Accuracy of remotely sensed data: Sampling and analysis procedures

NASA Technical Reports Server (NTRS)

Congalton, R. G.; Oderwald, R. G.; Mead, R. A.

1982-01-01

A review and update of the discrete multivariate analysis techniques used for accuracy assessment is given. A listing of the computer program written to implement these techniques is given. New work on evaluating accuracy assessment using Monte Carlo simulation with different sampling schemes is given. The results of matrices from the mapping effort of the San Juan National Forest is given. A method for estimating the sample size requirements for implementing the accuracy assessment procedures is given. A proposed method for determining the reliability of change detection between two maps of the same area produced at different times is given.

APPLICATION OF A "VITURAL FIELD REFERENCE DATABASE" TO ASSESS LAND-COVER MAP ACCURACIES

EPA Science Inventory

An accuracy assessment was performed for the Neuse River Basin, NC land-cover/use
(LCLU) mapping results using a "Virtual Field Reference Database (VFRDB)". The VFRDB was developed using field measurement and digital imagery (camera) data collected at 1,409 sites over a perio...

Influence of neighbourhood information on 'Local Climate Zone' mapping in heterogeneous cities

NASA Astrophysics Data System (ADS)

Verdonck, Marie-Leen; Okujeni, Akpona; van der Linden, Sebastian; Demuzere, Matthias; De Wulf, Robert; Van Coillie, Frieke

2017-10-01

Local climate zone (LCZ) mapping is an emerging field in urban climate research. LCZs potentially provide an objective framework to assess urban form and function worldwide. The scheme is currently being used to globally map LCZs as a part of the World Urban Database and Access Portal Tools (WUDAPT) initiative. So far, most of the LCZ maps lack proper quantitative assessment, challenging the generic character of the WUDAPT workflow. Using the standard method introduced by the WUDAPT community difficulties arose concerning the built zones due to high levels of heterogeneity. To overcome this problem a contextual classifier is adopted in the mapping process. This paper quantitatively assesses the influence of neighbourhood information on the LCZ mapping result of three cities in Belgium: Antwerp, Brussels and Ghent. Overall accuracies for the maps were respectively 85.7 ± 0.5, 79.6 ± 0.9, 90.2 ± 0.4%. The approach presented here results in overall accuracies of 93.6 ± 0.2, 92.6 ± 0.3 and 95.6 ± 0.3% for Antwerp, Brussels and Ghent. The results thus indicate a positive influence of neighbourhood information for all study areas with an increase in overall accuracies of 7.9, 13.0 and 5.4%. This paper reaches two main conclusions. Firstly, evidence was introduced on the relevance of a quantitative accuracy assessment in LCZ mapping, showing that the accuracies reported in previous papers are not easily achieved. Secondly, the method presented in this paper proves to be highly effective in Belgian cities, and given its open character shows promise for application in other heterogeneous cities worldwide.

Simulation of seagrass bed mapping by satellite images based on the radiative transfer model

NASA Astrophysics Data System (ADS)

Sagawa, Tatsuyuki; Komatsu, Teruhisa

2015-06-01

Seagrass and seaweed beds play important roles in coastal marine ecosystems. They are food sources and habitats for many marine organisms, and influence the physical, chemical, and biological environment. They are sensitive to human impacts such as reclamation and pollution. Therefore, their management and preservation are necessary for a healthy coastal environment. Satellite remote sensing is a useful tool for mapping and monitoring seagrass beds. The efficiency of seagrass mapping, seagrass bed classification in particular, has been evaluated by mapping accuracy using an error matrix. However, mapping accuracies are influenced by coastal environments such as seawater transparency, bathymetry, and substrate type. Coastal management requires sufficient accuracy and an understanding of mapping limitations for monitoring coastal habitats including seagrass beds. Previous studies are mainly based on case studies in specific regions and seasons. Extensive data are required to generalise assessments of classification accuracy from case studies, which has proven difficult. This study aims to build a simulator based on a radiative transfer model to produce modelled satellite images and assess the visual detectability of seagrass beds under different transparencies and seagrass coverages, as well as to examine mapping limitations and classification accuracy. Our simulations led to the development of a model of water transparency and the mapping of depth limits and indicated the possibility for seagrass density mapping under certain ideal conditions. The results show that modelling satellite images is useful in evaluating the accuracy of classification and that establishing seagrass bed monitoring by remote sensing is a reliable tool.

Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD)

USGS Publications Warehouse

Wickham, James; Stehman, Stephen V.; Gass, Leila; Dewitz, Jon; Sorenson, Daniel G.; Granneman, Brian J.; Poss, Richard V.; Baer, Lori Anne

2017-01-01

Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change reporting themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001–2006, 2006–2011, and 2001–2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Many class-specific user's accuracies met or exceeded a previously established nominal accuracy benchmark of 85%. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. The high Level I overall, user's, and producer's accuracies for the single-date eras in NLCD 2011 did not translate into high class-specific user's and producer's accuracies for many of the 17 change reporting themes. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest loss, forest gain, and urban gain had user's accuracies that exceeded 70%. Lower user's accuracies for the other change reporting themes may be attributable to the difficulty in determining the context of grass (e.g., open urban, grassland, agriculture) and between the components of the forest-shrubland-grassland gradient at either the mapping phase, reference label assignment phase, or both. NLCD 2011 user's accuracies for forest loss, forest gain, and urban gain compare favorably with results from other land cover change accuracy assessments.

Mapping invasive wetland plants in the Hudson River National Estuarine Research Reserve using quickbird satellite imagery

USGS Publications Warehouse

Laba, M.; Downs, R.; Smith, S.; Welsh, S.; Neider, C.; White, S.; Richmond, M.; Philpot, W.; Baveye, P.

2008-01-01

The National Estuarine Research Reserve (NERR) program is a nationally coordinated research and monitoring program that identifies and tracks changes in ecological resources of representative estuarine ecosystems and coastal watersheds. In recent years, attention has focused on using high spatial and spectral resolution satellite imagery to map and monitor wetland plant communities in the NERRs, particularly invasive plant species. The utility of this technology for that purpose has yet to be assessed in detail. To that end, a specific high spatial resolution satellite imagery, QuickBird, was used to map plant communities and monitor invasive plants within the Hudson River NERR (HRNERR). The HRNERR contains four diverse tidal wetlands (Stockport Flats, Tivoli Bays, Iona Island, and Piermont), each with unique water chemistry (i.e., brackish, oligotrophic and fresh) and, consequently, unique assemblages of plant communities, including three invasive plants (Trapa natans, Phragmites australis, and Lythrum salicaria). A maximum-likelihood classification was used to produce 20-class land cover maps for each of the four marshes within the HRNERR. Conventional contingency tables and a fuzzy set analysis served as a basis for an accuracy assessment of these maps. The overall accuracies, as assessed by the contingency tables, were 73.6%, 68.4%, 67.9%, and 64.9% for Tivoli Bays, Stockport Flats, Piermont, and Iona Island, respectively. Fuzzy assessment tables lead to higher estimates of map accuracies of 83%, 75%, 76%, and 76%, respectively. In general, the open water/tidal channel class was the most accurately mapped class and Scirpus sp. was the least accurately mapped. These encouraging accuracies suggest that high-resolution satellite imagery offers significant potential for the mapping of invasive plant species in estuarine environments. ?? 2007 Elsevier Inc. All rights reserved.

Survey methods for assessing land cover map accuracy

USGS Publications Warehouse

Nusser, S.M.; Klaas, E.E.

2003-01-01

The increasing availability of digital photographic materials has fueled efforts by agencies and organizations to generate land cover maps for states, regions, and the United States as a whole. Regardless of the information sources and classification methods used, land cover maps are subject to numerous sources of error. In order to understand the quality of the information contained in these maps, it is desirable to generate statistically valid estimates of accuracy rates describing misclassification errors. We explored a full sample survey framework for creating accuracy assessment study designs that balance statistical and operational considerations in relation to study objectives for a regional assessment of GAP land cover maps. We focused not only on appropriate sample designs and estimation approaches, but on aspects of the data collection process, such as gaining cooperation of land owners and using pixel clusters as an observation unit. The approach was tested in a pilot study to assess the accuracy of Iowa GAP land cover maps. A stratified two-stage cluster sampling design addressed sample size requirements for land covers and the need for geographic spread while minimizing operational effort. Recruitment methods used for private land owners yielded high response rates, minimizing a source of nonresponse error. Collecting data for a 9-pixel cluster centered on the sampled pixel was simple to implement, and provided better information on rarer vegetation classes as well as substantial gains in precision relative to observing data at a single-pixel.

THEMATIC ACCURACY OF MRLC LAND COVER FOR THE EASTERN UNITED STATES

EPA Science Inventory

One objective of the MultiResolution Land Characteristics (MRLC) consortium is to map general land-cover categories for the conterminous United States using Landsat Thematic Mapper (TM) data. Land-cover mapping and classification accuracy assessment are complete for the e...

Accuracy assessment for the U.S. Geological Survey Regional Land-Cover Mapping Program: New York and New Jersey Region

Treesearch

Zhiliang Zhu; Limin Yang; Stephen V. Stehman; Raymond L. Czaplewski

2000-01-01

The U.S. Geological Survey, in cooperation with other government and private organizations, is producing a conterminous U.S. land-cover map using Landsat Thematic Mapper 30-meter data for the Federal regions designated by the U.S. Environmental Protection Agency. Accuracy assessment is to be conducted for each Federal region to estimate overall and class-specific...

Accuracy assessments and areal estimates using two-phase stratified random sampling, cluster plots, and the multivariate composite estimator

Treesearch

Raymond L. Czaplewski

2000-01-01

Consider the following example of an accuracy assessment. Landsat data are used to build a thematic map of land cover for a multicounty region. The map classifier (e.g., a supervised classification algorithm) assigns each pixel into one category of land cover. The classification system includes 12 different types of forest and land cover: black spruce, balsam fir,...

Thematic Accuracy Assessment of the 2011 National Land ...

EPA Pesticide Factsheets

Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change reporting themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001–2006, 2006–2011, and 2001–2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Many class-specific user's accuracies met or exceeded a previously established nominal accuracy benchmark of 85%. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. The high Level I overall, user's, and producer's accuracies for the single-date eras in NLCD 2011 did not translate into high class-specific user's and producer's accuracies for many of the 17 change reporting themes. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest l

Variance approximations for assessments of classification accuracy

Treesearch

R. L. Czaplewski

1994-01-01

Variance approximations are derived for the weighted and unweighted kappa statistics, the conditional kappa statistic, and conditional probabilities. These statistics are useful to assess classification accuracy, such as accuracy of remotely sensed classifications in thematic maps when compared to a sample of reference classifications made in the field. Published...

SACRIFICING THE ECOLOGICAL RESOLUTION OF VEGETATION MAPS AT THE ALTAR OF THEMATIC ACCURACY: ASSESSED MAP ACCURACIES FOR HIERARCHICAL VEGETATION CLASSIFICATIONS IN THE EASTERN GREAT BASIN OF THE SOUTHWEST REGIONAL GAP ANALYSIS PROJECT (SW REGAP)

EPA Science Inventory

The Southwest Regional Gap Analysis Project (SW ReGAP) improves upon previous GAP projects conducted in Arizona, Colorado, Nevada, New Mexico, and Utah to provide a
consistent, seamless vegetation map for this large and ecologically diverse geographic region. Nevada's compone...

THE USE OF NTM DATA FOR THE ACCURACY ASSESSMENT OF LANDSAT DERIVED LAND USE/LAND COVER MAPS

EPA Science Inventory

National Technical Means (NTM) data were utilized to validate the accuracy of a series of LANDSAT derived Land Use / Land Cover (LU/LC) maps for the time frames mid- I 970s, early- I 990s and mid- I 990s. The area-of-interest for these maps is a 2000 square mile portion of the De...

Characterization and delineation of caribou habitat on Unimak Island using remote sensing techniques

NASA Astrophysics Data System (ADS)

Atkinson, Brain M.

The assessment of herbivore habitat quality is traditionally based on quantifying the forages available to the animal across their home range through ground-based techniques. While these methods are highly accurate, they can be time-consuming and highly expensive, especially for herbivores that occupy vast spatial landscapes. The Unimak Island caribou herd has been decreasing in the last decade at rates that have prompted discussion of management intervention. Frequent inclement weather in this region of Alaska has provided for little opportunity to study the caribou forage habitat on Unimak Island. The overall objectives of this study were two-fold 1) to assess the feasibility of using high-resolution color and near-infrared aerial imagery to map the forage distribution of caribou habitat on Unimak Island and 2) to assess the use of a new high-resolution multispectral satellite imagery platform, RapidEye, and use of the "red-edge" spectral band on vegetation classification accuracy. Maximum likelihood classification algorithms were used to create land cover maps in aerial and satellite imagery. Accuracy assessments and transformed divergence values were produced to assess vegetative spectral information and classification accuracy. By using RapidEye and aerial digital imagery in a hierarchical supervised classification technique, we were able to produce a high resolution land cover map of Unimak Island. We obtained overall accuracy rates of 71.4 percent which are comparable to other land cover maps using RapidEye imagery. The "red-edge" spectral band included in the RapidEye imagery provides additional spectral information that allows for a more accurate overall classification, raising overall accuracy 5.2 percent.

Accuracy assessment, using stratified plurality sampling, of portions of a LANDSAT classification of the Arctic National Wildlife Refuge Coastal Plain

NASA Technical Reports Server (NTRS)

Card, Don H.; Strong, Laurence L.

1989-01-01

An application of a classification accuracy assessment procedure is described for a vegetation and land cover map prepared by digital image processing of LANDSAT multispectral scanner data. A statistical sampling procedure called Stratified Plurality Sampling was used to assess the accuracy of portions of a map of the Arctic National Wildlife Refuge coastal plain. Results are tabulated as percent correct classification overall as well as per category with associated confidence intervals. Although values of percent correct were disappointingly low for most categories, the study was useful in highlighting sources of classification error and demonstrating shortcomings of the plurality sampling method.

Urban Land Cover Mapping Accuracy Assessment - A Cost-benefit Analysis Approach

NASA Astrophysics Data System (ADS)

Xiao, T.

2012-12-01

One of the most important components in urban land cover mapping is mapping accuracy assessment. Many statistical models have been developed to help design simple schemes based on both accuracy and confidence levels. It is intuitive that an increased number of samples increases the accuracy as well as the cost of an assessment. Understanding cost and sampling size is crucial in implementing efficient and effective of field data collection. Few studies have included a cost calculation component as part of the assessment. In this study, a cost-benefit sampling analysis model was created by combining sample size design and sampling cost calculation. The sampling cost included transportation cost, field data collection cost, and laboratory data analysis cost. Simple Random Sampling (SRS) and Modified Systematic Sampling (MSS) methods were used to design sample locations and to extract land cover data in ArcGIS. High resolution land cover data layers of Denver, CO and Sacramento, CA, street networks, and parcel GIS data layers were used in this study to test and verify the model. The relationship between the cost and accuracy was used to determine the effectiveness of each sample method. The results of this study can be applied to other environmental studies that require spatial sampling.

Nationwide forestry applications program. Analysis of forest classification accuracy

NASA Technical Reports Server (NTRS)

Congalton, R. G.; Mead, R. A.; Oderwald, R. G.; Heinen, J. (Principal Investigator)

1981-01-01

The development of LANDSAT classification accuracy assessment techniques, and of a computerized system for assessing wildlife habitat from land cover maps are considered. A literature review on accuracy assessment techniques and an explanation for the techniques development under both projects are included along with listings of the computer programs. The presentations and discussions at the National Working Conference on LANDSAT Classification Accuracy are summarized. Two symposium papers which were published on the results of this project are appended.

MapEdit: solution to continuous raster map creation

NASA Astrophysics Data System (ADS)

Rančić, Dejan; Djordjevi-Kajan, Slobodanka

2003-03-01

The paper describes MapEdit, MS Windows TM software for georeferencing and rectification of scanned paper maps. The software produces continuous raster maps which can be used as background in geographical information systems. Process of continuous raster map creation using MapEdit "mosaicking" function is also described as well as the georeferencing and rectification algorithms which are used in MapEdit. Our approach for georeferencing and rectification using four control points and two linear transformations for each scanned map part, together with nearest neighbor resampling method, represents low cost—high speed solution that produce continuous raster maps with satisfactory quality for many purposes (±1 pixel). Quality assessment of several continuous raster maps at different scales that have been created using our software and methodology, has been undertaken and results are presented in the paper. For the quality control of the produced raster maps we referred to three wide adopted standards: US Standard for Digital Cartographic Data, National Standard for Spatial Data Accuracy and US National Map Accuracy Standard. The results obtained during the quality assessment process are given in the paper and show that our maps meat all three standards.

Procedural Documentation and Accuracy Assessment of Bathymetric Maps and Area/Capacity Tables for Small Reservoirs

USGS Publications Warehouse

Wilson, Gary L.; Richards, Joseph M.

2006-01-01

Because of the increasing use and importance of lakes for water supply to communities, a repeatable and reliable procedure to determine lake bathymetry and capacity is needed. A method to determine the accuracy of the procedure will help ensure proper collection and use of the data and resulting products. It is important to clearly define the intended products and desired accuracy before conducting the bathymetric survey to ensure proper data collection. A survey-grade echo sounder and differential global positioning system receivers were used to collect water-depth and position data in December 2003 at Sugar Creek Lake near Moberly, Missouri. Data were collected along planned transects, with an additional set of quality-assurance data collected for use in accuracy computations. All collected data were imported into a geographic information system database. A bathymetric surface model, contour map, and area/capacity tables were created from the geographic information system database. An accuracy assessment was completed on the collected data, bathymetric surface model, area/capacity table, and contour map products. Using established vertical accuracy standards, the accuracy of the collected data, bathymetric surface model, and contour map product was 0.67 foot, 0.91 foot, and 1.51 feet at the 95 percent confidence level. By comparing results from different transect intervals with the quality-assurance transect data, it was determined that a transect interval of 1 percent of the longitudinal length of Sugar Creek Lake produced nearly as good results as 0.5 percent transect interval for the bathymetric surface model, area/capacity table, and contour map products.

THEMATIC ACCURACY OF THE 1992 NATIONAL LAND-COVER DATA (NLCD) FOR THE EASTERN UNITED STATES: STATISTICAL METHODOLOGY AND REGIONAL RESULTS

EPA Science Inventory

The accuracy of the National Land Cover Data (NLCD) map is assessed via a probability sampling design incorporating three levels of stratification and two stages of selection. Agreement between the map and reference land-cover labels is defined as a match between the primary or a...

Mapping of land cover in northern California with simulated hyperspectral satellite imagery

NASA Astrophysics Data System (ADS)

Clark, Matthew L.; Kilham, Nina E.

2016-09-01

Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Analysis of hyperspectral, or imaging spectrometer, imagery has shown an impressive capacity to map a wide range of natural and anthropogenic land cover. Applications have been mostly with single-date imagery from relatively small spatial extents. Future hyperspectral satellites will provide imagery at greater spatial and temporal scales, and there is a need to assess techniques for mapping land cover with these data. Here we used simulated multi-temporal HyspIRI satellite imagery over a 30,000 km2 area in the San Francisco Bay Area, California to assess its capabilities for mapping classes defined by the international Land Cover Classification System (LCCS). We employed a mapping methodology and analysis framework that is applicable to regional and global scales. We used the Random Forests classifier with three sets of predictor variables (reflectance, MNF, hyperspectral metrics), two temporal resolutions (summer, spring-summer-fall), two sample scales (pixel, polygon) and two levels of classification complexity (12, 20 classes). Hyperspectral metrics provided a 16.4-21.8% and 3.1-6.7% increase in overall accuracy relative to MNF and reflectance bands, respectively, depending on pixel or polygon scales of analysis. Multi-temporal metrics improved overall accuracy by 0.9-3.1% over summer metrics, yet increases were only significant at the pixel scale of analysis. Overall accuracy at pixel scales was 72.2% (Kappa 0.70) with three seasons of metrics. Anthropogenic and homogenous natural vegetation classes had relatively high confidence and producer and user accuracies were over 70%; in comparison, woodland and forest classes had considerable confusion. We next focused on plant functional types with relatively pure spectra by removing open-canopy shrublands, woodlands and mixed forests from the classification. This 12-class map had significantly improved accuracy of 85.1% (Kappa 0.83) and most classes had over 70% producer and user accuracies. Finally, we summarized important metrics from the multi-temporal Random Forests to infer the underlying chemical and structural properties that best discriminated our land-cover classes across seasons.

Sampling Simulations for Assessing the Accuracy of U.S. Agricultural Crop Mapping from Remotely Sensed Imagery

NASA Astrophysics Data System (ADS)

Dwyer, Linnea; Yadav, Kamini; Congalton, Russell G.

2017-04-01

Providing adequate food and water for a growing, global population continues to be a major challenge. Mapping and monitoring crops are useful tools for estimating the extent of crop productivity. GFSAD30 (Global Food Security Analysis Data at 30m) is a program, funded by NASA, that is producing global cropland maps by using field measurements and remote sensing images. This program studies 8 major crop types, and includes information on cropland area/extent, if crops are irrigated or rainfed, and the cropping intensities. Using results from the US and the extensive reference data available, CDL (USDA Crop Data Layer), we will experiment with various sampling simulations to determine optimal sampling for thematic map accuracy assessment. These simulations will include varying the sampling unit, the sampling strategy, and the sample number. Results of these simulations will allow us to recommend assessment approaches to handle different cropping scenarios.

A large-area, spatially continuous assessment of land cover map error and its impact on downstream analyses.

PubMed

Estes, Lyndon; Chen, Peng; Debats, Stephanie; Evans, Tom; Ferreira, Stefanus; Kuemmerle, Tobias; Ragazzo, Gabrielle; Sheffield, Justin; Wolf, Adam; Wood, Eric; Caylor, Kelly

2018-01-01

Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high-resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel-wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative "downstream" (map-based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps' spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to ∼45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National-scale maps derived from higher-resolution imagery were most accurate, followed by multi-map fusion products. Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to cropland-adjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%-500% greater than in input cropland maps, but ∼40% less for sparse cover types). The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land cover map users. © 2017 John Wiley & Sons Ltd.

Multitemporal Accuracy and Precision Assessment of Unmanned Aerial System Photogrammetry for Slope-Scale Snow Depth Maps in Alpine Terrain

NASA Astrophysics Data System (ADS)

Adams, Marc S.; Bühler, Yves; Fromm, Reinhard

2017-12-01

Reliable and timely information on the spatio-temporal distribution of snow in alpine terrain plays an important role for a wide range of applications. Unmanned aerial system (UAS) photogrammetry is increasingly applied to cost-efficiently map the snow depth at very high resolution with flexible applicability. However, crucial questions regarding quality and repeatability of this technique are still under discussion. Here we present a multitemporal accuracy and precision assessment of UAS photogrammetry for snow depth mapping on the slope-scale. We mapped a 0.12 km2 large snow-covered study site, located in a high-alpine valley in Western Austria. 12 UAS flights were performed to acquire imagery at 0.05 m ground sampling distance in visible (VIS) and near-infrared (NIR) wavelengths with a modified commercial, off-the-shelf sensor mounted on a custom-built fixed-wing UAS. The imagery was processed with structure-from-motion photogrammetry software to generate orthophotos, digital surface models (DSMs) and snow depth maps (SDMs). Accuracy of DSMs and SDMs were assessed with terrestrial laser scanning and manual snow depth probing, respectively. The results show that under good illumination conditions (study site in full sunlight), the DSMs and SDMs were acquired with an accuracy of ≤ 0.25 and ≤ 0.29 m (both at 1σ), respectively. In case of poorly illuminated snow surfaces (study site shadowed), the NIR imagery provided higher accuracy (0.19 m; 0.23 m) than VIS imagery (0.49 m; 0.37 m). The precision of the UASSDMs was 0.04 m for a small, stable area and below 0.33 m for the whole study site (both at 1σ).

AN ACCURACY ASSESSMENT OF 1997 LANDSAT THEMATIC MAPPER DERIVED LAND COVER FOR THE UPPER SAN PEDRO WATERSHED (U.S./MEXICO)

EPA Science Inventory

High-Resolution airborne color video data were used to evaluate the accuracy of a land cover map of the upper San Pedro River watershed, derived from June 1997 Landsat Thematic Mapper data. The land cover map was interpreted and generated by Instituto del Medio Ambiente y el Bes...

A PRIOR EVALUATION OF TWO-STAGE CLUSTER SAMPLING FOR ACCURACY ASSESSMENT OF LARGE-AREA LAND-COVER MAPS

EPA Science Inventory

Two-stage cluster sampling reduces the cost of collecting accuracy assessment reference data by constraining sample elements to fall within a limited number of geographic domains (clusters). However, because classification error is typically positively spatially correlated, withi...

Analysis of spatial distribution of land cover maps accuracy

NASA Astrophysics Data System (ADS)

Khatami, R.; Mountrakis, G.; Stehman, S. V.

2017-12-01

Land cover maps have become one of the most important products of remote sensing science. However, classification errors will exist in any classified map and affect the reliability of subsequent map usage. Moreover, classification accuracy often varies over different regions of a classified map. These variations of accuracy will affect the reliability of subsequent analyses of different regions based on the classified maps. The traditional approach of map accuracy assessment based on an error matrix does not capture the spatial variation in classification accuracy. Here, per-pixel accuracy prediction methods are proposed based on interpolating accuracy values from a test sample to produce wall-to-wall accuracy maps. Different accuracy prediction methods were developed based on four factors: predictive domain (spatial versus spectral), interpolation function (constant, linear, Gaussian, and logistic), incorporation of class information (interpolating each class separately versus grouping them together), and sample size. Incorporation of spectral domain as explanatory feature spaces of classification accuracy interpolation was done for the first time in this research. Performance of the prediction methods was evaluated using 26 test blocks, with 10 km × 10 km dimensions, dispersed throughout the United States. The performance of the predictions was evaluated using the area under the curve (AUC) of the receiver operating characteristic. Relative to existing accuracy prediction methods, our proposed methods resulted in improvements of AUC of 0.15 or greater. Evaluation of the four factors comprising the accuracy prediction methods demonstrated that: i) interpolations should be done separately for each class instead of grouping all classes together; ii) if an all-classes approach is used, the spectral domain will result in substantially greater AUC than the spatial domain; iii) for the smaller sample size and per-class predictions, the spectral and spatial domain yielded similar AUC; iv) for the larger sample size (i.e., very dense spatial sample) and per-class predictions, the spatial domain yielded larger AUC; v) increasing the sample size improved accuracy predictions with a greater benefit accruing to the spatial domain; and vi) the function used for interpolation had the smallest effect on AUC.

Characterization of Incidental Renal Mass With Dual-Energy CT: Diagnostic Accuracy of Effective Atomic Number Maps for Discriminating Nonenhancing Cysts From Enhancing Masses.

PubMed

Mileto, Achille; Allen, Brian C; Pietryga, Jason A; Farjat, Alfredo E; Zarzour, Jessica G; Bellini, Davide; Ebner, Lukas; Morgan, Desiree E

2017-10-01

The purpose of this study was to assess the diagnostic accuracy of effective atomic number maps reconstructed from dual-energy contrast-enhanced data for discriminating between nonenhancing renal cysts and enhancing masses. Two hundred six patients (128 men, 78 women; mean age, 64 years) underwent a CT renal mass protocol (single-energy unenhanced and dual-energy contrast-enhanced nephrographic imaging) at two different hospitals. For each set of patients, two blinded, independent observers performed measurements on effective atomic number maps from contrast-enhanced dual-energy data. Renal mass assessment on unenhanced and nephrographic images, corroborated by imaging and medical records, was the reference standard. The diagnostic accuracy of effective atomic number maps was assessed with ROC analysis. Significant differences in mean effective atomic numbers (Z eff ) were observed between nonenhancing and enhancing masses (set A, 8.19 vs 9.59 Z eff ; set B, 8.05 vs 9.19 Z eff ; sets combined, 8.13 vs 9.37 Z eff ) (p < 0.0001). An effective atomic number value of 8.36 Z eff was the optimal threshold, rendering an AUC of 0.92 (95% CI, 0.89-0.94), sensitivity of 90.8% (158/174 [95% CI, 85.5-94.7%]), specificity of 85.2% (445/522 [95% CI, 81.9-88.2%]), and overall diagnostic accuracy of 86.6% (603/696 [95% CI, 83.9-89.1%]). Nonenhancing renal cysts, including hyperattenuating cysts, can be discriminated from enhancing masses on effective atomic number maps generated from dual-energy contrast-enhanced CT data. This technique may be of clinical usefulness when a CT protocol for comprehensive assessment of renal masses is not available.

Land cover in Upper Egypt assessed using regional and global land cover products derived from MODIS imagery.

PubMed

Fuller, Douglas O; Parenti, Michael S; Gad, Adel M; Beier, John C

2012-01-01

Irrigation along the Nile River has resulted in dramatic changes in the biophysical environment of Upper Egypt. In this study we used a combination of MODIS 250 m NDVI data and Landsat imagery to identify areas that changed from 2001-2008 as a result of irrigation and water-level fluctuations in the Nile River and nearby water bodies. We used two different methods of time series analysis -- principal components (PCA) and harmonic decomposition (HD), applied to the MODIS 250 m NDVI images to derive simple three-class land cover maps and then assessed their accuracy using a set of reference polygons derived from 30 m Landsat 5 and 7 imagery. We analyzed our MODIS 250 m maps against a new MODIS global land cover product (MOD12Q1 collection 5) to assess whether regionally specific mapping approaches are superior to a standard global product. Results showed that the accuracy of the PCA-based product was greater than the accuracy of either the HD or MOD12Q1 products for the years 2001, 2003, and 2008. However, the accuracy of the PCA product was only slightly better than the MOD12Q1 for 2001 and 2003. Overall, the results suggest that our PCA-based approach produces a high level of user and producer accuracies, although the MOD12Q1 product also showed consistently high accuracy. Overlay of 2001-2008 PCA-based maps showed a net increase of 12 129 ha of irrigated vegetation, with the largest increase found from 2006-2008 around the Districts of Edfu and Kom Ombo. This result was unexpected in light of ambitious government plans to develop 336 000 ha of irrigated agriculture around the Toshka Lakes.

How Fit is Your Citizen Science Data?

NASA Astrophysics Data System (ADS)

Fischer, H. A.; Gerber, L. R.; Wentz, E. A.

2017-12-01

Data quality and accuracy is a fundamental concern with utilizing citizen science data. Although many methods can be used to assess quality and accuracy, these methods may not be sufficient to qualify citizen science data for widespread use in scientific research. While Data Fitness For Use (DFFU) does not provide a blanket assessment of data quality, it does assesses the data's ability to be used for a specific application, within a given area (Devillers and Bédard 2007). The STAAq (Spatial, Temporal, Aptness, and Accuracy) assessment was developed to assess the fitness for use of citizen science data, this assessment can be used on a stand alone dataset or be used to compare multiple datasets. The citizen science data used in this assessment was collected by volunteers of the Map of Life- Denali project, which is a tourist-centric citizen science project developed through a partnership with Arizona State University, Map of Life at Yale University, and Denali National Park and Preserve. Volunteers use the offline version of the Map of Life app to record their wildlife, insect, and plant observations in the park. To test the STAAq assessment data from different sources- Map of Life- Denali, Ride Observe and Record, and NPS wildlife surveys- were compared to determined which dataset is most fit for use for a specific research question; What is the recent Grizzly bear distribution in areas of high visitor use in Denali National Park and Preserve? These datasets were compared and ranked according to how well they performed in each of the components of the STAAq assessment. These components include spatial scale, temporal scale, aptness, and application. The Map of Life- Denali data and the ROAR program data were most for use for this research question. The STAAq assessment can be adjusted to assess the fitness for use of a single dataset or being used to compare any number of datasets. This data fitness for use assessment provides a means to assess data fitness instead of data quality for citizen science data.

On the accuracy and reproducibility of a novel probabilistic atlas-based generation for calculation of head attenuation maps on integrated PET/MR scanners.

PubMed

Chen, Kevin T; Izquierdo-Garcia, David; Poynton, Clare B; Chonde, Daniel B; Catana, Ciprian

2017-03-01

To propose an MR-based method for generating continuous-valued head attenuation maps and to assess its accuracy and reproducibility. Demonstrating that novel MR-based photon attenuation correction methods are both accurate and reproducible is essential prior to using them routinely in research and clinical studies on integrated PET/MR scanners. Continuous-valued linear attenuation coefficient maps ("μ-maps") were generated by combining atlases that provided the prior probability of voxel positions belonging to a certain tissue class (air, soft tissue, or bone) and an MR intensity-based likelihood classifier to produce posterior probability maps of tissue classes. These probabilities were used as weights to generate the μ-maps. The accuracy of this probabilistic atlas-based continuous-valued μ-map ("PAC-map") generation method was assessed by calculating the voxel-wise absolute relative change (RC) between the MR-based and scaled CT-based attenuation-corrected PET images. To assess reproducibility, we performed pair-wise comparisons of the RC values obtained from the PET images reconstructed using the μ-maps generated from the data acquired at three time points. The proposed method produced continuous-valued μ-maps that qualitatively reflected the variable anatomy in patients with brain tumor and agreed well with the scaled CT-based μ-maps. The absolute RC comparing the resulting PET volumes was 1.76 ± 2.33 %, quantitatively demonstrating that the method is accurate. Additionally, we also showed that the method is highly reproducible, the mean RC value for the PET images reconstructed using the μ-maps obtained at the three visits being 0.65 ± 0.95 %. Accurate and highly reproducible continuous-valued head μ-maps can be generated from MR data using a probabilistic atlas-based approach.

Mapping invasive Phragmites australis in the coastal Great Lakes with ALOS PALSAR satellite imagery for decision support

USGS Publications Warehouse

Bourgeau-Chavez, Laura L.; Kowalski, Kurt P.; Carlson Mazur, Martha L.; Scarbrough, Kirk A.; Powell, Richard B.; Brooks, Colin N.; Huberty, Brian; Jenkins, Liza K.; Banda, Elizabeth C.; Galbraith, David M.; Laubach, Zachary M.; Riordan, Kevin

2013-01-01

The invasive variety of Phragmites australis (common reed) forms dense stands that can cause negative impacts on coastal Great Lakes wetlands including habitat degradation and reduced biological diversity. Early treatment is key to controlling Phragmites, therefore a map of the current distribution is needed. ALOS PALSAR imagery was used to produce the first basin-wide distribution map showing the extent of large, dense invasive Phragmites-dominated habitats in wetlands and other coastal ecosystems along the U.S. shore of the Great Lakes. PALSAR is a satellite imaging radar sensor that is sensitive to differences in plant biomass and inundation patterns, allowing for the detection and delineation of these tall (up to 5 m), high density, high biomass invasive Phragmites stands. Classification was based on multi-season ALOS PALSAR L-band (23 cm wavelength) HH and HV polarization data. Seasonal (spring, summer, and fall) datasets were used to improve discrimination of Phragmites by taking advantage of phenological changes in vegetation and inundation patterns over the seasons. Extensive field collections of training and randomly selected validation data were conducted in 2010–2011 to aid in mapping and for accuracy assessments. Overall basin-wide map accuracy was 87%, with 86% producer's accuracy and 43% user's accuracy for invasive Phragmites. The invasive Phragmites maps are being used to identify major environmental drivers of this invader's distribution, to assess areas vulnerable to new invasion, and to provide information to regional stakeholders through a decision support tool.

a Free and Open Source Tool to Assess the Accuracy of Land Cover Maps: Implementation and Application to Lombardy Region (italy)

NASA Astrophysics Data System (ADS)

Bratic, G.; Brovelli, M. A.; Molinari, M. E.

2018-04-01

The availability of thematic maps has significantly increased over the last few years. Validation of these maps is a key factor in assessing their suitability for different applications. The evaluation of the accuracy of classified data is carried out through a comparison with a reference dataset and the generation of a confusion matrix from which many quality indexes can be derived. In this work, an ad hoc free and open source Python tool was implemented to automatically compute all the matrix confusion-derived accuracy indexes proposed by literature. The tool was integrated into GRASS GIS environment and successfully applied to evaluate the quality of three high-resolution global datasets (GlobeLand30, Global Urban Footprint, Global Human Settlement Layer Built-Up Grid) in the Lombardy Region area (Italy). In addition to the most commonly used accuracy measures, e.g. overall accuracy and Kappa, the tool allowed to compute and investigate less known indexes such as the Ground Truth and the Classification Success Index. The promising tool will be further extended with spatial autocorrelation analysis functions and made available to researcher and user community.

Mapping wetlands in Nova Scotia with multi-beam RADARSAT-2 Polarimetric SAR, optical satellite imagery, and Lidar data

NASA Astrophysics Data System (ADS)

Jahncke, Raymond; Leblon, Brigitte; Bush, Peter; LaRocque, Armand

2018-06-01

Wetland maps currently in use by the Province of Nova Scotia, namely the Department of Natural Resources (DNR) wetland inventory map and the swamp wetland classes of the DNR forest map, need to be updated. In this study, wetlands were mapped in an area southwest of Halifax, Nova Scotia by classifying a combination of multi-date and multi-beam RADARSAT-2 C-band polarimetric SAR (polSAR) images with spring Lidar, and fall QuickBird optical data using the Random Forests (RF) classifier. The resulting map has five wetland classes (open-water/marsh complex, open bog, open fen, shrub/treed fen/bog, swamp), plus lakes and various upland classes. Its accuracy was assessed using data from 156 GPS wetland sites collected in 2012 and compared to the one obtained with the current wetland map of Nova Scotia. The best overall classification was obtained using a combination of Lidar, RADARSAT-2 HH, HV, VH, VV intensity with polarimetric variables, and QuickBird multispectral (89.2%). The classified image was compared to GPS validation sites to assess the mapping accuracy of the wetlands. It was first done considering a group consisting of all wetland classes including lakes. This showed that only 69.9% of the wetland sites were correctly identified when only the QuickBird classified image was used in the classification. With the addition of variables derived from lidar, the number of correctly identified wetlands increased to 88.5%. The accuracy remained the same with the addition of RADARSAT-2 (88.5%). When we tested the accuracy for identifying wetland classes (e.g. marsh complex vs. open bog) instead of grouped wetlands, the resulting wetland map performed best with either QuickBird and Lidar, or QuickBird, Lidar, and RADARSAT-2 (66%). The Province of Nova Scotia's current wetland inventory and its associated wetland classes (aerial-photo interpreted) were also assessed against the GPS wetland sites. This provincial inventory correctly identified 62.2% of the grouped wetlands and only 18.6% of the wetland classes. The current inventory's poor performance demonstrates the value of incorporating a combination of new data sources into the provincial wetland mapping.

Comparison of Hyperspectral and Multispectral Satellites for Discriminating Land Cover in Northern California

NASA Astrophysics Data System (ADS)

Clark, M. L.; Kilham, N. E.

2015-12-01

Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Most land-cover maps at regional to global scales are produced with remote sensing techniques applied to multispectral satellite imagery with 30-500 m pixel sizes (e.g., Landsat, MODIS). Hyperspectral, or imaging spectrometer, imagery measuring the visible to shortwave infrared regions (VSWIR) of the spectrum have shown impressive capacity to map plant species and coarser land-cover associations, yet techniques have not been widely tested at regional and greater spatial scales. The Hyperspectral Infrared Imager (HyspIRI) mission is a VSWIR hyperspectral and thermal satellite being considered for development by NASA. The goal of this study was to assess multi-temporal, HyspIRI-like satellite imagery for improved land cover mapping relative to multispectral satellites. We mapped FAO Land Cover Classification System (LCCS) classes over 22,500 km2 in the San Francisco Bay Area, California using 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery simulated from data acquired by NASA's AVIRIS airborne sensor. Random Forests (RF) and Multiple-Endmember Spectral Mixture Analysis (MESMA) classifiers were applied to the simulated images and accuracies were compared to those from real Landsat 8 images. The RF classifier was superior to MESMA, and multi-temporal data yielded higher accuracy than summer-only data. With RF, hyperspectral data had overall accuracy of 72.2% and 85.1% with full 20-class and reduced 12-class schemes, respectively. Multispectral imagery had lower accuracy. For example, simulated and real Landsat data had 7.5% and 4.6% lower accuracy than HyspIRI data with 12 classes, respectively. In summary, our results indicate increased mapping accuracy using HyspIRI multi-temporal imagery, particularly in discriminating different natural vegetation types, such as spectrally-mixed woodlands and forests.

Spatial and thematic assessment of object-based forest stand delineation using an OFA-matrix

NASA Astrophysics Data System (ADS)

Hernando, A.; Tiede, D.; Albrecht, F.; Lang, S.

2012-10-01

The delineation and classification of forest stands is a crucial aspect of forest management. Object-based image analysis (OBIA) can be used to produce detailed maps of forest stands from either orthophotos or very high resolution satellite imagery. However, measures are then required for evaluating and quantifying both the spatial and thematic accuracy of the OBIA output. In this paper we present an approach for delineating forest stands and a new Object Fate Analysis (OFA) matrix for accuracy assessment. A two-level object-based orthophoto analysis was first carried out to delineate stands on the Dehesa Boyal public land in central Spain (Avila Province). Two structural features were first created for use in class modelling, enabling good differentiation between stands: a relational tree cover cluster feature, and an arithmetic ratio shadow/tree feature. We then extended the OFA comparison approach with an OFA-matrix to enable concurrent validation of thematic and spatial accuracies. Its diagonal shows the proportion of spatial and thematic coincidence between a reference data and the corresponding classification. New parameters for Spatial Thematic Loyalty (STL), Spatial Thematic Loyalty Overall (STLOVERALL) and Maximal Interfering Object (MIO) are introduced to summarise the OFA-matrix accuracy assessment. A stands map generated by OBIA (classification data) was compared with a map of the same area produced from photo interpretation and field data (reference data). In our example the OFA-matrix results indicate good spatial and thematic accuracies (>65%) for all stand classes except for the shrub stands (31.8%), and a good STLOVERALL (69.8%). The OFA-matrix has therefore been shown to be a valid tool for OBIA accuracy assessment.

Trajectory analysis of land use and land cover maps to improve spatial-temporal patterns, and impact assessment on groundwater recharge

NASA Astrophysics Data System (ADS)

Zomlot, Z.; Verbeiren, B.; Huysmans, M.; Batelaan, O.

2017-11-01

Land use/land cover (LULC) change is a consequence of human-induced global environmental change. It is also considered one of the major factors affecting groundwater recharge. Uncertainties and inconsistencies in LULC maps are one of the difficulties that LULC timeseries analysis face and which have a significant effect on hydrological impact analysis. Therefore, an accuracy assessment approach of LULC timeseries is needed for a more reliable hydrological analysis and prediction. The objective of this paper is to assess the impact of land use uncertainty and to improve the accuracy of a timeseries of CORINE (coordination of information on the environment) land cover maps by using a new approach of identifying spatial-temporal LULC change trajectories as a pre-processing tool. This ensures consistency of model input when dealing with land-use dynamics and as such improves the accuracy of land use maps and consequently groundwater recharge estimation. As a case study the impact of consistent land use changes from 1990 until 2013 on groundwater recharge for the Flanders-Brussels region is assessed. The change trajectory analysis successfully assigned a rational trajectory to 99% of all pixels. The methodology is shown to be powerful in correcting interpretation inconsistencies and overestimation errors in CORINE land cover maps. The overall kappa (cell-by-cell map comparison) improved from 0.6 to 0.8 and from 0.2 to 0.7 for forest and pasture land use classes respectively. The study shows that the inconsistencies in the land use maps introduce uncertainty in groundwater recharge estimation in a range of 10-30%. The analysis showed that during the period of 1990-2013 the LULC changes were mainly driven by urban expansion. The results show that the resolution at which the spatial analysis is performed is important; the recharge differences using original and corrected CORINE land cover maps increase considerably with increasing spatial resolution. This study indicates that improving consistency of land use map timeseries is of critical importance for assessing land use change and its environmental impact.

Assessment of the Thematic Accuracy of Land Cover Maps

NASA Astrophysics Data System (ADS)

Höhle, J.

2015-08-01

Several land cover maps are generated from aerial imagery and assessed by different approaches. The test site is an urban area in Europe for which six classes (`building', `hedge and bush', `grass', `road and parking lot', `tree', `wall and car port') had to be derived. Two classification methods were applied (`Decision Tree' and `Support Vector Machine') using only two attributes (height above ground and normalized difference vegetation index) which both are derived from the images. The assessment of the thematic accuracy applied a stratified design and was based on accuracy measures such as user's and producer's accuracy, and kappa coefficient. In addition, confidence intervals were computed for several accuracy measures. The achieved accuracies and confidence intervals are thoroughly analysed and recommendations are derived from the gained experiences. Reliable reference values are obtained using stereovision, false-colour image pairs, and positioning to the checkpoints with 3D coordinates. The influence of the training areas on the results is studied. Cross validation has been tested with a few reference points in order to derive approximate accuracy measures. The two classification methods perform equally for five classes. Trees are classified with a much better accuracy and a smaller confidence interval by means of the decision tree method. Buildings are classified by both methods with an accuracy of 99% (95% CI: 95%-100%) using independent 3D checkpoints. The average width of the confidence interval of six classes was 14% of the user's accuracy.

Thorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping

NASA Astrophysics Data System (ADS)

Drzewiecki, Wojciech

2017-12-01

We evaluated the performance of nine machine learning regression algorithms and their ensembles for sub-pixel estimation of impervious areas coverages from Landsat imagery. The accuracy of imperviousness mapping in individual time points was assessed based on RMSE, MAE and R2. These measures were also used for the assessment of imperviousness change intensity estimations. The applicability for detection of relevant changes in impervious areas coverages at sub-pixel level was evaluated using overall accuracy, F-measure and ROC Area Under Curve. The results proved that Cubist algorithm may be advised for Landsat-based mapping of imperviousness for single dates. Stochastic gradient boosting of regression trees (GBM) may be also considered for this purpose. However, Random Forest algorithm is endorsed for both imperviousness change detection and mapping of its intensity. In all applications the heterogeneous model ensembles performed at least as well as the best individual models or better. They may be recommended for improving the quality of sub-pixel imperviousness and imperviousness change mapping. The study revealed also limitations of the investigated methodology for detection of subtle changes of imperviousness inside the pixel. None of the tested approaches was able to reliably classify changed and non-changed pixels if the relevant change threshold was set as one or three percent. Also for fi ve percent change threshold most of algorithms did not ensure that the accuracy of change map is higher than the accuracy of random classifi er. For the threshold of relevant change set as ten percent all approaches performed satisfactory.

Performance Evaluation of sUAS Equipped with Velodyne HDL-32E LiDAR Sensor

NASA Astrophysics Data System (ADS)

Jozkow, G.; Wieczorek, P.; Karpina, M.; Walicka, A.; Borkowski, A.

2017-08-01

The Velodyne HDL-32E laser scanner is used more frequently as main mapping sensor in small commercial UASs. However, there is still little information about the actual accuracy of point clouds collected with such UASs. This work evaluates empirically the accuracy of the point cloud collected with such UAS. Accuracy assessment was conducted in four aspects: impact of sensors on theoretical point cloud accuracy, trajectory reconstruction quality, and internal and absolute point cloud accuracies. Theoretical point cloud accuracy was evaluated by calculating 3D position error knowing errors of used sensors. The quality of trajectory reconstruction was assessed by comparing position and attitude differences from forward and reverse EKF solution. Internal and absolute accuracies were evaluated by fitting planes to 8 point cloud samples extracted for planar surfaces. In addition, the absolute accuracy was also determined by calculating point 3D distances between LiDAR UAS and reference TLS point clouds. Test data consisted of point clouds collected in two separate flights performed over the same area. Executed experiments showed that in tested UAS, the trajectory reconstruction, especially attitude, has significant impact on point cloud accuracy. Estimated absolute accuracy of point clouds collected during both test flights was better than 10 cm, thus investigated UAS fits mapping-grade category.

Mapping seabed sediments: Comparison of manual, geostatistical, object-based image analysis and machine learning approaches

NASA Astrophysics Data System (ADS)

Diesing, Markus; Green, Sophie L.; Stephens, David; Lark, R. Murray; Stewart, Heather A.; Dove, Dayton

2014-08-01

Marine spatial planning and conservation need underpinning with sufficiently detailed and accurate seabed substrate and habitat maps. Although multibeam echosounders enable us to map the seabed with high resolution and spatial accuracy, there is still a lack of fit-for-purpose seabed maps. This is due to the high costs involved in carrying out systematic seabed mapping programmes and the fact that the development of validated, repeatable, quantitative and objective methods of swath acoustic data interpretation is still in its infancy. We compared a wide spectrum of approaches including manual interpretation, geostatistics, object-based image analysis and machine-learning to gain further insights into the accuracy and comparability of acoustic data interpretation approaches based on multibeam echosounder data (bathymetry, backscatter and derivatives) and seabed samples with the aim to derive seabed substrate maps. Sample data were split into a training and validation data set to allow us to carry out an accuracy assessment. Overall thematic classification accuracy ranged from 67% to 76% and Cohen's kappa varied between 0.34 and 0.52. However, these differences were not statistically significant at the 5% level. Misclassifications were mainly associated with uncommon classes, which were rarely sampled. Map outputs were between 68% and 87% identical. To improve classification accuracy in seabed mapping, we suggest that more studies on the effects of factors affecting the classification performance as well as comparative studies testing the performance of different approaches need to be carried out with a view to developing guidelines for selecting an appropriate method for a given dataset. In the meantime, classification accuracy might be improved by combining different techniques to hybrid approaches and multi-method ensembles.

Mapping the Daily Progression of Large Wildland Fires Using MODIS Active Fire Data

NASA Technical Reports Server (NTRS)

Veraverbeke, Sander; Sedano, Fernando; Hook, Simon J.; Randerson, James T.; Jin, Yufang; Rogers, Brendan

2013-01-01

High temporal resolution information on burned area is a prerequisite for incorporating bottom-up estimates of wildland fire emissions in regional air transport models and for improving models of fire behavior. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire product (MO(Y)D14) as input to a kriging interpolation to derive continuous maps of the evolution of nine large wildland fires. For each fire, local input parameters for the kriging model were defined using variogram analysis. The accuracy of the kriging model was assessed using high resolution daily fire perimeter data available from the U.S. Forest Service. We also assessed the temporal reporting accuracy of the MODIS burned area products (MCD45A1 and MCD64A1). Averaged over the nine fires, the kriging method correctly mapped 73% of the pixels within the accuracy of a single day, compared to 33% for MCD45A1 and 53% for MCD64A1.

Object-Based Retro-Classification Of A Agricultural Land Use: A Case Study Of Irrigated Croplands

NASA Astrophysics Data System (ADS)

Dubovyk, Olena; Conrad, Christopher; Khamzina, Asia; Menz, Gunter

2013-12-01

Availability of the historical crop maps is necessary for the assessment of land management practices and their effectiveness, as well as monitoring of environmental impacts of land uses. Lack of accurate current and past land-use information forestalls assessment of the occurred changes and their consequences and, thus, complicates knowledge-driven agrarian policy development. At the same time, lack of the sampling dataset for the past years often restrict mapping of historical land use. We proposed a methodology for a retro-assessment of several crops, based on multitemporal Landsat 5 TM imagery and a limited sampling dataset. The overall accuracy of the retro-map was 81% while accuracies for specific crop classes varied from 60% to 93%. If further elaborated, the developed method could be a useful tool for the generation of historical data on agricultural land use.

A Multitemporal, Multisensor Approach to Mapping the Canadian Boreal Forest

NASA Astrophysics Data System (ADS)

Reith, Ernest

The main anthropogenic source of CO2 emissions is the combustion of fossil fuels, while the clearing and burning of forests contribute significant amounts as well. Vegetation represents a major reservoir for terrestrial carbon stocks, and improving our ability to inventory vegetation will enhance our understanding of the impacts of land cover and climate change on carbon stocks and fluxes. These relationships may be an indication of a series of troubling biosphere-atmospheric feedback mechanisms that need to be better understood and modeled. Valuable land cover information can be provided to the global climate change modeling community using advanced remote sensing capabilities such as Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR). Individually and synergistically, data were successfully used to characterize the complex nature of the Canadian boreal forest land cover types. The multiple endmember spectral mixture analysis process was applied against seasonal AVIRIS data to produce species-level vegetated land cover maps of two study sites in the Canadian boreal forest: Old Black Spruce (OBS) and Old Jack Pine (OJP). The highest overall accuracy was assessed to be at least 66% accurate to the available reference map, providing evidence that high-quality, species-level land cover mapping of the Canadian boreal forest is achievable at accuracy levels greater than other previous research efforts in the region. Backscatter information from multichannel, polarimetric SAR utilizing a binary decision tree-based classification technique methodology was moderately successfully applied to AIRSAR to produce maps of the boreal land cover types at both sites, with overall accuracies at least 59%. A process, centered around noise whitening and principal component analysis features of the minimum noise fraction transform, was implemented to leverage synergies contained within spatially coregistered multitemporal and multisensor AVIRIS and AIRSAR data sets to successfully produce high-accuracy boreal forest land cover maps. Overall land cover map accuracies of 78% and 72% were assessed for OJP and OBS sites, respectively, for either seasonal or multitemporal data sets. High individual land cover accuracies appeared to be independent of site, season, or multisensor combination in the minimum-noise fraction-based approach.

EnviroAtlas -- Fresno, California -- One Meter Resolution Urban Land Cover Data (2010)

EPA Pesticide Factsheets

The Fresno, CA EnviroAtlas One-Meter-scale Urban Land Cover Data were generated via supervised classification of combined aerial photography and LiDAR data. The air photos were United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1-m spatial resolution. Aerial photography ('imagery') was collected on multiple dates in summer 2010. Seven land cover classes were mapped: Water, impervious surfaces (Impervious), soil and barren (Soil), trees and forest (Tree), and grass and herbaceous non-woody vegetation (Grass), agriculture (Ag), and Orchards. An accuracy assessment of 500 completely random and 103 stratified random points yielded an overall User's fuzzy accuracy of 81.1 percent (see below). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Fresno, CA plus a 1-km buffer. Where imagery was available, additional areas outside the 1-km boundary were also mapped but not included in the accuracy assessment. We expect the accuracy of the areas outside of the 1-km boundary to be consistent with those within. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The da

EnviroAtlas -- Fresno, California -- One Meter Resolution Urban Land Cover Data (2010) Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The Fresno, CA EnviroAtlas One-Meter-scale Urban Land Cover Data were generated via supervised classification of combined aerial photography and LiDAR data. The air photos were United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1-m spatial resolution. Aerial photography ('imagery') was collected on multiple dates in summer 2010. Seven land cover classes were mapped: Water, impervious surfaces (Impervious), soil and barren (Soil), trees and forest (Tree), and grass and herbaceous non-woody vegetation (Grass), agriculture (Ag), and Orchards. An accuracy assessment of 500 completely random and 103 stratified random points yielded an overall User's fuzzy accuracy of 81.1 percent (see below). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Fresno, CA plus a 1-km buffer. Where imagery was available, additional areas outside the 1-km boundary were also mapped but not included in the accuracy assessment. We expect the accuracy of the areas outside of the 1-km boundary to be consistent with those within. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with

Satellite Remote Sensing of Cropland Characteristics in 30m Resolution: The First North American Continental-Scale Classification on High Performance Computing Platforms

NASA Astrophysics Data System (ADS)

Massey, Richard

Cropland characteristics and accurate maps of their spatial distribution are required to develop strategies for global food security by continental-scale assessments and agricultural land use policies. North America is the major producer and exporter of coarse grains, wheat, and other crops. While cropland characteristics such as crop types are available at country-scales in North America, however, at continental-scale cropland products are lacking at fine sufficient resolution such as 30m. Additionally, applications of automated, open, and rapid methods to map cropland characteristics over large areas without the need of ground samples are needed on efficient high performance computing platforms for timely and long-term cropland monitoring. In this study, I developed novel, automated, and open methods to map cropland extent, crop intensity, and crop types in the North American continent using large remote sensing datasets on high-performance computing platforms. First, a novel method was developed in this study to fuse pixel-based classification of continental-scale Landsat data using Random Forest algorithm available on Google Earth Engine cloud computing platform with an object-based classification approach, recursive hierarchical segmentation (RHSeg) to map cropland extent at continental scale. Using the fusion method, a continental-scale cropland extent map for North America at 30m spatial resolution for the nominal year 2010 was produced. In this map, the total cropland area for North America was estimated at 275.2 million hectares (Mha). This map was assessed for accuracy using randomly distributed samples derived from United States Department of Agriculture (USDA) cropland data layer (CDL), Agriculture and Agri-Food Canada (AAFC) annual crop inventory (ACI), Servicio de Informacion Agroalimentaria y Pesquera (SIAP), Mexico's agricultural boundaries, and photo-interpretation of high-resolution imagery. The overall accuracies of the map are 93.4% with a producer's accuracy for crop class at 85.4% and user's accuracy of 74.5% across the continent. The sub-country statistics including state-wise and county-wise cropland statistics derived from this map compared well in regression models resulting in R2 > 0.84. Secondly, an automated phenological pattern matching (PPM) method to efficiently map cropping intensity was also developed in this study. This study presents a continental-scale cropping intensity map for the North American continent at 250m spatial resolution for 2010. In this map, the total areas for single crop, double crop, continuous crop, and fallow were estimated to be 123.5 Mha, 11.1 Mha, 64.0 Mha, and 83.4 Mha, respectively. This map was assessed using limited country-level reference datasets derived from United States Department of Agriculture cropland data layer and Agriculture and Agri-Food Canada annual crop inventory with overall accuracies of 79.8% and 80.2%, respectively. Third, two novel and automated decision tree classification approaches to map crop types across the conterminous United States (U.S.) using MODIS 250 m resolution data: 1) generalized, and 2) year-specific classification were developed. The classification approaches use similarities and dissimilarities in crop type phenology derived from NDVI time-series data for the two approaches. Annual crop type maps were produced for 8 major crop types in the United States using the generalized classification approach for 2001-2014 and the year-specific approach for 2008, 2010, 2011 and 2012. The year-specific classification had overall accuracies greater than 78%, while the generalized classifier had accuracies greater than 75% for the conterminous U.S. for 2008, 2010, 2011, and 2012. The generalized classifier enables automated and routine crop type mapping without repeated and expensive ground sample collection year after year with overall accuracies > 70% across all independent years. Taken together, these cropland products of extent, cropping intensity, and crop types, are significantly beneficial in agricultural and water use planning and monitoring to formulate policies towards global and North American food security issues.

Using known map category marginal frequencies to improve estimates of thematic map accuracy

NASA Technical Reports Server (NTRS)

Card, D. H.

1982-01-01

By means of two simple sampling plans suggested in the accuracy-assessment literature, it is shown how one can use knowledge of map-category relative sizes to improve estimates of various probabilities. The fact that maximum likelihood estimates of cell probabilities for the simple random sampling and map category-stratified sampling were identical has permitted a unified treatment of the contingency-table analysis. A rigorous analysis of the effect of sampling independently within map categories is made possible by results for the stratified case. It is noted that such matters as optimal sample size selection for the achievement of a desired level of precision in various estimators are irrelevant, since the estimators derived are valid irrespective of how sample sizes are chosen.

Generation of 2D Land Cover Maps for Urban Areas Using Decision Tree Classification

NASA Astrophysics Data System (ADS)

Höhle, J.

2014-09-01

A 2D land cover map can automatically and efficiently be generated from high-resolution multispectral aerial images. First, a digital surface model is produced and each cell of the elevation model is then supplemented with attributes. A decision tree classification is applied to extract map objects like buildings, roads, grassland, trees, hedges, and walls from such an "intelligent" point cloud. The decision tree is derived from training areas which borders are digitized on top of a false-colour orthoimage. The produced 2D land cover map with six classes is then subsequently refined by using image analysis techniques. The proposed methodology is described step by step. The classification, assessment, and refinement is carried out by the open source software "R"; the generation of the dense and accurate digital surface model by the "Match-T DSM" program of the Trimble Company. A practical example of a 2D land cover map generation is carried out. Images of a multispectral medium-format aerial camera covering an urban area in Switzerland are used. The assessment of the produced land cover map is based on class-wise stratified sampling where reference values of samples are determined by means of stereo-observations of false-colour stereopairs. The stratified statistical assessment of the produced land cover map with six classes and based on 91 points per class reveals a high thematic accuracy for classes "building" (99 %, 95 % CI: 95 %-100 %) and "road and parking lot" (90 %, 95 % CI: 83 %-95 %). Some other accuracy measures (overall accuracy, kappa value) and their 95 % confidence intervals are derived as well. The proposed methodology has a high potential for automation and fast processing and may be applied to other scenes and sensors.

Geometric accuracy of Landsat-4 and Landsat-5 Thematic Mapper images.

USGS Publications Warehouse

Borgeson, W.T.; Batson, R.M.; Kieffer, H.H.

1985-01-01

The geometric accuracy of the Landsat Thematic Mappers was assessed by a linear least-square comparison of the positions of conspicuous ground features in digital images with their geographic locations as determined from 1:24 000-scale maps. For a Landsat-5 image, the single-dimension standard deviations of the standard digital product, and of this image with additional linear corrections, are 11.2 and 10.3 m, respectively (0.4 pixel). An F-test showed that skew and affine distortion corrections are not significant. At this level of accuracy, the granularity of the digital image and the probable inaccuracy of the 1:24 000 maps began to affect the precision of the comparison. The tested image, even with a moderate accuracy loss in the digital-to-graphic conversion, meets National Horizontal Map Accuracy standards for scales of 1:100 000 and smaller. Two Landsat-4 images, obtained with the Multispectral Scanner on and off, and processed by an interim software system, contain significant skew and affine distortions. -Authors

Vegetation classification and distribution mapping report Mesa Verde National Park

USGS Publications Warehouse

Thomas, Kathryn A.; McTeague, Monica L.; Ogden, Lindsay; Floyd, M. Lisa; Schulz, Keith; Friesen, Beverly A.; Fancher, Tammy; Waltermire, Robert G.; Cully, Anne

2009-01-01

The classification and distribution mapping of the vegetation of Mesa Verde National Park (MEVE) and surrounding environment was achieved through a multi-agency effort between 2004 and 2007. The National Park Service’s Southern Colorado Plateau Network facilitated the team that conducted the work, which comprised the U.S. Geological Survey’s Southwest Biological Science Center, Fort Collins Research Center, and Rocky Mountain Geographic Science Center; Northern Arizona University; Prescott College; and NatureServe. The project team described 47 plant communities for MEVE, 34 of which were described from quantitative classification based on f eld-relevé data collected in 1993 and 2004. The team derived 13 additional plant communities from field observations during the photointerpretation phase of the project. The National Vegetation Classification Standard served as a framework for classifying these plant communities to the alliance and association level. Eleven of the 47 plant communities were classified as “park specials;” that is, plant communities with insufficient data to describe them as new alliances or associations. The project team also developed a spatial vegetation map database representing MEVE, with three different map-class schemas: base, group, and management map classes. The base map classes represent the fi nest level of spatial detail. Initial polygons were developed using Definiens Professional (at the time of our use, this software was called eCognition), assisted by interpretation of 1:12,000 true-color digital orthophoto quarter quadrangles (DOQQs). These polygons (base map classes) were labeled using manual photo interpretation of the DOQQs and 1:12,000 true-color aerial photography. Field visits verified interpretation concepts. The vegetation map database includes 46 base map classes, which consist of associations, alliances, and park specials classified with quantitative analysis, additional associations and park specials noted during photointerpretation, and non-vegetated land cover, such as infrastructure, land use, and geological land cover. The base map classes consist of 5,007 polygons in the project area. A field-based accuracy assessment of the base map classes showed overall accuracy to be 43.5%. Seven map classes comprise 89.1% of the park vegetated land cover. The group map classes represent aggregations of the base map classes, approximating the group level of the National Vegetation Classification Standard, version 2 (Federal Geographic Data Committee 2007), and reflecting physiognomy and floristics. Terrestrial ecological systems, as described by NatureServe (Comer et al. 2003), were used as the fi rst approximation of the group level. The project team identified 14 group map classes for this project. The overall accuracy of the group map classes was determined using the same accuracy assessment data as for the base map classes. The overall accuracy of the group representation of vegetation was 80.3%. In consultation with park staff , the team developed management map classes, consisting of park-defined groupings of base map classes intended to represent a balance between maintaining required accuracy and providing a focus on vegetation of particular interest or import to park managers. The 23 management map classes had an overall accuracy of 73.3%. While the main products of this project are the vegetation classification and the vegetation map database, a number of ancillary digital geographic information system and database products were also produced that can be used independently or to augment the main products. These products include shapefiles of the locations of field-collected data and relational databases of field-collected data.

Mapping river bathymetry with a small footprint green LiDAR: Applications and challenges

USGS Publications Warehouse

Kinzel, Paul J.; Legleiter, Carl; Nelson, Jonathan M.

2013-01-01

that environmental conditions and postprocessing algorithms can influence the accuracy and utility of these surveys and must be given consideration. These factors can lead to mapping errors that can have a direct bearing on derivative analyses such as hydraulic modeling and habitat assessment. We discuss the water and substrate characteristics of the sites, compare the conventional and remotely sensed river-bed topographies, and investigate the laser waveforms reflected from submerged targets to provide an evaluation as to the suitability and accuracy of the EAARL system and associated processing algorithms for riverine mapping applications.

Image Analysis for Facility Siting: a Comparison of Lowand High-altitude Image Interpretability for Land Use/land Cover Mapping

NASA Technical Reports Server (NTRS)

Borella, H. M.; Estes, J. E.; Ezra, C. E.; Scepan, J.; Tinney, L. R.

1982-01-01

For two test sites in Pennsylvania the interpretability of commercially acquired low-altitude and existing high-altitude aerial photography are documented in terms of time, costs, and accuracy for Anderson Level II land use/land cover mapping. Information extracted from the imagery is to be used in the evaluation process for siting energy facilities. Land use/land cover maps were drawn at 1:24,000 scale using commercially flown color infrared photography obtained from the United States Geological Surveys' EROS Data Center. Detailed accuracy assessment of the maps generated by manual image analysis was accomplished employing a stratified unaligned adequate class representation. Both 'area-weighted' and 'by-class' accuracies were documented and field-verified. A discrepancy map was also drawn to illustrate differences in classifications between the two map scales. Results show that the 1:24,000 scale map set was more accurate (99% to 94% area-weighted) than the 1:62,500 scale set, especially when sampled by class (96% to 66%). The 1:24,000 scale maps were also more time-consuming and costly to produce, due mainly to higher image acquisition costs.

Turkers in Africa: A Crowdsourcing Approach to Improving Agricultural Landcover Maps

NASA Astrophysics Data System (ADS)

Estes, L. D.; Caylor, K. K.; Choi, J.

2012-12-01

In the coming decades a substantial portion of Africa is expected to be transformed to agriculture. The scale of this conversion may match or exceed that which occurred in the Brazilian Cerrado and Argentinian Pampa in recent years. Tracking the rate and extent of this conversion will depend on having an accurate baseline of the current extent of croplands. Continent-wide baseline data do exist, but the accuracy of these relatively coarse resolution, remotely sensed assessments is suspect in many regions. To develop more accurate maps of the distribution and nature of African croplands, we develop a distributed "crowdsourcing" approach that harnesses human eyeballs and image interpretation capabilities. Our initial goal is to assess the accuracy of existing agricultural land cover maps, but ultimately we aim to generate "wall-to-wall" cropland maps that can be revisited and updated to track agricultural transformation. Our approach utilizes the freely avail- able, high-resolution satellite imagery provided by Google Earth, combined with Amazon.com's Mechanical Turk platform, an online service that provides a large, global pool of workers (known as "Turkers") who perform "Human Intelligence Tasks" (HITs) for a fee. Using open-source R and python software, we select a random sample of 1 km2 cells from a grid placed over our study area, stratified by field density classes drawn from one of the coarse-scale land cover maps, and send these in batches to Mechanical Turk for processing. Each Turker is required to conduct an initial training session, on the basis of which they are assigned an accuracy score that determines whether the Turker is allowed to proceed with mapping tasks. Completed mapping tasks are automatically retrieved and processed on our server, and subject to two further quality control measures. The first of these is a measure of the spatial accuracy of Turker mapped areas compared to a "gold standard" maps from selected locations that are randomly inserted (at relatively low frequency, ˜1/100) into batches sent to Mechanical Turk. This check provides a measure of overall map accuracy, and is used to update individual Turker's accuracy scores, which is the basis for determining pay rates. The second measure compares the area of each mapped Turkers' results with the expected area derived from existing land cover data, accepting or rejecting each Turker's batch based on how closely the two distributions match, with accuracy scores adjusted accordingly. Those two checks balance the need to ensure mapping quality with the overall cost of the project. Our initial study is developed for South Africa, where an existing dataset of hand digitized fields commissioned by the South African Department of Agriculture provides our validation and gold standard data. We compare our Turker-produced results with these existing maps, and with the the coarser-scaled land cover datasets, providing insight into their relative accuracies, classified according to cropland type (e.g. small-scale/subsistence cropping; large-scale commercial farms), and provide information on the cost effectiveness of our approach.

Pilot performance: assessing how scan patterns & navigational assessments vary by flight expertise.

PubMed

Yang, Ji Hyun; Kennedy, Quinn; Sullivan, Joseph; Fricker, Ronald D

2013-02-01

Helicopter overland navigation is a cognitively complex task that requires continuous monitoring of system and environmental parameters and many hours of training to master. This study investigated the effect of expertise on pilots' gaze measurements, navigation accuracy, and subjective assessment of their navigation accuracy in overland navigation on easy and difficult routes. A simulated overland task was completed by 12 military officers who ranged in flight experience as measured by total flight hours (TFH). They first studied a map of a route that included both easy and difficult route sections, and then had to 'fly' this simulated route in a fixed-base helicopter simulator. They also completed pre-task estimations and post-task assessments of the navigational difficulty of the transit to each waypoint in the route. Their scan pattern was tracked via eye tracking systems, which captured both the subject's out-the-window (OTW) and topographical map scan data. TFH was not associated with navigation accuracy or root mean square (RMS) error for any route section. For the easy routes, experts spent less time scanning out the window (p = 0.61) and had shorter OTW dwell (p = -0.66). For the difficult routes, experts appeared to slow down their scan by spending as much time scanning out the window as the novices while also having fewer Map fixations (p = -0.65) and shorter OTW dwell (p = -0.69). However, TFH was not significantly correlated with more accurate estimates of route difficulty. This study found that TFH did not predict navigation accuracy or subjective assessment, but was correlated with some gaze parameters.

Feature Orientation and Positional Accuracy Assessment of Digital Orthophoto and Line Map for Large Scale Mapping: the Case Study on Bahir Dar Town, Ethiopia

NASA Astrophysics Data System (ADS)

Sisay, Z. G.; Besha, T.; Gessesse, B.

2017-05-01

This study used in-situ GPS data to validate the accuracy of horizontal coordinates and orientation of linear features of orthophoto and line map for Bahir Dar city. GPS data is processed using GAMIT/GLOBK and Lieca GeoOfice (LGO) in a least square sense with a tie to local and regional GPS reference stations to predict horizontal coordinates at five checkpoints. Real-Time-Kinematic GPS measurement technique is used to collect the coordinates of road centerline to test the accuracy associated with the orientation of the photogrammetric line map. The accuracy of orthophoto was evaluated by comparing with in-situ GPS coordinates and it is in a good agreement with a root mean square error (RMSE) of 12.45 cm in x- and 13.97 cm in y-coordinates, on the other hand, 6.06 cm with 95 % confidence level - GPS coordinates from GAMIT/GLOBK. Whereas, the horizontal coordinates of the orthophoto are in agreement with in-situ GPS coordinates at an accuracy of 16.71 cm and 18.98 cm in x and y-directions respectively and 11.07 cm with 95 % confidence level - GPS data is processed by LGO and a tie to local GPS network. Similarly, the accuracy of linear feature is in a good fit with in-situ GPS measurement. The GPS coordinates of the road centerline deviates from the corresponding coordinates of line map by a mean value of 9.18 cm in x- direction and -14.96 cm in y-direction. Therefore, it can be concluded that, the accuracy of the orthophoto and line map is within the national standard of error budget ( 25 cm).

Testing of the Apollo 15 Metric Camera System.

NASA Technical Reports Server (NTRS)

Helmering, R. J.; Alspaugh, D. H.

1972-01-01

Description of tests conducted (1) to assess the quality of Apollo 15 Metric Camera System data and (2) to develop production procedures for total block reduction. Three strips of metric photography over the Hadley Rille area were selected for the tests. These photographs were utilized in a series of evaluation tests culminating in an orbitally constrained block triangulation solution. Results show that film deformations up to 25 and 5 microns are present in the mapping and stellar materials, respectively. Stellar reductions can provide mapping camera orientations with an accuracy that is consistent with the accuracies of other parameters in the triangulation solutions. Pointing accuracies of 4 to 10 microns can be expected for the mapping camera materials, depending on variations in resolution caused by changing sun angle conditions.

Mapping fire regimes from data you may already have: assessing LANDFIRE fire regime maps using local products

Treesearch

Melissa A. Thomas-Van Gundy

2014-01-01

LANDFIRE maps of fire regime groups are frequently used by land managers to help plan and execute prescribed burns for ecosystem restoration. Since LANDFIRE maps are generally applicable at coarse scales, questions often arise regarding their utility and accuracy. Here, the two recently published products from West Virginia, a rule-based and a witness tree-based model...

Vegetation classification, mapping, and monitoring at Voyageurs National Park, Minnesota: An application of the U.S. National Vegetation Classification

USGS Publications Warehouse

Faber-Langendoen, D.; Aaseng, N.; Hop, K.; Lew-Smith, M.; Drake, J.

2007-01-01

Question: How can the U.S. National Vegetation Classification (USNVC) serve as an effective tool for classifying and mapping vegetation, and inform assessments and monitoring? Location: Voyageurs National Park, northern Minnesota, U.S.A and environs. The park contains 54 243 ha of terrestrial habitat in the sub-boreal region of North America. Methods: We classified and mapped the natural vegetation using the USNVC, with 'alliance' and 'association' as base units. We compiled 259 classification plots and 1251 accuracy assessment test plots. Both plot and type ordinations were used to analyse vegetation and environmental patterns. Color infrared aerial photography (1:15840 scale) was used for mapping. Polygons were manually drawn, then transferred into digital form. Classification and mapping products are stored in publicly available databases. Past fire and logging events were used to assess distribution of forest types. Results and Discussion: Ordination and cluster analyses confirmed 49 associations and 42 alliances, with three associations ranked as globally vulnerable to extirpation. Ordination provided a useful summary of vegetation and ecological gradients. Overall map accuracy was 82.4%. Pinus banksiana - Picea mariana forests were less frequent in areas unburned since the 1930s. Conclusion: The USNVC provides a consistent ecological tool for summarizing and mapping vegetation. The products provide a baseline for assessing forests and wetlands, including fire management. The standardized classification and map units provide local to continental perspectives on park resources through linkages to state, provincial, and national classifications in the U.S. and Canada, and to NatureServe's Ecological Systems classification. ?? IAVS; Opulus Press.

An evaluation of satellite data for estimating the area of small forestland in the southern lower peninsula of Michigan. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Karteris, M. A. (Principal Investigator)

1980-01-01

A winter black and white band 5, a winter color, a fall color, and a diazo color composite of the fall scene were used to assess the use and potential of LANDSAT images for mapping and estimating acreage of small scattered forest tracts in Barry County, Michigan. Forests as small as 2.5 acres were mapped from each LANDSAT data source. The maps for each image were compared with an available forest-type map. Mapping errors detected were categorized as boundary and identification errors. The most frequently misclassified areas were agriculture lands, treed-bogs, brushlands and lowland and mixed hardwood stands. Stocking level affected interpretation more than stand size. The overall level of the interpretation performance was expressed through the estimation of classification, interpretation, and mapping accuracies. These accuracies ranged from 74 between 74% and 98%. Considering errors, accuracy, and cost, winter color imagery is the best LANDSAT alternative for mapping small forest tracts. However, since the availability of cloud-free winter images of the study area is significantly lower than images for other seasons, a diazo enhanced image of a fall scene is recommended as the best next best alternative.

GOFC-GOLD :: Global Observation of Forest and Land Cover Dynamics

Science.gov Websites

Validation: Recommendations for Evaluation and Accuracy Assessment of Global Land Cover Maps, A. Strahler et GOFC-GOLD-38: Report of the GOFC-GOLD/CEOS Workshop on Land Cover Change Accuracy Assessment as part of al., March 2006 860 kb GOFC-GOLD-24: A Revised Strategy for GOFC-GOLD, J.R. Townshend and M.A. Brady

Thematic accuracy of the 1992 National Land-Cover Data for the eastern United States: Statistical methodology and regional results

USGS Publications Warehouse

Stehman, S.V.; Wickham, J.D.; Smith, J.H.; Yang, L.

2003-01-01

The accuracy of the 1992 National Land-Cover Data (NLCD) map is assessed via a probability sampling design incorporating three levels of stratification and two stages of selection. Agreement between the map and reference land-cover labels is defined as a match between the primary or alternate reference label determined for a sample pixel and a mode class of the mapped 3×3 block of pixels centered on the sample pixel. Results are reported for each of the four regions comprising the eastern United States for both Anderson Level I and II classifications. Overall accuracies for Levels I and II are 80% and 46% for New England, 82% and 62% for New York/New Jersey (NY/NJ), 70% and 43% for the Mid-Atlantic, and 83% and 66% for the Southeast.

MAPPING SPATIAL THEMATIC ACCURACY WITH FUZZY SETS

EPA Science Inventory

Thematic map accuracy is not spatially homogenous but variable across a landscape. Properly analyzing and representing spatial pattern and degree of thematic map accuracy would provide valuable information for using thematic maps. However, current thematic map accuracy measures (...

A Stochastic Simulation Framework for the Prediction of Strategic Noise Mapping and Occupational Noise Exposure Using the Random Walk Approach

PubMed Central

Haron, Zaiton; Bakar, Suhaimi Abu; Dimon, Mohamad Ngasri

2015-01-01

Strategic noise mapping provides important information for noise impact assessment and noise abatement. However, producing reliable strategic noise mapping in a dynamic, complex working environment is difficult. This study proposes the implementation of the random walk approach as a new stochastic technique to simulate noise mapping and to predict the noise exposure level in a workplace. A stochastic simulation framework and software, namely RW-eNMS, were developed to facilitate the random walk approach in noise mapping prediction. This framework considers the randomness and complexity of machinery operation and noise emission levels. Also, it assesses the impact of noise on the workers and the surrounding environment. For data validation, three case studies were conducted to check the accuracy of the prediction data and to determine the efficiency and effectiveness of this approach. The results showed high accuracy of prediction results together with a majority of absolute differences of less than 2 dBA; also, the predicted noise doses were mostly in the range of measurement. Therefore, the random walk approach was effective in dealing with environmental noises. It could predict strategic noise mapping to facilitate noise monitoring and noise control in the workplaces. PMID:25875019

Spatial accuracy of a simplified disaggregation method for traffic emissions applied in seven mid-sized Chilean cities

NASA Astrophysics Data System (ADS)

Ossés de Eicker, Margarita; Zah, Rainer; Triviño, Rubén; Hurni, Hans

The spatial accuracy of top-down traffic emission inventory maps obtained with a simplified disaggregation method based on street density was assessed in seven mid-sized Chilean cities. Each top-down emission inventory map was compared against a reference, namely a more accurate bottom-up emission inventory map from the same study area. The comparison was carried out using a combination of numerical indicators and visual interpretation. Statistically significant differences were found between the seven cities with regard to the spatial accuracy of their top-down emission inventory maps. In compact cities with a simple street network and a single center, a good accuracy of the spatial distribution of emissions was achieved with correlation values>0.8 with respect to the bottom-up emission inventory of reference. In contrast, the simplified disaggregation method is not suitable for complex cities consisting of interconnected nuclei, resulting in correlation values<0.5. Although top-down disaggregation of traffic emissions generally exhibits low accuracy, the accuracy is significantly higher in compact cities and might be further improved by applying a correction factor for the city center. Therefore, the method can be used by local environmental authorities in cities with limited resources and with little knowledge on the pollution situation to get an overview on the spatial distribution of the emissions generated by traffic activities.

A Synergy Cropland of China by Fusing Multiple Existing Maps and Statistics.

PubMed

Lu, Miao; Wu, Wenbin; You, Liangzhi; Chen, Di; Zhang, Li; Yang, Peng; Tang, Huajun

2017-07-12

Accurate information on cropland extent is critical for scientific research and resource management. Several cropland products from remotely sensed datasets are available. Nevertheless, significant inconsistency exists among these products and the cropland areas estimated from these products differ considerably from statistics. In this study, we propose a hierarchical optimization synergy approach (HOSA) to develop a hybrid cropland map of China, circa 2010, by fusing five existing cropland products, i.e., GlobeLand30, Climate Change Initiative Land Cover (CCI-LC), GlobCover 2009, MODIS Collection 5 (MODIS C5), and MODIS Cropland, and sub-national statistics of cropland area. HOSA simplifies the widely used method of score assignment into two steps, including determination of optimal agreement level and identification of the best product combination. The accuracy assessment indicates that the synergy map has higher accuracy of spatial locations and better consistency with statistics than the five existing datasets individually. This suggests that the synergy approach can improve the accuracy of cropland mapping and enhance consistency with statistics.

Positional Accuracy Assessment of Googleearth in Riyadh

NASA Astrophysics Data System (ADS)

Farah, Ashraf; Algarni, Dafer

2014-06-01

Google Earth is a virtual globe, map and geographical information program that is controlled by Google corporation. It maps the Earth by the superimposition of images obtained from satellite imagery, aerial photography and GIS 3D globe. With millions of users all around the globe, GoogleEarth® has become the ultimate source of spatial data and information for private and public decision-support systems besides many types and forms of social interactions. Many users mostly in developing countries are also using it for surveying applications, the matter that raises questions about the positional accuracy of the Google Earth program. This research presents a small-scale assessment study of the positional accuracy of GoogleEarth® Imagery in Riyadh; capital of Kingdom of Saudi Arabia (KSA). The results show that the RMSE of the GoogleEarth imagery is 2.18 m and 1.51 m for the horizontal and height coordinates respectively.

The utilization of Depth Invariant Index and Principle Component Analysis for mapping seagrass ecosystem of Kotok Island and Karang Bongkok, Indonesia

NASA Astrophysics Data System (ADS)

Manuputty, Agnestesya; Lumban Gaol, Jonson; Bahri Agus, Syamsul; Wayan Nurjaya, I.

2017-01-01

Seagrass perform a variety of functions within ecosystems, and have both economic and ecological values, therefore it has to be kept sustainable. One of the stages to preserve seagrass ecosystems is monitoring by utilizing thespatial data accurately. The purpose of the study was to assess and compare the accuracy of DII and PCA transformationsfor mapping of seagrass ecosystems. Fieldstudy was carried out in Karang Bongkok and Kotok Island waters, in Agustus 2014 and in March 2015. A WorldView-2 image acquisition date of 5 October 2013 was used in the study. The transformations for image processing data were Depth Invariant Index (DII) and Principle Component Analysis (PCA) using Support Vector Machine (SVM) classification. The result shows that benthic habitat mapping of Karang Bongkok using DII and PCA transformations were 72%and 81% overall’s accuracy respectively, whereas of Kotok Island were 83% and 84% overall’s accuracy respectively. There were seven benthic habitat types found in karang Bongkok waters and in Kotok Island namely seagrass, sand, rubble, coral, logoon, sand mix seagrass, and sand mix rubble. PCA transformation was effectively to improve mapping accuracy of sea grass mapping in Kotok Island and Karang Bongkok.

American Society of Photogrammetry and American Congress on Surveying and Mapping, Fall Technical Meeting, ASP Technical Papers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1981-01-01

Various topics in the field of photogrammetry are addressed. Among the subjects discussed are: remote sensing of Gulf Stream dynamics using VHRR satellite imagery an interactive rectification system for remote sensing imagery use of a single photo and digital terrain matrix for point positioning crop type analysis using Landsat digital data use of a fisheye lens in solar energy assessment remote sensing inventory of Rocky Mountain elk habitat Washington state's large scale ortho program educational image processing. Also discussed are: operational advantages of on-line photogrammetric triangulation analysis of fracturation field photogrammetry as a tool for measuring glacier movement double modelmore » orthophotos used for forest inventory mapping map revisioning module for the Kern PG2 stereoplotter assessing accuracy of digital land-use and terrain data accuracy of earthwork calculations from digital elevation data.« less

Aerial photography flight quality assessment with GPS/INS and DEM data

NASA Astrophysics Data System (ADS)

Zhao, Haitao; Zhang, Bing; Shang, Jiali; Liu, Jiangui; Li, Dong; Chen, Yanyan; Zuo, Zhengli; Chen, Zhengchao

2018-01-01

The flight altitude, ground coverage, photo overlap, and other acquisition specifications of an aerial photography flight mission directly affect the quality and accuracy of the subsequent mapping tasks. To ensure smooth post-flight data processing and fulfill the pre-defined mapping accuracy, flight quality assessments should be carried out in time. This paper presents a novel and rigorous approach for flight quality evaluation of frame cameras with GPS/INS data and DEM, using geometric calculation rather than image analysis as in the conventional methods. This new approach is based mainly on the collinearity equations, in which the accuracy of a set of flight quality indicators is derived through a rigorous error propagation model and validated with scenario data. Theoretical analysis and practical flight test of an aerial photography mission using an UltraCamXp camera showed that the calculated photo overlap is accurate enough for flight quality assessment of 5 cm ground sample distance image, using the SRTMGL3 DEM and the POSAV510 GPS/INS data. An even better overlap accuracy could be achieved for coarser-resolution aerial photography. With this new approach, the flight quality evaluation can be conducted on site right after landing, providing accurate and timely information for decision making.

Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data

PubMed Central

Zhang, Geli; Xiao, Xiangming; Dong, Jinwei; Kou, Weili; Jin, Cui; Qin, Yuanwei; Zhou, Yuting; Wang, Jie; Menarguez, Michael Angelo; Biradar, Chandrashekhar

2016-01-01

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting. PMID:27667901

Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data.

PubMed

Zhang, Geli; Xiao, Xiangming; Dong, Jinwei; Kou, Weili; Jin, Cui; Qin, Yuanwei; Zhou, Yuting; Wang, Jie; Menarguez, Michael Angelo; Biradar, Chandrashekhar

2015-08-01

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting.

Spatial Collective Intelligence? credibility, accuracy, and Volunteered Geographic Information

PubMed Central

Spielman, Seth E.

2014-01-01

Collective intelligence is the idea that under the right circumstances collections of individuals are smarter than even the smartest individuals in the group (Suroweiki 2004), that is a group has an “intelligence” that is independent of the intelligence of its members. The ideology of collective intelligence undergirds much of the enthusiasm about the use of “volunteered” or crowdsourced geographic information. Literature from a variety of fields makes clear that not all groups possess collective intelligence, this paper identifies four pre-conditions for the emergence of collective intelligence and then examine the extent to which collectively generated mapping systems satisfy these conditions. However, the “intelligence” collectively generated maps is hard to assess because there are two difficult to reconcile perspectives on map quality- the credibility perspective and the accuracy perspective. Much of the current literature on user generated maps focuses on assessing the quality of individual contributions. However, because user generated maps are complex social systems and because the quality of a contribution is difficult to assess this strategy may not yield an “intelligent” end product. The existing literature on collective intelligence suggests that the structure of groups more important that the intelligence of group members. Applying this idea to user generated suggests that systems should be designed to foster conditions known to produce collective intelligence rather than privileging particular contributions/contributors. The paper concludes with some design recommendations and by considering the implications of collectively generated maps for both expert knowledge and traditional state sponsored mapping programs. PMID:25419184

Evaluation of a Moderate Resolution, Satellite-Based Impervious Surface Map Using an Independent, High-Resolution Validation Dataset

EPA Science Inventory

Given the relatively high cost of mapping impervious surfaces at regional scales, substantial effort is being expended in the development of moderate-resolution, satellite-based methods for estimating impervious surface area (ISA). To rigorously assess the accuracy of these data ...

Accuracy of lineaments mapping from space

NASA Technical Reports Server (NTRS)

Short, Nicholas M.

1989-01-01

The use of Landsat and other space imaging systems for lineaments detection is analyzed in terms of their effectiveness in recognizing and mapping fractures and faults, and the results of several studies providing a quantitative assessment of lineaments mapping accuracies are discussed. The cases under investigation include a Landsat image of the surface overlying a part of the Anadarko Basin of Oklahoma, the Landsat images and selected radar imagery of major lineaments systems distributed over much of Canadian Shield, and space imagery covering a part of the East African Rift in Kenya. It is demonstrated that space imagery can detect a significant portion of a region's fracture pattern, however, significant fractions of faults and fractures recorded on a field-produced geological map are missing from the imagery as it is evident in the Kenya case.

Optimizing remote sensing and GIS tools for mapping and managing the distribution of an invasive mangrove (Rhizophora mangle) on South Molokai, Hawaii

USGS Publications Warehouse

D'Iorio, M.; Jupiter, S.D.; Cochran, S.A.; Potts, D.C.

2007-01-01

In 1902, the Florida red mangrove, Rhizophora mangle L., was introduced to the island of Molokai, Hawaii, and has since colonized nearly 25% of the south coast shoreline. By classifying three kinds of remote sensing imagery, we compared abilities to detect invasive mangrove distributions and to discriminate mangroves from surrounding terrestrial vegetation. Using three analytical techniques, we compared mangrove mapping accuracy for various sensor-technique combinations. ANOVA of accuracy assessments demonstrated significant differences among techniques, but no significant differences among the three sensors. We summarize advantages and disadvantages of each sensor and technique for mapping mangrove distributions in tropical coastal environments.

Evaluation of Masimo signal extraction technology pulse oximetry in anaesthetized pregnant sheep.

PubMed

Quinn, Christopher T; Raisis, Anthea L; Musk, Gabrielle C

2013-03-01

Evaluation of the accuracy of Masimo signal extraction technology (SET) pulse oximetry in anaesthetized late gestational pregnant sheep. Prospective experimental study. Seventeen pregnant Merino ewes. Animals included in study were late gestation ewes undergoing general anaesthesia for Caesarean delivery or foetal surgery in a medical research laboratory. Masimo Radical-7 pulse oximetry (SpO(2) ) measurements were compared to co-oximetry (SaO(2) ) measurements from arterial blood gas analyses. The failure rate of the pulse oximeter was calculated. Accuracy was assessed by Bland & Altman's (2007) limits of agreement method. The effect of mean arterial blood pressure (MAP), perfusion index (PI) and haemoglobin (Hb) concentration on accuracy were assessed by regression analysis. Forty arterial blood samples paired with SpO(2) and blood pressure measurements were obtained. SpO(2) ranged from 42 to 99% and SaO(2) from 43.7 to 99.9%. MAP ranged from 24 to 82 mmHg, PI from 0.1 to 1.56 and Hb concentration from 71 to 114 g L(-1) . Masimo pulse oximetry measurements tended to underestimate oxyhaemoglobin saturation compared to co-oximetry with a bias (mean difference) of -2% and precision (standard deviation of the differences) of 6%. Accuracy appeared to decrease when SpO(2) was <75%, however numbers were too small for statistical comparisons. Hb concentration and PI had no significant effect on accuracy, whereas MAP was negatively correlated with SpO(2) bias. Masimo SET pulse oximetry can provide reliable and continuous monitoring of arterial oxyhaemoglobin saturation in anaesthetized pregnant sheep during clinically relevant levels of cardiopulmonary dysfunction. Further work is needed to assess pulse oximeter function during extreme hypotension and hypoxaemia. © 2012 The Authors. Veterinary Anaesthesia and Analgesia. © 2012 Association of Veterinary Anaesthetists and the American College of Veterinary Anesthesiologists.

A Vegetation Database for the Colorado River Ecosystem from Glen Canyon Dam to the Western Boundary of Grand Canyon National Park, Arizona

USGS Publications Warehouse

Ralston, Barbara E.; Davis, Philip A.; Weber, Robert M.; Rundall, Jill M.

2008-01-01

A vegetation database of the riparian vegetation located within the Colorado River ecosystem (CRE), a subsection of the Colorado River between Glen Canyon Dam and the western boundary of Grand Canyon National Park, was constructed using four-band image mosaics acquired in May 2002. A digital line scanner was flown over the Colorado River corridor in Arizona by ISTAR Americas, using a Leica ADS-40 digital camera to acquire a digital surface model and four-band image mosaics (blue, green, red, and near-infrared) for vegetation mapping. The primary objective of this mapping project was to develop a digital inventory map of vegetation to enable patch- and landscape-scale change detection, and to establish randomized sampling points for ground surveys of terrestrial fauna (principally, but not exclusively, birds). The vegetation base map was constructed through a combination of ground surveys to identify vegetation classes, image processing, and automated supervised classification procedures. Analysis of the imagery and subsequent supervised classification involved multiple steps to evaluate band quality, band ratios, and vegetation texture and density. Identification of vegetation classes involved collection of cover data throughout the river corridor and subsequent analysis using two-way indicator species analysis (TWINSPAN). Vegetation was classified into six vegetation classes, following the National Vegetation Classification Standard, based on cover dominance. This analysis indicated that total area covered by all vegetation within the CRE was 3,346 ha. Considering the six vegetation classes, the sparse shrub (SS) class accounted for the greatest amount of vegetation (627 ha) followed by Pluchea (PLSE) and Tamarix (TARA) at 494 and 366 ha, respectively. The wetland (WTLD) and Prosopis-Acacia (PRGL) classes both had similar areal cover values (227 and 213 ha, respectively). Baccharis-Salix (BAXX) was the least represented at 94 ha. Accuracy assessment of the supervised classification determined that accuracies varied among vegetation classes from 90% to 49%. Causes for low accuracies were similar spectral signatures among vegetation classes. Fuzzy accuracy assessment improved classification accuracies such that Federal mapping standards of 80% accuracies for all classes were met. The scale used to quantify vegetation adequately meets the needs of the stakeholder group. Increasing the scale to meet the U.S. Geological Survey (USGS)-National Park Service (NPS)National Mapping Program's minimum mapping unit of 0.5 ha is unwarranted because this scale would reduce the resolution of some classes (e.g., seep willow/coyote willow would likely be combined with tamarisk). While this would undoubtedly improve classification accuracies, it would not provide the community-level information about vegetation change that would benefit stakeholders. The identification of vegetation classes should follow NPS mapping approaches to complement the national effort and should incorporate the alternative analysis for community identification that is being incorporated into newer NPS mapping efforts. National Vegetation Classification is followed in this report for association- to formation-level categories. Accuracies could be improved by including more environmental variables such as stage elevation in the classification process and incorporating object-based classification methods. Another approach that may address the heterogeneous species issue and classification is to use spectral mixing analysis to estimate the fractional cover of species within each pixel and better quantify the cover of individual species that compose a cover class. Varying flights to capture vegetation at different times of the year might also help separate some vegetation classes, though the cost may be prohibitive. Lastly, photointerpretation instead of automated mapping could be tried. Photointerpretation would likely not improve accuracies in this case, howev

The Importance of Measurement Errors for Deriving Accurate Reference Leaf Area Index Maps for Validation of Moderate-Resolution Satellite LAI Products

NASA Technical Reports Server (NTRS)

Huang, Dong; Yang, Wenze; Tan, Bin; Rautiainen, Miina; Zhang, Ping; Hu, Jiannan; Shabanov, Nikolay V.; Linder, Sune; Knyazikhin, Yuri; Myneni, Ranga B.

2006-01-01

The validation of moderate-resolution satellite leaf area index (LAI) products such as those operationally generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor data requires reference LAI maps developed from field LAI measurements and fine-resolution satellite data. Errors in field measurements and satellite data determine the accuracy of the reference LAI maps. This paper describes a method by which reference maps of known accuracy can be generated with knowledge of errors in fine-resolution satellite data. The method is demonstrated with data from an international field campaign in a boreal coniferous forest in northern Sweden, and Enhanced Thematic Mapper Plus images. The reference LAI map thus generated is used to assess modifications to the MODIS LAI/fPAR algorithm recently implemented to derive the next generation of the MODIS LAI/fPAR product for this important biome type.

High resolution mapping of development in the wildland-urban interface using object based image extraction.

PubMed

Caggiano, Michael D; Tinkham, Wade T; Hoffman, Chad; Cheng, Antony S; Hawbaker, Todd J

2016-10-01

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m 2 ) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting.

High resolution mapping of development in the wildland-urban interface using object based image extraction

USGS Publications Warehouse

Caggiano, Michael D.; Tinkham, Wade T.; Hoffman, Chad; Cheng, Antony S.; Hawbaker, Todd J.

2016-01-01

The wildland-urban interface (WUI), the area where human development encroaches on undeveloped land, is expanding throughout the western United States resulting in increased wildfire risk to homes and communities. Although census based mapping efforts have provided insights into the pattern of development and expansion of the WUI at regional and national scales, these approaches do not provide sufficient detail for fine-scale fire and emergency management planning, which requires maps of individual building locations. Although fine-scale maps of the WUI have been developed, they are often limited in their spatial extent, have unknown accuracies and biases, and are costly to update over time. In this paper we assess a semi-automated Object Based Image Analysis (OBIA) approach that utilizes 4-band multispectral National Aerial Image Program (NAIP) imagery for the detection of individual buildings within the WUI. We evaluate this approach by comparing the accuracy and overall quality of extracted buildings to a building footprint control dataset. In addition, we assessed the effects of buffer distance, topographic conditions, and building characteristics on the accuracy and quality of building extraction. The overall accuracy and quality of our approach was positively related to buffer distance, with accuracies ranging from 50 to 95% for buffer distances from 0 to 100 m. Our results also indicate that building detection was sensitive to building size, with smaller outbuildings (footprints less than 75 m2) having detection rates below 80% and larger residential buildings having detection rates above 90%. These findings demonstrate that this approach can successfully identify buildings in the WUI in diverse landscapes while achieving high accuracies at buffer distances appropriate for most fire management applications while overcoming cost and time constraints associated with traditional approaches. This study is unique in that it evaluates the ability of an OBIA approach to extract highly detailed data on building locations in a WUI setting.

Vegetation inventory, mapping, and classification report, Fort Bowie National Historic Site

USGS Publications Warehouse

Studd, Sarah; Fallon, Elizabeth; Crumbacher, Laura; Drake, Sam; Villarreal, Miguel

2013-01-01

A vegetation mapping and characterization effort was conducted at Fort Bowie National Historic Site in 2008-10 by the Sonoran Desert Network office in collaboration with researchers from the Office of Arid lands studies, Remote Sensing Center at the University of Arizona. This vegetation mapping effort was completed under the National Park Service Vegetation Inventory program which aims to complete baseline mapping inventories at over 270 national park units. The vegetation map data was collected to provide park managers with a digital map product that met national standards of spatial and thematic accuracy, while also placing the vegetation into a regional and even national context. Work comprised of three major field phases 1) concurrent field-based classification data collection and mapping (map unit delineation), 2) development of vegetation community types at the National Vegetation Classification alliance or association level and 3) map accuracy assessment. Phase 1 was completed in late 2008 and early 2009. Community type descriptions were drafted to meet the then-current hierarchy (version 1) of the National Vegetation Classification System (NVCS) and these were applied to each of the mapped areas. This classification was developed from both plot level data and censused polygon data (map units) as this project was conducted as a concurrent mapping and classification effort. The third stage of accuracy assessment completed in the fall of 2010 consisted of a complete census of each map unit and was conducted almost entirely by park staff. Following accuracy assessment the map was amended where needed and final products were developed including this report, a digital map and full vegetation descriptions. Fort Bowie National Historic Site covers only 1000 acres yet has a relatively complex landscape, topography and geology. A total of 16 distinct communities were described and mapped at Fort Bowie NHS. These ranged from lush riparian woodlands lining the ephemeral washes dominated by Ash (Fraxinus), Walnut (Juglans) and Hackberry (Celtis) to drier upland sites typical of desert scrub and semi-desert grassland communities. These shrublands boast a diverse mixture of shrubs, succulents and perennial grasses. In many places the vegetation could be seen to echo the history of the fort site, with management of shrub encroachment apparent in the grasslands and the paucity of trees evidence of historic cutting for timber and fire wood. Seven of the 16 vegetation types were ‘accepted’ types within the NVC while the others have been described here as specific to FOBO and have proposed status within the NVC. The map was designed to facilitate ecologically-based natural resources management and research. The map is in digital format within a geodatabase structure that allows for complex relationships to be established between spatial and tabular data, and makes accessing the product easy and seamless. The GIS format allows user flexibility and will also enable updates to be made as new information becomes available (such as revised NVC codes or vegetation type names) or in the event of major disturbance events that could impact the vegetation.

Assessing Accuracy in Varying LIDAR Data Point Densities in Digital Elevation Maps

DTIC Science & Technology

2008-09-01

23 1. MOLA ...pentagon for a circular field-of-view that is centered on nadir (Dubayah 5)........................................23 Figure 13. Using MOLA data...through June of 2000, the MOLA Science Team has produced very high resolution topographic shade maps of Mars. This figure is from 0 to 360 degrees E

Inexpensive Tools To Quantify And Map Vegetative Cover For Large-Scale Research Or Management Decisions.

USDA-ARS?s Scientific Manuscript database

Vegetative cover can be quantified quickly and consistently and often at lower cost with image analysis of color digital images than with visual assessments. Image-based mapping of vegetative cover for large-scale research and management decisions can now be considered with the accuracy of these met...

Spatial distribution of arable and abandoned land across former Soviet Union countries

NASA Astrophysics Data System (ADS)

Lesiv, Myroslava; Schepaschenko, Dmitry; Moltchanova, Elena; Bun, Rostyslav; Dürauer, Martina; Prishchepov, Alexander V.; Schierhorn, Florian; Estel, Stephan; Kuemmerle, Tobias; Alcántara, Camilo; Kussul, Natalia; Shchepashchenko, Maria; Kutovaya, Olga; Martynenko, Olga; Karminov, Viktor; Shvidenko, Anatoly; Havlik, Petr; Kraxner, Florian; See, Linda; Fritz, Steffen

2018-04-01

Knowledge of the spatial distribution of agricultural abandonment following the collapse of the Soviet Union is highly uncertain. To help improve this situation, we have developed a new map of arable and abandoned land for 2010 at a 10 arc-second resolution. We have fused together existing land cover and land use maps at different temporal and spatial scales for the former Soviet Union (fSU) using a training data set collected from visual interpretation of very high resolution (VHR) imagery. We have also collected an independent validation data set to assess the map accuracy. The overall accuracies of the map by region and country, i.e. Caucasus, Belarus, Kazakhstan, Republic of Moldova, Russian Federation and Ukraine, are 90±2%, 84±2%, 92±1%, 78±3%, 95±1%, 83±2%, respectively. This new product can be used for numerous applications including the modelling of biogeochemical cycles, land-use modelling, the assessment of trade-offs between ecosystem services and land-use potentials (e.g., agricultural production), among others.

Spatial distribution of arable and abandoned land across former Soviet Union countries.

PubMed

Lesiv, Myroslava; Schepaschenko, Dmitry; Moltchanova, Elena; Bun, Rostyslav; Dürauer, Martina; Prishchepov, Alexander V; Schierhorn, Florian; Estel, Stephan; Kuemmerle, Tobias; Alcántara, Camilo; Kussul, Natalia; Shchepashchenko, Maria; Kutovaya, Olga; Martynenko, Olga; Karminov, Viktor; Shvidenko, Anatoly; Havlik, Petr; Kraxner, Florian; See, Linda; Fritz, Steffen

2018-04-03

Knowledge of the spatial distribution of agricultural abandonment following the collapse of the Soviet Union is highly uncertain. To help improve this situation, we have developed a new map of arable and abandoned land for 2010 at a 10 arc-second resolution. We have fused together existing land cover and land use maps at different temporal and spatial scales for the former Soviet Union (fSU) using a training data set collected from visual interpretation of very high resolution (VHR) imagery. We have also collected an independent validation data set to assess the map accuracy. The overall accuracies of the map by region and country, i.e. Caucasus, Belarus, Kazakhstan, Republic of Moldova, Russian Federation and Ukraine, are 90±2%, 84±2%, 92±1%, 78±3%, 95±1%, 83±2%, respectively. This new product can be used for numerous applications including the modelling of biogeochemical cycles, land-use modelling, the assessment of trade-offs between ecosystem services and land-use potentials (e.g., agricultural production), among others.

Spatial distribution of arable and abandoned land across former Soviet Union countries

PubMed Central

Lesiv, Myroslava; Schepaschenko, Dmitry; Moltchanova, Elena; Bun, Rostyslav; Dürauer, Martina; Prishchepov, Alexander V.; Schierhorn, Florian; Estel, Stephan; Kuemmerle, Tobias; Alcántara, Camilo; Kussul, Natalia; Shchepashchenko, Maria; Kutovaya, Olga; Martynenko, Olga; Karminov, Viktor; Shvidenko, Anatoly; Havlik, Petr; Kraxner, Florian; See, Linda; Fritz, Steffen

2018-01-01

Knowledge of the spatial distribution of agricultural abandonment following the collapse of the Soviet Union is highly uncertain. To help improve this situation, we have developed a new map of arable and abandoned land for 2010 at a 10 arc-second resolution. We have fused together existing land cover and land use maps at different temporal and spatial scales for the former Soviet Union (fSU) using a training data set collected from visual interpretation of very high resolution (VHR) imagery. We have also collected an independent validation data set to assess the map accuracy. The overall accuracies of the map by region and country, i.e. Caucasus, Belarus, Kazakhstan, Republic of Moldova, Russian Federation and Ukraine, are 90±2%, 84±2%, 92±1%, 78±3%, 95±1%, 83±2%, respectively. This new product can be used for numerous applications including the modelling of biogeochemical cycles, land-use modelling, the assessment of trade-offs between ecosystem services and land-use potentials (e.g., agricultural production), among others. PMID:29611843

Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited.

PubMed

Thomas, Cibu; Ye, Frank Q; Irfanoglu, M Okan; Modi, Pooja; Saleem, Kadharbatcha S; Leopold, David A; Pierpaoli, Carlo

2014-11-18

Tractography based on diffusion-weighted MRI (DWI) is widely used for mapping the structural connections of the human brain. Its accuracy is known to be limited by technical factors affecting in vivo data acquisition, such as noise, artifacts, and data undersampling resulting from scan time constraints. It generally is assumed that improvements in data quality and implementation of sophisticated tractography methods will lead to increasingly accurate maps of human anatomical connections. However, assessing the anatomical accuracy of DWI tractography is difficult because of the lack of independent knowledge of the true anatomical connections in humans. Here we investigate the future prospects of DWI-based connectional imaging by applying advanced tractography methods to an ex vivo DWI dataset of the macaque brain. The results of different tractography methods were compared with maps of known axonal projections from previous tracer studies in the macaque. Despite the exceptional quality of the DWI data, none of the methods demonstrated high anatomical accuracy. The methods that showed the highest sensitivity showed the lowest specificity, and vice versa. Additionally, anatomical accuracy was highly dependent upon parameters of the tractography algorithm, with different optimal values for mapping different pathways. These results suggest that there is an inherent limitation in determining long-range anatomical projections based on voxel-averaged estimates of local fiber orientation obtained from DWI data that is unlikely to be overcome by improvements in data acquisition and analysis alone.

Performance Assessment of Integrated Sensor Orientation with a Low-Cost Gnss Receiver

NASA Astrophysics Data System (ADS)

Rehak, M.; Skaloud, J.

2017-08-01

Mapping with Micro Aerial Vehicles (MAVs whose weight does not exceed 5 kg) is gaining importance in applications such as corridor mapping, road and pipeline inspections, or mapping of large areas with homogeneous surface structure, e.g. forest or agricultural fields. In these challenging scenarios, integrated sensor orientation (ISO) improves effectiveness and accuracy. Furthermore, in block geometry configurations, this mode of operation allows mapping without ground control points (GCPs). Accurate camera positions are traditionally determined by carrier-phase GNSS (Global Navigation Satellite System) positioning. However, such mode of positioning has strong requirements on receiver's and antenna's performance. In this article, we present a mapping project in which we employ a single-frequency, low-cost (< 100) GNSS receiver on a MAV. The performance of the low-cost receiver is assessed by comparing its trajectory with a reference trajectory obtained by a survey-grade, multi-frequency GNSS receiver. In addition, the camera positions derived from these two trajectories are used as observations in bundle adjustment (BA) projects and mapping accuracy is evaluated at check points (ChP). Several BA scenarios are considered with absolute and relative aerial position control. Additionally, the presented experiments show the possibility of BA to determine a camera-antenna spatial offset, so-called lever-arm.

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies.

PubMed

Devaney, John; Barrett, Brian; Barrett, Frank; Redmond, John; O Halloran, John

2015-01-01

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1-98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

PubMed Central

Devaney, John; Barrett, Brian; Barrett, Frank; Redmond, John; O`Halloran, John

2015-01-01

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting. PMID:26262681

Development and Assessment of a Geographic Knowledge-Based Model for Mapping Suitable Areas for Rift Valley Fever Transmission in Eastern Africa

PubMed Central

Tran, Annelise; Trevennec, Carlène; Lutwama, Julius; Sserugga, Joseph; Gély, Marie; Pittiglio, Claudia; Pinto, Julio; Chevalier, Véronique

2016-01-01

Rift Valley fever (RVF), a mosquito-borne disease affecting ruminants and humans, is one of the most important viral zoonoses in Africa. The objective of the present study was to develop a geographic knowledge-based method to map the areas suitable for RVF amplification and RVF spread in four East African countries, namely, Kenya, Tanzania, Uganda and Ethiopia, and to assess the predictive accuracy of the model using livestock outbreak data from Kenya and Tanzania. Risk factors and their relative importance regarding RVF amplification and spread were identified from a literature review. A numerical weight was calculated for each risk factor using an analytical hierarchy process. The corresponding geographic data were collected, standardized and combined based on a weighted linear combination to produce maps of the suitability for RVF transmission. The accuracy of the resulting maps was assessed using RVF outbreak locations in livestock reported in Kenya and Tanzania between 1998 and 2012 and the ROC curve analysis. Our results confirmed the capacity of the geographic information system-based multi-criteria evaluation method to synthesize available scientific knowledge and to accurately map (AUC = 0.786; 95% CI [0.730–0.842]) the spatial heterogeneity of RVF suitability in East Africa. This approach provides users with a straightforward and easy update of the maps according to data availability or the further development of scientific knowledge. PMID:27631374

Accuracy assessment of a mobile terrestrial lidar survey at Padre Island National Seashore

USGS Publications Warehouse

Lim, Samsung; Thatcher, Cindy A.; Brock, John C.; Kimbrow, Dustin R.; Danielson, Jeffrey J.; Reynolds, B.J.

2013-01-01

The higher point density and mobility of terrestrial laser scanning (light detection and ranging (lidar)) is desired when extremely detailed elevation data are needed for mapping vertically orientated complex features such as levees, dunes, and cliffs, or when highly accurate data are needed for monitoring geomorphic changes. Mobile terrestrial lidar scanners have the capability for rapid data collection on a larger spatial scale compared with tripod-based terrestrial lidar, but few studies have examined the accuracy of this relatively new mapping technology. For this reason, we conducted a field test at Padre Island National Seashore of a mobile lidar scanner mounted on a sport utility vehicle and integrated with a position and orientation system. The purpose of the study was to assess the vertical and horizontal accuracy of data collected by the mobile terrestrial lidar system, which is georeferenced to the Universal Transverse Mercator coordinate system and the North American Vertical Datum of 1988. To accomplish the study objectives, independent elevation data were collected by conducting a high-accuracy global positioning system survey to establish the coordinates and elevations of 12 targets spaced throughout the 12 km transect. These independent ground control data were compared to the lidar scanner-derived elevations to quantify the accuracy of the mobile lidar system. The performance of the mobile lidar system was also tested at various vehicle speeds and scan density settings (e.g. field of view and linear point spacing) to estimate the optimal parameters for desired point density. After adjustment of the lever arm parameters, the final point cloud accuracy was 0.060 m (east), 0.095 m (north), and 0.053 m (height). The very high density of the resulting point cloud was sufficient to map fine-scale topographic features, such as the complex shape of the sand dunes.

Coastal flood inundation monitoring with Satellite C-band and L-band Synthetic Aperture Radar data

USGS Publications Warehouse

Ramsey, Elijah W.; Rangoonwala, Amina; Bannister, Terri

2013-01-01

Satellite Synthetic Aperture Radar (SAR) was evaluated as a method to operationally monitor the occurrence and distribution of storm- and tidal-related flooding of spatially extensive coastal marshes within the north-central Gulf of Mexico. Maps representing the occurrence of marsh surface inundation were created from available Advanced Land Observation Satellite (ALOS) Phased Array type L-Band SAR (PALSAR) (L-band) (21 scenes with HH polarizations in Wide Beam [100 m]) data and Environmental Satellite (ENVISAT) Advanced SAR (ASAR) (C-band) data (24 scenes with VV and HH polarizations in Wide Swath [150 m]) during 2006-2009 covering 500 km of the Louisiana coastal zone. Mapping was primarily based on a decrease in backscatter between reference and target scenes, and as an extension of previous studies, the flood inundation mapping performance was assessed by the degree of correspondence between inundation mapping and inland water levels. Both PALSAR- and ASAR-based mapping at times were based on suboptimal reference scenes; however, ASAR performance seemed more sensitive to reference-scene quality and other types of scene variability. Related to water depth, PALSAR and ASAR mapping accuracies tended to be lower when water depths were shallow and increased as water levels decreased below or increased above the ground surface, but this pattern was more pronounced with ASAR. Overall, PALSAR-based inundation accuracies averaged 84% (n = 160), while ASAR-based mapping accuracies averaged 62% (n = 245).

The Effect of Training on Accuracy of Angle Estimation.

ERIC Educational Resources Information Center

Waller, T. Gary; Wright, Robert H.

This report describes a study to determine the effect of training on accuracy in estimating angles. The study was part of a research program directed toward improving navigation techniques for low-level flight in Army aircraft and was made to assess the feasibility of visually estimating angles on a map in order to determine angles of drift.…

Thematic accuracy of the NLCD 2001 land cover for the conterminous United States

USGS Publications Warehouse

Wickham, J.D.; Stehman, S.V.; Fry, J.A.; Smith, J.H.; Homer, Collin G.

2010-01-01

The land-cover thematic accuracy of NLCD 2001 was assessed from a probability-sample of 15,000 pixels. Nationwide, NLCD 2001 overall Anderson Level II and Level I accuracies were 78.7% and 85.3%, respectively. By comparison, overall accuracies at Level II and Level I for the NLCD 1992 were 58% and 80%. Forest and cropland were two classes showing substantial improvements in accuracy in NLCD 2001 relative to NLCD 1992. NLCD 2001 forest and cropland user's accuracies were 87% and 82%, respectively, compared to 80% and 43% for NLCD 1992. Accuracy results are reported for 10 geographic regions of the United States, with regional overall accuracies ranging from 68% to 86% for Level II and from 79% to 91% at Level I. Geographic variation in class-specific accuracy was strongly associated with the phenomenon that regionally more abundant land-cover classes had higher accuracy. Accuracy estimates based on several definitions of agreement are reported to provide an indication of the potential impact of reference data error on accuracy. Drawing on our experience from two NLCD national accuracy assessments, we discuss the use of designs incorporating auxiliary data to more seamlessly quantify reference data quality as a means to further advance thematic map accuracy assessment.

Developing Land Use Land Cover Maps for the Lower Mekong Basin to Aid SWAT Hydrologic Modeling

NASA Astrophysics Data System (ADS)

Spruce, J.; Bolten, J. D.; Srinivasan, R.

2017-12-01

This presentation discusses research to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB). Funded by a NASA ROSES Disasters grant, the main objective was to produce updated LULC maps to aid the Mekong River Commission's (MRC's) Soil and Water Assessment Tool (SWAT) hydrologic model. In producing needed LULC maps, temporally processed MODIS monthly NDVI data for 2010 were used as the primary data source for classifying regionally prominent forest and agricultural types. The MODIS NDVI data was derived from processing MOD09 and MYD09 8-day reflectance data with the Time Series Product Tool, a custom software package. Circa 2010 Landsat multispectral data from the dry season were processed into top of atmosphere reflectance mosaics and then classified to derive certain locally common LULC types, such as urban areas and industrial forest plantations. Unsupervised ISODATA clustering was used to derive most LULC classifications. GIS techniques were used to merge MODIS and Landsat classifications into final LULC maps for Sub-Basins (SBs) 1-8 of the LMB. The final LULC maps were produced at 250-meter resolution and delivered to the MRC for use in SWAT modeling for the LMB. A map accuracy assessment was performed for the SB 7 LULC map with 14 classes. This assessment was performed by comparing random locations for sampled LULC types to geospatial reference data such as Landsat RGBs, MODIS NDVI phenologic profiles, high resolution satellite data from Google Map/Earth, and other reference data from the MRC (e.g., crop calendars). LULC accuracy assessment results for SB 7 indicated an overall agreement to reference data of 81% at full scheme specificity. However, by grouping 3 deciduous forest classes into 1 class, the overall agreement improved to 87%. The project enabled updated LULC maps, plus more specific rice types were classified compared to the previous LULC maps. The LULC maps from this project should improve the use of SWAT for modeling hydrology in the LMB, plus improve water and disaster management in a region vulnerable to flooding, droughts, and anthropogenic change (e.g., from dam building and other LULC change).

A computerized model for integrating the physical environmental factors into metropolitan landscape planning

Treesearch

Julius Gy Fabos; Kimball H. Ferris

1977-01-01

This paper justifies and illustrates (in simplified form) a landscape planning approach to the environmental management of the metropolitan landscape. The model utilizes a computerized assessment and mapping system, which exhibits a recent advancement in computer technology that allows for greater accuracy and the weighting of different values when mapping at the...

Mapping simulated scenes with skeletal remains using differential GPS in open environments: an assessment of accuracy and practicality.

PubMed

Walter, Brittany S; Schultz, John J

2013-05-10

Scene mapping is an integral aspect of processing a scene with scattered human remains. By utilizing the appropriate mapping technique, investigators can accurately document the location of human remains and maintain a precise geospatial record of evidence. One option that has not received much attention for mapping forensic evidence is the differential global positioning (DGPS) unit, as this technology now provides decreased positional error suitable for mapping scenes. Because of the lack of knowledge concerning this utility in mapping a scene, controlled research is necessary to determine the practicality of using newer and enhanced DGPS units in mapping scattered human remains. The purpose of this research was to quantify the accuracy of a DGPS unit for mapping skeletal dispersals and to determine the applicability of this utility in mapping a scene with dispersed remains. First, the accuracy of the DGPS unit in open environments was determined using known survey markers in open areas. Secondly, three simulated scenes exhibiting different types of dispersals were constructed and mapped in an open environment using the DGPS. Variables considered during data collection included the extent of the dispersal, data collection time, data collected on different days, and different postprocessing techniques. Data were differentially postprocessed and compared in a geographic information system (GIS) to evaluate the most efficient recordation methods. Results of this study demonstrate that the DGPS is a viable option for mapping dispersed human remains in open areas. The accuracy of collected point data was 11.52 and 9.55 cm for 50- and 100-s collection times, respectfully, and the orientation and maximum length of long bones was maintained. Also, the use of error buffers for point data of bones in maps demonstrated the error of the DGPS unit, while showing that the context of the dispersed skeleton was accurately maintained. Furthermore, the application of a DGPS for accurate scene mapping is discussed and guidelines concerning the implementation of this technology for mapping human scattered skeletal remains in open environments are provided. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier

USGS Publications Warehouse

Friesz, Aaron M.; Wylie, Bruce K.; Howard, Daniel M.

2017-01-01

Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) are a series of commonly used crop cover maps for the conterminous United States (CONUS) that span from 2008 to 2013. In this investigation, we sought to contribute to the availability of consistent CONUS crop cover maps by extending temporal coverage of the NASS CDL archive back eight additional years to 2000 by creating annual NASS CDL-like crop cover maps derived from a classification tree model algorithm. We used over 11 million records to train a classification tree algorithm and develop a crop classification model (CCM). The model was used to create crop cover maps for the CONUS for years 2000–2013 at 250 m spatial resolution. The CCM and the maps for years 2008–2013 were assessed for accuracy relative to resampled NASS CDLs. The CCM performed well against a withheld test data set with a model prediction accuracy of over 90%. The assessment of the crop cover maps indicated that the model performed well spatially, placing crop cover pixels within their known domains; however, the model did show a bias towards the ‘Other’ crop cover class, which caused frequent misclassifications of pixels around the periphery of large crop cover patch clusters and of pixels that form small, sparsely dispersed crop cover patches.

Toward an operational framework for fine-scale urban land-cover mapping in Wallonia using submeter remote sensing and ancillary vector data

NASA Astrophysics Data System (ADS)

Beaumont, Benjamin; Grippa, Tais; Lennert, Moritz; Vanhuysse, Sabine; Stephenne, Nathalie; Wolff, Eléonore

2017-07-01

Encouraged by the EU INSPIRE directive requirements and recommendations, the Walloon authorities, similar to other EU regional or national authorities, want to develop operational land-cover (LC) and land-use (LU) mapping methods using existing geodata. Urban planners and environmental monitoring stakeholders of Wallonia have to rely on outdated, mixed, and incomplete LC and LU information. The current reference map is 10-years old. The two object-based classification methods, i.e., a rule- and a classifier-based method, for detailed regional urban LC mapping are compared. The added value of using the different existing geospatial datasets in the process is assessed. This includes the comparison between satellite and aerial optical data in terms of mapping accuracies, visual quality of the map, costs, processing, data availability, and property rights. The combination of spectral, tridimensional, and vector data provides accuracy values close to 0.90 for mapping the LC into nine categories with a minimum mapping unit of 15 m2. Such a detailed LC map offers opportunities for fine-scale environmental and spatial planning activities. Still, the regional application poses challenges regarding automation, big data handling, and processing time, which are discussed.

Quality Analysis of Open Street Map Data

NASA Astrophysics Data System (ADS)

Wang, M.; Li, Q.; Hu, Q.; Zhou, M.

2013-05-01

Crowd sourcing geographic data is an opensource geographic data which is contributed by lots of non-professionals and provided to the public. The typical crowd sourcing geographic data contains GPS track data like OpenStreetMap, collaborative map data like Wikimapia, social websites like Twitter and Facebook, POI signed by Jiepang user and so on. These data will provide canonical geographic information for pubic after treatment. As compared with conventional geographic data collection and update method, the crowd sourcing geographic data from the non-professional has characteristics or advantages of large data volume, high currency, abundance information and low cost and becomes a research hotspot of international geographic information science in the recent years. Large volume crowd sourcing geographic data with high currency provides a new solution for geospatial database updating while it need to solve the quality problem of crowd sourcing geographic data obtained from the non-professionals. In this paper, a quality analysis model for OpenStreetMap crowd sourcing geographic data is proposed. Firstly, a quality analysis framework is designed based on data characteristic analysis of OSM data. Secondly, a quality assessment model for OSM data by three different quality elements: completeness, thematic accuracy and positional accuracy is presented. Finally, take the OSM data of Wuhan for instance, the paper analyses and assesses the quality of OSM data with 2011 version of navigation map for reference. The result shows that the high-level roads and urban traffic network of OSM data has a high positional accuracy and completeness so that these OSM data can be used for updating of urban road network database.

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

NASA Astrophysics Data System (ADS)

Roy, P. S.; Behera, M. D.; Murthy, M. S. R.; Roy, Arijit; Singh, Sarnam; Kushwaha, S. P. S.; Jha, C. S.; Sudhakar, S.; Joshi, P. K.; Reddy, Ch. Sudhakar; Gupta, Stutee; Pujar, Girish; Dutt, C. B. S.; Srivastava, V. K.; Porwal, M. C.; Tripathi, Poonam; Singh, J. S.; Chitale, Vishwas; Skidmore, A. K.; Rajshekhar, G.; Kushwaha, Deepak; Karnatak, Harish; Saran, Sameer; Giriraj, A.; Padalia, Hitendra; Kale, Manish; Nandy, Subrato; Jeganathan, C.; Singh, C. P.; Biradar, C. M.; Pattanaik, Chiranjibi; Singh, D. K.; Devagiri, G. M.; Talukdar, Gautam; Panigrahy, Rabindra K.; Singh, Harnam; Sharma, J. R.; Haridasan, K.; Trivedi, Shivam; Singh, K. P.; Kannan, L.; Daniel, M.; Misra, M. K.; Niphadkar, Madhura; Nagabhatla, Nidhi; Prasad, Nupoor; Tripathi, O. P.; Prasad, P. Rama Chandra; Dash, Pushpa; Qureshi, Qamer; Tripathi, S. K.; Ramesh, B. R.; Gowda, Balakrishnan; Tomar, Sanjay; Romshoo, Shakil; Giriraj, Shilpa; Ravan, Shirish A.; Behera, Soumit Kumar; Paul, Subrato; Das, Ashesh Kumar; Ranganath, B. K.; Singh, T. P.; Sahu, T. R.; Shankar, Uma; Menon, A. R. R.; Srivastava, Gaurav; Neeti; Sharma, Subrat; Mohapatra, U. B.; Peddi, Ashok; Rashid, Humayun; Salroo, Irfan; Krishna, P. Hari; Hajra, P. K.; Vergheese, A. O.; Matin, Shafique; Chaudhary, Swapnil A.; Ghosh, Sonali; Lakshmi, Udaya; Rawat, Deepshikha; Ambastha, Kalpana; Malik, Akhtar H.; Devi, B. S. S.; Gowda, Balakrishna; Sharma, K. C.; Mukharjee, Prashant; Sharma, Ajay; Davidar, Priya; Raju, R. R. Venkata; Katewa, S. S.; Kant, Shashi; Raju, Vatsavaya S.; Uniyal, B. P.; Debnath, Bijan; Rout, D. K.; Thapa, Rajesh; Joseph, Shijo; Chhetri, Pradeep; Ramachandran, Reshma M.

2015-07-01

A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. We used 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge's life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in).

Spectral performance of a whole-body research photon counting detector CT: quantitative accuracy in derived image sets

NASA Astrophysics Data System (ADS)

Leng, Shuai; Zhou, Wei; Yu, Zhicong; Halaweish, Ahmed; Krauss, Bernhard; Schmidt, Bernhard; Yu, Lifeng; Kappler, Steffen; McCollough, Cynthia

2017-09-01

Photon-counting computed tomography (PCCT) uses a photon counting detector to count individual photons and allocate them to specific energy bins by comparing photon energy to preset thresholds. This enables simultaneous multi-energy CT with a single source and detector. Phantom studies were performed to assess the spectral performance of a research PCCT scanner by assessing the accuracy of derived images sets. Specifically, we assessed the accuracy of iodine quantification in iodine map images and of CT number accuracy in virtual monoenergetic images (VMI). Vials containing iodine with five known concentrations were scanned on the PCCT scanner after being placed in phantoms representing the attenuation of different size patients. For comparison, the same vials and phantoms were also scanned on 2nd and 3rd generation dual-source, dual-energy scanners. After material decomposition, iodine maps were generated, from which iodine concentration was measured for each vial and phantom size and compared with the known concentration. Additionally, VMIs were generated and CT number accuracy was compared to the reference standard, which was calculated based on known iodine concentration and attenuation coefficients at each keV obtained from the U.S. National Institute of Standards and Technology (NIST). Results showed accurate iodine quantification (root mean square error of 0.5 mgI/cc) and accurate CT number of VMIs (percentage error of 8.9%) using the PCCT scanner. The overall performance of the PCCT scanner, in terms of iodine quantification and VMI CT number accuracy, was comparable to that of EID-based dual-source, dual-energy scanners.

Accuracy assessment of seven global land cover datasets over China

NASA Astrophysics Data System (ADS)

Yang, Yongke; Xiao, Pengfeng; Feng, Xuezhi; Li, Haixing

2017-03-01

Land cover (LC) is the vital foundation to Earth science. Up to now, several global LC datasets have arisen with efforts of many scientific communities. To provide guidelines for data usage over China, nine LC maps from seven global LC datasets (IGBP DISCover, UMD, GLC, MCD12Q1, GLCNMO, CCI-LC, and GlobeLand30) were evaluated in this study. First, we compared their similarities and discrepancies in both area and spatial patterns, and analysed their inherent relations to data sources and classification schemes and methods. Next, five sets of validation sample units (VSUs) were collected to calculate their accuracy quantitatively. Further, we built a spatial analysis model and depicted their spatial variation in accuracy based on the five sets of VSUs. The results show that, there are evident discrepancies among these LC maps in both area and spatial patterns. For LC maps produced by different institutes, GLC 2000 and CCI-LC 2000 have the highest overall spatial agreement (53.8%). For LC maps produced by same institutes, overall spatial agreement of CCI-LC 2000 and 2010, and MCD12Q1 2001 and 2010 reach up to 99.8% and 73.2%, respectively; while more efforts are still needed if we hope to use these LC maps as time series data for model inputting, since both CCI-LC and MCD12Q1 fail to represent the rapid changing trend of several key LC classes in the early 21st century, in particular urban and built-up, snow and ice, water bodies, and permanent wetlands. With the highest spatial resolution, the overall accuracy of GlobeLand30 2010 is 82.39%. For the other six LC datasets with coarse resolution, CCI-LC 2010/2000 has the highest overall accuracy, and following are MCD12Q1 2010/2001, GLC 2000, GLCNMO 2008, IGBP DISCover, and UMD in turn. Beside that all maps exhibit high accuracy in homogeneous regions; local accuracies in other regions are quite different, particularly in Farming-Pastoral Zone of North China, mountains in Northeast China, and Southeast Hills. Special attention should be paid for data users who are interested in these regions.

Land cover

USGS Publications Warehouse

Jorgenson, Janet C.; Joria, Peter C.; Douglas, David C.; Douglas, David C.; Reynolds, Patricia E.; Rhode, E.B.

2002-01-01

Documenting the distribution of land-cover types on the Arctic National Wildlife Refuge coastal plain is the foundation for impact assessment and mitigation of potential oil exploration and development. Vegetation maps facilitate wildlife studies by allowing biologists to quantify the availability of important wildlife habitats, investigate the relationships between animal locations and the distribution or juxtaposition of habitat types, and assess or extrapolate habitat characteristics across regional areas.To meet the needs of refuge managers and biologists, satellite imagery was chosen as the most cost-effective method for mapping the large, remote landscape of the 1002 Area.Objectives of our study were the following: 1) evaluate a vegetation classification scheme for use in mapping. 2) determine optimal methods for producing a satellite-based vegetation map that adequately met the needs of the wildlife research and management objectives; 3) produce a digital vegetation map for the Arctic Refuge coastal plain using Lands at-Thematic Mapper(TM) satellite imagery, existing geobotanical classifications, ground data, and aerial photographs, and 4) perform an accuracy assessment of the map.

Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya.

PubMed

Richard, Kyalo; Abdel-Rahman, Elfatih M; Subramanian, Sevgan; Nyasani, Johnson O; Thiel, Michael; Jozani, Hosein; Borgemeister, Christian; Landmann, Tobias

2017-11-03

Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93% was attained for the LULC classification, while the class accuracies (PA: producer's accuracy and UA: user's accuracy) for the two cropping systems were consistently above 85%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10-20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.

Assessment of the Quality of Digital Terrain Model Produced from Unmanned Aerial System Imagery

NASA Astrophysics Data System (ADS)

Kosmatin Fras, M.; Kerin, A.; Mesarič, M.; Peterman, V.; Grigillo, D.

2016-06-01

Production of digital terrain model (DTM) is one of the most usual tasks when processing photogrammetric point cloud generated from Unmanned Aerial System (UAS) imagery. The quality of the DTM produced in this way depends on different factors: the quality of imagery, image orientation and camera calibration, point cloud filtering, interpolation methods etc. However, the assessment of the real quality of DTM is very important for its further use and applications. In this paper we first describe the main steps of UAS imagery acquisition and processing based on practical test field survey and data. The main focus of this paper is to present the approach to DTM quality assessment and to give a practical example on the test field data. For data processing and DTM quality assessment presented in this paper mainly the in-house developed computer programs have been used. The quality of DTM comprises its accuracy, density, and completeness. Different accuracy measures like RMSE, median, normalized median absolute deviation and their confidence interval, quantiles are computed. The completeness of the DTM is very often overlooked quality parameter, but when DTM is produced from the point cloud this should not be neglected as some areas might be very sparsely covered by points. The original density is presented with density plot or map. The completeness is presented by the map of point density and the map of distances between grid points and terrain points. The results in the test area show great potential of the DTM produced from UAS imagery, in the sense of detailed representation of the terrain as well as good height accuracy.

A Backpack-Mounted Omnidirectional Camera with Off-the-Shelf Navigation Sensors for Mobile Terrestrial Mapping: Development and Forest Application

PubMed Central

Prol, Fabricio dos Santos; El Issaoui, Aimad; Hakala, Teemu

2018-01-01

The use of Personal Mobile Terrestrial System (PMTS) has increased considerably for mobile mapping applications because these systems offer dynamic data acquisition with ground perspective in places where the use of wheeled platforms is unfeasible, such as forests and indoor buildings. PMTS has become more popular with emerging technologies, such as miniaturized navigation sensors and off-the-shelf omnidirectional cameras, which enable low-cost mobile mapping approaches. However, most of these sensors have not been developed for high-accuracy metric purposes and therefore require rigorous methods of data acquisition and data processing to obtain satisfactory results for some mapping applications. To contribute to the development of light, low-cost PMTS and potential applications of these off-the-shelf sensors for forest mapping, this paper presents a low-cost PMTS approach comprising an omnidirectional camera with off-the-shelf navigation systems and its evaluation in a forest environment. Experimental assessments showed that the integrated sensor orientation approach using navigation data as the initial information can increase the trajectory accuracy, especially in covered areas. The point cloud generated with the PMTS data had accuracy consistent with the Ground Sample Distance (GSD) range of omnidirectional images (3.5–7 cm). These results are consistent with those obtained for other PMTS approaches. PMID:29522467

Integrating multisource imagery and GIS analysis for mapping Bermuda`s benthic habitats

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vierros, M.K.

1997-06-01

Bermuda is a group of isolated oceanic situated in the northwest Atlantic Ocean and surrounded by the Sargasso Sea. Bermuda possesses the northernmost coral reefs and mangroves in the Atlantic Ocean, and because of its high population density, both the terrestrial and marine environments are under intense human pressure. Although a long record of scientific research exists, this study is the first attempt to comprehensively map the area`s benthic habitats, despite the need for such a map for resource assessment and management purposes. Multi-source and multi-date imagery were used for producing the habitat map due to lack of a completemore » up-to-date image. Classifications were performed with SPOT data, and the results verified from recent aerial photography and current aerial video, along with extensive ground truthing. Stratification of the image into regions prior to classification reduced the confusing effects of varying water depth. Classification accuracy in shallow areas was increased by derivation of a texture pseudo-channel, while bathymetry was used as a classification tool in deeper areas, where local patterns of zonation were well known. Because of seasonal variation in extent of seagrasses, a classification scheme based on density could not be used. Instead, a set of classes based on the seagrass area`s exposure to the open ocean were developed. The resulting habitat map is currently being assessed for accuracy with promising preliminary results, indicating its usefulness as a basis for future resource assessment studies.« less

A Low-Visibility Force Multiplier: Assessing China’s Cruise Missile Ambitions

DTIC Science & Technology

2014-04-01

terminal sensor to achieve 10–15 meter (m) accuracy. • The second-generation DH-10 has a GPS/inertial guidance system but may also use terrain...contour mapping for redundant midcourse guidance and a digital scene-matching sensor to permit an accuracy of 10 m. • Development of the Chinese Beidou...pictures of the target as seen from different perspectives. DSMAC permits LACMs to achieve accuracies of about 1 m. Other (for example, thermal) sensors

EnviroAtlas -Tampa, FL- One Meter Resolution Urban Land Cover (2010)

EPA Pesticide Factsheets

The EnviroAtlas Tampa, FL land cover map was generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from April-May 2010 at 1 m spatial resolution. Eight land cover classes were mapped: impervious surface, soil and barren, grass and herbaceous, trees and forest, water, agriculture, woody wetland, and emergent wetland. The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Tampa, and includes the cities of Clearwater and St. Petersburg, as well as additional out-lying areas. An accuracy assessment using a stratified random sampling of 600 samples (100 per class) yielded an overall accuracy of 70.67 percent and an area weighted accuracy of 81.87 percent using a minimum mapping unit of 9 pixels (3x3 pixel window). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Transition index maps for urban growth simulation: application of artificial neural networks, weight of evidence and fuzzy multi-criteria evaluation.

PubMed

Shafizadeh-Moghadam, Hossein; Tayyebi, Amin; Helbich, Marco

2017-06-01

Transition index maps (TIMs) are key products in urban growth simulation models. However, their operationalization is still conflicting. Our aim was to compare the prediction accuracy of three TIM-based spatially explicit land cover change (LCC) models in the mega city of Mumbai, India. These LCC models include two data-driven approaches, namely artificial neural networks (ANNs) and weight of evidence (WOE), and one knowledge-based approach which integrates an analytical hierarchical process with fuzzy membership functions (FAHP). Using the relative operating characteristics (ROC), the performance of these three LCC models were evaluated. The results showed 85%, 75%, and 73% accuracy for the ANN, FAHP, and WOE. The ANN was clearly superior compared to the other LCC models when simulating urban growth for the year 2010; hence, ANN was used to predict urban growth for 2020 and 2030. Projected urban growth maps were assessed using statistical measures, including figure of merit, average spatial distance deviation, producer accuracy, and overall accuracy. Based on our findings, we recomend ANNs as an and accurate method for simulating future patterns of urban growth.

A priori evaluation of two-stage cluster sampling for accuracy assessment of large-area land-cover maps

USGS Publications Warehouse

Wickham, J.D.; Stehman, S.V.; Smith, J.H.; Wade, T.G.; Yang, L.

2004-01-01

Two-stage cluster sampling reduces the cost of collecting accuracy assessment reference data by constraining sample elements to fall within a limited number of geographic domains (clusters). However, because classification error is typically positively spatially correlated, within-cluster correlation may reduce the precision of the accuracy estimates. The detailed population information to quantify a priori the effect of within-cluster correlation on precision is typically unavailable. Consequently, a convenient, practical approach to evaluate the likely performance of a two-stage cluster sample is needed. We describe such an a priori evaluation protocol focusing on the spatial distribution of the sample by land-cover class across different cluster sizes and costs of different sampling options, including options not imposing clustering. This protocol also assesses the two-stage design's adequacy for estimating the precision of accuracy estimates for rare land-cover classes. We illustrate the approach using two large-area, regional accuracy assessments from the National Land-Cover Data (NLCD), and describe how the a priorievaluation was used as a decision-making tool when implementing the NLCD design.

Accuracy, resolution, and cost comparisons between small format and mapping cameras for environmental mapping

NASA Technical Reports Server (NTRS)

Clegg, R. H.; Scherz, J. P.

1975-01-01

Successful aerial photography depends on aerial cameras providing acceptable photographs within cost restrictions of the job. For topographic mapping where ultimate accuracy is required only large format mapping cameras will suffice. For mapping environmental patterns of vegetation, soils, or water pollution, 9-inch cameras often exceed accuracy and cost requirements, and small formats may be better. In choosing the best camera for environmental mapping, relative capabilities and costs must be understood. This study compares resolution, photo interpretation potential, metric accuracy, and cost of 9-inch, 70mm, and 35mm cameras for obtaining simultaneous color and color infrared photography for environmental mapping purposes.

Perspectives of Maine Forest Cover Change from Landsat Imagery and Forest Inventory Analysis (FIA)

Treesearch

Steven Sader; Michael Hoppus; Jacob Metzler; Suming Jin

2005-01-01

A forest change detection map was developed to document forest gains and losses during the decade of the 1990s. The effectiveness of the Landsat imagery and methods for detecting Maine forest cover change are indicated by the good accuracy assessment results: forest-no change, forest loss, and forest gain accuracy were 90, 88, and 92% respectively, and the good...

MAPPING SPATIAL ACCURACY AND ESTIMATING LANDSCAPE INDICATORS FROM THEMATIC LAND COVER MAPS USING FUZZY SET THEORY

EPA Science Inventory

This paper presents a fuzzy set-based method of mapping spatial accuracy of thematic map and computing several ecological indicators while taking into account spatial variation of accuracy associated with different land cover types and other factors (e.g., slope, soil type, etc.)...

30 CFR 75.1200-2 - Accuracy and scale of mine maps.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Accuracy and scale of mine maps. 75.1200-2... SAFETY AND HEALTH MANDATORY SAFETY STANDARDS-UNDERGROUND COAL MINES Maps § 75.1200-2 Accuracy and scale of mine maps. (a) The scale of mine maps submitted to the Secretary shall not be less than 100 or...

Feasibility study of using the RoboEarth cloud engine for rapid mapping and tracking with small unmanned aerial systems

NASA Astrophysics Data System (ADS)

Li-Chee-Ming, J.; Armenakis, C.

2014-11-01

This paper presents the ongoing development of a small unmanned aerial mapping system (sUAMS) that in the future will track its trajectory and perform 3D mapping in near-real time. As both mapping and tracking algorithms require powerful computational capabilities and large data storage facilities, we propose to use the RoboEarth Cloud Engine (RCE) to offload heavy computation and store data to secure computing environments in the cloud. While the RCE's capabilities have been demonstrated with terrestrial robots in indoor environments, this paper explores the feasibility of using the RCE in mapping and tracking applications in outdoor environments by small UAMS. The experiments presented in this work assess the data processing strategies and evaluate the attainable tracking and mapping accuracies using the data obtained by the sUAMS. Testing was performed with an Aeryon Scout quadcopter. It flew over York University, up to approximately 40 metres above the ground. The quadcopter was equipped with a single-frequency GPS receiver providing positioning to about 3 meter accuracies, an AHRS (Attitude and Heading Reference System) estimating the attitude to about 3 degrees, and an FPV (First Person Viewing) camera. Video images captured from the onboard camera were processed using VisualSFM and SURE, which are being reformed as an Application-as-a-Service via the RCE. The 3D virtual building model of York University was used as a known environment to georeference the point cloud generated from the sUAMS' sensor data. The estimated position and orientation parameters of the video camera show increases in accuracy when compared to the sUAMS' autopilot solution, derived from the onboard GPS and AHRS. The paper presents the proposed approach and the results, along with their accuracies.

EXhype: A tool for mineral classification using hyperspectral data

NASA Astrophysics Data System (ADS)

Adep, Ramesh Nityanand; shetty, Amba; Ramesh, H.

2017-02-01

Various supervised classification algorithms have been developed to classify earth surface features using hyperspectral data. Each algorithm is modelled based on different human expertises. However, the performance of conventional algorithms is not satisfactory to map especially the minerals in view of their typical spectral responses. This study introduces a new expert system named 'EXhype (Expert system for hyperspectral data classification)' to map minerals. The system incorporates human expertise at several stages of it's implementation: (i) to deal with intra-class variation; (ii) to identify absorption features; (iii) to discriminate spectra by considering absorption features, non-absorption features and by full spectra comparison; and (iv) finally takes a decision based on learning and by emphasizing most important features. It is developed using a knowledge base consisting of an Optimal Spectral Library, Segmented Upper Hull method, Spectral Angle Mapper (SAM) and Artificial Neural Network. The performance of the EXhype is compared with a traditional, most commonly used SAM algorithm using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired over Cuprite, Nevada, USA. A virtual verification method is used to collect samples information for accuracy assessment. Further, a modified accuracy assessment method is used to get a real users accuracies in cases where only limited or desired classes are considered for classification. With the modified accuracy assessment method, SAM and EXhype yields an overall accuracy of 60.35% and 90.75% and the kappa coefficient of 0.51 and 0.89 respectively. It was also found that the virtual verification method allows to use most desired stratified random sampling method and eliminates all the difficulties associated with it. The experimental results show that EXhype is not only producing better accuracy compared to traditional SAM but, can also rightly classify the minerals. It is proficient in avoiding misclassification between target classes when applied on minerals.

Multi-site evaluation of IKONOS data for classification of tropical coral reef environments

USGS Publications Warehouse

Andrefouet, S.; Kramer, Philip; Torres-Pulliza, D.; Joyce, K.E.; Hochberg, E.J.; Garza-Perez, R.; Mumby, P.J.; Riegl, Bernhard; Yamano, H.; White, W.H.; Zubia, M.; Brock, J.C.; Phinn, S.R.; Naseer, A.; Hatcher, B.G.; Muller-Karger, F. E.

2003-01-01

Ten IKONOS images of different coral reef sites distributed around the world were processed to assess the potential of 4-m resolution multispectral data for coral reef habitat mapping. Complexity of reef environments, established by field observation, ranged from 3 to 15 classes of benthic habitats containing various combinations of sediments, carbonate pavement, seagrass, algae, and corals in different geomorphologic zones (forereef, lagoon, patch reef, reef flats). Processing included corrections for sea surface roughness and bathymetry, unsupervised or supervised classification, and accuracy assessment based on ground-truth data. IKONOS classification results were compared with classified Landsat 7 imagery for simple to moderate complexity of reef habitats (5-11 classes). For both sensors, overall accuracies of the classifications show a general linear trend of decreasing accuracy with increasing habitat complexity. The IKONOS sensor performed better, with a 15-20% improvement in accuracy compared to Landsat. For IKONOS, overall accuracy was 77% for 4-5 classes, 71% for 7-8 classes, 65% in 9-11 classes, and 53% for more than 13 classes. The Landsat classification accuracy was systematically lower, with an average of 56% for 5-10 classes. Within this general trend, inter-site comparisons and specificities demonstrate the benefits of different approaches. Pre-segmentation of the different geomorphologic zones and depth correction provided different advantages in different environments. Our results help guide scientists and managers in applying IKONOS-class data for coral reef mapping applications. ?? 2003 Elsevier Inc. All rights reserved.

Historical shoreline mapping (I): improving techniques and reducing positioning errors

USGS Publications Warehouse

Thieler, E. Robert; Danforth, William W.

1994-01-01

A critical need exists among coastal researchers and policy-makers for a precise method to obtain shoreline positions from historical maps and aerial photographs. A number of methods that vary widely in approach and accuracy have been developed to meet this need. None of the existing methods, however, address the entire range of cartographic and photogrammetric techniques required for accurate coastal mapping. Thus, their application to many typical shoreline mapping problems is limited. In addition, no shoreline mapping technique provides an adequate basis for quantifying the many errors inherent in shoreline mapping using maps and air photos. As a result, current assessments of errors in air photo mapping techniques generally (and falsely) assume that errors in shoreline positions are represented by the sum of a series of worst-case assumptions about digitizer operator resolution and ground control accuracy. These assessments also ignore altogether other errors that commonly approach ground distances of 10 m. This paper provides a conceptual and analytical framework for improved methods of extracting geographic data from maps and aerial photographs. We also present a new approach to shoreline mapping using air photos that revises and extends a number of photogrammetric techniques. These techniques include (1) developing spatially and temporally overlapping control networks for large groups of photos; (2) digitizing air photos for use in shoreline mapping; (3) preprocessing digitized photos to remove lens distortion and film deformation effects; (4) simultaneous aerotriangulation of large groups of spatially and temporally overlapping photos; and (5) using a single-ray intersection technique to determine geographic shoreline coordinates and express the horizontal and vertical error associated with a given digitized shoreline. As long as historical maps and air photos are used in studies of shoreline change, there will be a considerable amount of error (on the order of several meters) present in shoreline position and rate-of- change calculations. The techniques presented in this paper, however, provide a means to reduce and quantify these errors so that realistic assessments of the technological noise (as opposed to geological noise) in geographic shoreline positions can be made.

Land cover mapping after the tsunami event over Nanggroe Aceh Darussalam (NAD) province, Indonesia

NASA Astrophysics Data System (ADS)

Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Alias, A. N.; Mohd. Saleh, N.; Wong, C. J.; Surbakti, M. S.

2008-03-01

Remote sensing offers an important means of detecting and analyzing temporal changes occurring in our landscape. This research used remote sensing to quantify land use/land cover changes at the Nanggroe Aceh Darussalam (Nad) province, Indonesia on a regional scale. The objective of this paper is to assess the changed produced from the analysis of Landsat TM data. A Landsat TM image was used to develop land cover classification map for the 27 March 2005. Four supervised classifications techniques (Maximum Likelihood, Minimum Distance-to- Mean, Parallelepiped and Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier) were performed to the satellite image. Training sites and accuracy assessment were needed for supervised classification techniques. The training sites were established using polygons based on the colour image. High detection accuracy (>80%) and overall Kappa (>0.80) were achieved by the Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier in this study. This preliminary study has produced a promising result. This indicates that land cover mapping can be carried out using remote sensing classification method of the satellite digital imagery.

Historical shoreline mapping (II): Application of the Digital Shoreline Mapping and Analysis Systems (DSMS/DSAS) to shoreline change mapping in Puerto Rico

USGS Publications Warehouse

Thieler, E. Robert; Danforth, William W.

1994-01-01

A new, state-of-the-art method for mapping historical shorelines from maps and aerial photographs, the Digital Shoreline Mapping System (DSMS), has been developed. The DSMS is a freely available, public domain software package that meets the cartographic and photogrammetric requirements of precise coastal mapping, and provides a means to quantify and analyze different sources of error in the mapping process. The DSMS is also capable of resolving imperfections in aerial photography that commonly are assumed to be nonexistent. The DSMS utilizes commonly available computer hardware and software, and permits the entire shoreline mapping process to be executed rapidly by a single person in a small lab. The DSMS generates output shoreline position data that are compatible with a variety of Geographic Information Systems (GIS). A second suite of programs, the Digital Shoreline Analysis System (DSAS) has been developed to calculate shoreline rates-of-change from a series of shoreline data residing in a GIS. Four rate-of-change statistics are calculated simultaneously (end-point rate, average of rates, linear regression and jackknife) at a user-specified interval along the shoreline using a measurement baseline approach. An example of DSMS and DSAS application using historical maps and air photos of Punta Uvero, Puerto Rico provides a basis for assessing the errors associated with the source materials as well as the accuracy of computed shoreline positions and erosion rates. The maps and photos used here represent a common situation in shoreline mapping: marginal-quality source materials. The maps and photos are near the usable upper limit of scale and accuracy, yet the shoreline positions are still accurate ±9.25 m when all sources of error are considered. This level of accuracy yields a resolution of ±0.51 m/yr for shoreline rates-of-change in this example, and is sufficient to identify the short-term trend (36 years) of shoreline change in the study area.

Urban Land Cover/use Change Detection Using High Resolution SPOT 5 and SPOT 6 Images and Urban Atlas Nomenclature

NASA Astrophysics Data System (ADS)

Akay, S. S.; Sertel, E.

2016-06-01

Urban land cover/use changes like urbanization and urban sprawl have been impacting the urban ecosystems significantly therefore determination of urban land cover/use changes is an important task to understand trends and status of urban ecosystems, to support urban planning and to aid decision-making for urban-based projects. High resolution satellite images could be used to accurately, periodically and quickly map urban land cover/use and their changes by time. This paper aims to determine urban land cover/use changes in Gaziantep city centre between 2010 and 2105 using object based images analysis and high resolution SPOT 5 and SPOT 6 images. 2.5 m SPOT 5 image obtained in 5th of June 2010 and 1.5 m SPOT 6 image obtained in 7th of July 2015 were used in this research to precisely determine land changes in five-year period. In addition to satellite images, various ancillary data namely Normalized Difference Vegetation Index (NDVI), Difference Water Index (NDWI) maps, cadastral maps, OpenStreetMaps, road maps and Land Cover maps, were integrated into the classification process to produce high accuracy urban land cover/use maps for these two years. Both images were geometrically corrected to fulfil the 1/10,000 scale geometric accuracy. Decision tree based object oriented classification was applied to identify twenty different urban land cover/use classes defined in European Urban Atlas project. Not only satellite images and satellite image-derived indices but also different thematic maps were integrated into decision tree analysis to create rule sets for accurate mapping of each class. Rule sets of each satellite image for the object based classification involves spectral, spatial and geometric parameter to automatically produce urban map of the city centre region. Total area of each class per related year and their changes in five-year period were determined and change trend in terms of class transformation were presented. Classification accuracy assessment was conducted by creating a confusion matrix to illustrate the thematic accuracy of each class.

Mapping soil texture classes and optimization of the result by accuracy assessment

NASA Astrophysics Data System (ADS)

Laborczi, Annamária; Takács, Katalin; Bakacsi, Zsófia; Szabó, József; Pásztor, László

2014-05-01

There are increasing demands nowadays on spatial soil information in order to support environmental related and land use management decisions. The GlobalSoilMap.net (GSM) project aims to make a new digital soil map of the world using state-of-the-art and emerging technologies for soil mapping and predicting soil properties at fine resolution. Sand, silt and clay are among the mandatory GSM soil properties. Furthermore, soil texture class information is input data of significant agro-meteorological and hydrological models. Our present work aims to compare and evaluate different digital soil mapping methods and variables for producing the most accurate spatial prediction of texture classes in Hungary. In addition to the Hungarian Soil Information and Monitoring System as our basic data, digital elevation model and its derived components, geological database, and physical property maps of the Digital Kreybig Soil Information System have been applied as auxiliary elements. Two approaches have been applied for the mapping process. At first the sand, silt and clay rasters have been computed independently using regression kriging (RK). From these rasters, according to the USDA categories, we have compiled the texture class map. Different combinations of reference and training soil data and auxiliary covariables have resulted several different maps. However, these results consequentially include the uncertainty factor of the three kriged rasters. Therefore we have suited data mining methods as the other approach of digital soil mapping. By working out of classification trees and random forests we have got directly the texture class maps. In this way the various results can be compared to the RK maps. The performance of the different methods and data has been examined by testing the accuracy of the geostatistically computed and the directly classified results. We have used the GSM methodology to assess the most predictive and accurate way for getting the best among the several result maps. Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Cost, accuracy, and consistency comparisons of land use maps made from high-altitutde aircraft photography and ERTS imagery

USGS Publications Warehouse

Fitzpatrick, Katherine A.

1975-01-01

Accuracy analyses for the land use maps of the Central Atlantic Regional Ecological Test Site were performed for a 1-percent sample of the area. Researchers compared Level II land use maps produced at three scales, 1:24,000, 1:100,000, and 1:250,000 from high-altitude photography, with each other and with point data obtained in the field. They employed the same procedures to determine the accuracy of the Level I land use maps produced at 1:250,000 from high-altitude photography and color composite ERTS imagery. The accuracy of the Level II maps was 84.9 percent at 1:24,000, 77.4 percent at 1:100,000, and 73.0 percent at 1:250,000. The accuracy of the Level I 1:250,000 maps produced from high-altitude aircraft photography was 76.5 percent and for those produced from ERTS imagery was 69.5 percent The cost of Level II land use mapping at 1:24,000 was found to be high ($11.93 per km2 ). The cost of mapping at 1:100,000 ($1.75) was about 2 times as expensive as mapping at 1:250,000 ($.88), and the accuracy increased by only 4.4 percent. Level I land use maps, when mapped from highaltitude photography, were about 4 times as expensive as the maps produced from ERTS imagery, although the accuracy is 7.0 percent greater. The Level I land use category that is least accurately mapped from ERTS imagery is urban and built-up land in the non-urban areas; in the urbanized areas, built-up land is more reliably mapped.

Mapping Winter Wheat with Multi-Temporal SAR and Optical Images in an Urban Agricultural Region

PubMed Central

Zhou, Tao; Pan, Jianjun; Zhang, Peiyu; Wei, Shanbao; Han, Tao

2017-01-01

Winter wheat is the second largest food crop in China. It is important to obtain reliable winter wheat acreage to guarantee the food security for the most populous country in the world. This paper focuses on assessing the feasibility of in-season winter wheat mapping and investigating potential classification improvement by using SAR (Synthetic Aperture Radar) images, optical images, and the integration of both types of data in urban agricultural regions with complex planting structures in Southern China. Both SAR (Sentinel-1A) and optical (Landsat-8) data were acquired, and classification using different combinations of Sentinel-1A-derived information and optical images was performed using a support vector machine (SVM) and a random forest (RF) method. The interference coherence and texture images were obtained and used to assess the effect of adding them to the backscatter intensity images on the classification accuracy. The results showed that the use of four Sentinel-1A images acquired before the jointing period of winter wheat can provide satisfactory winter wheat classification accuracy, with an F1 measure of 87.89%. The combination of SAR and optical images for winter wheat mapping achieved the best F1 measure–up to 98.06%. The SVM was superior to RF in terms of the overall accuracy and the kappa coefficient, and was faster than RF, while the RF classifier was slightly better than SVM in terms of the F1 measure. In addition, the classification accuracy can be effectively improved by adding the texture and coherence images to the backscatter intensity data. PMID:28587066

Airborne Laser/GPS Mapping of Assateague National Seashore Beach

NASA Technical Reports Server (NTRS)

Kradill, W. B.; Wright, C. W.; Brock, John C.; Swift, R. N.; Frederick, E. B.; Manizade, S. S.; Yungel, J. K.; Martin, C. F.; Sonntag, J. G.; Duffy, Mark;

Harnessing Satellite Imageries in Feature Extraction Using Google Earth Pro

NASA Astrophysics Data System (ADS)

Fernandez, Sim Joseph; Milano, Alan

2016-07-01

Climate change has been a long-time concern worldwide. Impending flooding, for one, is among its unwanted consequences. The Phil-LiDAR 1 project of the Department of Science and Technology (DOST), Republic of the Philippines, has developed an early warning system in regards to flood hazards. The project utilizes the use of remote sensing technologies in determining the lives in probable dire danger by mapping and attributing building features using LiDAR dataset and satellite imageries. A free mapping software named Google Earth Pro (GEP) is used to load these satellite imageries as base maps. Geotagging of building features has been done so far with the use of handheld Global Positioning System (GPS). Alternatively, mapping and attribution of building features using GEP saves a substantial amount of resources such as manpower, time and budget. Accuracy-wise, geotagging by GEP is dependent on either the satellite imageries or orthophotograph images of half-meter resolution obtained during LiDAR acquisition and not on the GPS of three-meter accuracy. The attributed building features are overlain to the flood hazard map of Phil-LiDAR 1 in order to determine the exposed population. The building features as obtained from satellite imageries may not only be used in flood exposure assessment but may also be used in assessing other hazards and a number of other uses. Several other features may also be extracted from the satellite imageries.

A Unified Cropland Layer at 250-m for global agriculture monitoring

USGS Publications Warehouse

Waldner, François; Fritz, Steffen; Di Gregorio, Antonio; Plotnikov, Dmitry; Bartalev, Sergey; Kussul, Nataliia; Gong, Peng; Thenkabail, Prasad S.; Hazeu, Gerard; Klein, Igor; Löw, Fabian; Miettinen, Jukka; Dadhwal, Vinay Kumar; Lamarche, Céline; Bontemps, Sophie; Defourny, Pierre

2016-01-01

Accurate and timely information on the global cropland extent is critical for food security monitoring, water management and earth system modeling. Principally, it allows for analyzing satellite image time-series to assess the crop conditions and permits isolation of the agricultural component to focus on food security and impacts of various climatic scenarios. However, despite its critical importance, accurate information on the spatial extent, cropland mapping with remote sensing imagery remains a major challenge. Following an exhaustive identification and collection of existing land cover maps, a multi-criteria analysis was designed at the country level to evaluate the fitness of a cropland map with regards to four dimensions: its timeliness, its legend, its resolution adequacy and its confidence level. As a result, a Unified Cropland Layer that combines the fittest products into a 250 m global cropland map was assembled. With an evaluated accuracy ranging from 82% to 95%, the Unified Cropland Layer successfully improved the accuracy compared to single global products.

EnviroAtlas -Durham, NC- One Meter Resolution Urban Area Land Cover Map (2010)

EPA Pesticide Factsheets

The EnviroAtlas Durham, NC land cover map was generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from July 2010 at 1 m spatial resolution. Five land cover classes were mapped: impervious surface, soil and barren, grass and herbaceous, trees and forest, and water. An accuracy assessment using a stratified random sampling of 500 samples yielded an overall accuracy of 83 percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Durham, and includes the cities of Durham, Chapel Hill, Carrboro and Hillsborough, NC. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

Automated reference-free detection of motion artifacts in magnetic resonance images.

PubMed

Küstner, Thomas; Liebgott, Annika; Mauch, Lukas; Martirosian, Petros; Bamberg, Fabian; Nikolaou, Konstantin; Yang, Bin; Schick, Fritz; Gatidis, Sergios

2018-04-01

Our objectives were to provide an automated method for spatially resolved detection and quantification of motion artifacts in MR images of the head and abdomen as well as a quality control of the trained architecture. T1-weighted MR images of the head and the upper abdomen were acquired in 16 healthy volunteers under rest and under motion. Images were divided into overlapping patches of different sizes achieving spatial separation. Using these patches as input data, a convolutional neural network (CNN) was trained to derive probability maps for the presence of motion artifacts. A deep visualization offers a human-interpretable quality control of the trained CNN. Results were visually assessed on probability maps and as classification accuracy on a per-patch, per-slice and per-volunteer basis. On visual assessment, a clear difference of probability maps was observed between data sets with and without motion. The overall accuracy of motion detection on a per-patch/per-volunteer basis reached 97%/100% in the head and 75%/100% in the abdomen, respectively. Automated detection of motion artifacts in MRI is feasible with good accuracy in the head and abdomen. The proposed method provides quantification and localization of artifacts as well as a visualization of the learned content. It may be extended to other anatomic areas and used for quality assurance of MR images.

The verification of LANDSAT data in the geographical analysis of wetlands in west Tennessee

NASA Technical Reports Server (NTRS)

Rehder, J.; Quattrochi, D. A.

1978-01-01

The reliability of LANDSAT imagery as a medium for identifying, delimiting, monitoring, measuring, and mapping wetlands in west Tennessee was assessed to verify LANDSAT as an accurate, efficient cartographic tool that could be employed by a wide range of users to study wetland dynamics. The verification procedure was based on the visual interpretation and measurement of multispectral imagery. The accuracy testing procedure was predicated on surrogate ground truth data gleaned from medium altitude imagery of the wetlands. Fourteen sites or case study areas were selected from individual 9 x 9 inch photo frames on the aerial photography. These sites were then used as data control calibration parameters for assessing the cartography accuracy of the LANDSAT imagery. An analysis of results obtained from the verification tests indicated that 1:250,000 scale LANDSAT data were the most reliable scale of imagery for visually mapping and measuring wetlands using the area grid technique. The mean areal percentage of accuracy was 93.54 percent (real) and 96.93 percent (absolute). As a test of accuracy, the LANDSAT 1:250,000 scale overall wetland measurements were compared with an area cell mensuration of the swamplands from 1:130,000 scale color infrared U-2 aircraft imagery. The comparative totals substantiated the results from the LANDSAT verification procedure.

Accuracy assessment of NOAA gridded daily reference evapotranspiration for the Texas High Plains

USGS Publications Warehouse

Moorhead, Jerry; Gowda, Prasanna H.; Hobbins, Michael; Senay, Gabriel; Paul, George; Marek, Thomas; Porter, Dana

2015-01-01

The National Oceanic and Atmospheric Administration (NOAA) provides daily reference evapotranspiration (ETref) maps for the contiguous United States using climatic data from North American Land Data Assimilation System (NLDAS). This data provides large-scale spatial representation of ETref, which is essential for regional scale water resources management. Data used in the development of NOAA daily ETref maps are derived from observations over surfaces that are different from short (grass — ETos) or tall (alfalfa — ETrs) reference crops, often in nonagricultural settings, which carries an unknown discrepancy between assumed and actual conditions. In this study, NOAA daily ETos and ETrs maps were evaluated for accuracy, using observed data from the Texas High Plains Evapotranspiration (TXHPET) network. Daily ETos, ETrs and the climatic data (air temperature, wind speed, and solar radiation) used for calculating ETref were extracted from the NOAA maps for TXHPET locations and compared against ground measurements on reference grass surfaces. NOAA ETrefmaps generally overestimated the TXHPET observations (1.4 and 2.2 mm/day ETos and ETrs, respectively), which may be attributed to errors in the NLDAS modeled air temperature and wind speed, to which reference ETref is most sensitive. Therefore, a bias correction to NLDAS modeled air temperature and wind speed data, or adjustment to the resulting NOAA ETref, may be needed to improve the accuracy of NOAA ETref maps.

Determining the Extent and Characterizing Coral Reef Habitats of the Northern Latitudes of the Florida Reef Tract (Martin County)

PubMed Central

Walker, Brian K.; Gilliam, David S.

2013-01-01

Climate change has recently been implicated in poleward shifts of many tropical species including corals; thus attention focused on higher-latitude coral communities is warranted to investigate possible range expansions and ecosystem shifts due to global warming. As the northern extension of the Florida Reef Tract (FRT), the third-largest barrier reef ecosystem in the world, southeast Florida (25–27° N latitude) is a prime region to study such effects. Most of the shallow-water FRT benthic habitats have been mapped, however minimal data and limited knowledge exist about the coral reef communities of its northernmost reaches off Martin County. First benthic habitat mapping was conducted using newly acquired high resolution LIDAR bathymetry and aerial photography where possible to map the spatial extent of coral reef habitats. Quantitative data were collected to characterize benthic cover and stony coral demographics and a comprehensive accuracy assessment was performed. The data were then analyzed in a habitat biogeography context to determine if a new coral reef ecosystem region designation was warranted. Of the 374 km2 seafloor mapped, 95.2% was Sand, 4.1% was Coral Reef and Colonized Pavement, and 0.7% was Other Delineations. Map accuracy assessment yielded an overall accuracy of 94.9% once adjusted for known map marginal proportions. Cluster analysis of cross-shelf habitat type and widths indicated that the benthic habitats were different than those further south and warranted designation of a new coral reef ecosystem region. Unlike the FRT further south, coral communities were dominated by cold-water tolerant species and LIDAR morphology indicated no evidence of historic reef growth during warmer climates. Present-day hydrographic conditions may be inhibiting poleward expansion of coral communities along Florida. This study provides new information on the benthic community composition of the northern FRT, serving as a baseline for future community shift and range expansion investigations. PMID:24282542

Determining the extent and characterizing coral reef habitats of the northern latitudes of the Florida Reef Tract (Martin County).

PubMed

Walker, Brian K; Gilliam, David S

2013-01-01

Climate change has recently been implicated in poleward shifts of many tropical species including corals; thus attention focused on higher-latitude coral communities is warranted to investigate possible range expansions and ecosystem shifts due to global warming. As the northern extension of the Florida Reef Tract (FRT), the third-largest barrier reef ecosystem in the world, southeast Florida (25-27° N latitude) is a prime region to study such effects. Most of the shallow-water FRT benthic habitats have been mapped, however minimal data and limited knowledge exist about the coral reef communities of its northernmost reaches off Martin County. First benthic habitat mapping was conducted using newly acquired high resolution LIDAR bathymetry and aerial photography where possible to map the spatial extent of coral reef habitats. Quantitative data were collected to characterize benthic cover and stony coral demographics and a comprehensive accuracy assessment was performed. The data were then analyzed in a habitat biogeography context to determine if a new coral reef ecosystem region designation was warranted. Of the 374 km(2) seafloor mapped, 95.2% was Sand, 4.1% was Coral Reef and Colonized Pavement, and 0.7% was Other Delineations. Map accuracy assessment yielded an overall accuracy of 94.9% once adjusted for known map marginal proportions. Cluster analysis of cross-shelf habitat type and widths indicated that the benthic habitats were different than those further south and warranted designation of a new coral reef ecosystem region. Unlike the FRT further south, coral communities were dominated by cold-water tolerant species and LIDAR morphology indicated no evidence of historic reef growth during warmer climates. Present-day hydrographic conditions may be inhibiting poleward expansion of coral communities along Florida. This study provides new information on the benthic community composition of the northern FRT, serving as a baseline for future community shift and range expansion investigations.

A multi-temporal analysis approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan; van Aardt, Jan A. N.; McKeown, Donald M.; Messinger, David W.

2012-06-01

Remote sensing can be used to rapidly generate land use maps for assisting emergency response personnel with resource deployment decisions and impact assessments. In this study we focus on constructing accurate land cover maps to map the impacted area in the case of a nuclear material release. The proposed methodology involves integration of results from two different approaches to increase classification accuracy. The data used included RapidEye scenes over Nine Mile Point Nuclear Power Station (Oswego, NY). The first step was building a coarse-scale land cover map from freely available, high temporal resolution, MODIS data using a time-series approach. In the case of a nuclear accident, high spatial resolution commercial satellites such as RapidEye or IKONOS can acquire images of the affected area. Land use maps from the two image sources were integrated using a probability-based approach. Classification results were obtained for four land classes - forest, urban, water and vegetation - using Euclidean and Mahalanobis distances as metrics. Despite the coarse resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. The classifications were augmented using this fused approach, with few supplementary advantages such as correction for cloud cover and independence from time of year. We concluded that this method would generate highly accurate land maps, using coarse spatial resolution time series satellite imagery and a single date, high spatial resolution, multi-spectral image.

The accuracy of selected land use and land cover maps at scales of 1:250,000 and 1:100,000

USGS Publications Warehouse

Fitzpatrick-Lins, Katherine

1980-01-01

Land use and land cover maps produced by the U.S. Geological Survey are found to meet or exceed the established standard of accuracy. When analyzed using a point sampling technique and binomial probability theory, several maps, illustrative of those produced for different parts of the country, were found to meet or exceed accuracies of 85 percent. Those maps tested were Tampa, Fla., Portland, Me., Charleston, W. Va., and Greeley, Colo., published at a scale of 1:250,000, and Atlanta, Ga., and Seattle and Tacoma, Wash., published at a scale of 1:100,000. For each map, the values were determined by calculating the ratio of the total number of points correctly interpreted to the total number of points sampled. Six of the seven maps tested have accuracies of 85 percent or better at the 95-percent lower confidence limit. When the sample data for predominant categories (those sampled with a significant number of points) were grouped together for all maps, accuracies of those predominant categories met the 85-percent accuracy criterion, with one exception. One category, Residential, had less than 85-percent accuracy at the 95-percent lower confidence limit. Nearly all residential land sampled was mapped correctly, but some areas of other land uses were mapped incorrectly as Residential.

Environmental monitoring and assessment of landscape dynamics in southern coast of the Caspian Sea through intensity analysis and imprecise land-use data.

PubMed

Hasani, Mohammad; Sakieh, Yousef; Dezhkam, Sadeq; Ardakani, Tahereh; Salmanmahiny, Abdolrassoul

2017-04-01

A hierarchical intensity analysis of land-use change is applied to evaluate the dynamics of a coupled urban coastal system in Rasht County, Iran. Temporal land-use layers of 1987, 1999, and 2011 are employed, while spatial accuracy metrics are only available for 2011 data (overall accuracy of 94%). The errors in 1987 and 1999 layers are unknown, which can influence the accuracy of temporal change information. Such data were employed to examine the size and the type of errors that could justify deviations from uniform change intensities. Accordingly, errors comprising 3.31 and 7.47% of 1999 and 2011 maps, respectively, could explain all differences from uniform gains and errors including 5.21 and 1.81% of 1987 and 1999 maps, respectively, could explain all deviations from uniform losses. Additional historical information is also applied for uncertainty assessment and to separate probable map errors from actual land-use changes. In this regard, historical processes in Rasht County can explain different types of transition that are either consistent or inconsistent to known processes. The intensity analysis assisted in identification of systematic transitions and detection of competitive categories, which cannot be investigated through conventional change detection methods. Based on results, built-up area is the most active gaining category in the area and wetland category with less areal extent is more sensitive to intense land-use change processes. Uncertainty assessment results also indicated that there are no considerable classification errors in temporal land-use data and these imprecise layers can reliably provide implications for informed decision making.

Object-Based Classification of Ikonos Imagery for Mapping Large-Scale Vegetation Communities in Urban Areas.

PubMed

Mathieu, Renaud; Aryal, Jagannath; Chong, Albert K

2007-11-20

Effective assessment of biodiversity in cities requires detailed vegetation maps.To date, most remote sensing of urban vegetation has focused on thematically coarse landcover products. Detailed habitat maps are created by manual interpretation of aerialphotographs, but this is time consuming and costly at large scale. To address this issue, wetested the effectiveness of object-based classifications that use automated imagesegmentation to extract meaningful ground features from imagery. We applied thesetechniques to very high resolution multispectral Ikonos images to produce vegetationcommunity maps in Dunedin City, New Zealand. An Ikonos image was orthorectified and amulti-scale segmentation algorithm used to produce a hierarchical network of image objects.The upper level included four coarse strata: industrial/commercial (commercial buildings),residential (houses and backyard private gardens), vegetation (vegetation patches larger than0.8/1ha), and water. We focused on the vegetation stratum that was segmented at moredetailed level to extract and classify fifteen classes of vegetation communities. The firstclassification yielded a moderate overall classification accuracy (64%, κ = 0.52), which ledus to consider a simplified classification with ten vegetation classes. The overallclassification accuracy from the simplified classification was 77% with a κ value close tothe excellent range (κ = 0.74). These results compared favourably with similar studies inother environments. We conclude that this approach does not provide maps as detailed as those produced by manually interpreting aerial photographs, but it can still extract ecologically significant classes. It is an efficient way to generate accurate and detailed maps in significantly shorter time. The final map accuracy could be improved by integrating segmentation, automated and manual classification in the mapping process, especially when considering important vegetation classes with limited spectral contrast.

Performance analysis of a compact and low-cost mapping-grade mobile laser scanning system

NASA Astrophysics Data System (ADS)

Julge, Kalev; Vajakas, Toivo; Ellmann, Artu

2017-10-01

The performance of a low-cost, self-contained, compact, and easy to deploy mapping-grade mobile laser scanning (MLS) system, which is composed of a light detection and ranging sensor Velodyne VLP-16 and a dual antenna global navigation satellite system/inertial navigation system SBG Systems Ellipse-D, is analyzed. The field tests were carried out in car-mounted and backpack modes for surveying road engineering structures (such as roads, parking lots, underpasses, and tunnels) and coastal erosion zones, respectively. The impact of applied calculation principles on trajectory postprocessing, direct georeferencing, and the theoretical accuracy of the system is analyzed. A calibration method, based on Bound Optimization BY Quadratic Approximation, for finding the boresight angles of an MLS system is proposed. The resulting MLS point clouds are compared with high-accuracy static terrestrial laser scanning data and survey-grade MLS data from a commercially manufactured MLS system. The vertical, horizontal, and relative accuracy are assessed-the root-mean-square error (RMSE) values were determined to be 8, 15, and 3 cm, respectively. Thus, the achieved mapping-grade accuracy demonstrates that this relatively compact and inexpensive self-assembled MLS can be successfully used for surveying the geometry and deformations of terrain, buildings, road, and other engineering structures.

Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity

NASA Astrophysics Data System (ADS)

Quintano, C.; Fernández-Manso, A.; Fernández-Manso, O.

2018-02-01

Nowadays Earth observation satellites, in particular Landsat, provide a valuable help to forest managers in post-fire operations; being the base of post-fire damage maps that enable to analyze fire impacts and to develop vegetation recovery plans. Sentinel-2A MultiSpectral Instrument (MSI) records data in similar spectral wavelengths that Landsat 8 Operational Land Imager (OLI), and has higher spatial and temporal resolutions. This work compares two types of satellite-based maps for evaluating fire damage in a large wildfire (around 8000 ha) located in Sierra de Gata (central-western Spain) on 6-11 August 2015. 1) burn severity maps based exclusively on Landsat data; specifically, on differenced Normalized Burn Ratio (dNBR) and on its relative versions (Relative dNBR, RdNBR, and Relativized Burn Ratio, RBR) and 2) burn severity maps based on the same indexes but combining pre-fire data from Landsat 8 OLI with post-fire data from Sentinel-2A MSI data. Combination of both Landsat and Sentinel-2 data might reduce the time elapsed since forest fire to the availability of an initial fire damage map. Interpretation of ortho-photograph Pléiades 1 B data (1:10,000) provided us the ground reference data to measure the accuracy of both burn severity maps. Results showed that Landsat based burn severity maps presented an adequate assessment of the damage grade (κ statistic = 0.80) and its spatial distribution in wildfire emergency response. Further using both Landsat and Sentinel-2 MSI data the accuracy of burn severity maps, though slightly lower (κ statistic = 0.70) showed an adequate level for be used by forest managers.

Performance map of a cluster detection test using extended power

PubMed Central

2013-01-01

Background Conventional power studies possess limited ability to assess the performance of cluster detection tests. In particular, they cannot evaluate the accuracy of the cluster location, which is essential in such assessments. Furthermore, they usually estimate power for one or a few particular alternative hypotheses and thus cannot assess performance over an entire region. Takahashi and Tango developed the concept of extended power that indicates both the rate of null hypothesis rejection and the accuracy of the cluster location. We propose a systematic assessment method, using here extended power, to produce a map showing the performance of cluster detection tests over an entire region. Methods To explore the behavior of a cluster detection test on identical cluster types at any possible location, we successively applied four different spatial and epidemiological parameters. These parameters determined four cluster collections, each covering the entire study region. We simulated 1,000 datasets for each cluster and analyzed them with Kulldorff’s spatial scan statistic. From the area under the extended power curve, we constructed a map for each parameter set showing the performance of the test across the entire region. Results Consistent with previous studies, the performance of the spatial scan statistic increased with the baseline incidence of disease, the size of the at-risk population and the strength of the cluster (i.e., the relative risk). Performance was heterogeneous, however, even for very similar clusters (i.e., similar with respect to the aforementioned factors), suggesting the influence of other factors. Conclusions The area under the extended power curve is a single measure of performance and, although needing further exploration, it is suitable to conduct a systematic spatial evaluation of performance. The performance map we propose enables epidemiologists to assess cluster detection tests across an entire study region. PMID:24156765

Development of a 30 m Spatial Resolution Land Cover of Canada: Contribution to the Harmonized North America Land Cover Dataset

NASA Astrophysics Data System (ADS)

Pouliot, D.; Latifovic, R.; Olthof, I.

2017-12-01

Land cover is needed for a large range of environmental applications regarding climate impacts and adaption, emergency response, wildlife habitat, air quality, water yield, etc. In Canada a 2008 user survey revealed that the most practical scale for provision of land cover data is 30 m, nationwide, with an update frequency of five years (Ball, 2008). In response to this need the Canada Centre for Remote Sensing has generated a 30 m land cover of Canada for the base year 2010 as part of a planned series of maps at the recommended five year update frequency. This land cover is the Canadian contribution to the North American Land Change Monitoring System initiative, which seeks to provide harmonized land cover across Canada, the United States, and Mexico. The methodology developed in this research utilized a combination of unsupervised and machine learning techniques to map land cover, blend results between mapping units, locally optimize results, and process some thematic attributes with specific features sets. Accuracy assessment with available field data shows it was on average 75% for the five study areas assessed. In this presentation an overview of the unique processing aspects, example results, and initial accuracy assessment will be discussed.

Validation of Land Cover Maps Utilizing Astronaut Acquired Imagery

NASA Technical Reports Server (NTRS)

Estes, John E.; Gebelein, Jennifer

1999-01-01

This report is produced in accordance with the requirements outlined in the NASA Research Grant NAG9-1032 titled "Validation of Land Cover Maps Utilizing Astronaut Acquired Imagery". This grant funds the Remote Sensing Research Unit of the University of California, Santa Barbara. This document summarizes the research progress and accomplishments to date and describes current on-going research activities. Even though this grant has technically expired, in a contractual sense, work continues on this project. Therefore, this summary will include all work done through and 5 May 1999. The principal goal of this effort is to test the accuracy of a sub-regional portion of an AVHRR-based land cover product. Land cover mapped to three different classification systems, in the southwestern United States, have been subjected to two specific accuracy assessments. One assessment utilizing astronaut acquired photography, and a second assessment employing Landsat Thematic Mapper imagery, augmented in some cases, high aerial photography. Validation of these three land cover products has proceeded using a stratified sampling methodology. We believe this research will provide an important initial test of the potential use of imagery acquired from Shuttle and ultimately the International Space Station (ISS) for the operational validation of the Moderate Resolution Imaging Spectrometer (MODIS) land cover products.

Forest fire risk assessment-an integrated approach based on multicriteria evaluation.

PubMed

Goleiji, Elham; Hosseini, Seyed Mohsen; Khorasani, Nematollah; Monavari, Seyed Masoud

2017-11-06

The present study deals with application of the weighted linear combination method for zoning of forest fire risk in Dohezar and Sehezar region of Mazandaran province in northern Iran. In this study, the effective criteria for fires were identified by the Delphi method, and these included ecological and socioeconomic parameters. In this regard, the first step comprised of digital layers; the required data were provided from databases, related centers, and field data collected in the region. Then, the map of criteria was digitized in a geographic information system, and all criteria and indexes were normalized by fuzzy logic. After that, the geographic information system (GIS 10.3) was integrated with the Weighted Linear Combination and the Analytical Network Process, to produce zonation of the forest fire risk map in the Dohezar and Sehezar region. In order to analyze accuracy of the evaluation, the results obtained from the study were compared to records of former fire incidents in the region. This was done using the Kappa coefficient test and a receiver operating characteristic curve. The model showing estimations for forest fire risk explained that the prepared map had accuracy of 90% determined by the Kappa coefficient test and the value of 0.924 by receiver operating characteristic. These results showed that the prepared map had high accuracy and efficacy.

A methodology for small scale rural land use mapping in semi-arid developing countries using orbital imagery. Part 6: A low-cost method for land use mapping using simple visual techniques of interpretation. [Spain

NASA Technical Reports Server (NTRS)

Vangenderen, J. L. (Principal Investigator); Lock, B. F.

1976-01-01

The author has identified the following significant results. It was found that color composite transparencies and monocular magnification provided the best base for land use interpretation. New methods for determining optimum sample sizes and analyzing interpretation accuracy levels were developed. All stages of the methodology were assessed, in the operational sense, during the production of a 1:250,000 rural land use map of Murcia Province, Southeast Spain.

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

NASA Technical Reports Server (NTRS)

Xiong, Jun; Thenkabail, Prasad S.; Tilton, James C.; Gumma, Murali K.; Teluguntla, Pardhasaradhi; Oliphant, Adam; Congalton, Russell G.; Yadav, Kamini; Gorelick, Noel

2017-01-01

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Evaluating an Automated Approach for Monitoring Forest Disturbances in the Pacific Northwest from Logging, Fire and Insect Outbreaks with Landsat Time Series Data

NASA Technical Reports Server (NTRS)

R.Neigh, Christopher S.; Bolton, Douglas K.; Williams, Jennifer J.; Diabate, Mouhamad

2014-01-01

Forests are the largest aboveground sink for atmospheric carbon (C), and understanding how they change through time is critical to reduce our C-cycle uncertainties. We investigated a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 1991 in Pacific Northwest forests, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometers (AVHRRs). To understand the causal factors of this decline, we evaluated an automated classification method developed for Landsat time series stacks (LTSS) to map forest change. This method included: (1) multiple disturbance index thresholds; and (2) a spectral trajectory-based image analysis with multiple confidence thresholds. We produced 48 maps and verified their accuracy with air photos, monitoring trends in burn severity data and insect aerial detection survey data. Area-based accuracy estimates for change in forest cover resulted in producer's and user's accuracies of 0.21 +/- 0.06 to 0.38 +/- 0.05 for insect disturbance, 0.23 +/- 0.07 to 1 +/- 0 for burned area and 0.74 +/- 0.03 to 0.76 +/- 0.03 for logging. We believe that accuracy was low for insect disturbance because air photo reference data were temporally sparse, hence missing some outbreaks, and the annual anniversary time step is not dense enough to track defoliation and progressive stand mortality. Producer's and user's accuracy for burned area was low due to the temporally abrupt nature of fire and harvest with a similar response of spectral indices between the disturbance index and normalized burn ratio. We conclude that the spectral trajectory approach also captures multi-year stress that could be caused by climate, acid deposition, pathogens, partial harvest, thinning, etc. Our study focused on understanding the transferability of previously successful methods to new ecosystems and found that this automated method does not perform with the same accuracy in Pacific Northwest forests. Using a robust accuracy assessment, we demonstrate the difficulty of transferring change attribution methods to other ecosystems, which has implications for the development of automated detection/attribution approaches. Widespread disturbance was found within AVHRR-negative anomalies, but identifying causal factors in LTSS with adequate mapping accuracy for fire and insects proved to be elusive. Our results provide a background framework for future studies to improve methods for the accuracy assessment of automated LTSS classifications.

3D micro-mapping: Towards assessing the quality of crowdsourcing to support 3D point cloud analysis

NASA Astrophysics Data System (ADS)

Herfort, Benjamin; Höfle, Bernhard; Klonner, Carolin

2018-03-01

In this paper, we propose a method to crowdsource the task of complex three-dimensional information extraction from 3D point clouds. We design web-based 3D micro tasks tailored to assess segmented LiDAR point clouds of urban trees and investigate the quality of the approach in an empirical user study. Our results for three different experiments with increasing complexity indicate that a single crowdsourcing task can be solved in a very short time of less than five seconds on average. Furthermore, the results of our empirical case study reveal that the accuracy, sensitivity and precision of 3D crowdsourcing are high for most information extraction problems. For our first experiment (binary classification with single answer) we obtain an accuracy of 91%, a sensitivity of 95% and a precision of 92%. For the more complex tasks of the second Experiment 2 (multiple answer classification) the accuracy ranges from 65% to 99% depending on the label class. Regarding the third experiment - the determination of the crown base height of individual trees - our study highlights that crowdsourcing can be a tool to obtain values with even higher accuracy in comparison to an automated computer-based approach. Finally, we found out that the accuracy of the crowdsourced results for all experiments is hardly influenced by characteristics of the input point cloud data and of the users. Importantly, the results' accuracy can be estimated using agreement among volunteers as an intrinsic indicator, which makes a broad application of 3D micro-mapping very promising.

Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure

Treesearch

Harold S.J. Zald; Janet L. Ohmann; Heather M. Roberts; Matthew J. Gregory; Emilie B. Henderson; Robert J. McGaughey; Justin Braaten

2014-01-01

This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS) imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were developed for 539,000 ha in the...

EnviroAtlas -Portland, ME- One Meter Resolution Urban Land Cover (2010)

EPA Pesticide Factsheets

The EnviroAtlas Portland, ME land cover map was generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from Late Summer 2010 at 1 m spatial resolution. Eight land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, agriculture, and wetlands (woody and emergent). An accuracy assessment using a stratified random sampling of 600 samples yielded an overall accuracy of 87.5 percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Portland. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas -Milwaukee, WI- One Meter Resolution Urban Land Cover Data (2010)

EPA Pesticide Factsheets

The EnviroAtlas Milwaukee, WI land cover data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from Late Summer 2010 at 1 m spatial resolution. Nine land cover classes were mapped: water, impervious surfaces (dark and light), soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, agriculture, and wetlands (woody and emergent). An accuracy assessment using a completely random sampling of 600 samples yielded an overall accuracy of 85.39% percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Milwaukee. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas -- Woodbine, IA -- One Meter Resolution Urban Land Cover Data (2011)

EPA Pesticide Factsheets

The EnviroAtlas Woodbine, IA land cover (LC) data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from Late Summer 2011 at 1 m spatial resolution. Six land cover classes were mapped: water, impervious surfaces (dark and light), soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, and agriculture. An accuracy assessment using a completely random sampling of 600 samples yielded an overall accuracy of 87.03% percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Woodbine. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

GIS based optimal impervious surface map generation using various spatial data for urban nonpoint source management.

PubMed

Lee, Cholyoung; Kim, Kyehyun; Lee, Hyuk

2018-01-15

Impervious surfaces are mainly artificial structures such as rooftops, roads, and parking lots that are covered by impenetrable materials. These surfaces are becoming the major causes of nonpoint source (NPS) pollution in urban areas. The rapid progress of urban development is increasing the total amount of impervious surfaces and NPS pollution. Therefore, many cities worldwide have adopted a stormwater utility fee (SUF) that generates funds needed to manage NPS pollution. The amount of SUF is estimated based on the impervious ratio, which is calculated by dividing the total impervious surface area by the net area of an individual land parcel. Hence, in order to identify the exact impervious ratio, large-scale impervious surface maps (ISMs) are necessary. This study proposes and assesses various methods for generating large-scale ISMs for urban areas by using existing GIS data. Bupyeong-gu, a district in the city of Incheon, South Korea, was selected as the study area. Spatial data that were freely offered by national/local governments in S. Korea were collected. First, three types of ISMs were generated by using the land-cover map, digital topographic map, and orthophotographs, to validate three methods that had been proposed conceptually by Korea Environment Corporation. Then, to generate an ISM of higher accuracy, an integration method using all data was proposed. Error matrices were made and Kappa statistics were calculated to evaluate the accuracy. Overlay analyses were performed to examine the distribution of misclassified areas. From the results, the integration method delivered the highest accuracy (Kappa statistic of 0.99) compared to the three methods that use a single type of spatial data. However, a longer production time and higher cost were limiting factors. Among the three methods using a single type of data, the land-cover map showed the highest accuracy with a Kappa statistic of 0.91. Thus, it was judged that the mapping method using the land-cover map is more appropriate than the others. In conclusion, it is desirable to apply the integration method when generating the ISM with the highest accuracy. However, if time and cost are constrained, it would be effective to primarily use the land-cover map. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modified look-locker inversion recovery T1 mapping indices: assessment of accuracy and reproducibility between magnetic resonance scanners

PubMed Central

2013-01-01

Background Cardiovascular magnetic resonance (CMR) T1 mapping indices, such as T1 time and partition coefficient (λ), have shown potential to assess diffuse myocardial fibrosis. The purpose of this study was to investigate how scanner and field strength variation affect the accuracy and precision/reproducibility of T1 mapping indices. Methods CMR studies were performed on two 1.5T and three 3T scanners. Eight phantoms were made to mimic the T1/T2 of pre- and post-contrast myocardium and blood at 1.5T and 3T. T1 mapping using MOLLI was performed with simulated heart rate of 40-100 bpm. Inversion recovery spin echo (IR-SE) was the reference standard for T1 determination. Accuracy was defined as the percent error between MOLLI and IR-SE, and scan/re-scan reproducibility was defined as the relative percent mean difference between repeat MOLLI scans. Partition coefficient was estimated by ΔR1myocardium phantom/ΔR1blood phantom. Generalized linear mixed model was used to compare the accuracy and precision/reproducibility of T1 and λ across field strength, scanners, and protocols. Results Field strength significantly affected MOLLI T1 accuracy (6.3% error for 1.5T vs. 10.8% error for 3T, p<0.001) but not λ accuracy (8.8% error for 1.5T vs. 8.0% error for 3T, p=0.11). Partition coefficients of MOLLI were not different between two 1.5T scanners (47.2% vs. 47.9%, p=0.13), and showed only slight variation across three 3T scanners (49.2% vs. 49.8% vs. 49.9%, p=0.016). Partition coefficient also had significantly lower percent error for precision (better scan/re-scan reproducibility) than measurement of individual T1 values (3.6% for λ vs. 4.3%-4.8% for T1 values, approximately, for pre/post blood and myocardium values). Conclusion Based on phantom studies, T1 errors using MOLLI ranged from 6-14% across various MR scanners while errors for partition coefficient were less (6-10%). Compared with absolute T1 times, partition coefficient showed less variability across platforms and field strengths as well as higher precision. PMID:23890156

Accuracy assessment of minimum control points for UAV photography and georeferencing

NASA Astrophysics Data System (ADS)

Skarlatos, D.; Procopiou, E.; Stavrou, G.; Gregoriou, M.

2013-08-01

In recent years, Autonomous Unmanned Aerial Vehicles (AUAV) became popular among researchers across disciplines because they combine many advantages. One major application is monitoring and mapping. Their ability to fly beyond eye sight autonomously, collecting data over large areas whenever, wherever, makes them excellent platform for monitoring hazardous areas or disasters. In both cases rapid mapping is needed while human access isn't always a given. Indeed, current automatic processing of aerial photos using photogrammetry and computer vision algorithms allows for rapid orthophomap production and Digital Surface Model (DSM) generation, as tools for monitoring and damage assessment. In such cases, control point measurement using GPS is either impossible, or time consuming or costly. This work investigates accuracies that can be attained using few or none control points over areas of one square kilometer, in two test sites; a typical block and a corridor survey. On board GPS data logged during AUAV's flight are being used for direct georeferencing, while ground check points are being used for evaluation. In addition various control point layouts are being tested using bundle adjustment for accuracy evaluation. Results indicate that it is possible to use on board single frequency GPS for direct georeferencing in cases of disaster management or areas without easy access, or even over featureless areas. Due to large numbers of tie points in the bundle adjustment, horizontal accuracy can be fulfilled with a rather small number of control points, but vertical accuracy may not.

Accuracy of CNV Detection from GWAS Data.

PubMed

Zhang, Dandan; Qian, Yudong; Akula, Nirmala; Alliey-Rodriguez, Ney; Tang, Jinsong; Gershon, Elliot S; Liu, Chunyu

2011-01-13

Several computer programs are available for detecting copy number variants (CNVs) using genome-wide SNP arrays. We evaluated the performance of four CNV detection software suites--Birdsuite, Partek, HelixTree, and PennCNV-Affy--in the identification of both rare and common CNVs. Each program's performance was assessed in two ways. The first was its recovery rate, i.e., its ability to call 893 CNVs previously identified in eight HapMap samples by paired-end sequencing of whole-genome fosmid clones, and 51,440 CNVs identified by array Comparative Genome Hybridization (aCGH) followed by validation procedures, in 90 HapMap CEU samples. The second evaluation was program performance calling rare and common CNVs in the Bipolar Genome Study (BiGS) data set (1001 bipolar cases and 1033 controls, all of European ancestry) as measured by the Affymetrix SNP 6.0 array. Accuracy in calling rare CNVs was assessed by positive predictive value, based on the proportion of rare CNVs validated by quantitative real-time PCR (qPCR), while accuracy in calling common CNVs was assessed by false positive/false negative rates based on qPCR validation results from a subset of common CNVs. Birdsuite recovered the highest percentages of known HapMap CNVs containing >20 markers in two reference CNV datasets. The recovery rate increased with decreased CNV frequency. In the tested rare CNV data, Birdsuite and Partek had higher positive predictive values than the other software suites. In a test of three common CNVs in the BiGS dataset, Birdsuite's call was 98.8% consistent with qPCR quantification in one CNV region, but the other two regions showed an unacceptable degree of accuracy. We found relatively poor consistency between the two "gold standards," the sequence data of Kidd et al., and aCGH data of Conrad et al. Algorithms for calling CNVs especially common ones need substantial improvement, and a "gold standard" for detection of CNVs remains to be established.

Concept Mapping Improves Metacomprehension Accuracy among 7th Graders

ERIC Educational Resources Information Center

Redford, Joshua S.; Thiede, Keith W.; Wiley, Jennifer; Griffin, Thomas D.

2012-01-01

Two experiments explored concept map construction as a useful intervention to improve metacomprehension accuracy among 7th grade students. In the first experiment, metacomprehension was marginally better for a concept mapping group than for a rereading group. In the second experiment, metacomprehension accuracy was significantly greater for a…

Mapping rice areas of South Asia using MODIS multitemporal data

NASA Astrophysics Data System (ADS)

Gumma, Murali Krishna; Nelson, Andrew; Thenkabail, Prasad S.; Singh, Amrendra N.

2011-01-01

Our goal is to map the rice areas of six South Asian countries using moderate-resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74% of the population that lives on less than $2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas.

Mapping rice areas of South Asia using MODIS multitemporal data

USGS Publications Warehouse

Gumma, M.K.; Nelson, A.; Thenkabail, P.S.; Singh, A.N.

2011-01-01

Our goal is to map the rice areas of six South Asian countries using moderate-resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74% of the population that lives on less than $2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R2 values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas. ?? 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).

An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme.

PubMed

Valderrama-Landeros, L; Flores-de-Santiago, F; Kovacs, J M; Flores-Verdugo, F

2017-12-14

Optimizing the classification accuracy of a mangrove forest is of utmost importance for conservation practitioners. Mangrove forest mapping using satellite-based remote sensing techniques is by far the most common method of classification currently used given the logistical difficulties of field endeavors in these forested wetlands. However, there is now an abundance of options from which to choose in regards to satellite sensors, which has led to substantially different estimations of mangrove forest location and extent with particular concern for degraded systems. The objective of this study was to assess the accuracy of mangrove forest classification using different remotely sensed data sources (i.e., Landsat-8, SPOT-5, Sentinel-2, and WorldView-2) for a system located along the Pacific coast of Mexico. Specifically, we examined a stressed semiarid mangrove forest which offers a variety of conditions such as dead areas, degraded stands, healthy mangroves, and very dense mangrove island formations. The results indicated that Landsat-8 (30 m per pixel) had  the lowest overall accuracy at 64% and that WorldView-2 (1.6 m per pixel) had the highest at 93%. Moreover, the SPOT-5 and the Sentinel-2 classifications (10 m per pixel) were very similar having accuracies of 75 and 78%, respectively. In comparison to WorldView-2, the other sensors overestimated the extent of Laguncularia racemosa and underestimated the extent of Rhizophora mangle. When considering such type of sensors, the higher spatial resolution can be particularly important in mapping small mangrove islands that often occur in degraded mangrove systems.

Large Scale Crop Mapping in Ukraine Using Google Earth Engine

NASA Astrophysics Data System (ADS)

Shelestov, A.; Lavreniuk, M. S.; Kussul, N.

2016-12-01

There are no globally available high resolution satellite-derived crop specific maps at present. Only coarse-resolution imagery (> 250 m spatial resolution) has been utilized to derive global cropland extent. In 2016 we are going to carry out a country level demonstration of Sentinel-2 use for crop classification in Ukraine within the ESA Sen2-Agri project. But optical imagery can be contaminated by cloud cover that makes it difficult to acquire imagery in an optimal time range to discriminate certain crops. Due to the Copernicus program since 2015, a lot of Sentinel-1 SAR data at high spatial resolution is available for free for Ukraine. It allows us to use the time series of SAR data for crop classification. Our experiment for one administrative region in 2015 showed much higher crop classification accuracy with SAR data than with optical only time series [1, 2]. Therefore, in 2016 within the Google Earth Engine Research Award we use SAR data together with optical ones for large area crop mapping (entire territory of Ukraine) using cloud computing capabilities available at Google Earth Engine (GEE). This study compares different classification methods for crop mapping for the whole territory of Ukraine using data and algorithms from GEE. Classification performance assessed using overall classification accuracy, Kappa coefficients, and user's and producer's accuracies. Also, crop areas from derived classification maps compared to the official statistics [3]. S. Skakun et al., "Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine," IEEE Journal of Selected Topics in Applied Earth Observ. and Rem. Sens., 2015, DOI: 10.1109/JSTARS.2015.2454297. N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "The use of satellite SAR imagery to crop classification in Ukraine within JECAM project," IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.1497-1500, 13-18 July 2014, Quebec City, Canada. F.J. Gallego, N. Kussul, S. Skakun, O. Kravchenko, A. Shelestov, O. Kussul, "Efficiency assessment of using satellite data for crop area estimation in Ukraine," International Journal of Applied Earth Observation and Geoinformation vol. 29, pp. 22-30, 2014.

Comparative analysis of Worldview-2 and Landsat 8 for coastal saltmarsh mapping accuracy assessment

NASA Astrophysics Data System (ADS)

Rasel, Sikdar M. M.; Chang, Hsing-Chung; Diti, Israt Jahan; Ralph, Tim; Saintilan, Neil

2016-05-01

Coastal saltmarsh and their constituent components and processes are of an interest scientifically due to their ecological function and services. However, heterogeneity and seasonal dynamic of the coastal wetland system makes it challenging to map saltmarshes with remotely sensed data. This study selected four important saltmarsh species Pragmitis australis, Sporobolus virginicus, Ficiona nodosa and Schoeloplectus sp. as well as a Mangrove and Pine tree species, Avecinia and Casuarina sp respectively. High Spatial Resolution Worldview-2 data and Coarse Spatial resolution Landsat 8 imagery were selected in this study. Among the selected vegetation types some patches ware fragmented and close to the spatial resolution of Worldview-2 data while and some patch were larger than the 30 meter resolution of Landsat 8 data. This study aims to test the effectiveness of different classifier for the imagery with various spatial and spectral resolutions. Three different classification algorithm, Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were tested and compared with their mapping accuracy of the results derived from both satellite imagery. For Worldview-2 data SVM was giving the higher overall accuracy (92.12%, kappa =0.90) followed by ANN (90.82%, Kappa 0.89) and MLC (90.55%, kappa = 0.88). For Landsat 8 data, MLC (82.04%) showed the highest classification accuracy comparing to SVM (77.31%) and ANN (75.23%). The producer accuracy of the classification results were also presented in the paper.

Testing mapping algorithms of the cancer-specific EORTC QLQ-C30 onto EQ-5D in malignant mesothelioma.

PubMed

Arnold, David T; Rowen, Donna; Versteegh, Matthijs M; Morley, Anna; Hooper, Clare E; Maskell, Nicholas A

2015-01-23

In order to estimate utilities for cancer studies where the EQ-5D was not used, the EORTC QLQ-C30 can be used to estimate EQ-5D using existing mapping algorithms. Several mapping algorithms exist for this transformation, however, algorithms tend to lose accuracy in patients in poor health states. The aim of this study was to test all existing mapping algorithms of QLQ-C30 onto EQ-5D, in a dataset of patients with malignant pleural mesothelioma, an invariably fatal malignancy where no previous mapping estimation has been published. Health related quality of life (HRQoL) data where both the EQ-5D and QLQ-C30 were used simultaneously was obtained from the UK-based prospective observational SWAMP (South West Area Mesothelioma and Pemetrexed) trial. In the original trial 73 patients with pleural mesothelioma were offered palliative chemotherapy and their HRQoL was assessed across five time points. This data was used to test the nine available mapping algorithms found in the literature, comparing predicted against observed EQ-5D values. The ability of algorithms to predict the mean, minimise error and detect clinically significant differences was assessed. The dataset had a total of 250 observations across 5 timepoints. The linear regression mapping algorithms tested generally performed poorly, over-estimating the predicted compared to observed EQ-5D values, especially when observed EQ-5D was below 0.5. The best performing algorithm used a response mapping method and predicted the mean EQ-5D with accuracy with an average root mean squared error of 0.17 (Standard Deviation; 0.22). This algorithm reliably discriminated between clinically distinct subgroups seen in the primary dataset. This study tested mapping algorithms in a population with poor health states, where they have been previously shown to perform poorly. Further research into EQ-5D estimation should be directed at response mapping methods given its superior performance in this study.

An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping.

PubMed

Feizizadeh, Bakhtiar; Blaschke, Thomas

2014-03-04

GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster-Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster-Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC operation yielded poor results.

Mapping the EORTC QLQ-C30 onto the EQ-5D-3L: assessing the external validity of existing mapping algorithms.

PubMed

Doble, Brett; Lorgelly, Paula

2016-04-01

To determine the external validity of existing mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses and to establish their generalizability in different types of cancer. A main analysis (pooled) sample of 3560 observations (1727 patients) and two disease severity patient samples (496 and 93 patients) with repeated observations over time from Cancer 2015 were used to validate the existing algorithms. Errors were calculated between observed and predicted EQ-5D-3L utility values using a single pooled sample and ten pooled tumour type-specific samples. Predictive accuracy was assessed using mean absolute error (MAE) and standardized root-mean-squared error (RMSE). The association between observed and predicted EQ-5D utility values and other covariates across the distribution was tested using quantile regression. Quality-adjusted life years (QALYs) were calculated using observed and predicted values to test responsiveness. Ten 'preferred' mapping algorithms were identified. Two algorithms estimated via response mapping and ordinary least-squares regression using dummy variables performed well on number of validation criteria, including accurate prediction of the best and worst QLQ-C30 health states, predicted values within the EQ-5D tariff range, relatively small MAEs and RMSEs, and minimal differences between estimated QALYs. Comparison of predictive accuracy across ten tumour type-specific samples highlighted that algorithms are relatively insensitive to grouping by tumour type and affected more by differences in disease severity. Two of the 'preferred' mapping algorithms suggest more accurate predictions, but limitations exist. We recommend extensive scenario analyses if mapped utilities are used in cost-utility analyses.

MRLC-LAND COVER MAPPING, ACCURACY ASSESSMENT AND APPLICATION RESEARCH

EPA Science Inventory

The National Land Cover Database (NLCD), produced by the Multi-Resolution Land Characteristics (MRLC) provides consistently classified land-cover and ancillary data for the United States. These data support many of the modeling and monitoring efforts related to GPRA goals of Cle...

Classification and Accuracy Assessment for Coarse Resolution Mapping within the Great Lakes Basin, USA

EPA Science Inventory

This study applied a phenology-based land-cover classification approach across the Laurentian Great Lakes Basin (GLB) using time-series data consisting of 23 Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) composite images (250 ...

Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment

PubMed Central

Guttmann, Aline; Li, Xinran; Feschet, Fabien; Gaudart, Jean; Demongeot, Jacques; Boire, Jean-Yves; Ouchchane, Lemlih

2015-01-01

In cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps. PMID:26086911

Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping

NASA Astrophysics Data System (ADS)

Rapinel, Sébastien; Hubert-Moy, Laurence; Clément, Bernard

2015-05-01

Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, the inventory and characterization of wetland habitats are most often limited to small areas. This explains why the understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. While LiDAR data and multispectral Earth Observation (EO) images are often used separately to map wetland habitats, their combined use is currently being assessed for different habitat types. The aim of this study is to evaluate the combination of multispectral and multiseasonal imagery and LiDAR data to precisely map the distribution of wetland habitats. The image classification was performed combining an object-based approach and decision-tree modeling. Four multispectral images with high (SPOT-5) and very high spatial resolution (Quickbird, KOMPSAT-2, aerial photographs) were classified separately. Another classification was then applied integrating summer and winter multispectral image data and three layers derived from LiDAR data: vegetation height, microtopography and intensity return. The comparison of classification results shows that some habitats are better identified on the winter image and others on the summer image (overall accuracies = 58.5 and 57.6%). They also point out that classification accuracy is highly improved (overall accuracy = 86.5%) when combining LiDAR data and multispectral images. Moreover, this study highlights the advantage of integrating vegetation height, microtopography and intensity parameters in the classification process. This article demonstrates that information provided by the synergetic use of multispectral images and LiDAR data can help in wetland functional assessment

Assessment of Lexical and Non-Lexical Spelling in Students in Grades 1-7

ERIC Educational Resources Information Center

Kohnen, Saskia; Colenbrander, Danielle; Krajenbrink, Trudy; Nickels, Lyndsey

2015-01-01

The main aim of this study was to develop standardised tests that assess some of the most important spelling skills for children in primary school: sound-letter mappings (non-lexical spelling) and word spelling accuracy (lexical spelling). We present normative comparison data for children in Grades 1-7 as well as measures of validity and…

Application research on land use remote sensing dynamic monitoring: A case study of Anning district, Lanzhou

NASA Astrophysics Data System (ADS)

Zhu, Yunqiang; Zhu, Huazhong; Lu, Heli; Ni, Jianguang; Zhu, Shaoxia

2005-10-01

Remote sensing dynamic monitoring of land use can detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management of land resources. This paper discusses the technological procedure of remote sensing dynamic monitoring of land use including the process of remote sensing images, the extraction of annual change information of land use, field survey, indoor post processing and accuracy assessment. Especially, we emphasize on comparative research on the choice of remote sensing rectifying models, image fusion algorithms and accuracy assessment methods. Taking Anning district in Lanzhou as an example, we extract the land use change information of the district during 2002-2003, access monitoring accuracy and analyze the reason of land use change.

Data Processing and Quality Evaluation of a Boat-Based Mobile Laser Scanning System

PubMed Central

Vaaja, Matti; Kukko, Antero; Kaartinen, Harri; Kurkela, Matti; Kasvi, Elina; Flener, Claude; Hyyppä, Hannu; Hyyppä, Juha; Järvelä, Juha; Alho, Petteri

2013-01-01

Mobile mapping systems (MMSs) are used for mapping topographic and urban features which are difficult and time consuming to measure with other instruments. The benefits of MMSs include efficient data collection and versatile usability. This paper investigates the data processing steps and quality of a boat-based mobile mapping system (BoMMS) data for generating terrain and vegetation points in a river environment. Our aim in data processing was to filter noise points, detect shorelines as well as points below water surface and conduct ground point classification. Previous studies of BoMMS have investigated elevation accuracies and usability in detection of fluvial erosion and deposition areas. The new findings concerning BoMMS data are that the improved data processing approach allows for identification of multipath reflections and shoreline delineation. We demonstrate the possibility to measure bathymetry data in shallow (0–1 m) and clear water. Furthermore, we evaluate for the first time the accuracy of the BoMMS ground points classification compared to manually classified data. We also demonstrate the spatial variations of the ground point density and assess elevation and vertical accuracies of the BoMMS data. PMID:24048340

Data processing and quality evaluation of a boat-based mobile laser scanning system.

PubMed

Vaaja, Matti; Kukko, Antero; Kaartinen, Harri; Kurkela, Matti; Kasvi, Elina; Flener, Claude; Hyyppä, Hannu; Hyyppä, Juha; Järvelä, Juha; Alho, Petteri

2013-09-17

Mobile mapping systems (MMSs) are used for mapping topographic and urban features which are difficult and time consuming to measure with other instruments. The benefits of MMSs include efficient data collection and versatile usability. This paper investigates the data processing steps and quality of a boat-based mobile mapping system (BoMMS) data for generating terrain and vegetation points in a river environment. Our aim in data processing was to filter noise points, detect shorelines as well as points below water surface and conduct ground point classification. Previous studies of BoMMS have investigated elevation accuracies and usability in detection of fluvial erosion and deposition areas. The new findings concerning BoMMS data are that the improved data processing approach allows for identification of multipath reflections and shoreline delineation. We demonstrate the possibility to measure bathymetry data in shallow (0-1 m) and clear water. Furthermore, we evaluate for the first time the accuracy of the BoMMS ground points classification compared to manually classified data. We also demonstrate the spatial variations of the ground point density and assess elevation and vertical accuracies of the BoMMS data.

Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area

USGS Publications Warehouse

Norman, Laura M.; Middleton, Barry R.; Wilson, Natalie R.

2018-01-01

Mapping of vegetation types is of great importance to the San Carlos Apache Tribe and their management of forestry and fire fuels. Various remote sensing techniques were applied to classify multitemporal Landsat 8 satellite data, vegetation index, and digital elevation model data. A multitiered unsupervised classification generated over 900 classes that were then recoded to one of the 16 generalized vegetation/land cover classes using the Southwest Regional Gap Analysis Project (SWReGAP) map as a guide. A supervised classification was also run using field data collected in the SWReGAP project and our field campaign. Field data were gathered and accuracy assessments were generated to compare outputs. Our hypothesis was that a resulting map would update and potentially improve upon the vegetation/land cover class distributions of the older SWReGAP map over the 24,000  km2 study area. The estimated overall accuracies ranged between 43% and 75%, depending on which method and field dataset were used. The findings demonstrate the complexity of vegetation mapping, the importance of recent, high-quality-field data, and the potential for misleading results when insufficient field data are collected.

Quantitative Evaluation of Segmentation- and Atlas-Based Attenuation Correction for PET/MR on Pediatric Patients.

PubMed

Bezrukov, Ilja; Schmidt, Holger; Gatidis, Sergios; Mantlik, Frédéric; Schäfer, Jürgen F; Schwenzer, Nina; Pichler, Bernd J

2015-07-01

Pediatric imaging is regarded as a key application for combined PET/MR imaging systems. Because existing MR-based attenuation-correction methods were not designed specifically for pediatric patients, we assessed the impact of 2 potentially influential factors: inter- and intrapatient variability of attenuation coefficients and anatomic variability. Furthermore, we evaluated the quantification accuracy of 3 methods for MR-based attenuation correction without (SEGbase) and with bone prediction using an adult and a pediatric atlas (SEGwBONEad and SEGwBONEpe, respectively) on PET data of pediatric patients. The variability of attenuation coefficients between and within pediatric (5-17 y, n = 17) and adult (27-66 y, n = 16) patient collectives was assessed on volumes of interest (VOIs) in CT datasets for different tissue types. Anatomic variability was assessed on SEGwBONEad/pe attenuation maps by computing mean differences to CT-based attenuation maps for regions of bone tissue, lungs, and soft tissue. PET quantification was evaluated on VOIs with physiologic uptake and on 80% isocontour VOIs with elevated uptake in the thorax and abdomen/pelvis. Inter- and intrapatient variability of the bias was assessed for each VOI group and method. Statistically significant differences in mean VOI Hounsfield unit values and linear attenuation coefficients between adult and pediatric collectives were found in the lungs and femur. The prediction of attenuation maps using the pediatric atlas showed a reduced error in bone tissue and better delineation of bone structure. Evaluation of PET quantification accuracy showed statistically significant mean errors in mean standardized uptake values of -14% ± 5% and -23% ± 6% in bone marrow and femur-adjacent VOIs with physiologic uptake for SEGbase, which could be reduced to 0% ± 4% and -1% ± 5% using SEGwBONEpe attenuation maps. Bias in soft-tissue VOIs was less than 5% for all methods. Lung VOIs showed high SDs in the range of 15% for all methods. For VOIs with elevated uptake, mean and SD were less than 5% except in the thorax. The use of a dedicated atlas for the pediatric patient collective resulted in improved attenuation map prediction in osseous regions and reduced interpatient bias variation in femur-adjacent VOIs. For the lungs, in which intrapatient variation was higher for the pediatric collective, a patient- or group-specific attenuation coefficient might improve attenuation map accuracy. Mean errors of -14% and -23% in bone marrow and femur-adjacent VOIs can affect PET quantification in these regions when bone tissue is ignored. © 2015 by the Society of Nuclear Medicine and Molecular Imaging, Inc.

Automatic mapping of urban areas from Landsat data using impervious surface fraction algorithm

NASA Astrophysics Data System (ADS)

Nguyen, S. T.; Chen, C. F.; Chen, C. R.

2014-12-01

Urbanization is a result of aggregation of people in urban areas that can help advance socioeconomic development and pull out people from the poverty line. However, if not monitored well, it can also lead to loss of farmlands, natural forests as well as to societal impacts including burgeoning growth of slums, pollution, and crime. Thus, spatiotemporal information that shapes the urbanization is thus critical to the process of urban planning. The overall objective of this study is to develop an impervious surface fraction algorithm (ISFA) for automatically mapping urban areas from Landsat data. We processed the data for 1986, 2001 and 2014 to trace the multi-decadal spatiotemporal change of Honduran capital city using a three-step procedure: (1) data pre-processing to perform image normalization as well as to produce the difference in the values (DVSS) between the simple ratio (SR) of green and shortwave bands and the soil adjust vegetation index (SAVI), (2) quantification of urban areas using ISFA, and (3) accuracy assessment of mapping results using the ground reference data constructed using land-cover maps and FORMOSAT-2 imagery. The mapping accuracy assessment was performed for 2001 and 2014 by comparing with the ground reference data indicated satisfactory results with the overall accuracies and Kappa coefficients generally higher than 90% and 0.8, respectively. When examining the urbanization between these years, it could be observed that the urban area was significantly expanded from 1986 to 2014, mainly driven by two factors of rapid population growth and socioeconomic development. This study eventually leads to a realization of the merit of using ISFA for multi-decadal monitoring of the urbanization of Honduran capital city from Landsat data. Results from this research can be used by urban planners as a general indicator to quantify urban change and environmental impacts. The methods were thus transferable to monitor urban growth in cities and their peri areas around the world.

Coastal areas mapping using UAV photogrammetry

NASA Astrophysics Data System (ADS)

Nikolakopoulos, Konstantinos G.; Kozarski, Dimitrios; Kogkas, Stefanos

2017-10-01

The coastal areas in the Patras Gulf suffer degradation due to the sea action and other natural and human-induced causes. Changes in beaches, ports, and other man made constructions need to be assessed, both after severe events and on a regular basis, to build models that can predict the evolution in the future. Thus, reliable spatial data acquisition is a critical process for the identification of the coastline and the broader coastal zones for geologists and other scientists involved in the study of coastal morphology. High resolution satellite data, airphotos and airborne Lidar provided in the past the necessary data for the coastline monitoring. High-resolution digital surface models (DSMs) and orthophoto maps had become a necessity in order to map with accuracy all the variations in costal environments. Recently, unmanned aerial vehicles (UAV) photogrammetry offers an alternative solution to the acquisition of high accuracy spatial data along the coastline. This paper presents the use of UAV to map the coastline in Rio area Western Greece. Multiple photogrammetric aerial campaigns were performed. A small commercial UAV (DJI Phantom 3 Advance) was used to acquire thousands of images with spatial resolutions better than 5 cm. Different photogrammetric software's were used to orientate the images, extract point clouds, build a digital surface model and produce orthoimage mosaics. In order to achieve the best positional accuracy signalised ground control points were measured with a differential GNSS receiver. The results of this coastal monitoring programme proved that UAVs can replace many of the conventional surveys, with considerable gains in the cost of the data acquisition and without any loss in the accuracy.

Mathematical models application for mapping soils spatial distribution on the example of the farm from the North of Udmurt Republic of Russia

NASA Astrophysics Data System (ADS)

Dokuchaev, P. M.; Meshalkina, J. L.; Yaroslavtsev, A. M.

2018-01-01

Comparative analysis of soils geospatial modeling using multinomial logistic regression, decision trees, random forest, regression trees and support vector machines algorithms was conducted. The visual interpretation of the digital maps obtained and their comparison with the existing map, as well as the quantitative assessment of the individual soil groups detection overall accuracy and of the models kappa showed that multiple logistic regression, support vector method, and random forest models application with spatial prediction of the conditional soil groups distribution can be reliably used for mapping of the study area. It has shown the most accurate detection for sod-podzolics soils (Phaeozems Albic) lightly eroded and moderately eroded soils. In second place, according to the mean overall accuracy of the prediction, there are sod-podzolics soils - non-eroded and warp one, as well as sod-gley soils (Umbrisols Gleyic) and alluvial soils (Fluvisols Dystric, Umbric). Heavy eroded sod-podzolics and gray forest soils (Phaeozems Albic) were detected by methods of automatic classification worst of all.

EnviroAtlas - New York, NY - One Meter Resolution Urban Land Cover Data (2008) Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas ). The New York, NY EnviroAtlas Meter-scale Urban Land Cover (MULC) Data were generated by the University of Vermont Spatial Analysis Laboratory (SAL) under the direction of Jarlath O'Neil-Dunne as part of the United States Forest Service Urban Tree Canopy (UTC) assessment program. Seven classes were mapped using LiDAR and high resolution orthophotography: Tree Canopy, Grass/Shrub, Bare Soil, Water, Buildings, Roads/Railroads, and Other Paved Surfaces. These data were subsequently merged to fit with the EPA classification. The SAL project covered the five boroughs within the NYC city limits. However the EPA study area encompassed that area plus a 1 kilometer buffer. Additional land cover for the buffer area was generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution from July, 2011 and LiDAR from 2010. Six land cover classes were mapped: water, impervious surfaces, soil and barren land, trees, grass-herbaceous non-woody vegetation, and agriculture. An accuracy assessment of 600 completely random and 55 stratified random photo interpreted reference points yielded an overall User's fuzzy accuracy of 87 percent. The area mapped is the US Census Bureau's 2010 Urban Statistical Area for New Yor

Uncertainty Assessment and Weight Map Generation for Efficient Fusion of Tandem-X and CARTOSAT-1 Dems

NASA Astrophysics Data System (ADS)

Bagheri, H.; Schmitt, M.; Zhu, X. X.

2017-05-01

Recently, with InSAR data provided by the German TanDEM-X mission, a new global, high-resolution Digital Elevation Model (DEM) has been produced by the German Aerospace Center (DLR) with unprecedented height accuracy. However, due to SAR-inherent sensor specifics, its quality decreases over urban areas, making additional improvement necessary. On the other hand, DEMs derived from optical remote sensing imagery, such as Cartosat-1 data, have an apparently greater resolution in urban areas, making their fusion with TanDEM-X elevation data a promising perspective. The objective of this paper is two-fold: First, the height accuracies of TanDEM-X and Cartosat-1 elevation data over different land types are empirically evaluated in order to analyze the potential of TanDEM-XCartosat- 1 DEM data fusion. After the quality assessment, urban DEM fusion using weighted averaging is investigated. In this experiment, both weight maps derived from the height error maps delivered with the DEM data, as well as more sophisticated weight maps predicted by a procedure based on artificial neural networks (ANNs) are compared. The ANN framework employs several features that can describe the height residual performance to predict the weights used in the subsequent fusion step. The results demonstrate that especially the ANN-based framework is able to improve the quality of the final DEM through data fusion.

EnviroAtlas -Pittsburgh, PA- One Meter Resolution Urban Land Cover Data (2010)

EPA Pesticide Factsheets

The EnviroAtlas Pittsburgh, PA land cover map was generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution. Imagery was collected on multiple dates in June 2010. Five land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, and grass and herbaceous non-woody vegetation. An accuracy assessment of 500 completely random and 81 stratified random points yielded an overall accuracy of 86.57 percent. The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Pittsburgh, PA. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

CARETS: A prototype regional environmental information system. Volume 6: Cost, accuracy and consistency comparisons of land use maps made from high-altitude aircraft photography and ERTS imagery

NASA Technical Reports Server (NTRS)

Alexander, R. H. (Principal Investigator); Fitzpatrick, K. A.

1975-01-01

The author has identified the following significant results. Level 2 land use maps produced at three scales (1:24,000, 1:100,000, and 1:250,000) from high altitude photography were compared with each other and with point data obtained in the field. The same procedures were employed to determine the accuracy of the Level 1 land use maps produced at 1:250,000 from high altitude photography and color composite ERTS imagery. Accuracy of the Level 2 maps was 84.9 percent at 1:24,000, 77.4 percent at 1:100,000 and 73.0 percent at 1:250,000. Accuracy of the Level 1 1:250,000 maps was 76.5 percent for aerial photographs and 69.5 percent for ERTS imagery. The cost of Level 2 land use mapping at 1:24,000 was found to be high ($11.93 per sq km). The cost of mapping at 1:100,000 ($1.75) was about two times as expensive as mapping at 1:250,000 ($.88), and the accuracy increased by only 4.4 percent.

Proposed hybrid-classifier ensemble algorithm to map snow cover area

NASA Astrophysics Data System (ADS)

Nijhawan, Rahul; Raman, Balasubramanian; Das, Josodhir

2018-01-01

Metaclassification ensemble approach is known to improve the prediction performance of snow-covered area. The methodology adopted in this case is based on neural network along with four state-of-art machine learning algorithms: support vector machine, artificial neural networks, spectral angle mapper, K-mean clustering, and a snow index: normalized difference snow index. An AdaBoost ensemble algorithm related to decision tree for snow-cover mapping is also proposed. According to available literature, these methods have been rarely used for snow-cover mapping. Employing the above techniques, a study was conducted for Raktavarn and Chaturangi Bamak glaciers, Uttarakhand, Himalaya using multispectral Landsat 7 ETM+ (enhanced thematic mapper) image. The study also compares the results with those obtained from statistical combination methods (majority rule and belief functions) and accuracies of individual classifiers. Accuracy assessment is performed by computing the quantity and allocation disagreement, analyzing statistic measures (accuracy, precision, specificity, AUC, and sensitivity) and receiver operating characteristic curves. A total of 225 combinations of parameters for individual classifiers were trained and tested on the dataset and results were compared with the proposed approach. It was observed that the proposed methodology produced the highest classification accuracy (95.21%), close to (94.01%) that was produced by the proposed AdaBoost ensemble algorithm. From the sets of observations, it was concluded that the ensemble of classifiers produced better results compared to individual classifiers.

Vertical Accuracy Assessment of ZY-3 Digital Surface Model Using Icesat/glas Laser Altimeter Data

NASA Astrophysics Data System (ADS)

Li, G.; Tang, X.; Yuan, X.; Zhou, P.; Hu, F.

2017-05-01

The Ziyuan-3 (ZY-3) satellite, as the first civilian high resolution surveying and mapping satellite in China, has a very important role in national 1 : 50,000 stereo mapping project. High accuracy digital surface Model (DSMs) can be generated from the three line-array images of ZY-3, and ZY-3 DSMs of China can be produced without using any ground control points (GCPs) by selecting SRTM (Shuttle Radar Topography Mission) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geo-science Laser Altimeter System) as the datum reference in the Satellite Surveying and Mapping Application Center, which is the key institute that manages and distributes ZY-3 products. To conduct the vertical accuracy evaluation of ZY-3 DSMs of China, three representative regions were chosen and the results were compared to ICESat/GLAS data. The experimental results demonstrated that the root mean square error (RMSE) elevation accuracy of the ZY-3 DSMs was better than 5.0 m, and it even reached to less than 2.5 m in the second region of eastern China. While this work presents preliminary results, it is an important reference for expanding the application of ZY-3 satellite imagery to widespread regions. And the satellite laser altimetry data can be used as referenced data for wide-area DSM evaluation.

Accuracy assessment of NOAA gridded daily reference evapotranspiration for the Texas High Plains

USDA-ARS?s Scientific Manuscript database

The National Oceanic and Atmospheric Administration (NOAA) provides daily reference evapotranspiration (ETref) maps for the contiguous United States using climatic data from North American Land Data Assimilation System (NLDAS). This data provides large-scale spatial representation of ETref, which i...

Dynamics of national forests assessed using the Landsat record: Case studies in eastern United States

USGS Publications Warehouse

Huang, C.; Goward, S.N.; Schleeweis, K.; Thomas, N.; Masek, J.G.; Zhu, Z.

2009-01-01

The national forests (NFs) in the United States are protected areas managed for multiple purposes, and therefore are subject to both natural and anthropogenic disturbances. Monitoring forest changes arising from such disturbances and the post-disturbance recovery processes is essential for assessing the conditions of the NFs and the effectiveness of management approaches. In this study, we used time series stacks of Landsat images (LTSS) to evaluate the dynamics of seven NFs in eastern United States, including the De Soto NF, the Talladega NF, the Francis Marion NF, and the Uwharrie NF in southeastern U.S., and the Chequamegon NF, the Hiawatha NF, and the Superior NF in northern U.S. Each LTSS consisted of 12–14 Landsat images acquired for the same location, spanning from 1984 to 2006 with a nominal interval of one image every 2 years. Each LTSS was analyzed using a vegetation change tracker (VCT) algorithm to map forest disturbance. Accuracy assessments of the derived disturbance maps revealed that they had overall accuracy values of about 80%, with most of the disturbance classes having user's accuracies ranging from 70% to 95%. The producer's accuracies were generally lower, with the majority being in the range between 50% and 70%. While this may suggest that the disturbance maps could slightly underestimate disturbances, a more detailed assessment of the omission errors revealed that the majority of the disagreements were due to minor disturbances like thinning or storm damages that were identified by the image analysts but were not captured by the VCT algorithm.The derived disturbance year maps revealed that while each of the seven NFs consisted of 90% or more forest land, significant portions of the forests were disturbed since 1984. Mapped disturbances accounted for about 30%–45% of total land area in the four NFs in southeastern U.S. and about 10%–20% in the three NFs in northern U.S. The disturbance rates were generally higher in the buffer zones surrounding each NF, and varied considerably over time. The time series approach employed in this study represents a new approach for monitoring forest resources using the Landsat or similar satellite data records. The disturbance products derived using this approach were spatially explicit and contained much more temporal details than conventional bi-temporal change products, and likely will be found more useful by many users including ecologists and resources managers. The high disturbance rates found in the southeastern U.S. suggest that this region may have a more significant role in modulating the atmospheric carbon budget than currently recognized.

Airborne laser mapping of Assateague National Seashore Beach

USGS Publications Warehouse

Krabill, W.B.; Wright, C.W.; Swift, R.N.; Frederick, E.B.; Manizade, S.S.; Yungel, J.K.; Martin, C.F.; Sonntag, J.G.; Duffy, Mark; Hulslander, William; Brock, John C.

2000-01-01

Results are presented from topographic surveys of the Assateague Island National Seashore using an airborne scanning laser altimeter and kinematic Global Positioning System (GPS) technology. The instrument used was the Airborne Topographic Mapper (ATM), developed by the NASA Arctic Ice Mapping (AIM) group from the Goddard Space Flight Center's Wallops Flight Facility. In November, 1995, and again in May, 1996, these topographic surveys were flown as a functionality check prior to conducting missions to measure the elevation of extensive sections of the Greenland Ice Sheet as part of NASA's Global Climate Change program. Differences between overlapping portions of both surveys are compared for quality control. An independent assessment of the accuracy of the ATM survey is provided by comparison to surface surveys which were conducted using standard techniques. The goal of these projects is to make these measurements to an accuracy of ± 10 cm. Differences between the fall 1995 and 1996 surveys provides an assessment of net changes in the beach morphology over an annual cycle.

Benthic Habitat Mapping by Combining Lyzenga’s Optical Model and Relative Water Depth Model in Lintea Island, Southeast Sulawesi

NASA Astrophysics Data System (ADS)

Hafizt, M.; Manessa, M. D. M.; Adi, N. S.; Prayudha, B.

2017-12-01

Benthic habitat mapping using satellite data is one challenging task for practitioners and academician as benthic objects are covered by light-attenuating water column obscuring object discrimination. One common method to reduce this water-column effect is by using depth-invariant index (DII) image. However, the application of the correction in shallow coastal areas is challenging as a dark object such as seagrass could have a very low pixel value, preventing its reliable identification and classification. This limitation can be solved by specifically applying a classification process to areas with different water depth levels. The water depth level can be extracted from satellite imagery using Relative Water Depth Index (RWDI). This study proposed a new approach to improve the mapping accuracy, particularly for benthic dark objects by combining the DII of Lyzenga’s water column correction method and the RWDI of Stumpt’s method. This research was conducted in Lintea Island which has a high variation of benthic cover using Sentinel-2A imagery. To assess the effectiveness of the proposed new approach for benthic habitat mapping two different classification procedures are implemented. The first procedure is the commonly applied method in benthic habitat mapping where DII image is used as input data to all coastal area for image classification process regardless of depth variation. The second procedure is the proposed new approach where its initial step begins with the separation of the study area into shallow and deep waters using the RWDI image. Shallow area was then classified using the sunglint-corrected image as input data and the deep area was classified using DII image as input data. The final classification maps of those two areas were merged as a single benthic habitat map. A confusion matrix was then applied to evaluate the mapping accuracy of the final map. The result shows that the new proposed mapping approach can be used to map all benthic objects in all depth ranges and shows a better accuracy compared to that of classification map produced using only with DII.

EnviroAtlas -Durham, NC- One Meter Resolution Urban Area Land Cover Map (2010) Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas ). The EnviroAtlas Durham, NC land cover map was generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from July 2010 at 1 m spatial resolution. Five land cover classes were mapped: impervious surface, soil and barren, grass and herbaceous, trees and forest, and water. An accuracy assessment using a stratified random sampling of 500 samples yielded an overall accuracy of 83 percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Durham, and includes the cities of Durham, Chapel Hill, Carrboro and Hillsborough, NC. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

Classification of urban features using airborne hyperspectral data

NASA Astrophysics Data System (ADS)

Ganesh Babu, Bharath

Accurate mapping and modeling of urban environments are critical for their efficient and successful management. Superior understanding of complex urban environments is made possible by using modern geospatial technologies. This research focuses on thematic classification of urban land use and land cover (LULC) using 248 bands of 2.0 meter resolution hyperspectral data acquired from an airborne imaging spectrometer (AISA+) on 24th July 2006 in and near Terre Haute, Indiana. Three distinct study areas including two commercial classes, two residential classes, and two urban parks/recreational classes were selected for classification and analysis. Four commonly used classification methods -- maximum likelihood (ML), extraction and classification of homogeneous objects (ECHO), spectral angle mapper (SAM), and iterative self organizing data analysis (ISODATA) - were applied to each data set. Accuracy assessment was conducted and overall accuracies were compared between the twenty four resulting thematic maps. With the exception of SAM and ISODATA in a complex commercial area, all methods employed classified the designated urban features with more than 80% accuracy. The thematic classification from ECHO showed the best agreement with ground reference samples. The residential area with relatively homogeneous composition was classified consistently with highest accuracy by all four of the classification methods used. The average accuracy amongst the classifiers was 93.60% for this area. When individually observed, the complex recreational area (Deming Park) was classified with the highest accuracy by ECHO, with an accuracy of 96.80% and 96.10% Kappa. The average accuracy amongst all the classifiers was 92.07%. The commercial area with relatively high complexity was classified with the least accuracy by all classifiers. The lowest accuracy was achieved by SAM at 63.90% with 59.20% Kappa. This was also the lowest accuracy in the entire analysis. This study demonstrates the potential for using the visible and near infrared (VNIR) bands from AISA+ hyperspectral data in urban LULC classification. Based on their performance, the need for further research using ECHO and SAM is underscored. The importance incorporating imaging spectrometer data in high resolution urban feature mapping is emphasized.

Using principal component analysis and annual seasonal trend analysis to assess karst rocky desertification in southwestern China.

PubMed

Zhang, Zhiming; Ouyang, Zhiyun; Xiao, Yi; Xiao, Yang; Xu, Weihua

2017-06-01

Increasing exploitation of karst resources is causing severe environmental degradation because of the fragility and vulnerability of karst areas. By integrating principal component analysis (PCA) with annual seasonal trend analysis (ASTA), this study assessed karst rocky desertification (KRD) within a spatial context. We first produced fractional vegetation cover (FVC) data from a moderate-resolution imaging spectroradiometer normalized difference vegetation index using a dimidiate pixel model. Then, we generated three main components of the annual FVC data using PCA. Subsequently, we generated the slope image of the annual seasonal trends of FVC using median trend analysis. Finally, we combined the three PCA components and annual seasonal trends of FVC with the incidence of KRD for each type of carbonate rock to classify KRD into one of four categories based on K-means cluster analysis: high, moderate, low, and none. The results of accuracy assessments indicated that this combination approach produced greater accuracy and more reasonable KRD mapping than the average FVC based on the vegetation coverage standard. The KRD map for 2010 indicated that the total area of KRD was 78.76 × 10 3  km 2 , which constitutes about 4.06% of the eight southwest provinces of China. The largest KRD areas were found in Yunnan province. The combined PCA and ASTA approach was demonstrated to be an easily implemented, robust, and flexible method for the mapping and assessment of KRD, which can be used to enhance regional KRD management schemes or to address assessment of other environmental issues.

An assessment of a collaborative mapping approach for exploring land use patterns for several European metropolises

NASA Astrophysics Data System (ADS)

Jokar Arsanjani, Jamal; Vaz, Eric

2015-03-01

Until recently, land surveys and digital interpretation of remotely sensed imagery have been used to generate land use inventories. These techniques however, are often cumbersome and costly, allocating large amounts of technical and temporal costs. The technological advances of web 2.0 have brought a wide array of technological achievements, stimulating the participatory role in collaborative and crowd sourced mapping products. This has been fostered by GPS-enabled devices, and accessible tools that enable visual interpretation of high resolution satellite images/air photos provided in collaborative mapping projects. Such technologies offer an integrative approach to geography by means of promoting public participation and allowing accurate assessment and classification of land use as well as geographical features. OpenStreetMap (OSM) has supported the evolution of such techniques, contributing to the existence of a large inventory of spatial land use information. This paper explores the introduction of this novel participatory phenomenon for land use classification in Europe's metropolitan regions. We adopt a positivistic approach to assess comparatively the accuracy of these contributions of OSM for land use classifications in seven large European metropolitan regions. Thematic accuracy and degree of completeness of OSM data was compared to available Global Monitoring for Environment and Security Urban Atlas (GMESUA) datasets for the chosen metropolises. We further extend our findings of land use within a novel framework for geography, justifying that volunteered geographic information (VGI) sources are of great benefit for land use mapping depending on location and degree of VGI dynamism and offer a great alternative to traditional mapping techniques for metropolitan regions throughout Europe. Evaluation of several land use types at the local level suggests that a number of OSM classes (such as anthropogenic land use, agricultural and some natural environment classes) are viable alternatives for land use classification. These classes are highly accurate and can be integrated into planning decisions for stakeholders and policymakers.

EnviroAtlas -Pittsburgh, PA- One Meter Resolution Urban Land Cover Data (2010) Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas).The EnviroAtlas Pittsburgh, PA land cover map was generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution. Imagery was collected on multiple dates in June 2010. Five land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, and grass and herbaceous non-woody vegetation. An accuracy assessment of 500 completely random and 81 stratified random points yielded an overall accuracy of 86.57 percent. The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Pittsburgh, PA. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Elevation-Based Sea-Level Rise Vulnerability Assessment of Mindanao, Philippines: are Freely-Available 30-M Dems Good Enough?

NASA Astrophysics Data System (ADS)

Santillan, J. R.; Makinano-Santillan, M.

2017-09-01

We assessed the vertical accuracies and uncertainties of three freely-available global DEMs as inputs to elevation-based sea-level rise vulnerability assessment of Mindanao, Philippines - an area where above average SLR of 14.7 mm/year was recently found. These DEMs are the Shuttle Radar Topography Mission (SRTM) DEM, ASTER Global DEM (GDEM Version 2), and ALOS World 3D-30 (AW3D30). Using 2,076 ground control points, we computed each DEM's vertical accuracies and uncertainties, and from these we determined the smallest increment of sea-level rise (SLRImin) that should be considered when using the DEMs for SLR impact assessment, as well as the Minimum Planning Timeline (TLmin) for an elevation-based SLR assessment. Results of vertical accuracy assessment revealed Root Mean Square Errors of 9.80 m for ASTER GDEM V2, 5.16 m for SRTM DEM, and 4.32 m for AW3D30. Vertical uncertainties in terms of the Linear Error at 95 % Confidence (LE95) were found to be as follows: 19.21 m for ASTER GDEM V2, 10.12 m for SRTM DEM, and 8.47 m for AW3D30. From these, we found that ASTER GDEM2 is suitable to model SLR increments of at least 38.41 m and it will take 2,613 years for the cumulative water level increase of 14.7 mm/year to reach the minimum SLR increment afforded by this DEM. For the SRTM DEM, SLRImin and TLmin were computed as 20.24 m and 1,377 years, respectively. For the AW3D30, SLRImin and TLmin were computed as 16.92 m and 1,151 years, respectively. These results suggest that the readily available global DEMs' suitability for mapping coastal inundations due to SLR in our study area is limited by their low vertical accuracies and high uncertainties. All the three DEMs do not have the necessary accuracy and minimum uncertainties that will make them suitable for mapping inundations of Mindanao at smaller increments of SLR (e.g., SLR ≤ 5 m). Hence, users who apply any of these DEMs for SLR impact assessment at SLRIs lower than the DEM's SLRImin must be cautious in reporting the areas of SLR vulnerable zones. Reporting the inundated areas as a range instead of a singular value for a given SLR scenario can highlight the inherent accuracy and uncertainty of the DEM used in the assessment.

A Comparison of Fuzzy Models in Similarity Assessment of Misregistered Area Class Maps

NASA Astrophysics Data System (ADS)

Brown, Scott

Spatial uncertainty refers to unknown error and vagueness in geographic data. It is relevant to land change and urban growth modelers, soil and biome scientists, geological surveyors and others, who must assess thematic maps for similarity, or categorical agreement. In this paper I build upon prior map comparison research, testing the effectiveness of similarity measures on misregistered data. Though several methods compare uncertain thematic maps, few methods have been tested on misregistration. My objective is to test five map comparison methods for sensitivity to misregistration, including sub-pixel errors in both position and rotation. Methods included four fuzzy categorical models: fuzzy kappa's model, fuzzy inference, cell aggregation, and the epsilon band. The fifth method used conventional crisp classification. I applied these methods to a case study map and simulated data in two sets: a test set with misregistration error, and a control set with equivalent uniform random error. For all five methods, I used raw accuracy or the kappa statistic to measure similarity. Rough-set epsilon bands report the most similarity increase in test maps relative to control data. Conversely, the fuzzy inference model reports a decrease in test map similarity.

Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China.

PubMed

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-03-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran's I analysis was used to supplement the traditional geostatistics. According to Moran's I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran's I and the standardized Moran's I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran's I analysis was better than traditional geostatistics. Thus, Moran's I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals.

Combining Geostatistics with Moran’s I Analysis for Mapping Soil Heavy Metals in Beijing, China

PubMed Central

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-01-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran’s I analysis was used to supplement the traditional geostatistics. According to Moran’s I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran’s I and the standardized Moran’s I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran’s I analysis was better than traditional geostatistics. Thus, Moran’s I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals. PMID:22690179

Accuracy of stream habitat interpolations across spatial scales

USGS Publications Warehouse

Sheehan, Kenneth R.; Welsh, Stuart A.

2013-01-01

Stream habitat data are often collected across spatial scales because relationships among habitat, species occurrence, and management plans are linked at multiple spatial scales. Unfortunately, scale is often a factor limiting insight gained from spatial analysis of stream habitat data. Considerable cost is often expended to collect data at several spatial scales to provide accurate evaluation of spatial relationships in streams. To address utility of single scale set of stream habitat data used at varying scales, we examined the influence that data scaling had on accuracy of natural neighbor predictions of depth, flow, and benthic substrate. To achieve this goal, we measured two streams at gridded resolution of 0.33 × 0.33 meter cell size over a combined area of 934 m2 to create a baseline for natural neighbor interpolated maps at 12 incremental scales ranging from a raster cell size of 0.11 m2 to 16 m2 . Analysis of predictive maps showed a logarithmic linear decay pattern in RMSE values in interpolation accuracy for variables as resolution of data used to interpolate study areas became coarser. Proportional accuracy of interpolated models (r2 ) decreased, but it was maintained up to 78% as interpolation scale moved from 0.11 m2 to 16 m2 . Results indicated that accuracy retention was suitable for assessment and management purposes at various scales different from the data collection scale. Our study is relevant to spatial modeling, fish habitat assessment, and stream habitat management because it highlights the potential of using a single dataset to fulfill analysis needs rather than investing considerable cost to develop several scaled datasets.

ACCURACY ASSESSMENTS OF AIRBORNE HYSPERSPECTRAL DATA FOR MAPPING OPPORTUNISTIC PLANT SPECIES IN FRESHWATER COASTAL WETLANDS

EPA Science Inventory

Airbome hyperspectral data were used to detect dense patches of Phragmites australis, a native opportunist plant species, at the Pointe Mouillee coastal wetland complex (Wayne and Monroe Counties, Michigan). This study provides initial results from one of thirteen coastal wetland...

South San Francisco Bay 2004 topographic lidar survey: Data overview and preliminary quality assessment

USGS Publications Warehouse

Foxgrover, Amy C.; Jaffe, Bruce E.

2005-01-01

This report details the collection of lidar in South Bay, the ground-truthing efforts, preliminary accuracy assessments, and known limitations of the data set. We describe the data generated from the survey and how to obtain it. In addition, we present maps and sample imagery that provides a revealing look into the intricate topographic features of South Bay.

Automated peroperative assessment of stents apposition from OCT pullbacks.

PubMed

Dubuisson, Florian; Péry, Emilie; Ouchchane, Lemlih; Combaret, Nicolas; Kauffmann, Claude; Souteyrand, Géraud; Motreff, Pascal; Sarry, Laurent

2015-04-01

This study's aim was to control the stents apposition by automatically analyzing endovascular optical coherence tomography (OCT) sequences. Lumen is detected using threshold, morphological and gradient operators to run a Dijkstra algorithm. Wrong detection tagged by the user and caused by bifurcation, struts'presence, thrombotic lesions or dissections can be corrected using a morphing algorithm. Struts are also segmented by computing symmetrical and morphological operators. Euclidian distance between detected struts and wall artery initializes a stent's complete distance map and missing data are interpolated with thin-plate spline functions. Rejection of detected outliers, regularization of parameters by generalized cross-validation and using the one-side cyclic property of the map also optimize accuracy. Several indices computed from the map provide quantitative values of malapposition. Algorithm was run on four in-vivo OCT sequences including different incomplete stent apposition's cases. Comparison with manual expert measurements validates the segmentation׳s accuracy and shows an almost perfect concordance of automated results. Copyright © 2014 Elsevier Ltd. All rights reserved.

Annual Changes of Paddy Rice Planting Areas in Northeastern Asia from MODIS images in 2000-2014

NASA Astrophysics Data System (ADS)

Xiao, X.; Zhang, G.; Dong, J.; Menarguez, M. A.; Kou, W.; Jin, C.; Qin, Y.; Zhou, Y.; Wang, J.; Moore, B., III

2014-12-01

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, estimation of greenhouse gas (methane) emissions, and understanding avian influenza virus transmission. Over the past two decades, paddy rice cultivation has expanded northward in temperate and cold temperate zones, particularly in Northeastern China. There is a need to quantify and map changes in paddy rice planting areas in Northeastern Asia (Japan, North and South Korea, and northeast China) at annual interval. We developed a pixel- and phenology-based image analysis system, MODIS-RICE, to map the paddy rice in Northeastern Asia by using multi-temporal MODIS thermal and surface reflectance imagery. Paddy rice fields during the flooding and transplanting phases have unique physical and spectral characteristics, which make it possible for the development of an automated and robust algorithm to track flooding and transplanting phases of paddy rice fields over time. In this presentation, we will show the MODIS-based annual maps of paddy rice planting area in the Northeastern Asia from 2000-2014 (500-m spatial resolution). Accuracy assessments using high-resolution images show that the resultant paddy rice map of Northeastern Asia had a comparable accuracy to the existing products, including 2010 Landsat-based National Land Cover Dataset (NLCD) of China, the 2010 RapidEye-based paddy rice map in North Korea, and the 2010 AVNIR-2-based National Land Cover Dataset in Japan in terms of both area and spatial pattern of paddy rice. This study has demonstrated that our novel MODIS-Rice system, which use both thermal and optical MODIS data over a year, are simple and robust tools to identify and map paddy rice fields in temperate and cold temperate zones.

A 50-m forest cover map in Southeast Asia from ALOS/PALSAR and its application on forest fragmentation assessment.

PubMed

Dong, Jinwei; Xiao, Xiangming; Sheldon, Sage; Biradar, Chandrashekhar; Zhang, Geli; Duong, Nguyen Dinh; Hazarika, Manzul; Wikantika, Ketut; Takeuhci, Wataru; Moore, Berrien

2014-01-01

Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6) km(2) (GlobCover) to 2.69×10(6) km(2) (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity.

A 50-m Forest Cover Map in Southeast Asia from ALOS/PALSAR and Its Application on Forest Fragmentation Assessment

PubMed Central

Dong, Jinwei; Xiao, Xiangming; Sheldon, Sage; Biradar, Chandrashekhar; Zhang, Geli; Dinh Duong, Nguyen; Hazarika, Manzul; Wikantika, Ketut; Takeuhci, Wataru; Moore, Berrien

2014-01-01

Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×106 km2 (GlobCover) to 2.69×106 km2 (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity. PMID:24465714

Operational monitoring of land-cover change using multitemporal remote sensing data

NASA Astrophysics Data System (ADS)

Rogan, John

2005-11-01

Land-cover change, manifested as either land-cover modification and/or conversion, can occur at all spatial scales, and changes at local scales can have profound, cumulative impacts at broader scales. The implication of operational land-cover monitoring is that researchers have access to a continuous stream of remote sensing data, with the long term goal of providing for consistent and repetitive mapping. Effective large area monitoring of land-cover (i.e., >1000 km2) can only be accomplished by using remotely sensed images as an indirect data source in land-cover change mapping and as a source for land-cover change model projections. Large area monitoring programs face several challenges: (1) choice of appropriate classification scheme/map legend over large, topographically and phenologically diverse areas; (2) issues concerning data consistency and map accuracy (i.e., calibration and validation); (3) very large data volumes; (4) time consuming data processing and interpretation. Therefore, this dissertation research broadly addresses these challenges in the context of examining state-of-the-art image pre-processing, spectral enhancement, classification, and accuracy assessment techniques to assist the California Land-cover Mapping and Monitoring Program (LCMMP). The results of this dissertation revealed that spatially varying haze can be effectively corrected from Landsat data for the purposes of change detection. The Multitemporal Spectral Mixture Analysis (MSMA) spectral enhancement technique produced more accurate land-cover maps than those derived from the Multitemporal Kauth Thomas (MKT) transformation in northern and southern California study areas. A comparison of machine learning classifiers showed that Fuzzy ARTMAP outperformed two classification tree algorithms, based on map accuracy and algorithm robustness. Variation in spatial data error (positional and thematic) was explored in relation to environmental variables using geostatistical interpolation techniques. Finally, the land-cover modification maps generated for three time intervals (1985--1990--1996--2000), with nine change-classes revealed important variations in land-cover gain and loss between northern and southern California study areas.

Detecting peatland drains with Object Based Image Analysis and Geoeye-1 imagery.

PubMed

Connolly, J; Holden, N M

2017-12-01

Peatlands play an important role in the global carbon cycle. They provide important ecosystem services including carbon sequestration and storage. Drainage disturbs peatland ecosystem services. Mapping drains is difficult and expensive and their spatial extent is, in many cases, unknown. An object based image analysis (OBIA) was performed on a very high resolution satellite image (Geoeye-1) to extract information about drain location and extent on a blanket peatland in Ireland. Two accuracy assessment methods: Error matrix and the completeness, correctness and quality (CCQ) were used to assess the extracted data across the peatland and at several sub sites. The cost of the OBIA method was compared with manual digitisation and field survey. The drain maps were also used to assess the costs relating to blocking drains vs. a business-as-usual scenario and estimating the impact of each on carbon fluxes at the study site. The OBIA method performed well at almost all sites. Almost 500 km of drains were detected within the peatland. In the error matrix method, overall accuracy (OA) of detecting the drains was 94% and the kappa statistic was 0.66. The OA for all sub-areas, except one, was 95-97%. The CCQ was 85%, 85% and 71% respectively. The OBIA method was the most cost effective way to map peatland drains and was at least 55% cheaper than either field survey or manual digitisation, respectively. The extracted drain maps were used constrain the study area CO 2 flux which was 19% smaller than the prescribed Peatland Code value for drained peatlands. The OBIA method used in this study showed that it is possible to accurately extract maps of fine scale peatland drains over large areas in a cost effective manner. The development of methods to map the spatial extent of drains is important as they play a critical role in peatland carbon dynamics. The objective of this study was to extract data on the spatial extent of drains on a blanket bog in the west of Ireland. The results show that information on drain extent and location can be extracted from high resolution imagery and mapped with a high degree of accuracy. Under Article 3.4 of the Kyoto Protocol Annex 1 parties can account for greenhouse gas emission by sources and removals by sinks resulting from "wetlands drainage and rewetting". The ability to map the spatial extent, density and location of peatlands drains means that Annex 1 parties can develop strategies for drain blocking to aid reduction of CO 2 emissions, DOC runoff and water discoloration. This paper highlights some uncertainty around using one-size-fits-all emission factors for GHG in drained peatlands and re-wetting scenarios. However, the OBIA method is robust and accurate and could be used to assess the extent of drains in peatlands across the globe aiding the refinement of peatland carbon dynamics .

Detecting peatland drains with Object Based Image Analysis and Geoeye-1 imagery.

PubMed

Connolly, J; Holden, N M

2017-12-01

Peatlands play an important role in the global carbon cycle. They provide important ecosystem services including carbon sequestration and storage. Drainage disturbs peatland ecosystem services. Mapping drains is difficult and expensive and their spatial extent is, in many cases, unknown. An object based image analysis (OBIA) was performed on a very high resolution satellite image (Geoeye-1) to extract information about drain location and extent on a blanket peatland in Ireland. Two accuracy assessment methods: Error matrix and the completeness, correctness and quality ( CCQ ) were used to assess the extracted data across the peatland and at several sub sites. The cost of the OBIA method was compared with manual digitisation and field survey. The drain maps were also used to assess the costs relating to blocking drains vs. a business-as-usual scenario and estimating the impact of each on carbon fluxes at the study site. The OBIA method performed well at almost all sites. Almost 500 km of drains were detected within the peatland. In the error matrix method, overall accuracy (OA) of detecting the drains was 94% and the kappa statistic was 0.66. The OA for all sub-areas, except one, was 95-97%. The CCQ was 85%, 85% and 71% respectively. The OBIA method was the most cost effective way to map peatland drains and was at least 55% cheaper than either field survey or manual digitisation, respectively. The extracted drain maps were used constrain the study area CO 2 flux which was 19% smaller than the prescribed Peatland Code value for drained peatlands. The OBIA method used in this study showed that it is possible to accurately extract maps of fine scale peatland drains over large areas in a cost effective manner. The development of methods to map the spatial extent of drains is important as they play a critical role in peatland carbon dynamics. The objective of this study was to extract data on the spatial extent of drains on a blanket bog in the west of Ireland. The results show that information on drain extent and location can be extracted from high resolution imagery and mapped with a high degree of accuracy. Under Article 3.4 of the Kyoto Protocol Annex 1 parties can account for greenhouse gas emission by sources and removals by sinks resulting from "wetlands drainage and rewetting". The ability to map the spatial extent, density and location of peatlands drains means that Annex 1 parties can develop strategies for drain blocking to aid reduction of CO 2 emissions, DOC runoff and water discoloration. This paper highlights some uncertainty around using one-size-fits-all emission factors for GHG in drained peatlands and re-wetting scenarios. However, the OBIA method is robust and accurate and could be used to assess the extent of drains in peatlands across the globe aiding the refinement of peatland carbon dynamics .

Fusion of pan-tropical biomass maps using weighted averaging and regional calibration data

NASA Astrophysics Data System (ADS)

Ge, Yong; Avitabile, Valerio; Heuvelink, Gerard B. M.; Wang, Jianghao; Herold, Martin

2014-09-01

Biomass is a key environmental variable that influences many biosphere-atmosphere interactions. Recently, a number of biomass maps at national, regional and global scales have been produced using different approaches with a variety of input data, such as from field observations, remotely sensed imagery and other spatial datasets. However, the accuracy of these maps varies regionally and is largely unknown. This research proposes a fusion method to increase the accuracy of regional biomass estimates by using higher-quality calibration data. In this fusion method, the biases in the source maps were first adjusted to correct for over- and underestimation by comparison with the calibration data. Next, the biomass maps were combined linearly using weights derived from the variance-covariance matrix associated with the accuracies of the source maps. Because each map may have different biases and accuracies for different land use types, the biases and fusion weights were computed for each of the main land cover types separately. The conceptual arguments are substantiated by a case study conducted in East Africa. Evaluation analysis shows that fusing multiple source biomass maps may produce a more accurate map than when only one biomass map or unweighted averaging is used.

EnviroAtlas -Milwaukee, WI- One Meter Resolution Urban Land Cover Data (2010) Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The EnviroAtlas Milwaukee, WI land cover data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from Late Summer 2010 at 1 m spatial resolution. Nine land cover classes were mapped: water, impervious surfaces (dark and light), soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, agriculture, and wetlands (woody and emergent). An accuracy assessment using a completely random sampling of 600 samples yielded an overall accuracy of 85.39% percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Milwaukee. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-

EnviroAtlas -- Woodbine, IA -- One Meter Resolution Urban Land Cover Data (2011) Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The EnviroAtlas Woodbine, IA land cover (LC) data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from Late Summer 2011 at 1 m spatial resolution. Six land cover classes were mapped: water, impervious surfaces (dark and light), soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, and agriculture. An accuracy assessment using a completely random sampling of 600 samples yielded an overall accuracy of 87.03% percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Woodbine. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas -Portland, ME- One Meter Resolution Urban Land Cover (2010) Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The Portland, ME land cover map was generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from Late Summer 2010 at 1 m spatial resolution. Nine land cover classes were mapped: water, impervious surfaces (dark and light), soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, agriculture, and wetlands (woody and emergent). An accuracy assessment using a stratified random sampling of 600 samples yielded an overall accuracy of 87.5 percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Portland.This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Land use mapping and modelling for the Phoenix Quadrangle

NASA Technical Reports Server (NTRS)

Place, J. L. (Principal Investigator)

1974-01-01

The author has identified the following significant results. The mapping of generalized land use (level 1) from ERTS 1 images was shown to be feasible with better than 95% accuracy in the Phoenix quadrangle. The accuracy of level 2 mapping in urban areas is still a problem. Updating existing maps also proved to be feasible, especially in water categories and agricultural uses; however, expanding urban growth has presented with accuracy. ERTS 1 film images indicated where areas of change were occurring, thus aiding focusing-in for more detailed investigation. ERTS color composite transparencies provided a cost effective source of information for land use mapping of very large regions at small map scales.

Effectiveness of Vegetation Index Transformation for Land Use Identifying and Mapping in the Area of Oil palm Plantation based on SPOT-6 Imagery (Case Study: PT.Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu)

NASA Astrophysics Data System (ADS)

Setyowati, H. A.; S, S. H. Murti B.; Sukentyas, E. S.

2016-11-01

The reflection of land surface, atmosphere and vegetation conditions affect the reflectance value of the object is recorded on remote sensing image so that it can affect the outcome of information extraction from remote sensing imagery one multispectral classification. This study aims to assess the ability of the transformation of generic vegetation index (Wide Dynamic Range Vegetation Index), the vegetation index transformation that is capable reducing the influence of the atmosphere (Atmospherically Resistant Vegetation Index), and the transformation of vegetation index that is capable of reducing the influence of the background soil (Second Modified Soil Adjusted Vegetation Index) for the identification and mapping of land use in the oil palm plantation area based on SPOT-6 archived on June 13, 2013 from LAPAN. The study area selected oil palm plantations PT. Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu, Riau Province. The method is using the transformation of the vegetation index ARVI, MSAVI2, and WDRVI. Sample selection method used was stratified random sampling. The test method used mapping accuracy of the confusion matrix. The results showed that the best transformation of the vegetation index for the identification and mapping of land use in the plantation area is ARVI transformation with a total of accuracy is 96%. Accuracy of mapping land use settlements 100%, replanting 82.35%, 81.25% young oil palm, old oil palm 99.46%, 100% bush, body of water 100%, and 100% bare-soil.

Breaking new ground in mapping human settlements from space - The Global Urban Footprint

NASA Astrophysics Data System (ADS)

Esch, Thomas; Heldens, Wieke; Hirner, Andreas; Keil, Manfred; Marconcini, Mattia; Roth, Achim; Zeidler, Julian; Dech, Stefan; Strano, Emanuele

2017-12-01

Today, approximately 7.2 billion people inhabit the Earth and by 2050 this number will have risen to around nine billion, of which about 70% will be living in cities. The population growth and the related global urbanization pose one of the major challenges to a sustainable future. Hence, it is essential to understand drivers, dynamics, and impacts of the human settlements development. A key component in this context is the availability of an up-to-date and spatially consistent map of the location and distribution of human settlements. It is here that the Global Urban Footprint (GUF) raster map can make a valuable contribution. The new global GUF binary settlement mask shows a so far unprecedented spatial resolution of 0.4″ (∼ 12m) that provides - for the first time - a complete picture of the entirety of urban and rural settlements. The GUF has been derived by means of a fully automated processing framework - the Urban Footprint Processor (UFP) - that was used to analyze a global coverage of more than 180,000 TanDEM-X and TerraSAR-X radar images with 3 m ground resolution collected in 2011-2012. The UFP consists of five main technical modules for data management, feature extraction, unsupervised classification, mosaicking and post-editing. Various quality assessment studies to determine the absolute GUF accuracy based on ground truth data on the one hand and the relative accuracies compared to established settlements maps on the other hand, clearly indicate the added value of the new global GUF layer, in particular with respect to the representation of rural settlement patterns. The Kappa coefficient of agreement compared to absolute ground truth data, for instance, shows GUF accuracies which are frequently twice as high as those of established low resolution maps. Generally, the GUF layer achieves an overall absolute accuracy of about 85%, with observed minima around 65% and maxima around 98%. The GUF will be provided open and free for any scientific use in the full resolution and for any non-profit (but also non-scientific) use in a generalized version of 2.8″ (∼ 84m). Therewith, the new GUF layer can be expected to break new ground with respect to the analysis of global urbanization and peri-urbanization patterns, population estimation, vulnerability assessment, or the modeling of diseases and phenomena of global change in general.

Comparison of CT perfusion summary maps to early diffusion-weighted images in suspected acute middle cerebral artery stroke.

PubMed

Benson, John; Payabvash, Seyedmehdi; Salazar, Pascal; Jagadeesan, Bharathi; Palmer, Christopher S; Truwit, Charles L; McKinney, Alexander M

2015-04-01

To assess the accuracy and reliability of one vendor's (Vital Images, Toshiba Medical, Minnetonka, MN) automated CT perfusion (CTP) summary maps in identification and volume estimation of infarcted tissue in patients with acute middle cerebral artery (MCA) distribution infarcts. From 1085 CTP examinations over 5.5 years, 43 diffusion-weighted imaging (DWI)-positive patients were included who underwent both CTP and DWI <12 h after symptom onset, with another 43 age-matched patients as controls (DWI-negative). Automated delay-corrected postprocessing software (DC-SVD) generated both infarct "core only" and "core+penumbra" CTP summary maps. Three reviewers independently tabulated Alberta Stroke Program Early CT scores (ASPECTS) of both CTP summary maps and coregistered DWI. Of 86 included patients, 36 had DWI infarct volumes ≤70 ml, 7 had volumes >70 ml, and 43 were negative; the automated CTP "core only" map correctly classified each as >70 ml or ≤70 ml, while the "core+penumbra" map misclassified 4 as >70 ml. There were strong correlations between DWI volume with both summary map-based volumes: "core only" (r=0.93), and "core+penumbra" (r=0.77) (both p<0.0001). Agreement between ASPECTS scores of infarct core on DWI with summary maps was 0.65-0.74 for "core only" map, and 0.61-0.65 for "core+penumbra" (both p<0.0001). Using DWI-based ASPECTS scores as the standard, the accuracy of the CTP-based maps were 79.1-86.0% for the "core only" map, and 83.7-88.4% for "core+penumbra." Automated CTP summary maps appear to be relatively accurate in both the detection of acute MCA distribution infarcts, and the discrimination of volumes using a 70 ml threshold. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Mapping stand-age distribution of Russian forests from satellite data

NASA Astrophysics Data System (ADS)

Chen, D.; Loboda, T. V.; Hall, A.; Channan, S.; Weber, C. Y.

2013-12-01

Russian boreal forest is a critical component of the global boreal biome as approximately two thirds of the boreal forest is located in Russia. Numerous studies have shown that wildfire and logging have led to extensive modifications of forest cover in the region since 2000. Forest disturbance and subsequent regrowth influences carbon and energy budgets and, in turn, affect climate. Several global and regional satellite-based data products have been developed from coarse (>100m) and moderate (10-100m) resolution imagery to monitor forest cover change over the past decade, record of forest cover change pre-dating year 2000 is very fragmented. Although by using stacks of Landsat images, some information regarding the past disturbances can be obtained, the quantity and locations of such stacks with sufficient number of images are extremely limited, especially in Eastern Siberia. This paper describes a modified method which is built upon previous work to hindcast the disturbance history and map stand-age distribution in the Russian boreal forest. Utilizing data from both Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), a wall-to-wall map indicating the estimated age of forest in the Russian boreal forest is created. Our previous work has shown that disturbances can be mapped successfully up to 30 years in the past as the spectral signature of regrowing forests is statistically significantly different from that of mature forests. The presented algorithm ingests 55 multi-temporal stacks of Landsat imagery available over Russian forest before 2001 and processes through a standardized and semi-automated approach to extract training and validation data samples. Landsat data, dating back to 1984, are used to generate maps of forest disturbance using temporal shifts in Disturbance Index through the multi-temporal stack of imagery in selected locations. These maps are then used as reference data to train a decision tree classifier on 50 MODIS-based indices. The resultant map provides an estimate of forest age based on the regrowth curves observed from Landsat imagery. The accuracy of the resultant map is assessed against three datasets: 1) subset of the disturbance maps developed within the algorithm, 2) independent disturbance maps created by the Northern Eurasia Land Dynamics Analysis (NELDA) project, and 3) field-based stand-age distribution from forestry inventory units. The current version of the product presents a considerable improvement on the previous version which used Landsat data samples at a set of randomly selected locations, resulting a strong bias of the training samples towards the Landsat-rich regions (e.g. European Russia) whereas regions such as Siberia were under-sampled. Aiming at improving accuracy, the current method significantly increases the number of training Landsat samples compared to the previous work. Aside from the previously used data, the current method uses all available Landsat data for the under-sampled regions in order to increase the representativeness of the total samples. The finial accuracy assessment is still ongoing, however, the initial results suggested an overall accuracy expressed in Kappa > 0.8. We plan to release both the training data and the final disturbance map of the Russian boreal forest to the public after the validation is completed.

AN ACCURACY ASSESSMENT OF MULTIPLE MID-ATLANTIC SUB-PIXEL IMPERVIOUS SURFACE MAPS

EPA Science Inventory

Anthropogenic impervious surfaces have an important relationship with non-point source pollution (NPS) in urban watersheds. The amount of impervious surface area in a watershed is a key indicator of landscape change. As a single variable, it serves to integrate a number of conc...

Accuracy assessment of NOAA's daily reference evapotranspiration maps for the Texas High Plains

USDA-ARS?s Scientific Manuscript database

The National Oceanic and Atmospheric Administration (NOAA) provides daily reference ET for the continental U.S. using climatic data from North American Land Data Assimilation System (NLDAS). This data provides large scale spatial representation for reference ET, which is essential for regional scal...

Assessment Study of Using Online (CSRS) GPS-PPP Service for Mapping Applications in Egypt

NASA Astrophysics Data System (ADS)

Abd-Elazeem, Mohamed; Farah, Ashraf; Farrag, Farrag

2011-09-01

Many applications in navigation, land surveying, land title definitions and mapping have been made simpler and more precise due to accessibility of Global Positioning System (GPS) data, and thus the demand for using advanced GPS techniques in surveying applications has become essential. The differential technique was the only source of accurate positioning for many years, and remained in use despite of its cost. The precise point positioning (PPP) technique is a viable alternative to the differential positioning method in which a user with a single receiver can attain positioning accuracy at the centimeter or decimeter scale. In recent years, many organizations introduced online (GPS-PPP) processing services capable of determining accurate geocentric positions using GPS observations. These services provide the user with receiver coordinates in free and unlimited access formats via the internet. This paper investigates the accuracy of the Canadian Spatial Reference System (CSRS) Precise Point Positioning (PPP) (CSRS-PPP) service supervised by the Geodetic Survey Division (GSD), Canada. Single frequency static GPS observations have been collected at three points covering time spans of 60, 90 and 120 minutes. These three observed sites form baselines of 1.6, 7, and 10 km, respectively. In order to assess the CSRS-PPP accuracy, the discrepancies between the CSRS-PPP estimates and the regular differential GPS solutions were computed. The obtained results illustrate that the PPP produces a horizontal error at the scale of a few decimeters; this is accurate enough to serve many mapping applications in developing countries with a savings in both cost and experienced labor.

Accuracy Assessment of Satellite Derived Forest Cover Products in South and Southeast Asia

NASA Astrophysics Data System (ADS)

Gilani, H.; Xu, X.; Jain, A. K.

2017-12-01

South and Southeast Asia (SSEA) region occupies 16 % of worlds land area. It is home to over 50% of the world's population. The SSEA's countries are experiencing significant land-use and land-cover changes (LULCCs), primarily in agriculture, forest, and urban land. For this study, we compiled four existing global forest cover maps for year 2010 by Gong et al.(2015), Hansen et al. (2013), Sexton et al.(2013) and Shimada et al. (2014), which were all medium resolution (≤30 m) products based on Landsat and/or PALSAR satellite images. To evaluate the accuracy of these forest products, we used three types of information: (1) ground measurements, (2) high resolution satellite images and (3) forest cover maps produced at the national scale. The stratified random sampling technique was used to select a set of validation data points from the ground and high-resolution satellite images. Then the confusion matrix method was used to assess and rank the accuracy of the forest cover products for the entire SSEA region. We analyzed the spatial consistency of different forest cover maps, and further evaluated the consistency with terrain characteristics. Our study suggests that global forest cover mapping algorithms are trained and tested using limited ground measurement data. We found significant uncertainties in mountainous areas due to the topographical shadow effect and the dense tree canopies effects. The findings of this study will facilitate to improve our understanding of the forest cover dynamics and their impacts on the quantities and pathways of terrestrial carbon and nitrogen fluxes. Gong, P., et al. (2012). "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data." International Journal of Remote Sensing 34(7): 2607-2654. Hansen, M. C., et al. (2013). "High-Resolution Global Maps of 21st-Century Forest Cover Change." Science 342(6160): 850-853. Sexton, J. O., et al. (2013). "Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error." International Journal of Digital Earth: 1-22. Shimada, M., et al. (2014). "New global forest/non-forest maps from ALOS PALSAR data (2007-2010)." Remote Sensing of Environment 155: 13-31.

SECURE INTERNET OF THINGS-BASED CLOUD FRAMEWORK TO CONTROL ZIKA VIRUS OUTBREAK.

PubMed

Sareen, Sanjay; Sood, Sandeep K; Gupta, Sunil Kumar

2017-01-01

Zika virus (ZikaV) is currently one of the most important emerging viruses in the world which has caused outbreaks and epidemics and has also been associated with severe clinical manifestations and congenital malformations. Traditional approaches to combat the ZikaV outbreak are not effective for detection and control. The aim of this study is to propose a cloud-based system to prevent and control the spread of Zika virus disease using integration of mobile phones and Internet of Things (IoT). A Naive Bayesian Network (NBN) is used to diagnose the possibly infected users, and Google Maps Web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each ZikaV infected user, mosquito-dense sites, and breeding sites on the Google map that helps the government healthcare authorities to control such risk-prone areas effectively and efficiently. The performance and accuracy of the proposed system are evaluated using dataset for 2 million users. Our system provides high accuracy for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment. The cloud-based proposed system contributed to the accurate NBN-based classification of infected users and accurate identification of risk-prone areas using Google Maps.

Low Altitude AVIRIS Data for Mapping Landform Types on West Ship Island, Mississippi

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Otvos, Ervin; Giardino, Marco

2003-01-01

A chain of barrier islands provides protection against hurricanes and severe storms along the southern and southeastern shores of the Unites States. Barrier island landform types can be spectrally similar and as small as a few meters across, making highly detailed maps difficult to produce. To determine whether high-resolution airborne hyperspectral imagery could provide detailed maps of barrier island landform types, we used low-altitude hyperspectral and multispectral imagery to map surface environments of West Ship Island, Mississippi. We employed 3.4 meter AVIRIS hyperspectral imagery acquired in July 1999 and 0.5 meter ADAR multispectral data acquired in November 1997. The data were co-registered to digital ortho aerial imagery, and the AVIRIS data was scaled to ground reflectance using ATREM software. Unsupervised classification of AVIRIS and ADAR data proceeded using ISODATA clustering techniques. The resulting landform maps were field-checked and compared to aerial photography and digital elevation maps. Preliminary analyses indicated that the AVIRIS classification mapped more landform types, while the ADAR-based map enabled smaller patches to be identified. Used together, these maps provided a means to assess landform distributions of West Ship Island before and after Hurricane Georges. Classification accuracy is being assessed through photo-interpretation and field surveys of sample areas selected with stratified random sampling.

Developmental Changes in Cross-Situational Word Learning: The Inverse Effect of Initial Accuracy

ERIC Educational Resources Information Center

Fitneva, Stanka A.; Christiansen, Morten H.

2017-01-01

Intuitively, the accuracy of initial word-referent mappings should be positively correlated with the outcome of learning. Yet recent evidence suggests an inverse effect of initial accuracy in adults, whereby greater accuracy of initial mappings is associated with poorer outcomes in a cross-situational learning task. Here, we examine the impact of…

Vegetation mapping from high-resolution satellite images in the heterogeneous arid environments of Socotra Island (Yemen)

NASA Astrophysics Data System (ADS)

Malatesta, Luca; Attorre, Fabio; Altobelli, Alfredo; Adeeb, Ahmed; De Sanctis, Michele; Taleb, Nadim M.; Scholte, Paul T.; Vitale, Marcello

2013-01-01

Socotra Island (Yemen), a global biodiversity hotspot, is characterized by high geomorphological and biological diversity. In this study, we present a high-resolution vegetation map of the island based on combining vegetation analysis and classification with remote sensing. Two different image classification approaches were tested to assess the most accurate one in mapping the vegetation mosaic of Socotra. Spectral signatures of the vegetation classes were obtained through a Gaussian mixture distribution model, and a sequential maximum a posteriori (SMAP) classification was applied to account for the heterogeneity and the complex spatial pattern of the arid vegetation. This approach was compared to the traditional maximum likelihood (ML) classification. Satellite data were represented by a RapidEye image with 5 m pixel resolution and five spectral bands. Classified vegetation relevés were used to obtain the training and evaluation sets for the main plant communities. Postclassification sorting was performed to adjust the classification through various rule-based operations. Twenty-eight classes were mapped, and SMAP, with an accuracy of 87%, proved to be more effective than ML (accuracy: 66%). The resulting map will represent an important instrument for the elaboration of conservation strategies and the sustainable use of natural resources in the island.

Investigations on the Bundle Adjustment Results from Sfm-Based Software for Mapping Purposes

NASA Astrophysics Data System (ADS)

Lumban-Gaol, Y. A.; Murtiyoso, A.; Nugroho, B. H.

2018-05-01

Since its first inception, aerial photography has been used for topographic mapping. Large-scale aerial photography contributed to the creation of many of the topographic maps around the world. In Indonesia, a 2013 government directive on spatial management has re-stressed the need for topographic maps, with aerial photogrammetry providing the main method of acquisition. However, the large need to generate such maps is often limited by budgetary reasons. Today, SfM (Structure-from-Motion) offers quicker and less expensive solutions to this problem. However, considering the required precision for topographic missions, these solutions need to be assessed to see if they provide enough level of accuracy. In this paper, a popular SfM-based software Agisoft PhotoScan is used to perform bundle adjustment on a set of large-scale aerial images. The aim of the paper is to compare its bundle adjustment results with those generated by more classical photogrammetric software, namely Trimble Inpho and ERDAS IMAGINE. Furthermore, in order to provide more bundle adjustment statistics to be compared, the Damped Bundle Adjustment Toolbox (DBAT) was also used to reprocess the PhotoScan project. Results show that PhotoScan results are less stable than those generated by the two photogrammetric software programmes. This translates to lower accuracy, which may impact the final photogrammetric product.

EnviroAtlas -- Austin, TX -- One Meter Resolution Urban Land Cover Data (2010)

EPA Pesticide Factsheets

The Austin, TX EnviroAtlas One Meter-scale Urban Land Cover (MULC) Data were generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution from multiple dates in May, 2010. Six land cover classes were mapped: water, impervious surfaces, soil and barren land, trees, grass-herbaceous non-woody vegetation, and agriculture. An accuracy assessment of 600 completely random and 55 stratified random photo interpreted reference points yielded an overall User's fuzzy accuracy of 87 percent. The area mapped is the US Census Bureau's 2010 Urban Statistical Area for Austin, TX plus a 1 km buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Removing non-urban roads from the National Land Cover Database to create improved urban maps for the United States, 1992-2011

USGS Publications Warehouse

Soulard, Christopher E.; Acevedo, William; Stehman, Stephen V.

2018-01-01

Quantifying change in urban land provides important information to create empirical models examining the effects of human land use. Maps of developed land from the National Land Cover Database (NLCD) of the conterminous United States include rural roads in the developed land class and therefore overestimate the amount of urban land. To better map the urban class and understand how urban lands change over time, we removed rural roads and small patches of rural development from the NLCD developed class and created four wall-to-wall maps (1992, 2001, 2006, and 2011) of urban land. Removing rural roads from the NLCD developed class involved a multi-step filtering process, data fusion using geospatial road and developed land data, and manual editing. Reference data classified as urban or not urban from a stratified random sample was used to assess the accuracy of the 2001 and 2006 urban and NLCD maps. The newly created urban maps had higher overall accuracy (98.7 percent) than the NLCD maps (96.2 percent). More importantly, the urban maps resulted in lower commission error of the urban class (23 percent versus 57 percent for the NLCD in 2006) with the trade-off of slightly inflated omission error (20 percent for the urban map, 16 percent for NLCD in 2006). The removal of approximately 230,000 km2 of rural roads from the NLCD developed class resulted in maps that better characterize the urban footprint. These urban maps are more suited to modeling applications and policy decisions that rely on quantitative and spatially explicit information regarding urban lands.

Comparison of PV signal quality using a novel circular mapping and ablation catheter versus a standard circular mapping catheter.

PubMed

von Bary, Christian; Fredersdorf-Hahn, Sabine; Heinicke, Norbert; Jungbauer, Carsten; Schmid, Peter; Riegger, Günter A; Weber, Stefan

2011-08-01

Recently, new catheter technologies have been developed for atrial fibrillation (AF) ablation. We investigate the diagnostic accuracy of a circular mapping and pulmonary vein ablation catheter (PVAC) compared with a standard circular mapping catheter (Orbiter) and the influence of filter settings on signal quality. After reconstruction of the left atrium by three-dimensional atriography, baseline PV potentials (PVP) were recorded consecutively with PVAC and Orbiter in 20 patients with paroxysmal AF. PVPs were compared and attributed to predefined anatomical PV segments. Ablation was performed in 80 PVs using the PVAC. If isolation of the PVs was assumed, signal assessment of each PV was repeated with the Orbiter. If residual PV potentials could be uncovered, different filter settings were tested to improve mapping quality of the PVAC. Ablation was continued until complete PV isolation (PVI) was confirmed with the Orbiter. Baseline mapping demonstrated a good correlation between the Orbiter and PVAC. Mapping accuracy using the PVAC for mapping and ablation was 94% (74 of 79 PVs). Additional mapping with the Orbiter improved the PV isolation rate to 99%. Adjustment of filter settings failed to improve quality of the PV signals compared with standard filter settings. Using the PVAC as a stand-alone strategy for mapping and ablation, one should be aware that in some cases, different signal morphology mimics PVI isolation. Adjustment of filter settings failed to improve signal quality. The use of an additional mapping catheter is recommended to become familiar with the particular signal morphology during the first PVAC cases or whenever there is a doubt about successful isolation of the pulmonary veins.

Comparative assessment of LANDSAT-D MSS and TM data quality for mapping applications in the Southeast

NASA Technical Reports Server (NTRS)

1984-01-01

Rectifications of multispectral scanner and thematic mapper data sets for full and subscene areas, analyses of planimetric errors, assessments of the number and distribution of ground control points required to minimize errors, and factors contributing to error residual are examined. Other investigations include the generation of three dimensional terrain models and the effects of spatial resolution on digital classification accuracies.

Assessing Strain Mapping by Electron Backscatter Diffraction and Confocal Raman Microscopy Using Wedge-indented Si

PubMed Central

Friedman, Lawrence H.; Vaudin, Mark D.; Stranick, Stephan J.; Stan, Gheorghe; Gerbig, Yvonne B.; Osborn, William; Cook, Robert F.

2016-01-01

The accuracy of electron backscatter diffraction (EBSD) and confocal Raman microscopy (CRM) for small-scale strain mapping are assessed using the multi-axial strain field surrounding a wedge indentation in Si as a test vehicle. The strain field is modeled using finite element analysis (FEA) that is adapted to the near-indentation surface profile measured by atomic force microscopy (AFM). The assessment consists of (1) direct experimental comparisons of strain and deformation and (2) comparisons in which the modeled strain field is used as an intermediate step. Direct experimental methods (1) consist of comparisons of surface elevation and gradient measured by AFM and EBSD and of Raman shifts measured and predicted by CRM and EBSD, respectively. Comparisons that utilize the combined FEA-AFM model (2) consist of predictions of distortion, strain, and rotation for comparison with EBSD measurements and predictions of Raman shift for comparison with CRM measurements. For both EBSD and CRM, convolution of measurements in depth-varying strain fields is considered. The interconnected comparisons suggest that EBSD was able to provide an accurate assessment of the wedge indentation deformation field to within the precision of the measurements, approximately 2 × 10−4 in strain. CRM was similarly precise, but was limited in accuracy to several times this value. PMID:26939030

Mapping species distribution of Canarian Monteverde forest by field spectroradiometry and satellite imagery

NASA Astrophysics Data System (ADS)

Martín-Luis, Antonio; Arbelo, Manuel; Hernández-Leal, Pedro; Arbelo-Bayó, Manuel

2016-10-01

Reliable and updated maps of vegetation in protected natural areas are essential for a proper management and conservation. Remote sensing is a valid tool for this purpose. In this study, a methodology based on a WorldView-2 (WV-2) satellite image and in situ spectral signatures measurements was applied to map the Canarian Monteverde ecosystem located in the north of the Tenerife Island (Canary Islands, Spain). Due to the high spectral similarity of vegetation species in the study zone, a Multiple Endmember Spectral Mixture Analysis (MESMA) was performed. MESMA determines the fractional cover of different components within one pixel and it allows for a pixel-by-pixel variation of endmembers. Two libraries of endmembers were collected for the most abundant species in the test area. The first library was collected from in situ spectral signatures measured with an ASD spectroradiometer during a field campaign in June 2015. The second library was obtained from pure pixels identified in the satellite image for the same species. The accuracy of the mapping process was assessed from a set of independent validation plots. The overall accuracy for the ASD-based method was 60.51 % compared to the 86.67 % reached for the WV-2 based mapping. The results suggest the possibility of using WV-2 images for monitoring and regularly updating the maps of the Monteverde forest on the island of Tenerife.

EnviroAtlas -Phoenix, AZ- One Meter Resolution Urban Land Cover Data (2010) Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The EnviroAtlas Phoenix, AZ land cover data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near-infrared) aerial photography taken from June through September, 2010 at 1 m spatial resolution. Seven land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, shrubland, grass and herbaceous non-woody vegetation, and agriculture. An accuracy assessment using a completely random sampling of 598 land cover reference points yielded an overall accuracy of 69.2%. The area mapped includes the entirety of the Central Arizona-Phoenix Long-Term Ecological Research (CAP-LTER) area, which was classified by the Environmental Remote Sensing and Geoinformatics Lab (ERSG) at Arizona State University. The land cover dataset also includes an area of approximately 625 square kilometers which is located north of Phoenix. This section was classified by the EPA land cover classification team. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data

EnviroAtlas - Phoenix, AZ - One Meter Resolution Urban Land Cover Data (2010)

EPA Pesticide Factsheets

The EnviroAtlas Phoenix, AZ land cover (LC) data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near-infrared) aerial photography taken from June through September, 2010 at 1 m spatial resolution. Seven land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, shrubs, grass and herbaceous non-woody vegetation, and agriculture. An accuracy assessment using a completely random sampling of 598 land cover reference points yielded an overall accuracy of 69.2%. The area mapped includes the entirety of the Central Arizona-Phoenix Long-Term Ecological Research (CAP-LTER) area, which was classified by the Environmental Remote Sensing and Geoinformatics Lab (ERSG) at Arizona State University. The land cover dataset also includes an area of approximately 625 square kilometers which is located north of Phoenix. This section was classified by the EPA land cover classification team. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each at

EnviroAtlas -- Austin, TX -- One Meter Resolution Urban Land Cover Data (2010) Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas ). The Austin, TX EnviroAtlas One Meter-scale Urban Land Cover (MULC) Data were generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution from multiple dates in May, 2010. Six land cover classes were mapped: water, impervious surfaces, soil and barren land, trees, grass-herbaceous non-woody vegetation, and agriculture. An accuracy assessment of 600 completely random and 55 stratified random photo interpreted reference points yielded an overall User's fuzzy accuracy of 87 percent. The area mapped is the US Census Bureau's 2010 Urban Statistical Area for Austin, TX plus a 1 km buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas

Semi-automated extraction of landslides in Taiwan based on SPOT imagery and DEMs

NASA Astrophysics Data System (ADS)

Eisank, Clemens; Hölbling, Daniel; Friedl, Barbara; Chen, Yi-Chin; Chang, Kang-Tsung

2014-05-01

The vast availability and improved quality of optical satellite data and digital elevation models (DEMs), as well as the need for complete and up-to-date landslide inventories at various spatial scales have fostered the development of semi-automated landslide recognition systems. Among the tested approaches for designing such systems, object-based image analysis (OBIA) stepped out to be a highly promising methodology. OBIA offers a flexible, spatially enabled framework for effective landslide mapping. Most object-based landslide mapping systems, however, have been tailored to specific, mainly small-scale study areas or even to single landslides only. Even though reported mapping accuracies tend to be higher than for pixel-based approaches, accuracy values are still relatively low and depend on the particular study. There is still room to improve the applicability and objectivity of object-based landslide mapping systems. The presented study aims at developing a knowledge-based landslide mapping system implemented in an OBIA environment, i.e. Trimble eCognition. In comparison to previous knowledge-based approaches, the classification of segmentation-derived multi-scale image objects relies on digital landslide signatures. These signatures hold the common operational knowledge on digital landslide mapping, as reported by 25 Taiwanese landslide experts during personal semi-structured interviews. Specifically, the signatures include information on commonly used data layers, spectral and spatial features, and feature thresholds. The signatures guide the selection and implementation of mapping rules that were finally encoded in Cognition Network Language (CNL). Multi-scale image segmentation is optimized by using the improved Estimation of Scale Parameter (ESP) tool. The approach described above is developed and tested for mapping landslides in a sub-region of the Baichi catchment in Northern Taiwan based on SPOT imagery and a high-resolution DEM. An object-based accuracy assessment is conducted by quantitatively comparing extracted landslide objects with landslide polygons that were visually interpreted by local experts. The applicability and transferability of the mapping system are evaluated by comparing initial accuracies with those achieved for the following two tests: first, usage of a SPOT image from the same year, but for a different area within the Baichi catchment; second, usage of SPOT images from multiple years for the same region. The integration of the common knowledge via digital landslide signatures is new in object-based landslide studies. In combination with strategies to optimize image segmentation this may lead to a more objective, transferable and stable knowledge-based system for the mapping of landslides from optical satellite data and DEMs.

EnviroAtlas - New York, NY - One Meter Resolution Urban Land Cover Data (2008)

EPA Pesticide Factsheets

The New York, NY EnviroAtlas Meter-scale Urban Land Cover (MULC) Data were generated by the University of Vermont Spatial Analysis Laboratory (SAL) under the direction of Jarlath O'Neil-Dunne as part of the United States Forest Service Urban Tree Canopy (UTC) assessment program. Seven classes were mapped using LiDAR and high resolution orthophotography: Tree Canopy, Grass/Shrub, Bare Soil, Water, Buildings, Roads/Railroads, and Other Paved Surfaces. These data were subsequently merged to fit with the EPA classification. The SAL project covered the five boroughs within the NYC city limits. However the EPA study area encompassed that area plus a 1 kilometer buffer. Additional land cover for the buffer area was generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution from July, 2011 and LiDAR from 2010. Six land cover classes were mapped: water, impervious surfaces, soil and barren land, trees, grass-herbaceous non-woody vegetation, and agriculture. An accuracy assessment of 600 completely random and 55 stratified random photo interpreted reference points yielded an overall User's fuzzy accuracy of 87 percent. The area mapped is the US Census Bureau's 2010 Urban Statistical Area for New York City plus a 1 km buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAt

Whole-tumor histogram analysis of the cerebral blood volume map: tumor volume defined by 11C-methionine positron emission tomography image improves the diagnostic accuracy of cerebral glioma grading.

PubMed

Wu, Rongli; Watanabe, Yoshiyuki; Arisawa, Atsuko; Takahashi, Hiroto; Tanaka, Hisashi; Fujimoto, Yasunori; Watabe, Tadashi; Isohashi, Kayako; Hatazawa, Jun; Tomiyama, Noriyuki

2017-10-01

This study aimed to compare the tumor volume definition using conventional magnetic resonance (MR) and 11C-methionine positron emission tomography (MET/PET) images in the differentiation of the pre-operative glioma grade by using whole-tumor histogram analysis of normalized cerebral blood volume (nCBV) maps. Thirty-four patients with histopathologically proven primary brain low-grade gliomas (n = 15) and high-grade gliomas (n = 19) underwent pre-operative or pre-biopsy MET/PET, fluid-attenuated inversion recovery, dynamic susceptibility contrast perfusion-weighted magnetic resonance imaging, and contrast-enhanced T1-weighted at 3.0 T. The histogram distribution derived from the nCBV maps was obtained by co-registering the whole tumor volume delineated on conventional MR or MET/PET images, and eight histogram parameters were assessed. The mean nCBV value had the highest AUC value (0.906) based on MET/PET images. Diagnostic accuracy significantly improved when the tumor volume was measured from MET/PET images compared with conventional MR images for the parameters of mean, 50th, and 75th percentile nCBV value (p = 0.0246, 0.0223, and 0.0150, respectively). Whole-tumor histogram analysis of CBV map provides more valuable histogram parameters and increases diagnostic accuracy in the differentiation of pre-operative cerebral gliomas when the tumor volume is derived from MET/PET images.

High Resolution Insights into Snow Distribution Provided by Drone Photogrammetry

NASA Astrophysics Data System (ADS)

Redpath, T.; Sirguey, P. J.; Cullen, N. J.; Fitzsimons, S.

2017-12-01

Dynamic in time and space, New Zealand's seasonal snow is largely confined to remote alpine areas, complicating ongoing in situ measurement and characterisation. Improved understanding and modeling of the seasonal snowpack requires fine scale resolution of snow distribution and spatial variability. The potential of remotely piloted aircraft system (RPAS) photogrammetry to resolve spatial and temporal variability of snow depth and water equivalent in a New Zealand alpine catchment is assessed in the Pisa Range, Central Otago. This approach yielded orthophotomosaics and digital surface models (DSM) at 0.05 and 0.15 m spatial resolution, respectively. An autumn reference DSM allowed mapping of winter (02/08/2016) and spring (10/09/2016) snow depth at 0.15 m spatial resolution, via DSM differencing. The consistency and accuracy of the RPAS-derived surface was assessed by comparison of snow-free regions of the spring and autumn DSMs, while accuracy of RPAS retrieved snow depth was assessed with 86 in situ snow probe measurements. Results show a mean vertical residual of 0.024 m between DSMs acquired in autumn and spring. This residual approximated a Laplace distribution, reflecting the influence of large outliers on the small overall bias. Propagation of errors associated with successive DSMs saw snow depth mapped with an accuracy of ± 0.09 m (95% c.l.). Comparing RPAS and in situ snow depth measurements revealed the influence of geo-location uncertainty and interactions between vegetation and the snowpack on snow depth uncertainty and bias. Semi-variogram analysis revealed that the RPAS outperformed systematic in situ measurements in resolving fine scale spatial variability. Despite limitations accompanying RPAS photogrammetry, this study demonstrates a repeatable means of accurately mapping snow depth for an entire, yet relatively small, hydrological basin ( 0.5 km2), at high resolution. Resolving snowpack features associated with re-distribution and preferential accumulation and ablation, snow depth maps provide geostatistically robust insights into seasonal snow processes, with unprecedented detail. Such data may enhance understanding of physical processes controlling spatial and temporal distribution of seasonal snow, and their relative importance at varying spatial and temporal scales.

Development of an Asset Value Map for Disaster Risk Assessment in China by Spatial Disaggregation Using Ancillary Remote Sensing Data.

PubMed

Wu, Jidong; Li, Ying; Li, Ning; Shi, Peijun

2018-01-01

The extent of economic losses due to a natural hazard and disaster depends largely on the spatial distribution of asset values in relation to the hazard intensity distribution within the affected area. Given that statistical data on asset value are collected by administrative units in China, generating spatially explicit asset exposure maps remains a key challenge for rapid postdisaster economic loss assessment. The goal of this study is to introduce a top-down (or downscaling) approach to disaggregate administrative-unit level asset value to grid-cell level. To do so, finding the highly correlated "surrogate" indicators is the key. A combination of three data sets-nighttime light grid, LandScan population grid, and road density grid, is used as ancillary asset density distribution information for spatializing the asset value. As a result, a high spatial resolution asset value map of China for 2015 is generated. The spatial data set contains aggregated economic value at risk at 30 arc-second spatial resolution. Accuracy of the spatial disaggregation reflects redistribution errors introduced by the disaggregation process as well as errors from the original ancillary data sets. The overall accuracy of the results proves to be promising. The example of using the developed disaggregated asset value map in exposure assessment of watersheds demonstrates that the data set offers immense analytical flexibility for overlay analysis according to the hazard extent. This product will help current efforts to analyze spatial characteristics of exposure and to uncover the contributions of both physical and social drivers of natural hazard and disaster across space and time. © 2017 Society for Risk Analysis.

Spatial Patterns of NLCD Land Cover Change Thematic Accuracy (2001 - 2011)

EPA Science Inventory

Research on spatial non-stationarity of land cover classification accuracy has been ongoing for over two decades. We extend the understanding of thematic map accuracy spatial patterns by: 1) quantifying spatial patterns of map-reference agreement for class-specific land cover c...

Seagrass mapping in Greek territorial waters using Landsat-8 satellite images

NASA Astrophysics Data System (ADS)

Topouzelis, Konstantinos; Makri, Despina; Stoupas, Nikolaos; Papakonstantinou, Apostolos; Katsanevakis, Stelios

2018-05-01

Seagrass meadows are among the most valuable coastal ecosystems on earth due to their structural and functional roles in the coastal environment. This study demonstrates remote sensing's capacity to produce seagrass distribution maps on a regional scale. The seagrass coverage maps provided here describe and quantify for the first time the extent and the spatial distribution of seagrass meadows in Greek waters. This information is needed for identifying priority conservation sites and to help coastal ecosystem managers and stakeholders to develop conservation strategies and design a resilient network of protected marine areas. The results were based on an object-based image analysis of 50 Landsat-8 satellite images. The time window of image acquisition was between June 2013 and July 2015. In total, the seagrass coverage in Greek waters was estimated at 2619 km2. The largest coverages of individual seagrass meadows were found around Lemnos Island (124 km2), Corfu Island (46 km2), and East Peloponnese (47 km2). The accuracy assessment of the detected areas was based on 62 Natura 2000 sites, for which habitat maps were available. The mean total accuracy for all 62 sites was estimated at 76.3%.

Classification of leafy spurge with earth observing-1 advanced land imager

USGS Publications Warehouse

Stitt, S.; Root, R.; Brown, K.; Hager, S.; Mladinich, C.; Anderson, G.L.; Dudek, K.; Bustos, M.R.; Kokaly, R.

2006-01-01

Leafy spurge (Euphorbia esula L.) is an invasive exotic plant that can completely displace native plant communities. Automated techniques for monitoring the location and extent of leafy spurge, especially if available on a seasonal basis, could add greatly to the effectiveness of control measures. As part of a larger study including multiple sensors, this study examines the utility of mapping the location and extent of leafy spurge in Theodore Roosevelt National Park using Earth Observing-1 satellite Advanced Land Imager (ALI) scanner data. An unsupervised classification methodology was used producing accuracies in the range of 59% to 66%. Existing field studies, with their associated limitations, were used for identifying class membership and accuracy assessment. This sensor could be useful for broad landscape scale mapping of leafy spurge, from which control measures could be based.

Using Satellite Imagery to Assess Large-Scale Habitat Characteristics of Adirondack Park, New York, USA

NASA Astrophysics Data System (ADS)

McClain, Bobbi J.; Porter, William F.

2000-11-01

Satellite imagery is a useful tool for large-scale habitat analysis; however, its limitations need to be tested. We tested these limitations by varying the methods of a habitat evaluation for white-tailed deer ( Odocoileus virginianus) in the Adirondack Park, New York, USA, utilizing harvest data to create and validate the assessment models. We used two classified images, one with a large minimum mapping unit but high accuracy and one with no minimum mapping unit but slightly lower accuracy, to test the sensitivity of the evaluation to these differences. We tested the utility of two methods of assessment, habitat suitability index modeling, and pattern recognition modeling. We varied the scale at which the models were applied by using five separate sizes of analysis windows. Results showed that the presence of a large minimum mapping unit eliminates important details of the habitat. Window size is relatively unimportant if the data are averaged to a large resolution (i.e., township), but if the data are used at the smaller resolution, then the window size is an important consideration. In the Adirondacks, the proportion of hardwood and softwood in an area is most important to the spatial dynamics of deer populations. The low occurrence of open area in all parts of the park either limits the effect of this cover type on the population or limits our ability to detect the effect. The arrangement and interspersion of cover types were not significant to deer populations.

Landslide hazard assessment: recent trends and techniques.

PubMed

Pardeshi, Sudhakar D; Autade, Sumant E; Pardeshi, Suchitra S

2013-01-01

Landslide hazard assessment is an important step towards landslide hazard and risk management. There are several methods of Landslide Hazard Zonation (LHZ) viz. heuristic, semi quantitative, quantitative, probabilistic and multi-criteria decision making process. However, no one method is accepted universally for effective assessment of landslide hazards. In recent years, several attempts have been made to apply different methods of LHZ and to compare results in order to find the best suited model. This paper presents the review of researches on landslide hazard mapping published in recent years. The advanced multivariate techniques are proved to be effective in spatial prediction of landslides with high degree of accuracy. Physical process based models also perform well in LHZ mapping even in the areas with poor database. Multi-criteria decision making approach also play significant role in determining relative importance of landslide causative factors in slope instability process. Remote Sensing and Geographical Information System (GIS) are powerful tools to assess landslide hazards and are being used extensively in landslide researches since last decade. Aerial photographs and high resolution satellite data are useful in detection, mapping and monitoring landslide processes. GIS based LHZ models helps not only to map and monitor landslides but also to predict future slope failures. The advancements in Geo-spatial technologies have opened the doors for detailed and accurate assessment of landslide hazards.

The Joint Agency Commercial Imagery Evaluation (JACIE) Team: Overview and IKONOS Joint Characterization Approach

NASA Technical Reports Server (NTRS)

Zanoni, Vicki; Ryan, Robert; Pagnutti, Mary; Baldridge, Braxton; Roylance, Spencer; Snyder, Greg; Lee, George; Stanley, Tom

2002-01-01

An overview of the Joint Agency Commercial Imagery Evalation (JACIE) team is presented. JACIE, composed of the National Aeronautics and Space Administration (NASA), the National Imagery and Mapping Agency (NIMA), and the U.S. Geological Survey (USGS), was formed to leverage government agencies' capabilities for the characterization of commercial remote sensing data. Each JACIE agency purchases, or plans to purchase, commercial imagery to support its research and applications. It is critical that the data be assessed for its accuracy and utility. Through JACIE, NASA, NIMA, and USGS jointly characterized image products from Space Imaging's IKONOS satellite. Each JACIE agency performed an aspect of the characterization based on its expertise. NASA and its university partners performed a system characterization focusing on radiometric calibration, geopositional accuracy, and spatial resolution assessment; NIMA performed image interpretability and feature extraction evaluations; and USGS assessed geopositional accuracy of several IKONOS products. The JACIE team purchased IKONOS imagery of several study sites to perform the assessments and presented results at an industry-government workshop. Future plans for JACIE include the characterization of DigitalGlobe's QuickBird-2 image products.

Evaluating Crop Area Mapping from MODIS Time-Series as an Assessment Tool for Zimbabwe’s “Fast Track Land Reform Programme”

PubMed Central

2016-01-01

Moderate Resolution Imaging Spectroradiometer (MODIS) data forms the basis for numerous land use and land cover (LULC) mapping and analysis frameworks at regional scale. Compared to other satellite sensors, the spatial, temporal and spectral specifications of MODIS are considered as highly suitable for LULC classifications which support many different aspects of social, environmental and developmental research. The LULC mapping of this study was carried out in the context of the development of an evaluation approach for Zimbabwe’s land reform program. Within the discourse about the success of this program, a lack of spatially explicit methods to produce objective data, such as on the extent of agricultural area, is apparent. We therefore assessed the suitability of moderate spatial and high temporal resolution imagery and phenological parameters to retrieve regional figures about the extent of cropland area in former freehold tenure in a series of 13 years from 2001–2013. Time-series data was processed with TIMESAT and was stratified according to agro-ecological potential zoning of Zimbabwe. Random Forest (RF) classifications were used to produce annual binary crop/non crop maps which were evaluated with high spatial resolution data from other satellite sensors. We assessed the cropland products in former freehold tenure in terms of classification accuracy, inter-annual comparability and heterogeneity. Although general LULC patterns were depicted in classification results and an overall accuracy of over 80% was achieved, user accuracies for rainfed agriculture were limited to below 65%. We conclude that phenological analysis has to be treated with caution when rainfed agriculture and grassland in semi-humid tropical regions have to be separated based on MODIS spectral data and phenological parameters. Because classification results significantly underestimate redistributed commercial farmland in Zimbabwe, we argue that the method cannot be used to produce spatial information on land-use which could be linked to tenure change. Hence capabilities of moderate resolution data are limited to assess Zimbabwe’s land reform. To make use of the unquestionable potential of MODIS time-series analysis, we propose an analysis of plant productivity which allows to link annual growth and production of vegetation to ownership after Zimbabwe’s land reform. PMID:27253327

Assessment of a visually guided autonomous exploration robot

NASA Astrophysics Data System (ADS)

Harris, C.; Evans, R.; Tidey, E.

2008-10-01

A system has been developed to enable a robot vehicle to autonomously explore and map an indoor environment using only visual sensors. The vehicle is equipped with a single camera, whose output is wirelessly transmitted to an off-board standard PC for processing. Visual features within the camera imagery are extracted and tracked, and their 3D positions are calculated using a Structure from Motion algorithm. As the vehicle travels, obstacles in its surroundings are identified and a map of the explored region is generated. This paper discusses suitable criteria for assessing the performance of the system by computer-based simulation and practical experiments with a real vehicle. Performance measures identified include the positional accuracy of the 3D map and the vehicle's location, the efficiency and completeness of the exploration and the system reliability. Selected results are presented and the effect of key system parameters and algorithms on performance is assessed. This work was funded by the Systems Engineering for Autonomous Systems (SEAS) Defence Technology Centre established by the UK Ministry of Defence.

In the eye of the beholder: the effect of rater variability and different rating scales on QTL mapping.

PubMed

Poland, Jesse A; Nelson, Rebecca J

2011-02-01

The agronomic importance of developing durably resistant cultivars has led to substantial research in the field of quantitative disease resistance (QDR) and, in particular, mapping quantitative trait loci (QTL) for disease resistance. The assessment of QDR is typically conducted by visual estimation of disease severity, which raises concern over the accuracy and precision of visual estimates. Although previous studies have examined the factors affecting the accuracy and precision of visual disease assessment in relation to the true value of disease severity, the impact of this variability on the identification of disease resistance QTL has not been assessed. In this study, the effects of rater variability and rating scales on mapping QTL for northern leaf blight resistance in maize were evaluated in a recombinant inbred line population grown under field conditions. The population of 191 lines was evaluated by 22 different raters using a direct percentage estimate, a 0-to-9 ordinal rating scale, or both. It was found that more experienced raters had higher precision and that using a direct percentage estimation of diseased leaf area produced higher precision than using an ordinal scale. QTL mapping was then conducted using the disease estimates from each rater using stepwise general linear model selection (GLM) and inclusive composite interval mapping (ICIM). For GLM, the same QTL were largely found across raters, though some QTL were only identified by a subset of raters. The magnitudes of estimated allele effects at identified QTL varied drastically, sometimes by as much as threefold. ICIM produced highly consistent results across raters and for the different rating scales in identifying the location of QTL. We conclude that, despite variability between raters, the identification of QTL was largely consistent among raters, particularly when using ICIM. However, care should be taken in estimating QTL allele effects, because this was highly variable and rater dependent.

Demonstration of Airborne Wide Area Assessment Technologies at Pueblo Precision Bombing Ranges, Colorado. Hyperspectral Imaging, Version 2.0

DTIC Science & Technology

2007-09-27

the spatial and spectral resolution ...variety of geological and vegetation mapping efforts, the Hymap sensor offered the best available combination of spectral and spatial resolution , signal... The limitations of the technology currently relate to spatial and spectral resolution and geo- correction accuracy. Secondly, HSI datasets

Teacher Attunement: Supporting Students' Peer Experiences in the Early Elementary Classroom

ERIC Educational Resources Information Center

Hoffman, Abigail S.

2012-01-01

This multi-method, longitudinal study examines the role of teacher attunement (teacher accuracy in identifying the peer group memberships of individual students) in children's peer experiences in early elementary classrooms (1st-3rd grades). Social cognitive mapping (SCM) procedures assessed and compared students' and teachers'…

Forest/non-forest stratification in Georgia with Landsat Thematic Mapper data

Treesearch

William H. Cooke

2000-01-01

Geographically accurate Forest Inventory and Analysis (FIA) data may be useful for training, classification, and accuracy assessment of Landsat Thematic Mapper (TM) data. Minimum expectation for maps derived from Landsat data is accurate discrimination of several land cover classes. Landsat TM costs have decreased dramatically, but acquiring cloud-free scenes at...

MRI-guided attenuation correction in whole-body PET/MR: assessment of the effect of bone attenuation.

PubMed

Akbarzadeh, A; Ay, M R; Ahmadian, A; Alam, N Riahi; Zaidi, H

2013-02-01

Hybrid PET/MRI presents many advantages in comparison with its counterpart PET/CT in terms of improved soft-tissue contrast, decrease in radiation exposure, and truly simultaneous and multi-parametric imaging capabilities. However, the lack of well-established methodology for MR-based attenuation correction is hampering further development and wider acceptance of this technology. We assess the impact of ignoring bone attenuation and using different tissue classes for generation of the attenuation map on the accuracy of attenuation correction of PET data. This work was performed using simulation studies based on the XCAT phantom and clinical input data. For the latter, PET and CT images of patients were used as input for the analytic simulation model using realistic activity distributions where CT-based attenuation correction was utilized as reference for comparison. For both phantom and clinical studies, the reference attenuation map was classified into various numbers of tissue classes to produce three (air, soft tissue and lung), four (air, lungs, soft tissue and cortical bones) and five (air, lungs, soft tissue, cortical bones and spongeous bones) class attenuation maps. The phantom studies demonstrated that ignoring bone increases the relative error by up to 6.8% in the body and up to 31.0% for bony regions. Likewise, the simulated clinical studies showed that the mean relative error reached 15% for lesions located in the body and 30.7% for lesions located in bones, when neglecting bones. These results demonstrate an underestimation of about 30% of tracer uptake when neglecting bone, which in turn imposes substantial loss of quantitative accuracy for PET images produced by hybrid PET/MRI systems. Considering bones in the attenuation map will considerably improve the accuracy of MR-guided attenuation correction in hybrid PET/MR to enable quantitative PET imaging on hybrid PET/MR technologies.

An uncertainty and sensitivity analysis approach for GIS-based multicriteria landslide susceptibility mapping

PubMed Central

Feizizadeh, Bakhtiar; Blaschke, Thomas

2014-01-01

GIS-based multicriteria decision analysis (MCDA) methods are increasingly being used in landslide susceptibility mapping. However, the uncertainties that are associated with MCDA techniques may significantly impact the results. This may sometimes lead to inaccurate outcomes and undesirable consequences. This article introduces a new GIS-based MCDA approach. We illustrate the consequences of applying different MCDA methods within a decision-making process through uncertainty analysis. Three GIS-MCDA methods in conjunction with Monte Carlo simulation (MCS) and Dempster–Shafer theory are analyzed for landslide susceptibility mapping (LSM) in the Urmia lake basin in Iran, which is highly susceptible to landslide hazards. The methodology comprises three stages. First, the LSM criteria are ranked and a sensitivity analysis is implemented to simulate error propagation based on the MCS. The resulting weights are expressed through probability density functions. Accordingly, within the second stage, three MCDA methods, namely analytical hierarchy process (AHP), weighted linear combination (WLC) and ordered weighted average (OWA), are used to produce the landslide susceptibility maps. In the third stage, accuracy assessments are carried out and the uncertainties of the different results are measured. We compare the accuracies of the three MCDA methods based on (1) the Dempster–Shafer theory and (2) a validation of the results using an inventory of known landslides and their respective coverage based on object-based image analysis of IRS-ID satellite images. The results of this study reveal that through the integration of GIS and MCDA models, it is possible to identify strategies for choosing an appropriate method for LSM. Furthermore, our findings indicate that the integration of MCDA and MCS can significantly improve the accuracy of the results. In LSM, the AHP method performed best, while the OWA reveals better performance in the reliability assessment. The WLC operation yielded poor results. PMID:27019609

Health promotion capacity mapping: the Korean situation.

PubMed

Nam, Eun Woo; Engelhardt, Katrin

2007-06-01

Ten years ago the Republic of Korea enacted the National Health Promotion Act, setting the stage for health promotion action in the country. A National Health Promotion Fund was established, financed through tobacco taxes, which is now one of the largest in the world. However, despite abundant financial resources, the infrastructure needed to plan, implement, coordinate and evaluate health promotion efforts is still underdeveloped. Currently, health promotion capacity mapping efforts are emerging in Korea. Two international capacity mapping tools have been used to assess the Korean situation, namely HP-Source and the Health Promotion Capacity Profile, which was developed prior to the sixth Global Conference of Health Promotion, held in August 2005 in Bangkok, Thailand. The article summarizes and discusses the results of the capacity mapping exercise, highlights its challenges and suggest ways to improve the accuracy of health promotion capacity mapping.

Will it Blend? Visualization and Accuracy Evaluation of High-Resolution Fuzzy Vegetation Maps

NASA Astrophysics Data System (ADS)

Zlinszky, A.; Kania, A.

2016-06-01

Instead of assigning every map pixel to a single class, fuzzy classification includes information on the class assigned to each pixel but also the certainty of this class and the alternative possible classes based on fuzzy set theory. The advantages of fuzzy classification for vegetation mapping are well recognized, but the accuracy and uncertainty of fuzzy maps cannot be directly quantified with indices developed for hard-boundary categorizations. The rich information in such a map is impossible to convey with a single map product or accuracy figure. Here we introduce a suite of evaluation indices and visualization products for fuzzy maps generated with ensemble classifiers. We also propose a way of evaluating classwise prediction certainty with "dominance profiles" visualizing the number of pixels in bins according to the probability of the dominant class, also showing the probability of all the other classes. Together, these data products allow a quantitative understanding of the rich information in a fuzzy raster map both for individual classes and in terms of variability in space, and also establish the connection between spatially explicit class certainty and traditional accuracy metrics. These map products are directly comparable to widely used hard boundary evaluation procedures, support active learning-based iterative classification and can be applied for operational use.

Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data

NASA Astrophysics Data System (ADS)

Jiao, Xianfeng; Kovacs, John M.; Shang, Jiali; McNairn, Heather; Walters, Dan; Ma, Baoluo; Geng, Xiaoyuan

2014-10-01

The aim of this paper is to assess the accuracy of an object-oriented classification of polarimetric Synthetic Aperture Radar (PolSAR) data to map and monitor crops using 19 RADARSAT-2 fine beam polarimetric (FQ) images of an agricultural area in North-eastern Ontario, Canada. Polarimetric images and field data were acquired during the 2011 and 2012 growing seasons. The classification and field data collection focused on the main crop types grown in the region, which include: wheat, oat, soybean, canola and forage. The polarimetric parameters were extracted with PolSAR analysis using both the Cloude-Pottier and Freeman-Durden decompositions. The object-oriented classification, with a single date of PolSAR data, was able to classify all five crop types with an accuracy of 95% and Kappa of 0.93; a 6% improvement in comparison with linear-polarization only classification. However, the time of acquisition is crucial. The larger biomass crops of canola and soybean were most accurately mapped, whereas the identification of oat and wheat were more variable. The multi-temporal data using the Cloude-Pottier decomposition parameters provided the best classification accuracy compared to the linear polarizations and the Freeman-Durden decomposition parameters. In general, the object-oriented classifications were able to accurately map crop types by reducing the noise inherent in the SAR data. Furthermore, using the crop classification maps we were able to monitor crop growth stage based on a trend analysis of the radar response. Based on field data from canola crops, there was a strong relationship between the phenological growth stage based on the BBCH scale, and the HV backscatter and entropy.

Assessment of radargrammetric DSMs from TerraSAR-X Stripmap images in a mountainous relief area of the Amazon region

NASA Astrophysics Data System (ADS)

de Oliveira, Cleber Gonzales; Paradella, Waldir Renato; da Silva, Arnaldo de Queiroz

The Brazilian Amazon is a vast territory with an enormous need for mapping and monitoring of renewable and non-renewable resources. Due to the adverse environmental condition (rain, cloud, dense vegetation) and difficult access, topographic information is still poor, and when available needs to be updated or re-mapped. In this paper, the feasibility of using Digital Surface Models (DSMs) extracted from TerraSAR-X Stripmap stereo-pair images for detailed topographic mapping was investigated for a mountainous area in the Carajás Mineral Province, located on the easternmost border of the Brazilian Amazon. The quality of the radargrammetric DSMs was evaluated regarding field altimetric measurements. Precise topographic field information acquired from a Global Positioning System (GPS) was used as Ground Control Points (GCPs) for the modeling of the stereoscopic DSMs and as Independent Check Points (ICPs) for the calculation of elevation accuracies. The analysis was performed following two ways: (1) the use of Root Mean Square Error (RMSE) and (2) calculations of systematic error (bias) and precision. The test for significant systematic error was based on the Student's-t distribution and the test of precision was based on the Chi-squared distribution. The investigation has shown that the accuracy of the TerraSAR-X Stripmap DSMs met the requirements for 1:50,000 map (Class A) as requested by the Brazilian Standard for Cartographic Accuracy. Thus, the use of TerraSAR-X Stripmap images can be considered a promising alternative for detailed topographic mapping in similar environments of the Amazon region, where available topographic information is rare or presents low quality.

Mapping post-fire forest regeneration and vegetation recovery using a combination of very high spatial resolution and hyperspectral satellite imagery

NASA Astrophysics Data System (ADS)

Mitri, George H.; Gitas, Ioannis Z.

2013-02-01

Careful evaluation of forest regeneration and vegetation recovery after a fire event provides vital information useful in land management. The use of remotely sensed data is considered to be especially suitable for monitoring ecosystem dynamics after fire. The aim of this work was to map post-fire forest regeneration and vegetation recovery on the Mediterranean island of Thasos by using a combination of very high spatial (VHS) resolution (QuickBird) and hyperspectral (EO-1 Hyperion) imagery and by employing object-based image analysis. More specifically, the work focused on (1) the separation and mapping of three major post-fire classes (forest regeneration, other vegetation recovery, unburned vegetation) existing within the fire perimeter, and (2) the differentiation and mapping of the two main forest regeneration classes, namely, Pinus brutia regeneration, and Pinus nigra regeneration. The data used in this study consisted of satellite images and field observations of homogeneous regenerated and revegetated areas. The methodology followed two main steps: a three-level image segmentation, and, a classification of the segmented images. The process resulted in the separation of classes related to the aforementioned objectives. The overall accuracy assessment revealed very promising results (approximately 83.7% overall accuracy, with a Kappa Index of Agreement of 0.79). The achieved accuracy was 8% higher when compared to the results reported in a previous work in which only the EO-1 Hyperion image was employed in order to map the same classes. Some classification confusions involving the classes of P. brutia regeneration and P. nigra regeneration were observed. This could be attributed to the absence of large and dense homogeneous areas of regenerated pine trees in the study area.

Exploring Google Earth Engine platform for big data processing: classification of multi-temporal satellite imagery for crop mapping

NASA Astrophysics Data System (ADS)

Shelestov, Andrii; Lavreniuk, Mykola; Kussul, Nataliia; Novikov, Alexei; Skakun, Sergii

2017-02-01

Many applied problems arising in agricultural monitoring and food security require reliable crop maps at national or global scale. Large scale crop mapping requires processing and management of large amount of heterogeneous satellite imagery acquired by various sensors that consequently leads to a “Big Data” problem. The main objective of this study is to explore efficiency of using the Google Earth Engine (GEE) platform when classifying multi-temporal satellite imagery with potential to apply the platform for a larger scale (e.g. country level) and multiple sensors (e.g. Landsat-8 and Sentinel-2). In particular, multiple state-of-the-art classifiers available in the GEE platform are compared to produce a high resolution (30 m) crop classification map for a large territory ( 28,100 km2 and 1.0 M ha of cropland). Though this study does not involve large volumes of data, it does address efficiency of the GEE platform to effectively execute complex workflows of satellite data processing required with large scale applications such as crop mapping. The study discusses strengths and weaknesses of classifiers, assesses accuracies that can be achieved with different classifiers for the Ukrainian landscape, and compares them to the benchmark classifier using a neural network approach that was developed in our previous studies. The study is carried out for the Joint Experiment of Crop Assessment and Monitoring (JECAM) test site in Ukraine covering the Kyiv region (North of Ukraine) in 2013. We found that Google Earth Engine (GEE) provides very good performance in terms of enabling access to the remote sensing products through the cloud platform and providing pre-processing; however, in terms of classification accuracy, the neural network based approach outperformed support vector machine (SVM), decision tree and random forest classifiers available in GEE.

Coarse climate change projections for species living in a fine-scaled world.

PubMed

Nadeau, Christopher P; Urban, Mark C; Bridle, Jon R

2017-01-01

Accurately predicting biological impacts of climate change is necessary to guide policy. However, the resolution of climate data could be affecting the accuracy of climate change impact assessments. Here, we review the spatial and temporal resolution of climate data used in impact assessments and demonstrate that these resolutions are often too coarse relative to biologically relevant scales. We then develop a framework that partitions climate into three important components: trend, variance, and autocorrelation. We apply this framework to map different global climate regimes and identify where coarse climate data is most and least likely to reduce the accuracy of impact assessments. We show that impact assessments for many large mammals and birds use climate data with a spatial resolution similar to the biologically relevant area encompassing population dynamics. Conversely, impact assessments for many small mammals, herpetofauna, and plants use climate data with a spatial resolution that is orders of magnitude larger than the area encompassing population dynamics. Most impact assessments also use climate data with a coarse temporal resolution. We suggest that climate data with a coarse spatial resolution is likely to reduce the accuracy of impact assessments the most in climates with high spatial trend and variance (e.g., much of western North and South America) and the least in climates with low spatial trend and variance (e.g., the Great Plains of the USA). Climate data with a coarse temporal resolution is likely to reduce the accuracy of impact assessments the most in the northern half of the northern hemisphere where temporal climatic variance is high. Our framework provides one way to identify where improving the resolution of climate data will have the largest impact on the accuracy of biological predictions under climate change. © 2016 John Wiley & Sons Ltd.

Study of USGS/NASA land use classification system. [compatibility of land use classification system with computer processing techniques employed for land use mapping from ERTS data

NASA Technical Reports Server (NTRS)

Spann, G. W.; Faust, N. L.

1974-01-01

It is known from several previous investigations that many categories of land-use can be mapped via computer processing of Earth Resources Technology Satellite data. The results are presented of one such experiment using the USGS/NASA land-use classification system. Douglas County, Georgia, was chosen as the test site for this project. It was chosen primarily because of its recent rapid growth and future growth potential. Results of the investigation indicate an overall land-use mapping accuracy of 67% with higher accuracies in rural areas and lower accuracies in urban areas. It is estimated, however, that 95% of the State of Georgia could be mapped by these techniques with an accuracy of 80% to 90%.

Mapping Resource Selection Functions in Wildlife Studies: Concerns and Recommendations

PubMed Central

Morris, Lillian R.; Proffitt, Kelly M.; Blackburn, Jason K.

2018-01-01

Predicting the spatial distribution of animals is an important and widely used tool with applications in wildlife management, conservation, and population health. Wildlife telemetry technology coupled with the availability of spatial data and GIS software have facilitated advancements in species distribution modeling. There are also challenges related to these advancements including the accurate and appropriate implementation of species distribution modeling methodology. Resource Selection Function (RSF) modeling is a commonly used approach for understanding species distributions and habitat usage, and mapping the RSF results can enhance study findings and make them more accessible to researchers and wildlife managers. Currently, there is no consensus in the literature on the most appropriate method for mapping RSF results, methods are frequently not described, and mapping approaches are not always related to accuracy metrics. We conducted a systematic review of the RSF literature to summarize the methods used to map RSF outputs, discuss the relationship between mapping approaches and accuracy metrics, performed a case study on the implications of employing different mapping methods, and provide recommendations as to appropriate mapping techniques for RSF studies. We found extensive variability in methodology for mapping RSF results. Our case study revealed that the most commonly used approaches for mapping RSF results led to notable differences in the visual interpretation of RSF results, and there is a concerning disconnect between accuracy metrics and mapping methods. We make 5 recommendations for researchers mapping the results of RSF studies, which are focused on carefully selecting and describing the method used to map RSF studies, and relating mapping approaches to accuracy metrics. PMID:29887652

Fixed-Wing Micro Aerial Vehicle for Accurate Corridor Mapping

NASA Astrophysics Data System (ADS)

Rehak, M.; Skaloud, J.

2015-08-01

In this study we present a Micro Aerial Vehicle (MAV) equipped with precise position and attitude sensors that together with a pre-calibrated camera enables accurate corridor mapping. The design of the platform is based on widely available model components to which we integrate an open-source autopilot, customized mass-market camera and navigation sensors. We adapt the concepts of system calibration from larger mapping platforms to MAV and evaluate them practically for their achievable accuracy. We present case studies for accurate mapping without ground control points: first for a block configuration, later for a narrow corridor. We evaluate the mapping accuracy with respect to checkpoints and digital terrain model. We show that while it is possible to achieve pixel (3-5 cm) mapping accuracy in both cases, precise aerial position control is sufficient for block configuration, the precise position and attitude control is required for corridor mapping.

Accuracy assessment of the U.S. Geological Survey National Elevation Dataset, and comparison with other large-area elevation datasets: SRTM and ASTER

USGS Publications Warehouse

Gesch, Dean B.; Oimoen, Michael J.; Evans, Gayla A.

2014-01-01

The National Elevation Dataset (NED) is the primary elevation data product produced and distributed by the U.S. Geological Survey. The NED provides seamless raster elevation data of the conterminous United States, Alaska, Hawaii, U.S. island territories, Mexico, and Canada. The NED is derived from diverse source datasets that are processed to a specification with consistent resolutions, coordinate system, elevation units, and horizontal and vertical datums. The NED serves as the elevation layer of The National Map, and it provides basic elevation information for earth science studies and mapping applications in the United States and most of North America. An important part of supporting scientific and operational use of the NED is provision of thorough dataset documentation including data quality and accuracy metrics. The focus of this report is on the vertical accuracy of the NED and on comparison of the NED with other similar large-area elevation datasets, namely data from the Shuttle Radar Topography Mission (SRTM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER).

A ground truth based comparative study on clustering of gene expression data.

PubMed

Zhu, Yitan; Wang, Zuyi; Miller, David J; Clarke, Robert; Xuan, Jianhua; Hoffman, Eric P; Wang, Yue

2008-05-01

Given the variety of available clustering methods for gene expression data analysis, it is important to develop an appropriate and rigorous validation scheme to assess the performance and limitations of the most widely used clustering algorithms. In this paper, we present a ground truth based comparative study on the functionality, accuracy, and stability of five data clustering methods, namely hierarchical clustering, K-means clustering, self-organizing maps, standard finite normal mixture fitting, and a caBIG toolkit (VIsual Statistical Data Analyzer--VISDA), tested on sample clustering of seven published microarray gene expression datasets and one synthetic dataset. We examined the performance of these algorithms in both data-sufficient and data-insufficient cases using quantitative performance measures, including cluster number detection accuracy and mean and standard deviation of partition accuracy. The experimental results showed that VISDA, an interactive coarse-to-fine maximum likelihood fitting algorithm, is a solid performer on most of the datasets, while K-means clustering and self-organizing maps optimized by the mean squared compactness criterion generally produce more stable solutions than the other methods.

Evaluation of Empirical Tropospheric Models Using Satellite-Tracking Tropospheric Wet Delays with Water Vapor Radiometer at Tongji, China

PubMed Central

Wang, Miaomiao; Li, Bofeng

2016-01-01

An empirical tropospheric delay model, together with a mapping function, is commonly used to correct the tropospheric errors in global navigation satellite system (GNSS) processing. As is well-known, the accuracy of tropospheric delay models relies mainly on the correction efficiency for tropospheric wet delays. In this paper, we evaluate the accuracy of three tropospheric delay models, together with five mapping functions in wet delays calculation. The evaluations are conducted by comparing their slant wet delays with those measured by water vapor radiometer based on its satellite-tracking function (collected data with large liquid water path is removed). For all 15 combinations of three tropospheric models and five mapping functions, their accuracies as a function of elevation are statistically analyzed by using nine-day data in two scenarios, with and without meteorological data. The results show that (1) no matter with or without meteorological data, there is no practical difference between mapping functions, i.e., Chao, Ifadis, Vienna Mapping Function 1 (VMF1), Niell Mapping Function (NMF), and MTT Mapping Function (MTT); (2) without meteorological data, the UNB3 is much better than Saastamoinen and Hopfield models, while the Saastamoinen model performed slightly better than the Hopfield model; (3) with meteorological data, the accuracies of all three tropospheric delay models are improved to be comparable, especially for lower elevations. In addition, the kinematic precise point positioning where no parameter is set up for tropospheric delay modification is conducted to further evaluate the performance of tropospheric delay models in positioning accuracy. It is shown that the UNB3 model is best and can achieve about 10 cm accuracy for the N and E coordinate component while 20 cm accuracy for the U coordinate component no matter the meteorological data is available or not. This accuracy can be obtained by the Saastamoinen model only when meteorological data is available, and degraded to 46 cm for the U component if the meteorological data is not available. PMID:26848662

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome.

PubMed

Guitet, Stéphane; Hérault, Bruno; Molto, Quentin; Brunaux, Olivier; Couteron, Pierre

2015-01-01

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate "wall-to-wall" remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution.

The Utility and Validity of Kinematic GPS Positioning for the Geosar Airborne Terrain Mapping Radar System

NASA Technical Reports Server (NTRS)

Freedman, Adam; Hensley, Scott; Chapin, Elaine; Kroger, Peter; Hussain, Mushtaq; Allred, Bruce

1999-01-01

GeoSAR is an airborne, interferometric Synthetic Aperture Radar (IFSAR) system for terrain mapping, currently under development by a consortium including NASA's Jet Propulsion Laboratory (JPL), Calgis, Inc., a California mapping sciences company, and the California Department of Conservation (CaIDOC), with funding provided by the U.S. Army Corps of Engineers Topographic Engineering Center (TEC) and the U.S. Defense Advanced Research Projects Agency (DARPA). IFSAR data processing requires high-accuracy platform position and attitude knowledge. On 9 GeoSAR, these are provided by one or two Honeywell Embedded GPS Inertial Navigation Units (EGI) and an Ashtech Z12 GPS receiver. The EGIs provide real-time high-accuracy attitude and moderate-accuracy position data, while the Ashtech data, post-processed differentially with data from a nearby ground station using Ashtech PNAV software, provide high-accuracy differential GPS positions. These data are optimally combined using a Kalman filter within the GeoSAR motion measurement software, and the resultant position and orientation information are used to process the dual frequency (X-band and P-band) radar data to generate high-accuracy, high -resolution terrain imagery and digital elevation models (DEMs). GeoSAR requirements specify sub-meter level planimetric and vertical accuracies for the resultant DEMS. To achieve this, platform positioning errors well below one meter are needed. The goal of GeoSAR is to obtain 25 cm or better 3-D positions from the GPS systems on board the aircraft. By imaging a set of known point target corner-cube reflectors, the GeoSAR system can be calibrated. This calibration process yields the true position of the aircraft with an uncertainty of 20- 50 cm. This process thus allows an independent assessment of the accuracy of our GPS-based positioning systems. We will present an overview of the GeoSAR motion measurement system, focusing on the use of GPS and the blending of position data from the various systems. We will present the results of our calibration studies that relate to the accuracy the GPS positioning. We will discuss the effects these positioning, errors have on the resultant DEM products and imagery.

Thematic accuracy of the National Land Cover Database (NLCD) 2001 land cover for Alaska

USGS Publications Warehouse

Selkowitz, D.J.; Stehman, S.V.

2011-01-01

The National Land Cover Database (NLCD) 2001 Alaska land cover classification is the first 30-m resolution land cover product available covering the entire state of Alaska. The accuracy assessment of the NLCD 2001 Alaska land cover classification employed a geographically stratified three-stage sampling design to select the reference sample of pixels. Reference land cover class labels were determined via fixed wing aircraft, as the high resolution imagery used for determining the reference land cover classification in the conterminous U.S. was not available for most of Alaska. Overall thematic accuracy for the Alaska NLCD was 76.2% (s.e. 2.8%) at Level II (12 classes evaluated) and 83.9% (s.e. 2.1%) at Level I (6 classes evaluated) when agreement was defined as a match between the map class and either the primary or alternate reference class label. When agreement was defined as a match between the map class and primary reference label only, overall accuracy was 59.4% at Level II and 69.3% at Level I. The majority of classification errors occurred at Level I of the classification hierarchy (i.e., misclassifications were generally to a different Level I class, not to a Level II class within the same Level I class). Classification accuracy was higher for more abundant land cover classes and for pixels located in the interior of homogeneous land cover patches. ?? 2011.

Summer Crop Classification by Multi-Temporal COSMO-SkyMed® Data

NASA Astrophysics Data System (ADS)

Guarini, Rocchina; Bruzzone, Lorenzo; Santoni, Massimo; Vuolo, Francesco; Luigi, Dini

2016-08-01

In this study, we propose a multi-temporal and multi- polarization approach to discriminate different crop types in the Marchefel region, Austria. The sensitivity of X-band COSMO-SkyMed® (CSK®) data with respect to five crop classes, namely carrot, corn, potato, soybean and sugarbeet is investigated. In particular, the capabilities of dual-polarization (StripMap PingPong) HH/HV, and single-polarization (StripMap Himage), HH and VH, in distinguishing among the five crop types are evaluated. A total of twenty-one Himage and ten PingPong images were acquired in a seven-months period, from April to October 2014. Therefore, the backscattering coefficient was extracted for each dataset and the classification was performed using a pixel-based support vector machine (SVM) approach. The accuracy of the obtained crop classifications was assessed by comparing them with ground truth. The dual-polarization results are contrasted between the HH and HV polarization, and with single-polarization ones (HH and VH polarizations). The best accuracy is obtained by using time-series of StripMap Himage data, at VH polarization, covering the whole season period.

Development of predictive mapping techniques for soil survey and salinity mapping

NASA Astrophysics Data System (ADS)

Elnaggar, Abdelhamid A.

Conventional soil maps represent a valuable source of information about soil characteristics, however they are subjective, very expensive, and time-consuming to prepare. Also, they do not include explicit information about the conceptual mental model used in developing them nor information about their accuracy, in addition to the error associated with them. Decision tree analysis (DTA) was successfully used in retrieving the expert knowledge embedded in old soil survey data. This knowledge was efficiently used in developing predictive soil maps for the study areas in Benton and Malheur Counties, Oregon and accessing their consistency. A retrieved soil-landscape model from a reference area in Harney County was extrapolated to develop a preliminary soil map for the neighboring unmapped part of Malheur County. The developed map had a low prediction accuracy and only a few soil map units (SMUs) were predicted with significant accuracy, mostly those shallow SMUs that have either a lithic contact with the bedrock or developed on a duripan. On the other hand, the developed soil map based on field data was predicted with very high accuracy (overall was about 97%). Salt-affected areas of the Malheur County study area are indicated by their high spectral reflectance and they are easily discriminated from the remote sensing data. However, remote sensing data fails to distinguish between the different classes of soil salinity. Using the DTA method, five classes of soil salinity were successfully predicted with an overall accuracy of about 99%. Moreover, the calculated area of salt-affected soil was overestimated when mapped using remote sensing data compared to that predicted by using DTA. Hence, DTA could be a very helpful approach in developing soil survey and soil salinity maps in more objective, effective, less-expensive and quicker ways based on field data.

Two- and three-dimensional accuracy of dental impression materials: effects of storage time and moisture contamination.

PubMed

Chandran, Deepa T; Jagger, Daryll C; Jagger, Robert G; Barbour, Michele E

2010-01-01

Dental impression materials are used to create an inverse replica of the dental hard and soft tissues, and are used in processes such as the fabrication of crowns and bridges. The accuracy and dimensional stability of impression materials are of paramount importance to the accuracy of fit of the resultant prosthesis. Conventional methods for assessing the dimensional stability of impression materials are two-dimensional (2D), and assess shrinkage or expansion between selected fixed points on the impression. In this study, dimensional changes in four impression materials were assessed using an established 2D and an experimental three-dimensional (3D) technique. The former involved measurement of the distance between reference points on the impression; the latter a contact scanning method for producing a computer map of the impression surface showing localised expansion, contraction and warpage. Dimensional changes were assessed as a function of storage times and moisture contamination comparable to that found in clinical situations. It was evident that dimensional changes observed using the 3D technique were not always apparent using the 2D technique, and that the former offers certain advantages in terms of assessing dimensional accuracy and predictability of impression methods. There are, however, drawbacks associated with 3D techniques such as the more time-consuming nature of the data acquisition and difficulty in statistically analysing the data.

Assessment of landslide distribution map reliability in Niigata prefecture - Japan using frequency ratio approach

NASA Astrophysics Data System (ADS)

Rahardianto, Trias; Saputra, Aditya; Gomez, Christopher

2017-07-01

Research on landslide susceptibility has evolved rapidly over the few last decades thanks to the availability of large databases. Landslide research used to be focused on discreet events but the usage of large inventory dataset has become a central pillar of landslide susceptibility, hazard, and risk assessment. Indeed, extracting meaningful information from the large database is now at the forth of geoscientific research, following the big-data research trend. Indeed, the more comprehensive information of the past landslide available in a particular area is, the better the produced map will be, in order to support the effective decision making, planning, and engineering practice. The landslide inventory data which is freely accessible online gives an opportunity for many researchers and decision makers to prevent casualties and economic loss caused by future landslides. This data is advantageous especially for areas with poor landslide historical data. Since the construction criteria of landslide inventory map and its quality evaluation remain poorly defined, the assessment of open source landslide inventory map reliability is required. The present contribution aims to assess the reliability of open-source landslide inventory data based on the particular topographical setting of the observed area in Niigata prefecture, Japan. Geographic Information System (GIS) platform and statistical approach are applied to analyze the data. Frequency ratio method is utilized to model and assess the landslide map. The outcomes of the generated model showed unsatisfactory results with AUC value of 0.603 indicate the low prediction accuracy and unreliability of the model.

Characteristics of Marine Gravity Anomaly Reference Maps and Accuracy Analysis of Gravity Matching-Aided Navigation.

PubMed

Wang, Hubiao; Wu, Lin; Chai, Hua; Xiao, Yaofei; Hsu, Houtse; Wang, Yong

2017-08-10

The variation of a marine gravity anomaly reference map is one of the important factors that affect the location accuracy of INS/Gravity integrated navigation systems in underwater navigation. In this study, based on marine gravity anomaly reference maps, new characteristic parameters of the gravity anomaly were constructed. Those characteristic values were calculated for 13 zones (105°-145° E, 0°-40° N) in the Western Pacific area, and simulation experiments of gravity matching-aided navigation were run. The influence of gravity variations on the accuracy of gravity matching-aided navigation was analyzed, and location accuracy of gravity matching in different zones was determined. Studies indicate that the new parameters may better characterize the marine gravity anomaly. Given the precision of current gravimeters and the resolution and accuracy of reference maps, the location accuracy of gravity matching in China's Western Pacific area is ~1.0-4.0 nautical miles (n miles). In particular, accuracy in regions around the South China Sea and Sulu Sea was the highest, better than 1.5 n miles. The gravity characteristic parameters identified herein and characteristic values calculated in various zones provide a reference for the selection of navigation area and planning of sailing routes under conditions requiring certain navigational accuracy.

Characteristics of Marine Gravity Anomaly Reference Maps and Accuracy Analysis of Gravity Matching-Aided Navigation

PubMed Central

Wang, Hubiao; Chai, Hua; Xiao, Yaofei; Hsu, Houtse; Wang, Yong

2017-01-01

The variation of a marine gravity anomaly reference map is one of the important factors that affect the location accuracy of INS/Gravity integrated navigation systems in underwater navigation. In this study, based on marine gravity anomaly reference maps, new characteristic parameters of the gravity anomaly were constructed. Those characteristic values were calculated for 13 zones (105°–145° E, 0°–40° N) in the Western Pacific area, and simulation experiments of gravity matching-aided navigation were run. The influence of gravity variations on the accuracy of gravity matching-aided navigation was analyzed, and location accuracy of gravity matching in different zones was determined. Studies indicate that the new parameters may better characterize the marine gravity anomaly. Given the precision of current gravimeters and the resolution and accuracy of reference maps, the location accuracy of gravity matching in China’s Western Pacific area is ~1.0–4.0 nautical miles (n miles). In particular, accuracy in regions around the South China Sea and Sulu Sea was the highest, better than 1.5 n miles. The gravity characteristic parameters identified herein and characteristic values calculated in various zones provide a reference for the selection of navigation area and planning of sailing routes under conditions requiring certain navigational accuracy. PMID:28796158

Potential of VIIRS Time Series Data for Aiding the USDA Forest Service Early Warning System for Forest Health Threats: A Gypsy Moth Defoliation Case Study

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Ryan, Robert E.; Smoot, James; Kuper, Phillip; Prados, Donald; Russell, Jeffrey; Ross, Kenton; Gasser, Gerald; Sader, Steven; McKellip, Rodney

2007-01-01

This report details one of three experiments performed during FY 2007 for the NASA RPC (Rapid Prototyping Capability) at Stennis Space Center. This RPC experiment assesses the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) and MODIS (Moderate Resolution Imaging Spectroradiometer) data for detecting and monitoring forest defoliation from the non-native Eurasian gypsy moth (Lymantria dispar). The intent of the RPC experiment was to assess the degree to which VIIRS data can provide forest disturbance monitoring information as an input to a forest threat EWS (Early Warning System) as compared to the level of information that can be obtained from MODIS data. The USDA Forest Service (USFS) plans to use MODIS products for generating broad-scaled, regional monitoring products as input to an EWS for forest health threat assessment. NASA SSC is helping the USFS to evaluate and integrate currently available satellite remote sensing technologies and data products for the EWS, including the use of MODIS products for regional monitoring of forest disturbance. Gypsy moth defoliation of the mid-Appalachian highland region was selected as a case study. Gypsy moth is one of eight major forest insect threats listed in the Healthy Forest Restoration Act (HFRA) of 2003; the gypsy moth threatens eastern U.S. hardwood forests, which are also a concern highlighted in the HFRA of 2003. This region was selected for the project because extensive gypsy moth defoliation occurred there over multiple years during the MODIS operational period. This RPC experiment is relevant to several nationally important mapping applications, including agricultural efficiency, coastal management, ecological forecasting, disaster management, and carbon management. In this experiment, MODIS data and VIIRS data simulated from MODIS were assessed for their ability to contribute broad, regional geospatial information on gypsy moth defoliation. Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) data were used to assess the quality of gypsy moth defoliation mapping products derived from MODIS data and from simulated VIIRS data. The project focused on use of data from MODIS Terra as opposed to MODIS Aqua mainly because only MODIS Terra data was collected during 2000 and 2001-years with comparatively high amounts of gypsy moth defoliation within the study area. The project assessed the quality of VIIRS data simulation products. Hyperion data was employed to assess the quality of MODIS-based VIIRS simulation datasets using image correlation analysis techniques. The ART (Application Research Toolbox) software was used for data simulation. Correlation analysis between MODIS-simulated VIIRS data and Hyperion-simulated VIIRS data for red, NIR (near-infrared), and NDVI (Normalized Difference Vegetation Index) image data products collectively indicate that useful, effective VIIRS simulations can be produced using Hyperion and MODIS data sources. The r(exp 2) for red, NIR, and NDVI products were 0.56, 0.63, and 0.62, respectively, indicating a moderately high correlation between the 2 data sources. Temporal decorrelation from different data acquisition times and image misregistration may have lowered correlation results. The RPC experiment also generated MODIS-based time series data products using the TSPT (Time Series Product Tool) software. Time series of simulated VIIRS NDVI products were produced at approximately 400-meter resolution GSD (Ground Sampling Distance) at nadir for comparison to MODIS NDVI products at either 250- or 500-meter GSD. The project also computed MODIS (MOD02) NDMI (Normalized Difference Moisture Index) products at 500-meter GSD for comparison to NDVI-based products. For each year during 2000-2006, MODIS and VIIRS (simulated from MOD02) time series were computed during the peak gypsy moth defoliation time frame in the study area (approximately June 10 through July 27). Gypsy moth defoliation mapping products from simated VIIRS and MOD02 time series were produced using multiple methods, including image classification and change detection via image differencing. The latter enabled an automated defoliation detection product computed using percent change in maximum NDVI for a peak defoliation period during 2001 compared to maximum NDVI across the entire 2000-2006 time frame. Final gypsy moth defoliation mapping products were assessed for accuracy using randomly sampled locations found on available geospatial reference data (Landsat and ASTER data in conjunction with defoliation map data from the USFS). Extensive gypsy moth defoliation patches were evident on screen displays of multitemporal color composites derived from MODIS data and from simulated VIIRS vegetation index data. Such defoliation was particularly evident for 2001, although widespread denuded forests were also seen for 2000 and 2003. These visualizations were validated using aforementioned reference data. Defoliation patches were visible on displays of MODIS-based NDVI and NDMI data. The viewing of apparent defoliation patches on all of these products necessitated adoption of a specialized temporal data processing method (e.g., maximum NDVI during the peak defoliation time frame). The frequency of cloud cover necessitated this approach. Multitemporal simulated VIIRS and MODIS Terra data both produced effective general classifications of defoliated forest versus other land cover. For 2001, the MOD02-simulated VIIRS 400-meter NDVI classification produced a similar yet slightly lower overall accuracy (87.28 percent with 0.72 Kappa) than the MOD02 250-meter NDVI classification (88.44 percent with 0.75 Kappa). The MOD13 250-meter NDVI classification had a lower overall accuracy (79.13 percent) and a much lower Kappa (0.46). The report discusses accuracy assessment results in much more detail, comparing overall classification and individual class accuracy statistics for simulated VIIRS 400-meter NDVI, MOD02 250-meter NDVI, MOD02-500 meter NDVI, MOD13 250-meter NDVI, and MOD02 500-meter NDMI classifications. Automated defoliation detection products from simulated VIIRS and MOD02 data for 2001 also yielded similar, relatively high overall classification accuracy (85.55 percent for the VIIRS 400-meter NDVI versus 87.28 percent for the MOD02 250-meter NDVI). In contrast, the USFS aerial sketch map of gypsy moth defoliation showed a lower overall classification accuracy at 73.64 percent. The overall classification Kappa values were also similar for the VIIRS (approximately 0.67 Kappa) versus the MOD02 (approximately 0.72 Kappa) automated defoliation detection product, which were much higher than the values exhibited by the USFS sketch map product (overall Kappa of approximately 0.47). The report provides additional details on the accuracy of automated gypsy moth defoliation detection products compared with USFS sketch maps. The results suggest that VIIRS data can be effectively simulated from MODIS data and that VIIRS data will produce gypsy moth defoliation mapping products that are similar to MODIS-based products. The results of the RPC experiment indicate that VIIRS and MODIS data products have good potential for integration into the forest threat EWS. The accuracy assessment was performed only for 2001 because of time constraints and a relative scarcity of cloud-free Landsat and ASTER data for the peak defoliation period of the other years in the 2000-2006 time series. Additional work should be performed to assess the accuracy of gypsy moth defoliation detection products for additional years.The study area (mid-Appalachian highlands) and application (gypsy moth forest defoliation) are not necessarily representative of all forested regions and of all forest threat disturbance agents. Additional work should be performed on other inland and coastal regions as well as for other major forest threats.

Analysis of Sampling Methodologies for Noise Pollution Assessment and the Impact on the Population.

PubMed

Rey Gozalo, Guillermo; Barrigón Morillas, Juan Miguel

2016-05-11

Today, noise pollution is an increasing environmental stressor. Noise maps are recognised as the main tool for assessing and managing environmental noise, but their accuracy largely depends on the sampling method used. The sampling methods most commonly used by different researchers (grid, legislative road types and categorisation methods) were analysed and compared using the city of Talca (Chile) as a test case. The results show that the stratification of sound values in road categories has a significantly lower prediction error and a higher capacity for discrimination and prediction than in the legislative road types used by the Ministry of Transport and Telecommunications in Chile. Also, the use of one or another method implies significant differences in the assessment of population exposure to noise pollution. Thus, the selection of a suitable method for performing noise maps through measurements is essential to achieve an accurate assessment of the impact of noise pollution on the population.

Mediterranean Land Use and Land Cover Classification Assessment Using High Spatial Resolution Data

NASA Astrophysics Data System (ADS)

Elhag, Mohamed; Boteva, Silvena

2016-10-01

Landscape fragmentation is noticeably practiced in Mediterranean regions and imposes substantial complications in several satellite image classification methods. To some extent, high spatial resolution data were able to overcome such complications. For better classification performances in Land Use Land Cover (LULC) mapping, the current research adopts different classification methods comparison for LULC mapping using Sentinel-2 satellite as a source of high spatial resolution. Both of pixel-based and an object-based classification algorithms were assessed; the pixel-based approach employs Maximum Likelihood (ML), Artificial Neural Network (ANN) algorithms, Support Vector Machine (SVM), and, the object-based classification uses the Nearest Neighbour (NN) classifier. Stratified Masking Process (SMP) that integrates a ranking process within the classes based on spectral fluctuation of the sum of the training and testing sites was implemented. An analysis of the overall and individual accuracy of the classification results of all four methods reveals that the SVM classifier was the most efficient overall by distinguishing most of the classes with the highest accuracy. NN succeeded to deal with artificial surface classes in general while agriculture area classes, and forest and semi-natural area classes were segregated successfully with SVM. Furthermore, a comparative analysis indicates that the conventional classification method yielded better accuracy results than the SMP method overall with both classifiers used, ML and SVM.

Mapping irrigated lands at 250-m scale by merging MODIS data and National Agricultural Statistics

USGS Publications Warehouse

Pervez, Md Shahriar; Brown, Jesslyn F.

2010-01-01

Accurate geospatial information on the extent of irrigated land improves our understanding of agricultural water use, local land surface processes, conservation or depletion of water resources, and components of the hydrologic budget. We have developed a method in a geospatial modeling framework that assimilates irrigation statistics with remotely sensed parameters describing vegetation growth conditions in areas with agricultural land cover to spatially identify irrigated lands at 250-m cell size across the conterminous United States for 2002. The geospatial model result, known as the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset (MIrAD-US), identified irrigated lands with reasonable accuracy in California and semiarid Great Plains states with overall accuracies of 92% and 75% and kappa statistics of 0.75 and 0.51, respectively. A quantitative accuracy assessment of MIrAD-US for the eastern region has not yet been conducted, and qualitative assessment shows that model improvements are needed for the humid eastern regions where the distinction in annual peak NDVI between irrigated and non-irrigated crops is minimal and county sizes are relatively small. This modeling approach enables consistent mapping of irrigated lands based upon USDA irrigation statistics and should lead to better understanding of spatial trends in irrigated lands across the conterminous United States. An improved version of the model with revised datasets is planned and will employ 2007 USDA irrigation statistics.

Theoretical Accuracy of Global Snow-Cover Mapping Using Satellite Data in the Earth Observing System (EOS) Era

NASA Technical Reports Server (NTRS)

Hall, D. K.; Foster, J. L.; Salomonson, V. V.; Klein, A. G.; Chien, J. Y. L.

1998-01-01

Following the launch of the Earth Observing System first morning (EOS-AM1) satellite, daily, global snow-cover mapping will be performed automatically at a spatial resolution of 500 m, cloud-cover permitting, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. A technique to calculate theoretical accuracy of the MODIS-derived snow maps is presented. Field studies demonstrate that under cloud-free conditions when snow cover is complete, snow-mapping errors are small (less than 1%) in all land covers studied except forests where errors are greater and more variable. The theoretical accuracy of MODIS snow-cover maps is largely determined by percent forest cover north of the snowline. Using the 17-class International Geosphere-Biosphere Program (IGBP) land-cover maps of North America and Eurasia, the Northern Hemisphere is classified into seven land-cover classes and water. Snow-mapping errors estimated for each of the seven land-cover classes are extrapolated to the entire Northern Hemisphere for areas north of the average continental snowline for each month. Average monthly errors for the Northern Hemisphere are expected to range from 5 - 10%, and the theoretical accuracy of the future global snow-cover maps is 92% or higher. Error estimates will be refined after the first full year that MODIS data are available.

Satellite image based methods for fuels maps updating

NASA Astrophysics Data System (ADS)

Alonso-Benito, Alfonso; Hernandez-Leal, Pedro A.; Arbelo, Manuel; Gonzalez-Calvo, Alejandro; Moreno-Ruiz, Jose A.; Garcia-Lazaro, Jose R.

2016-10-01

Regular updating of fuels maps is important for forest fire management. Nevertheless complex and time consuming field work is usually necessary for this purpose, which prevents a more frequent update. That is why the assessment of the usefulness of satellite data and the development of remote sensing techniques that enable the automatic updating of these maps, is of vital interest. In this work, we have tested the use of the spectral bands of OLI (Operational Land Imager) sensor on board Landsat 8 satellite, for updating the fuels map of El Hierro Island (Spain). From previously digitized map, a set of 200 reference plots for different fuel types was created. A 50% of the plots were randomly used as a training set and the rest were considered for validation. Six supervised and 2 unsupervised classification methods were applied, considering two levels of detail. A first level with only 5 classes (Meadow, Brushwood, Undergrowth canopy cover >50%, Undergrowth canopy cover <15%, and Xeric formations), and the second one containing 19 fuel types. The level 1 classification methods yielded an overall accuracy ranging from 44% for Parellelepided to an 84% for Maximun Likelihood. Meanwhile, level 2 results showed at best, an unacceptable overall accuracy of 34%, which prevents the use of this data for such a detailed characterization. Anyway it has been demonstrated that in some conditions, images of medium spatial resolution, like Landsat 8-OLI, could be a valid tool for an automatic upgrade of fuels maps, minimizing costs and complementing traditional methodologies.

Augmented reality and dynamic infrared thermography for perforator mapping in the anterolateral thigh.

PubMed

Cifuentes, Ignacio Javier; Dagnino, Bruno Leonardo; Salisbury, María Carolina; Perez, María Eliana; Ortega, Claudia; Maldonado, Daniela

2018-05-01

Dynamic infrared thermography (DIRT) has been used for the preoperative mapping of cutaneous perforators. This technique has shown a positive correlation with intraoperative findings. Our aim was to evaluate the accuracy of perforator mapping with DIRT and augmented reality using a portable projector. For this purpose, three volunteers had both of their anterolateral thighs assessed for the presence and location of cutaneous perforators using DIRT. The obtained image of these "hotspots" was projected back onto the thigh and the presence of Doppler signals within a 10-cm diameter from the midpoint between the lateral patella and the anterior superior iliac spine was assessed using a handheld Doppler device. Hotspots were identified in all six anterolateral thighs and were successfully projected onto the skin. The median number of perforators identified within the area of interest was 5 (range, 3-8) and the median time needed to identify them was 3.5 minutes (range, 3.3-4.0 minutes). Every hotspot was correlated to a Doppler sound signal. In conclusion, augmented reality can be a reliable method for transferring the location of perforators identified by DIRT onto the thigh, facilitating its assessment and yielding a reliable map of potential perforators for flap raising.

Mapping regional distribution of a single tree species: Whitebark pine in the Greater Yellowstone Ecosystem

USGS Publications Warehouse

Landenburger, L.; Lawrence, R.L.; Podruzny, S.; Schwartz, C.C.

2008-01-01

Moderate resolution satellite imagery traditionally has been thought to be inadequate for mapping vegetation at the species level. This has made comprehensive mapping of regional distributions of sensitive species, such as whitebark pine, either impractical or extremely time consuming. We sought to determine whether using a combination of moderate resolution satellite imagery (Landsat Enhanced Thematic Mapper Plus), extensive stand data collected by land management agencies for other purposes, and modern statistical classification techniques (boosted classification trees) could result in successful mapping of whitebark pine. Overall classification accuracies exceeded 90%, with similar individual class accuracies. Accuracies on a localized basis varied based on elevation. Accuracies also varied among administrative units, although we were not able to determine whether these differences related to inherent spatial variations or differences in the quality of available reference data.

Similarity and accuracy of mental models formed during nursing handovers: A concept mapping approach.

PubMed

Drach-Zahavy, Anat; Broyer, Chaya; Dagan, Efrat

2017-09-01

Shared mental models are crucial for constructing mutual understanding of the patient's condition during a clinical handover. Yet, scant research, if any, has empirically explored mental models of the parties involved in a clinical handover. This study aimed to examine the similarities among mental models of incoming and outgoing nurses, and to test their accuracy by comparing them with mental models of expert nurses. A cross-sectional study, exploring nurses' mental models via the concept mapping technique. 40 clinical handovers. Data were collected via concept mapping of the incoming, outgoing, and expert nurses' mental models (total of 120 concept maps). Similarity and accuracy for concepts and associations indexes were calculated to compare the different maps. About one fifth of the concepts emerged in both outgoing and incoming nurses' concept maps (concept similarity=23%±10.6). Concept accuracy indexes were 35%±18.8 for incoming and 62%±19.6 for outgoing nurses' maps. Although incoming nurses absorbed fewer number of concepts and associations (23% and 12%, respectively), they partially closed the gap (35% and 22%, respectively) relative to expert nurses' maps. The correlations between concept similarities, and incoming as well as outgoing nurses' concept accuracy, were significant (r=0.43, p<0.01; r=0.68 p<0.01, respectively). Finally, in 90% of the maps, outgoing nurses added information concerning the processes enacted during the shift, beyond the expert nurses' gold standard. Two seemingly contradicting processes in the handover were identified. "Information loss", captured by the low similarity indexes among the mental models of incoming and outgoing nurses; and "information restoration", based on accuracy measures indexes among the mental models of the incoming nurses. Based on mental model theory, we propose possible explanations for these processes and derive implications for how to improve a clinical handover. Copyright © 2017 Elsevier Ltd. All rights reserved.

High-spatial resolution multispectral and panchromatic satellite imagery for mapping perennial desert plants

NASA Astrophysics Data System (ADS)

Alsharrah, Saad A.; Bruce, David A.; Bouabid, Rachid; Somenahalli, Sekhar; Corcoran, Paul A.

2015-10-01

The use of remote sensing techniques to extract vegetation cover information for the assessment and monitoring of land degradation in arid environments has gained increased interest in recent years. However, such a task can be challenging, especially for medium-spatial resolution satellite sensors, due to soil background effects and the distribution and structure of perennial desert vegetation. In this study, we utilised Pleiades high-spatial resolution, multispectral (2m) and panchromatic (0.5m) imagery and focused on mapping small shrubs and low-lying trees using three classification techniques: 1) vegetation indices (VI) threshold analysis, 2) pre-built object-oriented image analysis (OBIA), and 3) a developed vegetation shadow model (VSM). We evaluated the success of each approach using a root of the sum of the squares (RSS) metric, which incorporated field data as control and three error metrics relating to commission, omission, and percent cover. Results showed that optimum VI performers returned good vegetation cover estimates at certain thresholds, but failed to accurately map the distribution of the desert plants. Using the pre-built IMAGINE Objective OBIA approach, we improved the vegetation distribution mapping accuracy, but this came at the cost of over classification, similar to results of lowering VI thresholds. We further introduced the VSM which takes into account shadow for further refining vegetation cover classification derived from VI. The results showed significant improvements in vegetation cover and distribution accuracy compared to the other techniques. We argue that the VSM approach using high-spatial resolution imagery provides a more accurate representation of desert landscape vegetation and should be considered in assessments of desertification.

Improved estimation of forest area in tropical Africa through ALOS/PALSAR 50-m orthorectified mosaic images

NASA Astrophysics Data System (ADS)

Dong, J.; Xiao, X.; Li, L.; Tenku, S. N.; Zhang, G.; Biradar, C. M.

2013-12-01

Tropical and moist Africa has one of the largest rainforests in the world. However, our knowledge about its forest area and spatial extent is still very limited. Forest area datasets from the Food and Agriculture Organization (FAO) Forest Resource Assessment (FRA) and the analyses of optical images (e.g., MODIS and MERIS) had a significant discrepancy, and they cannot meet the requirements to support the studies of forest carbon cycle and biodiversity, as well as the implementation of reducing emissions from deforestation and forest degradation (REDD+). The reasons for the large data discrepancy are complex and may attribute to the frequent cloud cover, coarse spatial resolution of images (MODIS, MERIS), diverse forest definition and classification approaches. In this study we generated a forest cover map in central Africa at 50-m resolution through the use of the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) 50-m orthorectified mosaic imagery in 2009. The resultant forest map was evaluated by the ground-reference data collected from the Geo-referenced Field Photo Library and Google Earth, and it has a reasonably high accuracy (producer's accuracy 83% and user's accuracy 94%). We also compared the PALSAR-based forest map with other three forest cover products (MCD12Q1 2009, GlobCover 2009 and VCF tree cover 2009) at the scales of (1) entire study domain and (2) selected sample regions. This new PALSAR-based 50-m forest cover map is likely to help reduce the uncertainty in forest area estimation, and better quantify and track deforestation, REDD+ implementation, and biodiversity conservation in central Africa.

Influence of mapping resolution on assessments of stream and streamside conditions: lessons from coastal Oregon, USA

Treesearch

Ken Vance-Borland; Kelly Burnett; Sharon Clarke

2009-01-01

1. Digital hydrographic data are commonly employed in research, planning, and monitoring for freshwater conservation, but hydrographic data sets differ in spatial resolution and accuracy of spatial representation, possibly leading to inaccurate conclusions or unsuitable policies for streams and streamside areas. 2. To examine and illustrate the potential for...

CONFIRMING THE RESULTS: AN ACCURACY ASSESSMENT OF REMOTE PRODUCTS, AN EXAMPLE COMPARING MULTIPLE MID-ATLANTIC SUB-PIXEL IMPERVIOUS SURFACE MAPS

EPA Science Inventory

Anthropogenic impervious surfaces have an important relationship with non-point source pollution (NPS) in urban watersheds. The amount of impervious surface area in a watershed is a key indicator of landscape change. As a single variable, it serves to intcgrate a number of concur...

Assessment of Classification Accuracies of SENTINEL-2 and LANDSAT-8 Data for Land Cover / Use Mapping

NASA Astrophysics Data System (ADS)

Hale Topaloğlu, Raziye; Sertel, Elif; Musaoğlu, Nebiye

2016-06-01

This study aims to compare classification accuracies of land cover/use maps created from Sentinel-2 and Landsat-8 data. Istanbul metropolitan city of Turkey, with a population of around 14 million, having different landscape characteristics was selected as study area. Water, forest, agricultural areas, grasslands, transport network, urban, airport- industrial units and barren land- mine land cover/use classes adapted from CORINE nomenclature were used as main land cover/use classes to identify. To fulfil the aims of this research, recently acquired dated 08/02/2016 Sentinel-2 and dated 22/02/2016 Landsat-8 images of Istanbul were obtained and image pre-processing steps like atmospheric and geometric correction were employed. Both Sentinel-2 and Landsat-8 images were resampled to 30m pixel size after geometric correction and similar spectral bands for both satellites were selected to create a similar base for these multi-sensor data. Maximum Likelihood (MLC) and Support Vector Machine (SVM) supervised classification methods were applied to both data sets to accurately identify eight different land cover/ use classes. Error matrix was created using same reference points for Sentinel-2 and Landsat-8 classifications. After the classification accuracy, results were compared to find out the best approach to create current land cover/use map of the region. The results of MLC and SVM classification methods were compared for both images.

A Comparative Object-Based Sugarcane Classification from Sentinel-2 Data Using Random Forests and Support Vector Machines

NASA Astrophysics Data System (ADS)

Chen, C. R.; Chen, C. F.; Nguyen, S. T.; Lau, K.; Lay, J. G.

2016-12-01

Sugarcane mostly grown in tropical and subtropical regions is one of the important commercial crops worldwide, providing significant employment, foreign exchange earnings, and other social and environmental benefits. The sugar industry is a vital component of Belize's economy as it provides employment to 15% of the country's population and 60% of the national agricultural exports. Sugarcane mapping is thus an important task due to official initiatives to provide reliable information on sugarcane-growing areas in respect to improved accuracy in monitoring sugarcane production and yield estimates. Policymakers need such monitoring information to formulate timely plans to ensure sustainably socioeconomic development. Sugarcane monitoring in Belize is traditionally carried out through time-consuming and costly field surveys. Remote sensing is an indispensable tool for crop monitoring on national, regional and global scales. The use of high and low resolution satellites for sugarcane monitoring in Belize is often restricted due to cost limitations and mixed pixel problems because sugarcane fields are small and fragmental. With the launch of Sentinel-2 satellite, it is possible to collectively map small patches of sugarcane fields over a large region as the data are free of charge and have high spectral, spatial, and temporal resolutions. This study aims to develop an object-based classification approach to comparatively map sugarcane fields in Belize from Sentinel-2 data using random forests (RF) and support vector machines (SVM). The data were processed through four main steps: (1) data pre-processing, (2) image segmentation, (3) sugarcane classification, and (4) accuracy assessment. The mapping results compared with the ground reference data indicated satisfactory results. The overall accuracies and Kappa coefficients were generally higher than 80% and 0.7, in both cases. The RF produced slightly more accurate mapping results than SVM. This study demonstrates the realization of the potential application of Sentinel-2 data for sugarcane mapping in Belize with the aid of RF and SVM methods. The methods are thus proposed for monitoring purposes in the country.

Spatial tools for managing hemlock woolly adelgid in the southern Appalachians

NASA Astrophysics Data System (ADS)

Koch, Frank Henry, Jr.

The hemlock woolly adelgid (Adelges tsugae) has recently spread into the southern Appalachians. This insect attacks both native hemlock species (Tsuga canadensis and T. caroliniana ), has no natural enemies, and can kill hemlocks within four years. Biological control displays promise for combating the pest, but counter-measures are impeded because adelgid and hemlock distribution patterns have been detailed poorly. We developed a spatial management system to better target control efforts, with two components: (1) a protocol for mapping hemlock stands, and (2) a technique to map areas at risk of imminent infestation. To construct a hemlock classifier, we used topographically normalized satellite images from Great Smoky Mountains National Park. Employing a decision tree approach that supplemented image spectral data with several environmental variables, we generated rules distinguishing hemlock areas from other forest types. We then implemented these rules in a geographic information system and generated hemlock distribution maps. Assessment yielded an overall thematic accuracy of 90% for one study area, and 75% accuracy in capturing hemlocks in a second study area. To map areas at risk, we combined first-year infestation locations from Great Smoky Mountains National Park and the Blue Ridge Parkway with points from uninfested hemlock stands, recording a suite of environmental variables for each point. We applied four different multivariate classification techniques to generate models from this sample predicting locations with high infestation risk, and used the resulting models to generate risk maps for the study region. All techniques performed well, accurately capturing 70--90% of training and validation samples, with the logistic regression model best balancing accuracy and regional applicability. Areas close to trails, roads, and streams appear to have the highest initial risk, perhaps due to bird- or human-mediated dispersal. Both components of our management system are general enough for use throughout the southern Appalachians. Overlay of derived maps will allow forest managers to reduce the area where they must focus their control efforts and thus allocate resources more efficiently.

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

USGS Publications Warehouse

Wu, Zhuoting; Thenkabail, Prasad S.; Mueller, Rick; Zakzeski, Audra; Melton, Forrest; Johnson, Lee; Rosevelt, Carolyn; Dwyer, John; Jones, Jeanine; Verdin, James P.

2014-01-01

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer’s accuracy of 93% and a user’s accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

Seasonal cultivated and fallow cropland mapping using MODIS-based automated cropland classification algorithm

NASA Astrophysics Data System (ADS)

Wu, Zhuoting; Thenkabail, Prasad S.; Mueller, Rick; Zakzeski, Audra; Melton, Forrest; Johnson, Lee; Rosevelt, Carolyn; Dwyer, John; Jones, Jeanine; Verdin, James P.

2014-01-01

Increasing drought occurrences and growing populations demand accurate, routine, and consistent cultivated and fallow cropland products to enable water and food security analysis. The overarching goal of this research was to develop and test automated cropland classification algorithm (ACCA) that provide accurate, consistent, and repeatable information on seasonal cultivated as well as seasonal fallow cropland extents and areas based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Seasonal ACCA development process involves writing series of iterative decision tree codes to separate cultivated and fallow croplands from noncroplands, aiming to accurately mirror reliable reference data sources. A pixel-by-pixel accuracy assessment when compared with the U.S. Department of Agriculture (USDA) cropland data showed, on average, a producer's accuracy of 93% and a user's accuracy of 85% across all months. Further, ACCA-derived cropland maps agreed well with the USDA Farm Service Agency crop acreage-reported data for both cultivated and fallow croplands with R-square values over 0.7 and field surveys with an accuracy of ≥95% for cultivated croplands and ≥76% for fallow croplands. Our results demonstrated the ability of ACCA to generate cropland products, such as cultivated and fallow cropland extents and areas, accurately, automatically, and repeatedly throughout the growing season.

A strategy to assess the pointing accuracy of the CERES FM1-FM5 scanners

NASA Astrophysics Data System (ADS)

Smith, Nathaniel P.; Szewczyk, Z. Peter; Hess, Phillip C.; Priestley, Kory J.

2017-09-01

The Clouds and the Earth's Radiant Energy System (CERES) scanning radiometer is designed to measure the solar radiation reflected by the Earth and thermal radiation emitted by the Earth. Five CERES instruments are currently in service; two aboard the Terra spacecraft, launched in 1999; two aboard the Aqua spacecraft, launched in 2002; and one instrument about the NPP spacecraft, launched in 2011. Verifying the pointing accuracy of the CERES instruments is required to assure that all earth viewing data is correctly geolocated. The CERES team has developed an on-orbit technique for assessing the pointing accuracy of the CERES sensors that relies on a rapid gradient change of measurements taken over a well-defined and known Earth target, such as a coastline, where a strong contrast in brightness and temperature exists. The computed coastline is then compared with World Bank II map to verify the accuracy of the measurement location. This paper briefly restates the algorithm used in the study, describes collection of coastline data, and summarizes the results of the study the CERES FM1, FM2, FM3, and FM5 instruments.

Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia.

PubMed

Hüttich, Christian; Herold, Martin; Strohbach, Ben J; Dech, Stefan

2011-05-01

Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional land-cover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of life-form composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.

Development and validation of a complementary map to enhance the existing 1998 to 2008 Abbreviated Injury Scale map

PubMed Central

2011-01-01

Introduction Many trauma registries have used the Abbreviated Injury Scale 1990 Revision Update 98 (AIS98) to classify injuries. In the current AIS version (Abbreviated Injury Scale 2005 Update 2008 - AIS08), injury classification and specificity differ substantially from AIS98, and the mapping tools provided in the AIS08 dictionary are incomplete. As a result, data from different AIS versions cannot currently be compared. The aim of this study was to develop an additional AIS98 to AIS08 mapping tool to complement the current AIS dictionary map, and then to evaluate the completed map (produced by combining these two maps) using double-coded data. The value of additional information provided by free text descriptions accompanying assigned codes was also assessed. Methods Using a modified Delphi process, a panel of expert AIS coders established plausible AIS08 equivalents for the 153 AIS98 codes which currently have no AIS08 map. A series of major trauma patients whose injuries had been double-coded in AIS98 and AIS08 was used to assess the maps; both of the AIS datasets had already been mapped to another AIS version using the AIS dictionary maps. Following application of the completed (enhanced) map with or without free text evaluation, up to six AIS codes were available for each injury. Datasets were assessed for agreement in injury severity measures, and the relative performances of the maps in accurately describing the trauma population were evaluated. Results The double-coded injuries sustained by 109 patients were used to assess the maps. For data conversion from AIS98, both the enhanced map and the enhanced map with free text description resulted in higher levels of accuracy and agreement with directly coded AIS08 data than the currently available dictionary map. Paired comparisons demonstrated significant differences between direct coding and the dictionary maps, but not with either of the enhanced maps. Conclusions The newly-developed AIS98 to AIS08 complementary map enabled transformation of the trauma population description given by AIS98 into an AIS08 estimate which was statistically indistinguishable from directly coded AIS08 data. It is recommended that the enhanced map should be adopted for dataset conversion, using free text descriptions if available. PMID:21548991

Development and validation of a complementary map to enhance the existing 1998 to 2008 Abbreviated Injury Scale map.

PubMed

Palmer, Cameron S; Franklyn, Melanie; Read-Allsopp, Christine; McLellan, Susan; Niggemeyer, Louise E

2011-05-08

Many trauma registries have used the Abbreviated Injury Scale 1990 Revision Update 98 (AIS98) to classify injuries. In the current AIS version (Abbreviated Injury Scale 2005 Update 2008 - AIS08), injury classification and specificity differ substantially from AIS98, and the mapping tools provided in the AIS08 dictionary are incomplete. As a result, data from different AIS versions cannot currently be compared. The aim of this study was to develop an additional AIS98 to AIS08 mapping tool to complement the current AIS dictionary map, and then to evaluate the completed map (produced by combining these two maps) using double-coded data. The value of additional information provided by free text descriptions accompanying assigned codes was also assessed. Using a modified Delphi process, a panel of expert AIS coders established plausible AIS08 equivalents for the 153 AIS98 codes which currently have no AIS08 map. A series of major trauma patients whose injuries had been double-coded in AIS98 and AIS08 was used to assess the maps; both of the AIS datasets had already been mapped to another AIS version using the AIS dictionary maps. Following application of the completed (enhanced) map with or without free text evaluation, up to six AIS codes were available for each injury. Datasets were assessed for agreement in injury severity measures, and the relative performances of the maps in accurately describing the trauma population were evaluated. The double-coded injuries sustained by 109 patients were used to assess the maps. For data conversion from AIS98, both the enhanced map and the enhanced map with free text description resulted in higher levels of accuracy and agreement with directly coded AIS08 data than the currently available dictionary map. Paired comparisons demonstrated significant differences between direct coding and the dictionary maps, but not with either of the enhanced maps. The newly-developed AIS98 to AIS08 complementary map enabled transformation of the trauma population description given by AIS98 into an AIS08 estimate which was statistically indistinguishable from directly coded AIS08 data. It is recommended that the enhanced map should be adopted for dataset conversion, using free text descriptions if available.

Selecting the optimum plot size for a California design-based stream and wetland mapping program.

PubMed

Lackey, Leila G; Stein, Eric D

2014-04-01

Accurate estimates of the extent and distribution of wetlands and streams are the foundation of wetland monitoring, management, restoration, and regulatory programs. Traditionally, these estimates have relied on comprehensive mapping. However, this approach is prohibitively resource-intensive over large areas, making it both impractical and statistically unreliable. Probabilistic (design-based) approaches to evaluating status and trends provide a more cost-effective alternative because, compared with comprehensive mapping, overall extent is inferred from mapping a statistically representative, randomly selected subset of the target area. In this type of design, the size of sample plots has a significant impact on program costs and on statistical precision and accuracy; however, no consensus exists on the appropriate plot size for remote monitoring of stream and wetland extent. This study utilized simulated sampling to assess the performance of four plot sizes (1, 4, 9, and 16 km(2)) for three geographic regions of California. Simulation results showed smaller plot sizes (1 and 4 km(2)) were most efficient for achieving desired levels of statistical accuracy and precision. However, larger plot sizes were more likely to contain rare and spatially limited wetland subtypes. Balancing these considerations led to selection of 4 km(2) for the California status and trends program.

Assessment of tropospheric delay mapping function models in Egypt: Using PTD database model

NASA Astrophysics Data System (ADS)

Abdelfatah, M. A.; Mousa, Ashraf E.; El-Fiky, Gamal S.

2018-06-01

For space geodetic measurements, estimates of tropospheric delays are highly correlated with site coordinates and receiver clock biases. Thus, it is important to use the most accurate models for the tropospheric delay to reduce errors in the estimates of the other parameters. Both the zenith delay value and mapping function should be assigned correctly to reduce such errors. Several mapping function models can treat the troposphere slant delay. The recent models were not evaluated for the Egyptian local climate conditions. An assessment of these models is needed to choose the most suitable one. The goal of this paper is to test the quality of global mapping function which provides high consistency with precise troposphere delay (PTD) mapping functions. The PTD model is derived from radiosonde data using ray tracing, which consider in this paper as true value. The PTD mapping functions were compared, with three recent total mapping functions model and another three separate dry and wet mapping function model. The results of the research indicate that models are very close up to zenith angle 80°. Saastamoinen and 1/cos z model are behind accuracy. Niell model is better than VMF model. The model of Black and Eisner is a good model. The results also indicate that the geometric range error has insignificant effect on slant delay and the fluctuation of azimuth anti-symmetric is about 1%.

Updating flood maps efficiently using existing hydraulic models, very-high-accuracy elevation data, and a geographic information system; a pilot study on the Nisqually River, Washington

USGS Publications Warehouse

Jones, Joseph L.; Haluska, Tana L.; Kresch, David L.

2001-01-01

A method of updating flood inundation maps at a fraction of the expense of using traditional methods was piloted in Washington State as part of the U.S. Geological Survey Urban Geologic and Hydrologic Hazards Initiative. Large savings in expense may be achieved by building upon previous Flood Insurance Studies and automating the process of flood delineation with a Geographic Information System (GIS); increases in accuracy and detail result from the use of very-high-accuracy elevation data and automated delineation; and the resulting digital data sets contain valuable ancillary information such as flood depth, as well as greatly facilitating map storage and utility. The method consists of creating stage-discharge relations from the archived output of the existing hydraulic model, using these relations to create updated flood stages for recalculated flood discharges, and using a GIS to automate the map generation process. Many of the effective flood maps were created in the late 1970?s and early 1980?s, and suffer from a number of well recognized deficiencies such as out-of-date or inaccurate estimates of discharges for selected recurrence intervals, changes in basin characteristics, and relatively low quality elevation data used for flood delineation. FEMA estimates that 45 percent of effective maps are over 10 years old (FEMA, 1997). Consequently, Congress has mandated the updating and periodic review of existing maps, which have cost the Nation almost 3 billion (1997) dollars. The need to update maps and the cost of doing so were the primary motivations for piloting a more cost-effective and efficient updating method. New technologies such as Geographic Information Systems and LIDAR (Light Detection and Ranging) elevation mapping are key to improving the efficiency of flood map updating, but they also improve the accuracy, detail, and usefulness of the resulting digital flood maps. GISs produce digital maps without manual estimation of inundated areas between cross sections, and can generate working maps across a broad range of scales, for any selected area, and overlayed with easily updated cultural features. Local governments are aggressively collecting very-high-accuracy elevation data for numerous reasons; this not only lowers the cost and increases accuracy of flood maps, but also inherently boosts the level of community involvement in the mapping process. These elevation data are also ideal for hydraulic modeling, should an existing model be judged inadequate.

Applying high resolution remote sensing image and DEM to falling boulder hazard assessment

NASA Astrophysics Data System (ADS)

Huang, Changqing; Shi, Wenzhong; Ng, K. C.

2005-10-01

Boulder fall hazard assessing generally requires gaining the boulder information. The extensive mapping and surveying fieldwork is a time-consuming, laborious and dangerous conventional method. So this paper proposes an applying image processing technology to extract boulder and assess boulder fall hazard from high resolution remote sensing image. The method can replace the conventional method and extract the boulder information in high accuracy, include boulder size, shape, height and the slope and aspect of its position. With above boulder information, it can be satisfied for assessing, prevention and cure boulder fall hazard.

Mapping wetlands in the Lower Mekong Basin for wetland resource and conservation management using Landsat ETM images and field survey data.

PubMed

MacAlister, Charlotte; Mahaxay, Manithaphone

2009-05-01

The Mekong River Basin is considered to be the second most species rich river basin in the world. The 795,000 km(2) catchment encompasses several ecoregions, incorporating biodiverse and productive wetland systems. Eighty percent of the rapidly expanding population of the Lower Mekong Basin (LMB), made up in part by Lao PDR, Thailand, Cambodia and Viet Nam, live in rural areas and are heavily reliant on wetland resources. As the populations of Cambodia and Lao PDR will double in the next 20 years, pressure on natural resources and particularly wetlands can only increase. For development planning, resource and conservation management to incorporate wetland issues, information on the distribution and character of Mekong wetlands is essential. The existing but outdated wetland maps were compiled from secondary landuse-landcover data, have limited coverage, poor thematic accuracy and no meta-data. Therefore the Mekong River Commission (MRC) undertook to produce new wetland coverage for the LMB. As resources, funding and regional capacity are limited, it was determined that the method applied should use existing facilities, be easily adaptable, and replicable locally. For the product to be useful it must be accepted by local governments and decision makers. The results must be of acceptable accuracy (>75%) and the methodology should be relatively understandable to non-experts. In the first stage of this exercise, field survey was conducted at five pilot sites covering a range of typical wetland habitats (MRC wetland classification) to supply data for a supervised classification of Landsat ETM images from the existing MRC archive. Images were analysed using ERDAS IMAGINE and applying Maximum Likelihood Classification. Field data were reserved to apply formal accuracy assessment to the final wetland habitat maps, with resulting accuracy ranging from 77 to 94%. The maps produced are now in use at a Provincial and National level in three countries for resource and conservation planning and management applications, including designation of a Ramsar wetland site of international importance.

Mapping land use changes in the carboniferous region of Santa Catarina, report 2

NASA Technical Reports Server (NTRS)

Valeriano, D. D. (Principal Investigator); Bitencourtpereira, M. D.

1983-01-01

The techniques applied to MSS-LANDSAT data in the land-use mapping of Criciuma region (Santa Catarina state, Brazil) are presented along with the results of a classification accuracy estimate tested on the resulting map. The MSS-LANDSAT data digital processing involves noise suppression, features selection and a hybrid classifier. The accuracy test is made through comparisons with aerial photographs of sampled points. The utilization of digital processing to map the classes agricultural lands, forest lands and urban areas is recommended, while the coal refuse areas should be mapped visually.

Block Adjustment and Image Matching of WORLDVIEW-3 Stereo Pairs and Accuracy Evaluation

NASA Astrophysics Data System (ADS)

Zuo, C.; Xiao, X.; Hou, Q.; Li, B.

2018-05-01

WorldView-3, as a high-resolution commercial earth observation satellite, which is launched by Digital Global, provides panchromatic imagery of 0.31 m resolution. The positioning accuracy is less than 3.5 meter CE90 without ground control, which can use for large scale topographic mapping. This paper presented the block adjustment for WorldView-3 based on RPC model and achieved the accuracy of 1 : 2000 scale topographic mapping with few control points. On the base of stereo orientation result, this paper applied two kinds of image matching algorithm for DSM extraction: LQM and SGM. Finally, this paper compared the accuracy of the point cloud generated by the two image matching methods with the reference data which was acquired by an airborne laser scanner. The results showed that the RPC adjustment model of WorldView-3 image with small number of GCPs could satisfy the requirement of Chinese Surveying and Mapping regulations for 1 : 2000 scale topographic maps. And the point cloud result obtained through WorldView-3 stereo image matching had higher elevation accuracy, the RMS error of elevation for bare ground area is 0.45 m, while for buildings the accuracy can almost reach 1 meter.

Three-dimensional T1 and T2* mapping of human lung parenchyma using interleaved saturation recovery with dual echo ultrashort echo time imaging (ITSR-DUTE).

PubMed

Gai, Neville D; Malayeri, Ashkan A; Bluemke, David A

2017-04-01

To develop and assess a new technique for three-dimensional (3D) full lung T1 and T2* mapping using a single free breathing scan during a clinically feasible time. A 3D stack of dual-echo ultrashort echo time (UTE) radial acquisition interleaved with and without a WET (water suppression enhanced through T1 effects) saturation pulse was used to map T1 and T2* simultaneously in a single scan. Correction for modulation due to multiple views per segment was derived. Bloch simulations were performed to study saturation pulse excitation profile on lung tissue. Optimization of the saturation delay time (for T1 mapping) and echo time (for T2* mapping) was performed. Monte Carlo simulation was done to predict accuracy and precision of the sequence with signal-to-noise ratio of in vivo images used in the simulation. A phantom study was carried out using the 3D interleaved saturation recovery with dual echo ultrashort echo time imaging (ITSR-DUTE) sequence and reference standard inversion recovery spin echo sequence (IR-SE) to compare accuracy of the sequence. Nine healthy volunteers were imaged and mean (SD) of T1 and T2* in lung parenchyma at 3T were estimated through manually assisted segmentation. 3D lung coverage with a resolution of 2.5 × 2.5 × 6 mm 3 was performed and nominal scan time was recorded for the scans. Repeatability was assessed in three of the volunteers. Regional differences in T1/T2* values were also assessed. The phantom study showed accuracy of T1 values to be within 2.3% of values obtained from IR-SE. Mean T1 value in lung parenchyma was 1002 ± 82 ms while T2* was 0.85 ± 0.1 ms. Scan time was ∼10 min for volunteer scans. Mean coefficient of variation (CV) across slices was 0.057 and 0.09, respectively. Regional variation along the gravitational direction and between right and left lung were not significant (P = 0.25 and P = 0.06, respectively) for T1. T2* showed significant variation (P = 0.03) along the gravitational direction. Repeatability for three volunteers was within 0.7% for T1 and 1.9% for T2*. 3D T1 and T2* maps of the entire lung can be obtained in a single scan of ∼10 min with a resolution of 2.5 × 2.5 × 6 mm 3 . 2 J. Magn. Reson. Imaging 2017;45:1097-1104. 2016 International Society for Magnetic Resonance in Medicine.

GIM-TEC adaptive ionospheric weather assessment and forecast system

NASA Astrophysics Data System (ADS)

Gulyaeva, T. L.; Arikan, F.; Hernandez-Pajares, M.; Stanislawska, I.

2013-09-01

The Ionospheric Weather Assessment and Forecast (IWAF) system is a computer software package designed to assess and predict the world-wide representation of 3-D electron density profiles from the Global Ionospheric Maps of Total Electron Content (GIM-TEC). The unique system products include daily-hourly numerical global maps of the F2 layer critical frequency (foF2) and the peak height (hmF2) generated with the International Reference Ionosphere extended to the plasmasphere, IRI-Plas, upgraded by importing the daily-hourly GIM-TEC as a new model driving parameter. Since GIM-TEC maps are provided with 1- or 2-days latency, the global maps forecast for 1 day and 2 days ahead are derived using an harmonic analysis applied to the temporal changes of TEC, foF2 and hmF2 at 5112 grid points of a map encapsulated in IONEX format (-87.5°:2.5°:87.5°N in latitude, -180°:5°:180°E in longitude). The system provides online the ionospheric disturbance warnings in the global W-index map establishing categories of the ionospheric weather from the quiet state (W=±1) to intense storm (W=±4) according to the thresholds set for instant TEC perturbations regarding quiet reference median for the preceding 7 days. The accuracy of IWAF system predictions of TEC, foF2 and hmF2 maps is superior to the standard persistence model with prediction equal to the most recent ‘true’ map. The paper presents outcomes of the new service expressed by the global ionospheric foF2, hmF2 and W-index maps demonstrating the process of origin and propagation of positive and negative ionosphere disturbances in space and time and their forecast under different scenarios.

Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome

PubMed Central

Guitet, Stéphane; Hérault, Bruno; Molto, Quentin; Brunaux, Olivier; Couteron, Pierre

2015-01-01

Precise mapping of above-ground biomass (AGB) is a major challenge for the success of REDD+ processes in tropical rainforest. The usual mapping methods are based on two hypotheses: a large and long-ranged spatial autocorrelation and a strong environment influence at the regional scale. However, there are no studies of the spatial structure of AGB at the landscapes scale to support these assumptions. We studied spatial variation in AGB at various scales using two large forest inventories conducted in French Guiana. The dataset comprised 2507 plots (0.4 to 0.5 ha) of undisturbed rainforest distributed over the whole region. After checking the uncertainties of estimates obtained from these data, we used half of the dataset to develop explicit predictive models including spatial and environmental effects and tested the accuracy of the resulting maps according to their resolution using the rest of the data. Forest inventories provided accurate AGB estimates at the plot scale, for a mean of 325 Mg.ha-1. They revealed high local variability combined with a weak autocorrelation up to distances of no more than10 km. Environmental variables accounted for a minor part of spatial variation. Accuracy of the best model including spatial effects was 90 Mg.ha-1 at plot scale but coarse graining up to 2-km resolution allowed mapping AGB with accuracy lower than 50 Mg.ha-1. Whatever the resolution, no agreement was found with available pan-tropical reference maps at all resolutions. We concluded that the combined weak autocorrelation and weak environmental effect limit AGB maps accuracy in rainforest, and that a trade-off has to be found between spatial resolution and effective accuracy until adequate “wall-to-wall” remote sensing signals provide reliable AGB predictions. Waiting for this, using large forest inventories with low sampling rate (<0.5%) may be an efficient way to increase the global coverage of AGB maps with acceptable accuracy at kilometric resolution. PMID:26402522

Smartphone-based noise mapping: Integrating sound level meter app data into the strategic noise mapping process.

PubMed

Murphy, Enda; King, Eoin A

2016-08-15

The strategic noise mapping process of the EU has now been ongoing for more than ten years. However, despite the fact that a significant volume of research has been conducted on the process and related issues there has been little change or innovation in how relevant authorities and policymakers are conducting the process since its inception. This paper reports on research undertaken to assess the possibility for smartphone-based noise mapping data to be integrated into the traditional strategic noise mapping process. We compare maps generated using the traditional approach with those generated using smartphone-based measurement data. The advantage of the latter approach is that it has the potential to remove the need for exhaustive input data into the source calculation model for noise prediction. In addition, the study also tests the accuracy of smartphone-based measurements against simultaneous measurements taken using traditional sound level meters in the field. Copyright © 2016 Elsevier B.V. All rights reserved.

Mapping of Coral Reef Environment in the Arabian Gulf Using Multispectral Remote Sensing

NASA Astrophysics Data System (ADS)

Ben-Romdhane, H.; Marpu, P. R.; Ghedira, H.; Ouarda, T. B. M. J.

2016-06-01

Coral reefs of the Arabian Gulf are subject to several pressures, thus requiring conservation actions. Well-designed conservation plans involve efficient mapping and monitoring systems. Satellite remote sensing is a cost-effective tool for seafloor mapping at large scales. Multispectral remote sensing of coastal habitats, like those of the Arabian Gulf, presents a special challenge due to their complexity and heterogeneity. The present study evaluates the potential of multispectral sensor DubaiSat-2 in mapping benthic communities of United Arab Emirates. We propose to use a spectral-spatial method that includes multilevel segmentation, nonlinear feature analysis and ensemble learning methods. Support Vector Machine (SVM) is used for comparison of classification performances. Comparative data were derived from the habitat maps published by the Environment Agency-Abu Dhabi. The spectral-spatial method produced 96.41% mapping accuracy. SVM classification is assessed to be 94.17% accurate. The adaptation of these methods can help achieving well-designed coastal management plans in the region.

Evaluating ASTER satellite imagery and gradient modeling for mapping and characterizing wildland fire fuels

Treesearch

Michael J. Falkowski; Paul Gessler; Penelope Morgan; Alistair M. S. Smith; Andrew T. Hudak

2004-01-01

Land managers need cost-effective methods for mapping and characterizing fire fuels quickly and accurately. The advent of sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection...

Evaluating the ASTER sensor for mapping and characterizing forest fire fuels in northern Idaho

Treesearch

Michael J. Falkowski; Paul Gessler; Penelope Morgan; Alistair M. S. Smith; Andrew T. Hudak

2004-01-01

Land managers need cost-effective methods for mapping and characterizing fire fuels quickly and accurately. The advent of sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection...

Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling

Treesearch

Michael J. Falkowski; Paul E. Gessler; Penelope Morgan; Andrew T. Hudak; Alistair M. S. Smith

2005-01-01

Land managers need cost-effective methods for mapping and characterizing forest fuels quickly and accurately. The launch of satellite sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the advanced spaceborne thermal emission and...

Automatic photointerpretation for land use management in Minnesota

NASA Technical Reports Server (NTRS)

Swanlund, G. D. (Principal Investigator); Kirvida, L.; Cheung, M.; Pile, D.; Zirkle, R.

1974-01-01

The author has identified the following significant results. Automatic photointerpretation techniques were utilized to evaluate the feasibility of data for land use management. It was shown that ERTS-1 MSS data can produce thematic maps of adequate resolution and accuracy to update land use maps. In particular, five typical land use areas were mapped with classification accuracies ranging from 77% to over 90%.

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

DOE PAGES

Preciat Gonzalez, German A.; El Assal, Lemmer R. P.; Noronha, Alberto; ...

2017-06-14

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, manymore » algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D

DOE Office of Scientific and Technical Information (OSTI.GOV)

Preciat Gonzalez, German A.; El Assal, Lemmer R. P.; Noronha, Alberto

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, manymore » algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.« less

Comparative evaluation of atom mapping algorithms for balanced metabolic reactions: application to Recon 3D.

PubMed

Preciat Gonzalez, German A; El Assal, Lemmer R P; Noronha, Alberto; Thiele, Ines; Haraldsdóttir, Hulda S; Fleming, Ronan M T

2017-06-14

The mechanism of each chemical reaction in a metabolic network can be represented as a set of atom mappings, each of which relates an atom in a substrate metabolite to an atom of the same element in a product metabolite. Genome-scale metabolic network reconstructions typically represent biochemistry at the level of reaction stoichiometry. However, a more detailed representation at the underlying level of atom mappings opens the possibility for a broader range of biological, biomedical and biotechnological applications than with stoichiometry alone. Complete manual acquisition of atom mapping data for a genome-scale metabolic network is a laborious process. However, many algorithms exist to predict atom mappings. How do their predictions compare to each other and to manually curated atom mappings? For more than four thousand metabolic reactions in the latest human metabolic reconstruction, Recon 3D, we compared the atom mappings predicted by six atom mapping algorithms. We also compared these predictions to those obtained by manual curation of atom mappings for over five hundred reactions distributed among all top level Enzyme Commission number classes. Five of the evaluated algorithms had similarly high prediction accuracy of over 91% when compared to manually curated atom mapped reactions. On average, the accuracy of the prediction was highest for reactions catalysed by oxidoreductases and lowest for reactions catalysed by ligases. In addition to prediction accuracy, the algorithms were evaluated on their accessibility, their advanced features, such as the ability to identify equivalent atoms, and their ability to map hydrogen atoms. In addition to prediction accuracy, we found that software accessibility and advanced features were fundamental to the selection of an atom mapping algorithm in practice.

Inventory and analysis of rangeland resources of the state land block on Parker Mountain, Utah

NASA Technical Reports Server (NTRS)

Jaynes, R. A. (Principal Investigator)

1983-01-01

High altitude color infrared (CIR) photography was interpreted to provide an 1:24,000 overlay to U.S.G.S. topographic maps. The inventory and analysis of rangeland resources was augmented by the digital analysis of LANDSAT MSS data. Available geology, soils, and precipitation maps were used to sort out areas of confusion on the CIR photography. The map overlay from photo interpretation was also prepared with reference to print maps developed from LANDSAT MSS data. The resulting map overlay has a high degree of interpretive and spatial accuracy. An unacceptable level of confusion between the several sagebrush types in the MSS mapping was largely corrected by introducing ancillary data. Boundaries from geology, soils, and precipitation maps, as well as field observations, were digitized and pixel classes were adjusted according to the location of pixels with particular spectral signatures with respect to such boundaries. The resulting map, with six major cover classes, has an overall accuracy of 89%. Overall accuracy was 74% when these six classes were expanded to 20 classes.

Delineating Beach and Dune Morphology from Massive Terrestrial Laser Scanning Data Using the Generic Mapping Tools

NASA Astrophysics Data System (ADS)

Zhou, X.; Wang, G.; Yan, B.; Kearns, T.

2016-12-01

Terrestrial laser scanning (TLS) techniques have been proven to be efficient tools to collect three-dimensional high-density and high-accuracy point clouds for coastal research and resource management. However, the processing and presenting of massive TLS data is always a challenge for research when targeting a large area with high-resolution. This article introduces a workflow using shell-scripting techniques to chain together tools from the Generic Mapping Tools (GMT), Geographic Resources Analysis Support System (GRASS), and other command-based open-source utilities for automating TLS data processing. TLS point clouds acquired in the beach and dune area near Freeport, Texas in May 2015 were used for the case study. Shell scripts for rotating the coordinate system, removing anomalous points, assessing data quality, generating high-accuracy bare-earth DEMs, and quantifying beach and sand dune features (shoreline, cross-dune section, dune ridge, toe, and volume) are presented in this article. According to this investigation, the accuracy of the laser measurements (distance from the scanner to the targets) is within a couple of centimeters. However, the positional accuracy of TLS points with respect to a global coordinate system is about 5 cm, which is dominated by the accuracy of GPS solutions for obtaining the positions of the scanner and reflector. The accuracy of TLS-derived bare-earth DEM is primarily determined by the size of grid cells and roughness of the terrain surface for the case study. A DEM with grid cells of 4m x 1m (shoreline by cross-shore) provides a suitable spatial resolution and accuracy for deriving major beach and dune features.

Production of high-resolution forest-ecosite maps based on model predictions of soil moisture and nutrient regimes over a large forested area.

PubMed

Yang, Qi; Meng, Fan-Rui; Bourque, Charles P-A; Zhao, Zhengyong

2017-09-08

Forest ecosite reflects the local site conditions that are meaningful to forest productivity as well as basic ecological functions. Field assessments of vegetation and soil types are often used to identify forest ecosites. However, the production of high-resolution ecosite maps for large areas from interpolating field data is difficult because of high spatial variation and associated costs and time requirements. Indices of soil moisture and nutrient regimes (i.e., SMR and SNR) introduced in this study reflect the combined effects of biogeochemical and topographic factors on forest growth. The objective of this research is to present a method for creating high-resolution forest ecosite maps based on computer-generated predictions of SMR and SNR for an area in Atlantic Canada covering about 4.3 × 10 6 hectares (ha) of forestland. Field data from 1,507 forest ecosystem classification plots were used to assess the accuracy of the ecosite maps produced. Using model predictions of SMR and SNR alone, ecosite maps were 61 and 59% correct in identifying 10 Acadian- and Maritime-Boreal-region ecosite types, respectively. This method provides an operational framework for the production of high-resolution maps of forest ecosites over large areas without the need for data from expensive, supplementary field surveys.

TAxonomy of Self-reported Sedentary behaviour Tools (TASST) framework for development, comparison and evaluation of self-report tools: content analysis and systematic review

PubMed Central

Dall, PM; Coulter, EH; Fitzsimons, CF; Skelton, DA; Chastin, SFM

2017-01-01

Objective Sedentary behaviour (SB) has distinct deleterious health outcomes, yet there is no consensus on best practice for measurement. This study aimed to identify the optimal self-report tool for population surveillance of SB, using a systematic framework. Design A framework, TAxonomy of Self-reported Sedentary behaviour Tools (TASST), consisting of four domains (type of assessment, recall period, temporal unit and assessment period), was developed based on a systematic inventory of existing tools. The inventory was achieved through a systematic review of studies reporting SB and tracing back to the original description. A systematic review of the accuracy and sensitivity to change of these tools was then mapped against TASST domains. Data sources Systematic searches were conducted via EBSCO, reference lists and expert opinion. Eligibility criteria for selecting studies The inventory included tools measuring SB in adults that could be self-completed at one sitting, and excluded tools measuring SB in specific populations or contexts. The systematic review included studies reporting on the accuracy against an objective measure of SB and/or sensitivity to change of a tool in the inventory. Results The systematic review initially identified 32 distinct tools (141 questions), which were used to develop the TASST framework. Twenty-two studies evaluated accuracy and/or sensitivity to change representing only eight taxa. Assessing SB as a sum of behaviours and using a previous day recall were the most promising features of existing tools. Accuracy was poor for all existing tools, with underestimation and overestimation of SB. There was a lack of evidence about sensitivity to change. Conclusions Despite the limited evidence, mapping existing SB tools onto the TASST framework has enabled informed recommendations to be made about the most promising features for a surveillance tool, identified aspects on which future research and development of SB surveillance tools should focus. Trial registration number International prospective register of systematic reviews (PROPSPERO)/CRD42014009851. PMID:28391233

Combining Human and Machine Learning to Map Cropland in the 21st Century's Major Agricultural Frontier

NASA Astrophysics Data System (ADS)

Estes, L. D.; Debats, S. R.; Caylor, K. K.; Evans, T. P.; Gower, D.; McRitchie, D.; Searchinger, T.; Thompson, D. R.; Wood, E. F.; Zeng, L.

2016-12-01

In the coming decades, large areas of new cropland will be created to meet the world's rapidly growing food demands. Much of this new cropland will be in sub-Saharan Africa, where food needs will increase most and the area of remaining potential farmland is greatest. If we are to understand the impacts of global change, it is critical to accurately identify Africa's existing croplands and how they are changing. Yet the continent's smallholder-dominated agricultural systems are unusually challenging for remote sensing analyses, making accurate area estimates difficult to obtain, let alone important details related to field size and geometry. Fortunately, the rapidly growing archives of moderate to high-resolution satellite imagery hosted on open servers now offer an unprecedented opportunity to improve landcover maps. We present a system that integrates two critical components needed to capitalize on this opportunity: 1) human image interpretation and 2) machine learning (ML). Human judgment is needed to accurately delineate training sites within noisy imagery and a highly variable cover type, while ML provides the ability to scale and to interpret large feature spaces that defy human comprehension. Because large amounts of training data are needed (a major impediment for analysts), we use a crowdsourcing platform that connects amazon.com's Mechanical Turk service to satellite imagery hosted on open image servers. Workers map visible fields at pre-assigned sites, and are paid according to their mapping accuracy. Initial tests show overall high map accuracy and mapping rates >1800 km2/hour. The ML classifier uses random forests and randomized quasi-exhaustive feature selection, and is highly effective in classifying diverse agricultural types in southern Africa (AUC > 0.9). We connect the ML and crowdsourcing components to make an interactive learning framework. The ML algorithm performs an initial classification using a first batch of crowd-sourced maps, using thresholds of posterior probabilities to segregate sub-images classified with high or low confidence. Workers are then directed to collect new training data in low confidence sub-images, after which classification is repeated and re-assessed, and the entire process iterated until maximum possible accuracy is realized.

Effects of bathymetric lidar errors on flow properties predicted with a multi-dimensional hydraulic model

Treesearch

J. McKean; D. Tonina; C. Bohn; C. W. Wright

2014-01-01

New remote sensing technologies and improved computer performance now allow numerical flow modeling over large stream domains. However, there has been limited testing of whether channel topography can be remotely mapped with accuracy necessary for such modeling. We assessed the ability of the Experimental Advanced Airborne Research Lidar, to support a multi-dimensional...

Role of interoceptive accuracy in topographical changes in emotion-induced bodily sensations

PubMed Central

Jung, Won-Mo; Ryu, Yeonhee; Lee, Ye-Seul; Wallraven, Christian; Chae, Younbyoung

2017-01-01

The emotion-associated bodily sensation map is composed of a specific topographical distribution of bodily sensations to categorical emotions. The present study investigated whether or not interoceptive accuracy was associated with topographical changes in this map following emotion-induced bodily sensations. This study included 31 participants who observed short video clips containing emotional stimuli and then reported their sensations on the body map. Interoceptive accuracy was evaluated with a heartbeat detection task and the spatial patterns of bodily sensations to specific emotions, including anger, fear, disgust, happiness, sadness, and neutral, were visualized using Statistical Parametric Mapping (SPM) analyses. Distinct patterns of bodily sensations were identified for different emotional states. In addition, positive correlations were found between the magnitude of sensation in emotion-specific regions and interoceptive accuracy across individuals. A greater degree of interoceptive accuracy was associated with more specific topographical changes after emotional stimuli. These results suggest that the awareness of one’s internal bodily states might play a crucial role as a required messenger of sensory information during the affective process. PMID:28877218

Performance Evaluation of Dsm Extraction from ZY-3 Three-Line Arrays Imagery

NASA Astrophysics Data System (ADS)

Xue, Y.; Xie, W.; Du, Q.; Sang, H.

2015-08-01

ZiYuan-3 (ZY-3), launched in January 09, 2012, is China's first civilian high-resolution stereo mapping satellite. ZY-3 is equipped with three-line scanners (nadir, backward and forward) for stereo mapping, the resolutions of the panchromatic (PAN) stereo mapping images are 2.1-m at nadir looking and 3.6-m at tilt angles of ±22° forward and backward looking, respectively. The stereo base-height ratio is 0.85-0.95. Compared with stereo mapping from two views images, three-line arrays images of ZY-3 can be used for DSM generation taking advantage of one more view than conventional photogrammetric methods. It would enrich the information for image matching and enhance the accuracy of DSM generated. The primary result of positioning accuracy of ZY-3 images has been reported, while before the massive mapping applications of utilizing ZY-3 images for DSM generation, the performance evaluation of DSM extraction from three-line arrays imagery of ZY-3 has significant meaning for the routine mapping applications. The goal of this research is to clarify the mapping performance of ZY-3 three-line arrays scanners on china's first civilian high-resolution stereo mapping satellite of ZY-3 through the accuracy evaluation of DSM generation. The comparison of DSM product in different topographic areas generated with three views images with different two views combination images of ZY-3 would be presented. Besides the comparison within different topographic study area, the accuracy deviation of the DSM products with different grid size including 25-m, 10-m and 5-m is delineated in order to clarify the impact of grid size on accuracy evaluation.

Accuracy of mapping the Earth's gravity field fine structure with a spaceborne gravity gradiometer mission

NASA Technical Reports Server (NTRS)

Kahn, W. D.

1984-01-01

The spaceborne gravity gradiometer is a potential sensor for mapping the fine structure of the Earth's gravity field. Error analyses were performed to investigate the accuracy of the determination of the Earth's gravity field from a gravity field satellite mission. The orbital height of the spacecraft is the dominating parameter as far as gravity field resolution and accuracies are concerned.

Evaluation of Techniques Used to Estimate Cortical Feature Maps

PubMed Central

Katta, Nalin; Chen, Thomas L.; Watkins, Paul V.; Barbour, Dennis L.

2011-01-01

Functional properties of neurons are often distributed nonrandomly within a cortical area and form topographic maps that reveal insights into neuronal organization and interconnection. Some functional maps, such as in visual cortex, are fairly straightforward to discern with a variety of techniques, while other maps, such as in auditory cortex, have resisted easy characterization. In order to determine appropriate protocols for establishing accurate functional maps in auditory cortex, artificial topographic maps were probed under various conditions, and the accuracy of estimates formed from the actual maps was quantified. Under these conditions, low-complexity maps such as sound frequency can be estimated accurately with as few as 25 total samples (e.g., electrode penetrations or imaging pixels) if neural responses are averaged together. More samples are required to achieve the highest estimation accuracy for higher complexity maps, and averaging improves map estimate accuracy even more than increasing sampling density. Undersampling without averaging can result in misleading map estimates, while undersampling with averaging can lead to the false conclusion of no map when one actually exists. Uniform sample spacing only slightly improves map estimation over nonuniform sample spacing typical of serial electrode penetrations. Tessellation plots commonly used to visualize maps estimated using nonuniform sampling are always inferior to linearly interpolated estimates, although differences are slight at higher sampling densities. Within primary auditory cortex, then, multiunit sampling with at least 100 samples would likely result in reasonable feature map estimates for all but the highest complexity maps and the highest variability that might be expected. PMID:21889537

Synergy of airborne LiDAR and Worldview-2 satellite imagery for land cover and habitat mapping: A BIO_SOS-EODHaM case study for the Netherlands

NASA Astrophysics Data System (ADS)

Mücher, C. A.; Roupioz, L.; Kramer, H.; Bogers, M. M. B.; Jongman, R. H. G.; Lucas, R. M.; Kosmidou, V. E.; Petrou, Z.; Manakos, I.; Padoa-Schioppa, E.; Adamo, M.; Blonda, P.

2015-05-01

A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classification results for a Dutch case study. The EODHaM system was developed within the BIO_SOS (The BIOdiversity multi-SOurce monitoring System: from Space TO Species) project and contains the decision rules for each land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall accuracy was lower (69.0%) for the habitat map based on the General Habitat Category (GHC) system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, in preparation for new habitat classifications.

Application of Satellite Data for Early Season Assessment of Fallowed Agricultural Lands for Drought Impact Reporting

NASA Astrophysics Data System (ADS)

Rosevelt, C.; Melton, F. S.; Johnson, L.; Verdin, J. P.; Thenkabail, P. S.; mueller, R.; Zakzeski, A.; Jones, J.

2013-12-01

Rapid assessment of drought impacts can aid water managers in assessing mitigation options, and guide decision making with respect to requests for local water transfers, county drought disaster designations, or state emergency proclamations. Satellite remote sensing offers an efficient way to provide quantitative assessments of drought impacts on agricultural production and land fallowing associated with reductions in water supply. A key advantage of satellite-based assessments is that they can provide a measure of land fallowing that is consistent across both space and time. Here we describe an approach for monthly mapping of land fallowing developed as part of a joint effort by USGS, USDA, and NASA to provide timely assessments of land fallowing during drought events. This effort has used the Central Valley of California as a pilot region for development and testing of an operational approach. To provide quantitative measures of fallowed land from satellite data early in the season, we developed a decision tree algorithm and applied it to timeseries of normalized difference vegetation index (NDVI) data from Landsat TM, ETM+, and MODIS. Our effort has been focused on development of leading indicators of drought impacts in the March - June timeframe based on measures of crop development patterns relative to a reference period with average or above average rainfall. This capability complements ongoing work by USDA to produce and publicly release within-season estimates of fallowed acreage from the USDA Cropland Data Layer. To assess the accuracy of the algorithms, monthly ground validation surveys were conducted along transects across the Central Valley at more than 200 fields per month from March - June, 2013. Here we present the algorithm for mapping fallowed acreage early in the season along with results from the accuracy assessment, and discuss potential applications to other regions.

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

NASA Astrophysics Data System (ADS)

Yilmaz, Işık

2009-06-01

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). Digital elevation model (DEM) was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index (TWI) and stream power index (SPI) were used in the landslide susceptibility analyses. Landslide susceptibility maps were produced from the frequency ratio, logistic regression and neural networks models, and they were then compared by means of their validations. The higher accuracies of the susceptibility maps for all three models were obtained from the comparison of the landslide susceptibility maps with the known landslide locations. However, respective area under curve (AUC) values of 0.826, 0.842 and 0.852 for frequency ratio, logistic regression and artificial neural networks showed that the map obtained from ANN model is more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results obtained in this study also showed that the frequency ratio model can be used as a simple tool in assessment of landslide susceptibility when a sufficient number of data were obtained. Input process, calculations and output process are very simple and can be readily understood in the frequency ratio model, however logistic regression and neural networks require the conversion of data to ASCII or other formats. Moreover, it is also very hard to process the large amount of data in the statistical package.

A risk management model for familial breast cancer: A new application using Fuzzy Cognitive Map method.

PubMed

Papageorgiou, Elpiniki I; Jayashree Subramanian; Karmegam, Akila; Papandrianos, Nikolaos

2015-11-01

Breast cancer is the most deadly disease affecting women and thus it is natural for women aged 40-49 years (who have a family history of breast cancer or other related cancers) to assess their personal risk for developing familial breast cancer (FBC). Besides, as each individual woman possesses different levels of risk of developing breast cancer depending on their family history, genetic predispositions and personal medical history, individualized care setting mechanism needs to be identified so that appropriate risk assessment, counseling, screening, and prevention options can be determined by the health care professionals. The presented work aims at developing a soft computing based medical decision support system using Fuzzy Cognitive Map (FCM) that assists health care professionals in deciding the individualized care setting mechanisms based on the FBC risk level of the given women. The FCM based FBC risk management system uses NHL to learn causal weights from 40 patient records and achieves a 95% diagnostic accuracy. The results obtained from the proposed model are in concurrence with the comprehensive risk evaluation tool based on Tyrer-Cuzick model for 38/40 patient cases (95%). Besides, the proposed model identifies high risk women by calculating higher accuracy of prediction than the standard Gail and NSAPB models. The testing accuracy of the proposed model using 10-fold cross validation technique outperforms other standard machine learning based inference engines as well as previous FCM-based risk prediction methods for BC. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Conflation and aggregation of spatial data improve predictive models for species with limited habitats: a case of the threatened yellow-billed cuckoo in Arizona, USA

USGS Publications Warehouse

Villarreal, Miguel L.; van Riper, Charles; Petrakis, Roy E.

2013-01-01

Riparian vegetation provides important wildlife habitat in the Southwestern United States, but limited distributions and spatial complexity often leads to inaccurate representation in maps used to guide conservation. We test the use of data conflation and aggregation on multiple vegetation/land-cover maps to improve the accuracy of habitat models for the threatened western yellow-billed cuckoo (Coccyzus americanus occidentalis). We used species observations (n = 479) from a state-wide survey to develop habitat models from 1) three vegetation/land-cover maps produced at different geographic scales ranging from state to national, and 2) new aggregate maps defined by the spatial agreement of cover types, which were defined as high (agreement = all data sets), moderate (agreement ≥ 2), and low (no agreement required). Model accuracies, predicted habitat locations, and total area of predicted habitat varied considerably, illustrating the effects of input data quality on habitat predictions and resulting potential impacts on conservation planning. Habitat models based on aggregated and conflated data were more accurate and had higher model sensitivity than original vegetation/land-cover, but this accuracy came at the cost of reduced geographic extent of predicted habitat. Using the highest performing models, we assessed cuckoo habitat preference and distribution in Arizona and found that major watersheds containing high-probably habitat are fragmented by a wide swath of low-probability habitat. Focus on riparian restoration in these areas could provide more breeding habitat for the threatened cuckoo, offset potential future habitat losses in adjacent watershed, and increase regional connectivity for other threatened vertebrates that also use riparian corridors.

Evaluating the potential for remote bathymetric mapping of a turbid, sand-bed river: 2. Application to hyperspectral image data from the Platte River

USGS Publications Warehouse

Legleiter, C.J.; Kinzel, P.J.; Overstreet, B.T.

2011-01-01

This study examined the possibility of mapping depth from optical image data in turbid, sediment-laden channels. Analysis of hyperspectral images from the Platte River indicated that depth retrieval in these environments is feasible, but might not be highly accurate. Four methods of calibrating image-derived depth estimates were evaluated. The first involved extracting image spectra at survey point locations throughout the reach. These paired observations of depth and reflectance were subjected to optimal band ratio analysis (OBRA) to relate (R2 = 0.596) a spectrally based quantity to flow depth. Two other methods were based on OBRA of data from individual cross sections. A fourth strategy used ground-based reflectance measurements to derive an OBRA relation (R2 = 0.944) that was then applied to the image. Depth retrieval accuracy was assessed by visually inspecting cross sections and calculating various error metrics. Calibration via field spectroscopy resulted in a shallow bias but provided relative accuracies similar to image-based methods. Reach-aggregated OBRA was marginally superior to calibrations based on individual cross sections, and depth retrieval accuracy varied considerably along each reach. Errors were lower and observed versus predicted regression R2 values higher for a relatively simple, deeper site than a shallower, braided reach; errors were 1/3 and 1/2 the mean depth for the two reaches. Bathymetric maps were coherent and hydraulically reasonable, however, and might be more reliable than implied by numerical metrics. As an example application, linear discriminant analysis was used to produce a series of depth threshold maps for characterizing shallow-water habitat for roosting cranes. ?? 2011 by the American Geophysical Union.

Evaluating the potential for remote bathymetric mapping of a turbid, sand-bed river: 2. application to hyperspectral image data from the Platte River

USGS Publications Warehouse

Legleiter, Carl J.; Kinzel, Paul J.; Overstreet, Brandon T.

2011-01-01

This study examined the possibility of mapping depth from optical image data in turbid, sediment-laden channels. Analysis of hyperspectral images from the Platte River indicated that depth retrieval in these environments is feasible, but might not be highly accurate. Four methods of calibrating image-derived depth estimates were evaluated. The first involved extracting image spectra at survey point locations throughout the reach. These paired observations of depth and reflectance were subjected to optimal band ratio analysis (OBRA) to relate (R2 = 0.596) a spectrally based quantity to flow depth. Two other methods were based on OBRA of data from individual cross sections. A fourth strategy used ground-based reflectance measurements to derive an OBRA relation (R2 = 0.944) that was then applied to the image. Depth retrieval accuracy was assessed by visually inspecting cross sections and calculating various error metrics. Calibration via field spectroscopy resulted in a shallow bias but provided relative accuracies similar to image-based methods. Reach-aggregated OBRA was marginally superior to calibrations based on individual cross sections, and depth retrieval accuracy varied considerably along each reach. Errors were lower and observed versus predicted regression R2 values higher for a relatively simple, deeper site than a shallower, braided reach; errors were 1/3 and 1/2 the mean depth for the two reaches. Bathymetric maps were coherent and hydraulically reasonable, however, and might be more reliable than implied by numerical metrics. As an example application, linear discriminant analysis was used to produce a series of depth threshold maps for characterizing shallow-water habitat for roosting cranes.

Geomorphological mapping with a small unmanned aircraft system (sUAS): Feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model

NASA Astrophysics Data System (ADS)

Hugenholtz, Chris H.; Whitehead, Ken; Brown, Owen W.; Barchyn, Thomas E.; Moorman, Brian J.; LeClair, Adam; Riddell, Kevin; Hamilton, Tayler

2013-07-01

Small unmanned aircraft systems (sUAS) are a relatively new type of aerial platform for acquiring high-resolution remote sensing measurements of Earth surface processes and landforms. However, despite growing application there has been little quantitative assessment of sUAS performance. Here we present results from a field experiment designed to evaluate the accuracy of a photogrammetrically-derived digital terrain model (DTM) developed from imagery acquired with a low-cost digital camera onboard an sUAS. We also show the utility of the high-resolution (0.1 m) sUAS imagery for resolving small-scale biogeomorphic features. The experiment was conducted in an area with active and stabilized aeolian landforms in the southern Canadian Prairies. Images were acquired with a Hawkeye RQ-84Z Areohawk fixed-wing sUAS. A total of 280 images were acquired along 14 flight lines, covering an area of 1.95 km2. The survey was completed in 4.5 h, including GPS surveying, sUAS setup and flight time. Standard image processing and photogrammetric techniques were used to produce a 1 m resolution DTM and a 0.1 m resolution orthorectified image mosaic. The latter revealed previously un-mapped bioturbation features. The vertical accuracy of the DTM was evaluated with 99 Real-Time Kinematic GPS points, while 20 of these points were used to quantify horizontal accuracy. The horizontal root mean squared error (RMSE) of the orthoimage was 0.18 m, while the vertical RMSE of the DTM was 0.29 m, which is equivalent to the RMSE of a bare earth LiDAR DTM for the same site. The combined error from both datasets was used to define a threshold of the minimum elevation difference that could be reliably attributed to erosion or deposition in the seven years separating the sUAS and LiDAR datasets. Overall, our results suggest that sUAS-acquired imagery may provide a low-cost, rapid, and flexible alternative to airborne LiDAR for geomorphological mapping.

Peatland classification of West Siberia based on Landsat imagery

NASA Astrophysics Data System (ADS)

Terentieva, I.; Glagolev, M.; Lapshina, E.; Maksyutov, S. S.

2014-12-01

Increasing interest in peatlands for prediction of environmental changes requires an understanding of its geographical distribution. West Siberia Plain is the biggest peatland area in Eurasia and is situated in the high latitudes experiencing enhanced rate of climate change. West Siberian taiga mires are important globally, accounting for about 12.5% of the global wetland area. A number of peatland maps of the West Siberia was developed in 1970s, but their accuracy is limited. Here we report the effort in mapping West Siberian peatlands using 30 m resolution Landsat imagery. As a first step, peatland classification scheme oriented on environmental parameter upscaling was developed. The overall workflow involves data pre-processing, training data collection, image classification on a scene-by-scene basis, regrouping of the derived classes into final peatland types and accuracy assessment. To avoid misclassification peatlands were distinguished from other landscapes using threshold method: for each scene, Green-Red Vegetation Indices was used for peatland masking and 5th channel was used for masking water bodies. Peatland image masks were made in Quantum GIS, filtered in MATLAB and then classified in Multispec (Purdue Research Foundation) using maximum likelihood algorithm of supervised classification method. Training sample selection was mostly based on spectral signatures due to limited ancillary and high-resolution image data. As an additional source of information, we applied our field knowledge resulting from more than 10 years of fieldwork in West Siberia summarized in an extensive dataset of botanical relevés, field photos, pH and electrical conductivity data from 40 test sites. After the classification procedure, discriminated spectral classes were generalized into 12 peatland types. Overall accuracy assessment was based on 439 randomly assigned test sites showing final map accuracy was 80%. Total peatland area was estimated at 73.0 Mha. Various ridge-hollow and ridge-hollow-pool bog complexes prevail here occupying 34.5 Mha. They are followed by lakes (11.1 Mha), fens (10.7 Mha), pine-dwarf-shrub sphagnum bogs (9.3 Mha) and palsa complexes (7.4 Mha).

Black-backed woodpecker habitat suitability mapping using conifer snag basal area estimated from airborne laser scanning

NASA Astrophysics Data System (ADS)

Casas Planes, Á.; Garcia, M.; Siegel, R.; Koltunov, A.; Ramirez, C.; Ustin, S.

2015-12-01

Occupancy and habitat suitability models for snag-dependent wildlife species are commonly defined as a function of snag basal area. Although critical for predicting or assessing habitat suitability, spatially distributed estimates of snag basal area are not generally available across landscapes at spatial scales relevant for conservation planning. This study evaluates the use of airborne laser scanning (ALS) to 1) identify individual conifer snags and map their basal area across a recently burned forest, and 2) map habitat suitability for a wildlife species known to be dependent on snag basal area, specifically the black-backed woodpecker (Picoides arcticus). This study focuses on the Rim Fire, a megafire that took place in 2013 in the Sierra Nevada Mountains of California, creating large patches of medium- and high-severity burned forest. We use forest inventory plots, single-tree ALS-derived metrics and Gaussian processes classification and regression to identify conifer snags and estimate their stem diameter and basal area. Then, we use the results to map habitat suitability for the black-backed woodpecker using thresholds for conifer basal area from a previously published habitat suitability model. Local maxima detection and watershed segmentation algorithms resulted in 75% detection of trees with stem diameter larger than 30 cm. Snags are identified with an overall accuracy of 91.8 % and conifer snags are identified with an overall accuracy of 84.8 %. Finally, Gaussian process regression reliably estimated stem diameter (R2 = 0.8) using height and crown area. This work provides a fast and efficient methodology to characterize the extent of a burned forest at the tree level and a critical tool for early wildlife assessment in post-fire forest management and biodiversity conservation.

The Reference Elevation Model of Antarctica (REMA): A High Resolution, Time-Stamped Digital Elevation Model for the Antarctic Ice Sheet

NASA Astrophysics Data System (ADS)

Howat, I.; Noh, M. J.; Porter, C. C.; Smith, B. E.; Morin, P. J.

2017-12-01

We are creating the Reference Elevation Model of Antarctica (REMA), a continuous, high resolution (2-8 m), high precision (accuracy better than 1 m) reference surface for a wide range of glaciological and geodetic applications. REMA will be constructed from stereo-photogrammetric Digital Surface Models (DSM) extracted from pairs of submeter resolution DigitalGlobe satellite imagery and vertically registred to precise elevations from near-coincident airborne LiDAR, ground-based GPS surveys and Cryosat-2 radar altimetry. Both a seamless mosaic and individual, time-stamped DSM strips, collected primarily between 2012 and 2016, will be distributed to enable change measurement. These data will be used for mapping bed topography from ice thickness, measuring ice thickness changes, constraining ice flow and geodynamic models, mapping glacial geomorphology, terrain corrections and filtering of remote sensing observations, and many other science tasks. Is will also be critical for mapping ice traverse routes, landing sites and other field logistics planning. REMA will also provide a critical elevation benchmark for future satellite altimetry missions including ICESat-2. Here we report on REMA production progress, initial accuracy assessment and data availability.

Sub-pixel mineral mapping using EO-1 Hyperion hyperspectral data

NASA Astrophysics Data System (ADS)

Kumar, C.; Shetty, A.; Raval, S.; Champatiray, P. K.; Sharma, R.

2014-11-01

This study describes the utility of Earth Observation (EO)-1 Hyperion data for sub-pixel mineral investigation using Mixture Tuned Target Constrained Interference Minimized Filter (MTTCIMF) algorithm in hostile mountainous terrain of Rajsamand district of Rajasthan, which hosts economic mineralization such as lead, zinc, and copper etc. The study encompasses pre-processing, data reduction, Pixel Purity Index (PPI) and endmember extraction from reflectance image of surface minerals such as illite, montmorillonite, phlogopite, dolomite and chlorite. These endmembers were then assessed with USGS mineral spectral library and lab spectra of rock samples collected from field for spectral inspection. Subsequently, MTTCIMF algorithm was implemented on processed image to obtain mineral distribution map of each detected mineral. A virtual verification method has been adopted to evaluate the classified image, which uses directly image information to evaluate the result and confirm the overall accuracy and kappa coefficient of 68 % and 0.6 respectively. The sub-pixel level mineral information with reasonable accuracy could be a valuable guide to geological and exploration community for expensive ground and/or lab experiments to discover economic deposits. Thus, the study demonstrates the feasibility of Hyperion data for sub-pixel mineral mapping using MTTCIMF algorithm with cost and time effective approach.

Brain Gliomas: Multicenter Standardized Assessment of Dynamic Contrast-enhanced and Dynamic Susceptibility Contrast MR Images.

PubMed

Anzalone, Nicoletta; Castellano, Antonella; Cadioli, Marcello; Conte, Gian Marco; Cuccarini, Valeria; Bizzi, Alberto; Grimaldi, Marco; Costa, Antonella; Grillea, Giovanni; Vitali, Paolo; Aquino, Domenico; Terreni, Maria Rosa; Torri, Valter; Erickson, Bradley J; Caulo, Massimo

2018-06-01

Purpose To evaluate the feasibility of a standardized protocol for acquisition and analysis of dynamic contrast material-enhanced (DCE) and dynamic susceptibility contrast (DSC) magnetic resonance (MR) imaging in a multicenter clinical setting and to verify its accuracy in predicting glioma grade according to the new World Health Organization 2016 classification. Materials and Methods The local research ethics committees of all centers approved the study, and informed consent was obtained from patients. One hundred patients with glioma were prospectively examined at 3.0 T in seven centers that performed the same preoperative MR imaging protocol, including DCE and DSC sequences. Two independent readers identified the perfusion hotspots on maps of volume transfer constant (K trans ), plasma (v p ) and extravascular-extracellular space (v e ) volumes, initial area under the concentration curve, and relative cerebral blood volume (rCBV). Differences in parameters between grades and molecular subtypes were assessed by using Kruskal-Wallis and Mann-Whitney U tests. Diagnostic accuracy was evaluated by using receiver operating characteristic curve analysis. Results The whole protocol was tolerated in all patients. Perfusion maps were successfully obtained in 94 patients. An excellent interreader reproducibility of DSC- and DCE-derived measures was found. Among DCE-derived parameters, v p and v e had the highest accuracy (are under the receiver operating characteristic curve [A z ] = 0.847 and 0.853) for glioma grading. DSC-derived rCBV had the highest accuracy (A z = 0.894), but the difference was not statistically significant (P > .05). Among lower-grade gliomas, a moderate increase in both v p and rCBV was evident in isocitrate dehydrogenase wild-type tumors, although this was not significant (P > .05). Conclusion A standardized multicenter acquisition and analysis protocol of DCE and DSC MR imaging is feasible and highly reproducible. Both techniques showed a comparable, high diagnostic accuracy for grading gliomas. © RSNA, 2018 Online supplemental material is available for this article.

An evaluation of a UAV guidance system with consumer grade GPS receivers

NASA Astrophysics Data System (ADS)

Rosenberg, Abigail Stella

Remote sensing has been demonstrated an important tool in agricultural and natural resource management and research applications, however there are limitations that exist with traditional platforms (i.e., hand held sensors, linear moves, vehicle mounted, airplanes, remotely piloted vehicles (RPVs), unmanned aerial vehicles (UAVs) and satellites). Rapid technological advances in electronics, computers, software applications, and the aerospace industry have dramatically reduced the cost and increased the availability of remote sensing technologies. Remote sensing imagery vary in spectral, spatial, and temporal resolutions and are available from numerous providers. Appendix A presented results of a test project that acquired high-resolution aerial photography with a RPV to map the boundary of a 0.42 km2 fire area. The project mapped the boundaries of the fire area from a mosaic of the aerial images collected and compared this with ground-based measurements. The project achieved a 92.4% correlation between the aerial assessment and the ground truth data. Appendix B used multi-objective analysis to quantitatively assess the tradeoffs between different sensor platform attributes to identify the best overall technology. Experts were surveyed to identify the best overall technology at three different pixel sizes. Appendix C evaluated the positional accuracy of a relatively low cost UAV designed for high resolution remote sensing of small areas in order to determine the positional accuracy of sensor readings. The study evaluated the accuracy and uncertainty of a UAV flight route with respect to the programmed waypoints and of the UAV's GPS position, respectively. In addition, the potential displacement of sensor data was evaluated based on (1) GPS measurements on board the aircraft and (2) the autopilot's circuit board with 3-axis gyros and accelerometers (i.e., roll, pitch, and yaw). The accuracies were estimated based on a 95% confidence interval or similar methods. The accuracy achieved in the second and third manuscripts demonstrates that reasonably priced, high resolution remote sensing via RPVs and UAVs is practical for agriculture and natural resource professionals.

Generating high-accuracy urban distribution map for short-term change monitoring based on convolutional neural network by utilizing SAR imagery

NASA Astrophysics Data System (ADS)

Iino, Shota; Ito, Riho; Doi, Kento; Imaizumi, Tomoyuki; Hikosaka, Shuhei

2017-10-01

In the developing countries, urban areas are expanding rapidly. With the rapid developments, a short term monitoring of urban changes is important. A constant observation and creation of urban distribution map of high accuracy and without noise pollution are the key issues for the short term monitoring. SAR satellites are highly suitable for day or night and regardless of atmospheric weather condition observations for this type of study. The current study highlights the methodology of generating high-accuracy urban distribution maps derived from the SAR satellite imagery based on Convolutional Neural Network (CNN), which showed the outstanding results for image classification. Several improvements on SAR polarization combinations and dataset construction were performed for increasing the accuracy. As an additional data, Digital Surface Model (DSM), which are useful to classify land cover, were added to improve the accuracy. From the obtained result, high-accuracy urban distribution map satisfying the quality for short-term monitoring was generated. For the evaluation, urban changes were extracted by taking the difference of urban distribution maps. The change analysis with time series of imageries revealed the locations of urban change areas for short-term. Comparisons with optical satellites were performed for validating the results. Finally, analysis of the urban changes combining X-band, L-band and C-band SAR satellites was attempted to increase the opportunity of acquiring satellite imageries. Further analysis will be conducted as future work of the present study

Vibrational near-field mapping of planar and buried three-dimensional plasmonic nanostructures

PubMed Central

Dregely, Daniel; Neubrech, Frank; Duan, Huigao; Vogelgesang, Ralf; Giessen, Harald

2013-01-01

Nanoantennas confine electromagnetic fields at visible and infrared wavelengths to volumes of only a few cubic nanometres. Assessing their near-field distribution offers fundamental insight into light–matter coupling and is of special interest for applications such as radiation engineering, attomolar sensing and nonlinear optics. Most experimental approaches to measure near-fields employ either diffraction-limited far-field methods or intricate near-field scanning techniques. Here, using diffraction-unlimited far-field spectroscopy in the infrared, we directly map the intensity of the electric field close to plasmonic nanoantennas. We place a patch of probe molecules with 10 nm accuracy at different locations in the near-field of a resonant antenna and extract the molecular vibrational excitation. We map the field intensity along a dipole antenna and gap-type antennas. Moreover, this method is able to assess the near-field intensity of complex buried plasmonic structures. We demonstrate this by measuring for the first time the near-field intensity of a three-dimensional plasmonic electromagnetically induced transparency structure. PMID:23892519

Improved scheme for Cross-track Infrared Sounder geolocation assessment and optimization

NASA Astrophysics Data System (ADS)

Wang, Likun; Zhang, Bin; Tremblay, Denis; Han, Yong

2017-01-01

An improved scheme for Cross-track Infrared Sounder (CrIS) geolocation assessment for all scan angles (from -48.5° to 48.5°) is developed in this study. The method uses spatially collocated radiance measurements from the Visible Infrared Imaging Radiometer Suite (VIIRS) image band I5 to evaluate the geolocation performance of the CrIS Sensor Data Records (SDR) by taking advantage of its high spatial resolution (375 m at nadir) and accurate geolocation. The basic idea is to perturb CrIS line-of-sight vectors along the in-track and cross-track directions to find a position where CrIS and VIIRS data matches more closely. The perturbation angles at this best matched position are then used to evaluate the CrIS geolocation accuracy. More importantly, the new method is capable of performing postlaunch on-orbit geometric calibration by optimizing mapping angle parameters based on the assessment results and thus can be further extended to the following CrIS sensors on new satellites. Finally, the proposed method is employed to evaluate the CrIS geolocation accuracy on current Suomi National Polar-orbiting Partnership satellite. The error characteristics are revealed along the scan positions in the in-track and cross-track directions. It is found that there are relatively large errors ( 4 km) in the cross-track direction close to the end of scan positions. With newly updated mapping angles, the geolocation accuracy is greatly improved for all scan positions (less than 0.3 km). This makes CrIS and VIIRS spatially align together and thus benefits the application that needs combination of CrIS and VIIRS measurements and products.

Forest tree species discrimination in western Himalaya using EO-1 Hyperion

NASA Astrophysics Data System (ADS)

George, Rajee; Padalia, Hitendra; Kushwaha, S. P. S.

2014-05-01

The information acquired in the narrow bands of hyperspectral remote sensing data has potential to capture plant species spectral variability, thereby improving forest tree species mapping. This study assessed the utility of spaceborne EO-1 Hyperion data in discrimination and classification of broadleaved evergreen and conifer forest tree species in western Himalaya. The pre-processing of 242 bands of Hyperion data resulted into 160 noise-free and vertical stripe corrected reflectance bands. Of these, 29 bands were selected through step-wise exclusion of bands (Wilk's Lambda). Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) algorithms were applied to the selected bands to assess their effectiveness in classification. SVM was also applied to broadband data (Landsat TM) to compare the variation in classification accuracy. All commonly occurring six gregarious tree species, viz., white oak, brown oak, chir pine, blue pine, cedar and fir in western Himalaya could be effectively discriminated. SVM produced a better species classification (overall accuracy 82.27%, kappa statistic 0.79) than SAM (overall accuracy 74.68%, kappa statistic 0.70). It was noticed that classification accuracy achieved with Hyperion bands was significantly higher than Landsat TM bands (overall accuracy 69.62%, kappa statistic 0.65). Study demonstrated the potential utility of narrow spectral bands of Hyperion data in discriminating tree species in a hilly terrain.

Mapping forest tree species over large areas with partially cloudy Landsat imagery

NASA Astrophysics Data System (ADS)

Turlej, K.; Radeloff, V.

2017-12-01

Forests provide numerous services to natural systems and humankind, but which services forest provide depends greatly on their tree species composition. That makes it important to track not only changes in forest extent, something that remote sensing excels in, but also to map tree species. The main goal of our work was to map tree species with Landsat imagery, and to identify how to maximize mapping accuracy by including partially cloudy imagery. Our study area covered one Landsat footprint (26/28) in Northern Wisconsin, USA, with temperate and boreal forests. We selected this area because it contains numerous tree species and variable forest composition providing an ideal study area to test the limits of Landsat data. We quantified how species-level classification accuracy was affected by a) the number of acquisitions, b) the seasonal distribution of observations, and c) the amount of cloud contamination. We classified a single year stack of Landsat-7, and -8 images data with a decision tree algorithm to generate a map of dominant tree species at the pixel- and stand-level. We obtained three important results. First, we achieved producer's accuracies in the range 70-80% and user's accuracies in range 80-90% for the most abundant tree species in our study area. Second, classification accuracy improved with more acquisitions, when observations were available from all seasons, and is the best when images with up to 40% cloud cover are included. Finally, classifications for pure stands were 10 to 30 percentage points better than those for mixed stands. We conclude that including partially cloudy Landsat imagery allows to map forest tree species with accuracies that were previously only possible for rare years with many cloud-free observations. Our approach thus provides important information for both forest management and science.

Functional brain microstate predicts the outcome in a visuospatial working memory task.

PubMed

Muthukrishnan, Suriya-Prakash; Ahuja, Navdeep; Mehta, Nalin; Sharma, Ratna

2016-11-01

Humans have limited capacity of processing just up to 4 integrated items of information in the working memory. Thus, it is inevitable to commit more errors when challenged with high memory loads. However, the neural mechanisms that determine the accuracy of response at high memory loads still remain unclear. High temporal resolution of Electroencephalography (EEG) technique makes it the best tool to resolve the temporal dynamics of brain networks. EEG-defined microstate is the quasi-stable scalp electrical potential topography that represents the momentary functional state of brain. Thus, it has been possible to assess the information processing currently performed by the brain using EEG microstate analysis. We hypothesize that the EEG microstate preceding the trial could determine its outcome in a visuospatial working memory (VSWM) task. Twenty-four healthy participants performed a high memory load VSWM task, while their brain activity was recorded using EEG. Four microstate maps were found to represent the functional brain state prior to the trials in the VSWM task. One pre-trial microstate map was found to determine the accuracy of subsequent behavioural response. The intracranial generators of the pre-trial microstate map that determined the response accuracy were localized to the visuospatial processing areas at bilateral occipital, right temporal and limbic cortices. Our results imply that the behavioural outcome in a VSWM task could be determined by the intensity of activation of memory representations in the visuospatial processing brain regions prior to the trial. Copyright © 2016 Elsevier B.V. All rights reserved.

An automated approach to measuring child movement and location in the early childhood classroom.

PubMed

Irvin, Dwight W; Crutchfield, Stephen A; Greenwood, Charles R; Kearns, William D; Buzhardt, Jay

2018-06-01

Children's movement is an important issue in child development and outcome in early childhood research, intervention, and practice. Digital sensor technologies offer improvements in naturalistic movement measurement and analysis. We conducted validity and feasibility testing of a real-time, indoor mapping and location system (Ubisense, Inc.) within a preschool classroom. Real-time indoor mapping has several implications with respect to efficiently and conveniently: (a) determining the activity areas where children are spending the most and least time per day (e.g., music); and (b) mapping a focal child's atypical real-time movements (e.g., lapping behavior). We calibrated the accuracy of Ubisense point-by-point location estimates (i.e., X and Y coordinates) against laser rangefinder measurements using several stationary points and atypical movement patterns as reference standards. Our results indicate that activity areas occupied and atypical movement patterns could be plotted with an accuracy of 30.48 cm (1 ft) using a Ubisense transponder tag attached to the participating child's shirt. The accuracy parallels findings of other researchers employing Ubisense to study atypical movement patterns in individuals at risk for dementia in an assisted living facility. The feasibility of Ubisense was tested in an approximately 90-min assessment of two children, one typically developing and one with Down syndrome, during natural classroom activities, and the results proved positive. Implications for employing Ubisense in early childhood classrooms as a data-based decision-making tool to support children's development and its potential integration with other wearable sensor technologies are discussed.

Photogrammetric Accuracy and Modeling of Rolling Shutter Cameras

NASA Astrophysics Data System (ADS)

Ye, W.; Qiao, G.; Kong, F.; Guo, S.; Ma, X.; Tong, X.; Li, R.

2016-06-01

Global climate change is one of the major challenges that all nations are commonly facing. Long-term observations of the Antarctic ice sheet have been playing a critical role in quantitatively estimating and predicting effects resulting from the global changes. The film-based ARGON reconnaissance imagery provides a remarkable data source for studying the Antarctic ice-sheet in 1960s, thus greatly extending the time period of Antarctica surface observations. To deal with the low-quality images and the unavailability of camera poses, a systematic photogrammetric approach is proposed to reconstruct the interior and exterior orientation information for further glacial mapping applications, including ice flow velocity mapping and mass balance estimation. Some noteworthy details while performing geometric modelling using the ARGON images were introduced, including methods and results for handling specific effects of film deformation, damaged or missing fiducial marks and calibration report, automatic fiducial mark detection, control point selection through Antarctic shadow and ice surface terrain analysis, and others. Several sites in East Antarctica were tested. As an example, four images in the Byrd glacier region were used to assess the accuracy of the geometric modelling. A digital elevation model (DEM) and an orthophoto map of Byrd glacier were generated. The accuracy of the ground positions estimated by using independent check points is within one nominal pixel of 140 m of ARGON imagery. Furthermore, a number of significant features, such as ice flow velocity and regional change patterns, will be extracted and analysed.

Augmented reality and dynamic infrared thermography for perforator mapping in the anterolateral thigh

PubMed Central

Cifuentes, Ignacio Javier; Dagnino, Bruno Leonardo; Salisbury, María Carolina; Perez, María Eliana; Ortega, Claudia; Maldonado, Daniela

2018-01-01

Dynamic infrared thermography (DIRT) has been used for the preoperative mapping of cutaneous perforators. This technique has shown a positive correlation with intraoperative findings. Our aim was to evaluate the accuracy of perforator mapping with DIRT and augmented reality using a portable projector. For this purpose, three volunteers had both of their anterolateral thighs assessed for the presence and location of cutaneous perforators using DIRT. The obtained image of these “hotspots” was projected back onto the thigh and the presence of Doppler signals within a 10-cm diameter from the midpoint between the lateral patella and the anterior superior iliac spine was assessed using a handheld Doppler device. Hotspots were identified in all six anterolateral thighs and were successfully projected onto the skin. The median number of perforators identified within the area of interest was 5 (range, 3–8) and the median time needed to identify them was 3.5 minutes (range, 3.3–4.0 minutes). Every hotspot was correlated to a Doppler sound signal. In conclusion, augmented reality can be a reliable method for transferring the location of perforators identified by DIRT onto the thigh, facilitating its assessment and yielding a reliable map of potential perforators for flap raising. PMID:29788686

Development of a New Branded UK Food Composition Database for an Online Dietary Assessment Tool

PubMed Central

Carter, Michelle C.; Hancock, Neil; Albar, Salwa A.; Brown, Helen; Greenwood, Darren C.; Hardie, Laura J.; Frost, Gary S.; Wark, Petra A.; Cade, Janet E.

2016-01-01

The current UK food composition tables are limited, containing ~3300 mostly generic food and drink items. To reflect the wide range of food products available to British consumers and to potentially improve accuracy of dietary assessment, a large UK specific electronic food composition database (FCDB) has been developed. A mapping exercise has been conducted that matched micronutrient data from generic food codes to “Back of Pack” data from branded food products using a semi-automated process. After cleaning and processing, version 1.0 of the new FCDB contains 40,274 generic and branded items with associated 120 macronutrient and micronutrient data and 5669 items with portion images. Over 50% of food and drink items were individually mapped to within 10% agreement with the generic food item for energy. Several quality checking procedures were applied after mapping including; identifying foods above and below the expected range for a particular nutrient within that food group and cross-checking the mapping of items such as concentrated and raw/dried products. The new electronic FCDB has substantially increased the size of the current, publically available, UK food tables. The FCDB has been incorporated into myfood24, a new fully automated online dietary assessment tool and, a smartphone application for weight loss. PMID:27527214

Development of a New Branded UK Food Composition Database for an Online Dietary Assessment Tool.

PubMed

Carter, Michelle C; Hancock, Neil; Albar, Salwa A; Brown, Helen; Greenwood, Darren C; Hardie, Laura J; Frost, Gary S; Wark, Petra A; Cade, Janet E

2016-08-05

The current UK food composition tables are limited, containing ~3300 mostly generic food and drink items. To reflect the wide range of food products available to British consumers and to potentially improve accuracy of dietary assessment, a large UK specific electronic food composition database (FCDB) has been developed. A mapping exercise has been conducted that matched micronutrient data from generic food codes to "Back of Pack" data from branded food products using a semi-automated process. After cleaning and processing, version 1.0 of the new FCDB contains 40,274 generic and branded items with associated 120 macronutrient and micronutrient data and 5669 items with portion images. Over 50% of food and drink items were individually mapped to within 10% agreement with the generic food item for energy. Several quality checking procedures were applied after mapping including; identifying foods above and below the expected range for a particular nutrient within that food group and cross-checking the mapping of items such as concentrated and raw/dried products. The new electronic FCDB has substantially increased the size of the current, publically available, UK food tables. The FCDB has been incorporated into myfood24, a new fully automated online dietary assessment tool and, a smartphone application for weight loss.

Accurate Mobile Urban Mapping via Digital Map-Based SLAM †

PubMed Central

Roh, Hyunchul; Jeong, Jinyong; Cho, Younggun; Kim, Ayoung

2016-01-01

This paper presents accurate urban map generation using digital map-based Simultaneous Localization and Mapping (SLAM). Throughout this work, our main objective is generating a 3D and lane map aiming for sub-meter accuracy. In conventional mapping approaches, achieving extremely high accuracy was performed by either (i) exploiting costly airborne sensors or (ii) surveying with a static mapping system in a stationary platform. Mobile scanning systems recently have gathered popularity but are mostly limited by the availability of the Global Positioning System (GPS). We focus on the fact that the availability of GPS and urban structures are both sporadic but complementary. By modeling both GPS and digital map data as measurements and integrating them with other sensor measurements, we leverage SLAM for an accurate mobile mapping system. Our proposed algorithm generates an efficient graph SLAM and achieves a framework running in real-time and targeting sub-meter accuracy with a mobile platform. Integrated with the SLAM framework, we implement a motion-adaptive model for the Inverse Perspective Mapping (IPM). Using motion estimation derived from SLAM, the experimental results show that the proposed approaches provide stable bird’s-eye view images, even with significant motion during the drive. Our real-time map generation framework is validated via a long-distance urban test and evaluated at randomly sampled points using Real-Time Kinematic (RTK)-GPS. PMID:27548175

Behavior Analysis of Novel Wearable Indoor Mapping System Based on 3D-SLAM.

PubMed

Lagüela, Susana; Dorado, Iago; Gesto, Manuel; Arias, Pedro; González-Aguilera, Diego; Lorenzo, Henrique

2018-03-02

This paper presents a Wearable Prototype for indoor mapping developed by the University of Vigo. The system is based on a Velodyne LiDAR, acquiring points with 16 rays for a simplistic or low-density 3D representation of reality. With this, a Simultaneous Localization and Mapping (3D-SLAM) method is developed for the mapping and generation of 3D point clouds of scenarios deprived from GNSS signal. The quality of the system presented is validated through the comparison with a commercial indoor mapping system, Zeb-Revo, from the company GeoSLAM and with a terrestrial LiDAR, Faro Focus 3D X330. The first is considered as a relative reference with other mobile systems and is chosen due to its use of the same principle for mapping: SLAM techniques based on Robot Operating System (ROS), while the second is taken as ground-truth for the determination of the final accuracy of the system regarding reality. Results show that the accuracy of the system is mainly determined by the accuracy of the sensor, with little increment in the error introduced by the mapping algorithm.

Improved spatial accuracy of functional maps in the rat olfactory bulb using supervised machine learning approach.

PubMed

Murphy, Matthew C; Poplawsky, Alexander J; Vazquez, Alberto L; Chan, Kevin C; Kim, Seong-Gi; Fukuda, Mitsuhiro

2016-08-15

Functional MRI (fMRI) is a popular and important tool for noninvasive mapping of neural activity. As fMRI measures the hemodynamic response, the resulting activation maps do not perfectly reflect the underlying neural activity. The purpose of this work was to design a data-driven model to improve the spatial accuracy of fMRI maps in the rat olfactory bulb. This system is an ideal choice for this investigation since the bulb circuit is well characterized, allowing for an accurate definition of activity patterns in order to train the model. We generated models for both cerebral blood volume weighted (CBVw) and blood oxygen level dependent (BOLD) fMRI data. The results indicate that the spatial accuracy of the activation maps is either significantly improved or at worst not significantly different when using the learned models compared to a conventional general linear model approach, particularly for BOLD images and activity patterns involving deep layers of the bulb. Furthermore, the activation maps computed by CBVw and BOLD data show increased agreement when using the learned models, lending more confidence to their accuracy. The models presented here could have an immediate impact on studies of the olfactory bulb, but perhaps more importantly, demonstrate the potential for similar flexible, data-driven models to improve the quality of activation maps calculated using fMRI data. Copyright © 2016 Elsevier Inc. All rights reserved.

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

NASA Technical Reports Server (NTRS)

Fagan, Matthew E.; Defries, Ruth S.; Sesnie, Steven E.; Arroyo-Mora, J. Pablo; Soto, Carlomagno; Singh, Aditya; Townsend, Philip A.; Chazdon, Robin L.

2015-01-01

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.

Flood mapping with multitemporal MODIS data

NASA Astrophysics Data System (ADS)

Son, Nguyen-Thanh; Chen, Chi-Farn; Chen, Cheng-Ru

2014-05-01

Flood is one of the most devastating and frequent disasters resulting in loss of human life and serve damage to infrastructure and agricultural production. Flood is phenomenal in the Mekong River Delta (MRD), Vietnam. It annually lasts from July to November. Information on spatiotemporal flood dynamics is thus important for planners to devise successful strategies for flood monitoring and mitigation of its negative effects. The main objective of this study is to develop an approach for weekly mapping flood dynamics with the Moderate Resolution Imaging Spectroradiometer data in MRD using the water fraction model (WFM). The data processed for 2009 comprises three main steps: (1) data pre-processing to construct smooth time series of the difference in the values (DVLE) between land surface water index (LSWI) and enhanced vegetation index (EVI) using the empirical mode decomposition (EMD), (2) flood derivation using WFM, and (3) accuracy assessment. The mapping results were compared with the ground reference data, which were constructed from Envisat Advanced Synthetic Aperture Radar (ASAR) data. As several error sources, including mixed-pixel problems and low-resolution bias between the mapping results and ground reference data, could lower the level of classification accuracy, the comparisons indicated satisfactory results with the overall accuracy of 80.5% and Kappa coefficient of 0.61, respectively. These results were reaffirmed by a close correlation between the MODIS-derived flood area and that of the ground reference map at the provincial level, with the correlation coefficients (R2) of 0.93. Considering the importance of remote sensing for monitoring floods and mitigating the damage caused by floods to crops and infrastructure, this study eventually leads to the realization of the value of using time-series MODIS DVLE data for weekly flood monitoring in MRD with the aid of EMD and WFM. Such an approach that could provide quantitative information on spatiotemporal flood dynamics for monitoring purposes was completely transferable to other regions in the world.

The Use of LIDAR and Volunteered Geographic Information to Map Flood Extents and Inundation

NASA Astrophysics Data System (ADS)

McDougall, K.; Temple-Watts, P.

2012-07-01

Floods are one of the most destructive natural disasters that threaten communities and properties. In recent decades, flooding has claimed more lives, destroyed more houses and ruined more agricultural land than any other natural hazard. The accurate prediction of the areas of inundation from flooding is critical to saving lives and property, but relies heavily on accurate digital elevation and hydrologic models. The 2011 Brisbane floods provided a unique opportunity to capture high resolution digital aerial imagery as the floods neared their peak, allowing the capture of areas of inundation over the various city suburbs. This high quality imagery, together with accurate LiDAR data over the area and publically available volunteered geographic imagery through repositories such as Flickr, enabled the reconstruction of flood extents and the assessment of both area and depth of inundation for the assessment of damage. In this study, approximately 20 images of flood damaged properties were utilised to identify the peak of the flood. Accurate position and height values were determined through the use of RTK GPS and conventional survey methods. This information was then utilised in conjunction with river gauge information to generate a digital flood surface. The LiDAR generated DEM was then intersected with the flood surface to reconstruct the area of inundation. The model determined areas of inundation were then compared to the mapped flood extent from the high resolution digital imagery to assess the accuracy of the process. The paper concludes that accurate flood extent prediction or mapping is possible through this method, although its accuracy is dependent on the number and location of sampled points. The utilisation of LiDAR generated DEMs and DSMs can also provide an excellent mechanism to estimate depths of inundation and hence flood damage

Crew performance and communication: Performing a terrain navigation task

NASA Technical Reports Server (NTRS)

Battiste, Vernol; Delzell, Susanne

1993-01-01

A study was conducted to examine the map and route cues pilots use while navigating under controlled, but realistic, nap-of-the-earth (NOE) flight conditions. US Army helicopter flight crews were presented a map and route overlay and asked to perform normal mission planning. They then viewed a video-recording of the out-the-window scene during low-level flights, without the route overlay, and were asked periodically to locate their current position on the map. The pilots and navigators were asked to communicate normally during the planning and flight phases. During each flight the navigator's response time, accuracy, and subjective workload were assessed. Post-flight NASA-TLX workload ratings were collected. No main effect of map orientation (north-up vs. track-up) was found for errors or response times on any of the tasks evaluated. Navigators in the north-up group rated their workload lower than those in the track-up group.

Improved Topographic Mapping Through Multi-Baseline SAR Interferometry with MAP Estimation

NASA Astrophysics Data System (ADS)

Dong, Yuting; Jiang, Houjun; Zhang, Lu; Liao, Mingsheng; Shi, Xuguo

2015-05-01

There is an inherent contradiction between the sensitivity of height measurement and the accuracy of phase unwrapping for SAR interferometry (InSAR) over rough terrain. This contradiction can be resolved by multi-baseline InSAR analysis, which exploits multiple phase observations with different normal baselines to improve phase unwrapping accuracy, or even avoid phase unwrapping. In this paper we propose a maximum a posteriori (MAP) estimation method assisted by SRTM DEM data for multi-baseline InSAR topographic mapping. Based on our method, a data processing flow is established and applied in processing multi-baseline ALOS/PALSAR dataset. The accuracy of resultant DEMs is evaluated by using a standard Chinese national DEM of scale 1:10,000 as reference. The results show that multi-baseline InSAR can improve DEM accuracy compared with single-baseline case. It is noteworthy that phase unwrapping is avoided and the quality of multi-baseline InSAR DEM can meet the DTED-2 standard.

Thematic accuracy of the 1992 National Land-Cover Data for the western United States

USGS Publications Warehouse

Wickham, J.D.; Stehman, S.V.; Smith, J.H.; Yang, L.

2004-01-01

The MultiResolution Land Characteristics (MRLC) consortium sponsored production of the National Land Cover Data (NLCD) for the conterminous United States, using Landsat imagery collected on a target year of 1992 (1992 NLCD). Here we report the thematic accuracy of the 1992 NLCD for the six western mapping regions. Reference data were collected in each region for a probability sample of pixels stratified by map land-cover class. Results are reported for each of the six mapping regions with agreement defined as a match between the primary or alternate reference land-cover label and a mode class of the mapped 3×3 block of pixels centered on the sample pixel. Overall accuracy at Anderson Level II was low and variable across the regions, ranging from 38% for the Midwest to 70% for the Southwest. Overall accuracy at Anderson Level I was higher and more consistent across the regions, ranging from 82% to 85% for five of the six regions, but only 74% for the South-central region.

A Statistical Examination of Magnetic Field Model Accuracy for Mapping Geosynchronous Solar Energetic Particle Observations to Lower Earth Orbits

NASA Astrophysics Data System (ADS)

Young, S. L.; Kress, B. T.; Rodriguez, J. V.; McCollough, J. P.

2013-12-01

Operational specifications of space environmental hazards can be an important input used by decision makers. Ideally the specification would come from on-board sensors, but for satellites where that capability is not available another option is to map data from remote observations to the location of the satellite. This requires a model of the physical environment and an understanding of its accuracy for mapping applications. We present a statistical comparison between magnetic field model mappings of solar energetic particle observations made by NOAA's Geostationary Operational Environmental Satellites (GOES) to the location of the Combined Release and Radiation Effects Satellite (CRRES). Because CRRES followed a geosynchronous transfer orbit which precessed in local time this allows us to examine the model accuracy between LEO and GEO orbits across a range of local times. We examine the accuracy of multiple magnetic field models using a variety of statistics and examine their utility for operational purposes.

Comparison of MSS and TM Data for Landcover Classification in the Chesapeake Bay Area: a Preliminary Report. [Taylor's Island, Maryland

NASA Technical Reports Server (NTRS)

Mulligan, P. J.; Gervin, J. C.; Lu, Y. C.

1985-01-01

An area bordering the Eastern Shore of the Chesapeake Bay was selected for study and classified using unsupervised techniques applied to LANDSAT-2 MSS data and several band combinations of LANDSAT-4 TM data. The accuracies of these Level I land cover classifications were verified using the Taylor's Island USGS 7.5 minute topographic map which was photointerpreted, digitized and rasterized. The the Taylor's Island map, comparing the MSS and TM three band (2 3 4) classifications, the increased resolution of TM produced a small improvement in overall accuracy of 1% correct due primarily to a small improvement, and 1% and 3%, in areas such as water and woodland. This was expected as the MSS data typically produce high accuracies for categories which cover large contiguous areas. However, in the categories covering smaller areas within the map there was generally an improvement of at least 10%. Classification of the important residential category improved 12%, and wetlands were mapped with 11% greater accuracy.

Drought Impacts on Agricultural Production and Land Fallowing in California's Central Valley in 2015

NASA Technical Reports Server (NTRS)

Rosevelt, Carolyn; Melton, Forrest S.; Johnson, Lee; Guzman, Alberto; Verdin, James P.; Thenkabail, Prasad S.; Mueller, Rick; Jones, Jeanine; Willis, Patrick

2016-01-01

The ongoing drought in California substantially reduced surface water supplies for millions of acres of irrigated farmland in California's Central Valley. Rapid assessment of drought impacts on agricultural production can aid water managers in assessing mitigation options, and guide decision making with respect to mitigation of drought impacts. Satellite remote sensing offers an efficient way to provide quantitative assessments of drought impacts on agricultural production and increases in fallow acreage associated with reductions in water supply. A key advantage of satellite-based assessments is that they can provide a measure of land fallowing that is consistent across both space and time. We describe an approach for monthly and seasonal mapping of uncultivated agricultural acreage developed as part of a joint effort by USGS, USDA, NASA, and the California Department of Water Resources to provide timely assessments of land fallowing during drought events. This effort has used the Central Valley of California as a pilot region for development and testing of an operational approach. To provide quantitative measures of uncultivated agricultural acreage from satellite data early in the season, we developed a decision tree algorithm and applied it to time-series data from Landsat TM (Thematic Mapper), ETM+ (Enhanced Thematic Mapper Plus), OLI (Operational Land Imager), and MODIS (Moderate Resolution Imaging Spectroradiometer). Our effort has been focused on development of indicators of drought impacts in the March-August timeframe based on measures of crop development patterns relative to a reference period with average or above average rainfall. To assess the accuracy of the algorithms, monthly ground validation surveys were conducted across 650 fields from March-September in 2014 and 2015. We present the algorithm along with updated results from the accuracy assessment, and data and maps of land fallowing in the Central Valley in 2015.

Drought Impacts on Agricultural Production and Land Fallowing in California's Central Valley in 2015

NASA Astrophysics Data System (ADS)

Rosevelt, C.; Melton, F. S.; Johnson, L.; Guzman, A.; Verdin, J. P.; Thenkabail, P. S.; Mueller, R.; Jones, J.; Willis, P.

2015-12-01

The ongoing drought in California substantially reduced surface water supplies for millions of acres of irrigated farmland in California's Central Valley. Rapid assessment of drought impacts on agricultural production can aid water managers in assessing mitigation options, and guide decision making with respect to mitigation of drought impacts. Satellite remote sensing offers an efficient way to provide quantitative assessments of drought impacts on agricultural production and increases in fallow acreage associated with reductions in water supply. A key advantage of satellite-based assessments is that they can provide a measure of land fallowing that is consistent across both space and time. We describe an approach for monthly and seasonal mapping of uncultivated agricultural acreage developed as part of a joint effort by USGS, USDA, NASA, and the California Department of Water Resources to provide timely assessments of land fallowing during drought events. This effort has used the Central Valley of California as a pilot region for development and testing of an operational approach. To provide quantitative measures of uncultivated agricultural acreage from satellite data early in the season, we developed a decision tree algorithm and applied it to timeseries of data from Landsat TM, ETM+, OLI, and MODIS. Our effort has been focused on development of indicators of drought impacts in the March - August timeframe based on measures of crop development patterns relative to a reference period with average or above average rainfall. To assess the accuracy of the algorithms, monthly ground validation surveys were conducted across 650 fields from March - September in 2014 and 2015. We present the algorithm along with updated results from the accuracy assessment, and data and maps of land fallowing in the Central Valley in 2015.

Mapping Drought Impacts on Agricultural Production in California's Central Valley

NASA Astrophysics Data System (ADS)

Melton, F. S.; Guzman, A.; Johnson, L.; Rosevelt, C.; Verdin, J. P.; Dwyer, J. L.; Mueller, R.; Zakzeski, A.; Thenkabail, P. S.; Wallace, C.; Jones, J.; Windell, S.; Urness, J.; Teaby, A.; Hamblin, D.; Post, K. M.; Nemani, R. R.

2014-12-01

The ongoing drought in California has substantially reduced surface water supplies for millions of acres of irrigated farmland in California's Central Valley. Rapid assessment of drought impacts on agricultural production can aid water managers in assessing mitigation options, and guide decision making with respect to requests for local water transfers, county drought disaster designations, and allocation of emergency funds to mitigate drought impacts. Satellite remote sensing offers an efficient way to provide quantitative assessments of drought impacts on agricultural production and increases in idle acreage associated with reductions in water supply. A key advantage of satellite-based assessments is that they can provide a measure of land fallowing that is consistent across both space and time. We describe an approach for monthly and seasonal mapping of uncultivated agricultural acreage developed as part of a joint effort by USGS, USDA, NASA, and the California Department of Water Resources to provide timely assessments of land fallowing during drought events. This effort has used the Central Valley of California as a pilot region for development and testing of an operational approach. To provide quantitative measures of uncultivated agricultural acreage from satellite data early in the season, we developed a decision tree algorithm and applied it to timeseries of data from Landsat TM, ETM+, OLI, and MODIS. Our effort has been focused on development of indicators of drought impacts in the March - August timeframe based on measures of crop development patterns relative to a reference period with average or above average rainfall. To assess the accuracy of the algorithms, monthly ground validation surveys were conducted across 640 fields from March - September, 2014. We present the algorithm along with updated results from the accuracy assessment, and discuss potential applications to other regions.

Advancing UAS methods for monitoring coastal environments

NASA Astrophysics Data System (ADS)

Ridge, J.; Seymour, A.; Rodriguez, A. B.; Dale, J.; Newton, E.; Johnston, D. W.

2017-12-01

Utilizing fixed-wing Unmanned Aircraft Systems (UAS), we are working to improve coastal monitoring by increasing the accuracy, precision, temporal resolution, and spatial coverage of habitat distribution maps. Generally, multirotor aircraft are preferred for precision imaging, but recent advances in fixed-wing technology have greatly increased their capabilities and application for fine-scale (decimeter-centimeter) measurements. Present mapping methods employed by North Carolina coastal managers involve expensive, time consuming and localized observation of coastal environments, which often lack the necessary frequency to make timely management decisions. For example, it has taken several decades to fully map oyster reefs along the NC coast, making it nearly impossible to track trends in oyster reef populations responding to harvesting pressure and water quality degradation. It is difficult for the state to employ manned flights for collecting aerial imagery to monitor intertidal oyster reefs, because flights are usually conducted after seasonal increases in turbidity. In addition, post-storm monitoring of coastal erosion from manned platforms is often conducted days after the event and collects oblique aerial photographs which are difficult to use for accurately measuring change. Here, we describe how fixed wing UAS and standard RGB sensors can be used to rapidly quantify and assess critical coastal habitats (e.g., barrier islands, oyster reefs, etc.), providing for increased temporal frequency to isolate long-term and event-driven (storms, harvesting) impacts. Furthermore, drone-based approaches can accurately image intertidal habitats as well as resolve information such as vegetation density and bathymetry from shallow submerged areas. We obtain UAS imagery of a barrier island and oyster reefs under ideal conditions (low tide, turbidity, and sun angle) to create high resolution (cm scale) maps and digital elevation models to assess habitat condition. Concurrently, we test the accuracy of UAS platforms and image analysis tools against traditional high-resolution mapping equipment (GPS and terrestrial lidar) and in situ sampling (density quadrats) to conduct error analysis of UAS orthoimagery and data processing.

Changes in the Extent of Surface Mining and Reclamation in the Central Appalachians Detected Using a 1976-2006 Landsat Time Series

NASA Technical Reports Server (NTRS)

Townsend, Philip A.; Helmers, David P.; Kingdon, Clayton C.; McNeil, Brenden E.; de Beurs, Kirsten M.; Eshleman, Keith N.

2009-01-01

Surface mining and reclamation is the dominant driver of land cover land use change (LCLUC) in the Central Appalachian Mountain region of the Eastern U.S. Accurate quantification of the extent of mining activities is important for assessing how this LCLUC affects ecosystem services such as aesthetics, biodiversity, and mitigation of flooding.We used Landsat imagery from 1976, 1987, 1999 and 2006 to map the extent of surface mines and mine reclamation for eight large watersheds in the Central Appalachian region of West Virginia, Maryland and Pennsylvania. We employed standard image processing techniques in conjunction with a temporal decision tree and GIS maps of mine permits and wetlands to map active and reclaimed mines and track changes through time. For the entire study area, active surface mine extent was highest in 1976, prior to implementation of the Surface Mine Control and Reclamation Act in 1977, with 1.76% of the study area in active mines, declining to 0.44% in 2006. The most extensively mined watershed, Georges Creek in Maryland, was 5.45% active mines in 1976, declining to 1.83% in 2006. For the entire study area, the area of reclaimed mines increased from 1.35% to 4.99% from 1976 to 2006, and from 4.71% to 15.42% in Georges Creek. Land cover conversion to mines and then reclaimed mines after 1976 was almost exclusively from forest. Accuracy levels for mined and reclaimed cover was above 85% for all time periods, and was generally above 80% for mapping active and reclaimed mines separately, especially for the later time periods in which good accuracy assessment data were available. Among other implications, the mapped patterns of LCLUC are likely to significantly affect watershed hydrology, as mined and reclaimed areas have lower infiltration capacity and thus more rapid runoff than unmined forest watersheds, leading to greater potential for extreme flooding during heavy rainfall events.

Multispectral Thermal Imagery and Its Application to the Geologic Mapping of the Koobi Fora Formation, Northwestern Kenya

DOE Office of Scientific and Technical Information (OSTI.GOV)

Green, Mary K.

The Koobi Fora Formation in northwestern Kenya has yielded more hominin fossils dated between 2.1 and 1.2 Ma than any other location on Earth. This research was undertaken to discover the spectral signatures of a portion of the Koobi Fora Formation using imagery from the DOE's Multispectral Thermal Imager (MTI) satellite. Creation of a digital geologic map from MTI imagery was a secondary goal of this research. MTI is unique amongst multispectral satellites in that it co-collects data from 15 spectral bands ranging from the visible to the thermal infrared with a ground sample distance of 5 meters per pixelmore » in the visible and 20 meters in the infrared. The map was created in two stages. The first was to correct the base MTI image using spatial accuracy assessment points collected in the field. The second was to mosaic various MTI images together to create the final Koobi Fora map. Absolute spatial accuracy of the final map product is 73 meters. The geologic classification of the Koobi Fora MTI map also took place in two stages. The field work stage involved location of outcrops of different lithologies within the Koobi Fora Formation. Field descriptions of these outcrops were made and their locations recorded. During the second stage, a linear spectral unmixing algorithm was applied to the MTI mosaic. In order to train the linear spectra unmixing algorithm, regions of interest representing four different classes of geologic material (tuff, alluvium, carbonate, and basalt), as well as a vegetation class were defined within the MTI mosaic. The regions of interest were based upon the aforementioned field data as well as overlays of geologic maps from the 1976 Iowa State mapping project. Pure spectra were generated for each class from the regions of interest, and then the unmixing algorithm classified each pixel according to relative percentage of classes found within the pixel based upon the pure spectra values. A total of four unique combinations of geologic classes were analyzed using the algorithm. The tuffs within the Koobi Fora Formation were defined with 100% accuracy using a combination of pure spectra from the basalt, vegetation, and tuff.« less

Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey).

PubMed

Akgun, Aykut; Kıncal, Cem; Pradhan, Biswajeet

2012-09-01

In this study, landslide risk assessment for Izmir city (west Turkey) was carried out, and the environmental effects of landslides on further urban development were evaluated using geographical information systems and remote sensing techniques. For this purpose, two different data groups, namely conditioning and triggering data, were produced. With the help of conditioning data such as lithology, slope gradient, slope aspect, distance from roads, distance from faults and distance from drainage lines, a landslide susceptibility model was constructed by using logistic regression modelling approach. The accuracy assessment of the susceptibility map was carried out by the area under curvature (AUC) approach, and a 0.810 AUC value was obtained. This value shows that the map obtained is successful. Due to the fact that the study area is located in an active seismic region, earthquake data were considered as primary triggering factor contributing to landslide occurrence. In addition to this, precipitation data were also taken into account as a secondary triggering factor. Considering the susceptibility data and triggering factors, a landslide hazard index was obtained. Furthermore, using the Aster data, a land-cover map was produced with an overall kappa value of 0.94. From this map, settlement areas were extracted, and these extracted data were assessed as elements at risk in the study area. Next, a vulnerability index was created by using these data. Finally, the hazard index and the vulnerability index were combined, and a landslide risk map for Izmir city was obtained. Based on this final risk map, it was observed that especially south and north parts of the Izmir Bay, where urbanization is dense, are threatened to future landsliding. This result can be used for preliminary land use planning by local governmental authorities.

Temporal Comparison Between NIRS and EEG Signals During a Mental Arithmetic Task Evaluated with Self-Organizing Maps.

PubMed

Oyama, Katsunori; Sakatani, Kaoru

2016-01-01

Simultaneous monitoring of brain activity with near-infrared spectroscopy and electroencephalography allows spatiotemporal reconstruction of the hemodynamic response regarding the concentration changes in oxyhemoglobin and deoxyhemoglobin that are associated with recorded brain activity such as cognitive functions. However, the accuracy of state estimation during mental arithmetic tasks is often different depending on the length of the segment for sampling of NIRS and EEG signals. This study compared the results of a self-organizing map and ANOVA, which were both used to assess the accuracy of state estimation. We conducted an experiment with a mental arithmetic task performed by 10 participants. The lengths of the segment in each time frame for observation of NIRS and EEG signals were compared with the 30-s, 1-min, and 2-min segment lengths. The optimal segment lengths were different for NIRS and EEG signals in the case of classification of feature vectors into the states of performing a mental arithmetic task and being at rest.

Cognitive impairment categorized in community-dwelling older adults with and without dementia using in-home sensors that recognise activities of daily living

NASA Astrophysics Data System (ADS)

Urwyler, Prabitha; Stucki, Reto; Rampa, Luca; Müri, René; Mosimann, Urs P.; Nef, Tobias

2017-02-01

Cognitive impairment due to dementia decreases functionality in Activities of Daily Living (ADL). Its assessment is useful to identify care needs, risks and monitor disease progression. This study investigates differences in ADL pattern-performance between dementia patients and healthy controls using unobtrusive sensors. Around 9,600 person-hours of activity data were collected from the home of ten dementia patients and ten healthy controls using a wireless-unobtrusive sensors and analysed to detect ADL. Recognised ADL were visualized using activity maps, the heterogeneity and accuracy to discriminate patients from healthy were analysed. Activity maps of dementia patients reveal unorganised behaviour patterns and heterogeneity differed significantly between the healthy and diseased. The discriminating accuracy increases with observation duration (0.95 for 20 days). Unobtrusive sensors quantify ADL-relevant behaviour, useful to uncover the effect of cognitive impairment, to quantify ADL-relevant changes in the course of dementia and to measure outcomes of anti-dementia treatments.

Comparative study of landslides susceptibility mapping methods: Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN)

NASA Astrophysics Data System (ADS)

Salleh, S. A.; Rahman, A. S. A. Abd; Othman, A. N.; Mohd, W. M. N. Wan

2018-02-01

As different approach produces different results, it is crucial to determine the methods that are accurate in order to perform analysis towards the event. This research aim is to compare the Rank Reciprocal (MCDM) and Artificial Neural Network (ANN) analysis techniques in determining susceptible zones of landslide hazard. The study is based on data obtained from various sources such as local authority; Dewan Bandaraya Kuala Lumpur (DBKL), Jabatan Kerja Raya (JKR) and other agencies. The data were analysed and processed using Arc GIS. The results were compared by quantifying the risk ranking and area differential. It was also compared with the zonation map classified by DBKL. The results suggested that ANN method gives better accuracy compared to MCDM with 18.18% higher accuracy assessment of the MCDM approach. This indicated that ANN provides more reliable results and it is probably due to its ability to learn from the environment thus portraying realistic and accurate result.

Mapping Prosopis spp. with Landsat 8 data in arid environments: Evaluating effectiveness of different methods and temporal imagery selection for Hargeisa, Somaliland

NASA Astrophysics Data System (ADS)

Ng, Wai-Tim; Meroni, Michele; Immitzer, Markus; Böck, Sebastian; Leonardi, Ugo; Rembold, Felix; Gadain, Hussein; Atzberger, Clement

2016-12-01

Prosopis spp. is a fast and aggressive invader threatening many arid and semi-arid areas globally. The species is native to the American dry zones and was introduced in Somaliland for dune stabilization and fuel wood production in the 1970⿿s and 1980⿿s. Its deep rooting system is capable of tapping into the groundwater table thereby reducing its reliance on infrequent rainfalls and near-surface water. The competitive advantage of Prosopis is further fuelled by the hybridization of the many introduced subspecies that made the plant capable of adapting to the new environment and replacing endemic species. This study aimed to test the mapping accuracy achievable with Landsat 8 data acquired during the wet and the dry seasons within a Random Forest (RF) classifier, using both pixel- and object-based approaches. Maps are produced for the Hargeisa area (Somaliland), where reference data was collected during the dry season of 2015. Results were assessed through a 10-fold cross-validation procedure. In our study, the highest overall accuracy (74%) was achieved when applying a pixel-based classification using a combination of the wet and dry season Earth observation data. Object-based mapping were less reliable due to the limitations in spatial resolution of the Landsat data (15⿿30 m) and problems in finding an appropriate segmentation scale.

The automated reference toolset: A soil-geomorphic ecological potential matching algorithm

USGS Publications Warehouse

Nauman, Travis; Duniway, Michael C.

2016-01-01

Ecological inventory and monitoring data need referential context for interpretation. Identification of appropriate reference areas of similar ecological potential for site comparison is demonstrated using a newly developed automated reference toolset (ART). Foundational to identification of reference areas was a soil map of particle size in the control section (PSCS), a theme in US Soil Taxonomy. A 30-m resolution PSCS map of the Colorado Plateau (366,000 km2) was created by interpolating ∼5000 field soil observations using a random forest model and a suite of raster environmental spatial layers representing topography, climate, general ecological community, and satellite imagery ratios. The PSCS map had overall out of bag accuracy of 61.8% (Kappa of 0.54, p < 0.0001), and an independent validation accuracy of 93.2% at a set of 356 field plots along the southern edge of Canyonlands National Park, Utah. The ART process was also tested at these plots, and matched plots with the same ecological sites (ESs) 67% of the time where sites fell within 2-km buffers of each other. These results show that the PSCS and ART have strong application for ecological monitoring and sampling design, as well as assessing impacts of disturbance and land management action using an ecological potential framework. Results also demonstrate that PSCS could be a key mapping layer for the USDA-NRCS provisional ES development initiative.

Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing.

PubMed

William, David J; Rybicki, Nancy B; Lombana, Alfonso V; O'Brien, Tim M; Gomez, Richard B

2003-01-01

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.

Analyzing thematic maps and mapping for accuracy

USGS Publications Warehouse

Rosenfield, G.H.

1982-01-01

Two problems which exist while attempting to test the accuracy of thematic maps and mapping are: (1) evaluating the accuracy of thematic content, and (2) evaluating the effects of the variables on thematic mapping. Statistical analysis techniques are applicable to both these problems and include techniques for sampling the data and determining their accuracy. In addition, techniques for hypothesis testing, or inferential statistics, are used when comparing the effects of variables. A comprehensive and valid accuracy test of a classification project, such as thematic mapping from remotely sensed data, includes the following components of statistical analysis: (1) sample design, including the sample distribution, sample size, size of the sample unit, and sampling procedure; and (2) accuracy estimation, including estimation of the variance and confidence limits. Careful consideration must be given to the minimum sample size necessary to validate the accuracy of a given. classification category. The results of an accuracy test are presented in a contingency table sometimes called a classification error matrix. Usually the rows represent the interpretation, and the columns represent the verification. The diagonal elements represent the correct classifications. The remaining elements of the rows represent errors by commission, and the remaining elements of the columns represent the errors of omission. For tests of hypothesis that compare variables, the general practice has been to use only the diagonal elements from several related classification error matrices. These data are arranged in the form of another contingency table. The columns of the table represent the different variables being compared, such as different scales of mapping. The rows represent the blocking characteristics, such as the various categories of classification. The values in the cells of the tables might be the counts of correct classification or the binomial proportions of these counts divided by either the row totals or the column totals from the original classification error matrices. In hypothesis testing, when the results of tests of multiple sample cases prove to be significant, some form of statistical test must be used to separate any results that differ significantly from the others. In the past, many analyses of the data in this error matrix were made by comparing the relative magnitudes of the percentage of correct classifications, for either individual categories, the entire map or both. More rigorous analyses have used data transformations and (or) two-way classification analysis of variance. A more sophisticated step of data analysis techniques would be to use the entire classification error matrices using the methods of discrete multivariate analysis or of multiviariate analysis of variance.

Gates to Gregg High Voltage Transmission Line Study. [California

NASA Technical Reports Server (NTRS)

Bergis, V.; Maw, K.; Newland, W.; Sinnott, D.; Thornbury, G.; Easterwood, P.; Bonderud, J.

1982-01-01

The usefulness of LANDSAT data in the planning of transmission line routes was assessed. LANDSAT digital data and image processing techniques, specifically a multi-date supervised classification aproach, were used to develop a land cover map for an agricultural area near Fresno, California. Twenty-six land cover classes were identified, of which twenty classes were agricultural crops. High classification accuracies (greater than 80%) were attained for several classes, including cotton, grain, and vineyards. The primary products generated were 1:24,000, 1:100,000 and 1:250,000 scale maps of the classification and acreage summaries for all land cover classes within four alternate transmission line routes.

A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery.

PubMed

Huang, Huasheng; Deng, Jizhong; Lan, Yubin; Yang, Aqing; Deng, Xiaoling; Zhang, Lei

2018-01-01

Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.

Accuracy and precision of stream reach water surface slopes estimated in the field and from maps

USGS Publications Warehouse

Isaak, D.J.; Hubert, W.A.; Krueger, K.L.

1999-01-01

The accuracy and precision of five tools used to measure stream water surface slope (WSS) were evaluated. Water surface slopes estimated in the field with a clinometer or from topographic maps used in conjunction with a map wheel or geographic information system (GIS) were significantly higher than WSS estimated in the field with a surveying level (biases of 34, 41, and 53%, respectively). Accuracy of WSS estimates obtained with an Abney level did not differ from surveying level estimates, but conclusions regarding the accuracy of Abney levels and clinometers were weakened by intratool variability. The surveying level estimated WSS most precisely (coefficient of variation [CV] = 0.26%), followed by the GIS (CV = 1.87%), map wheel (CV = 6.18%), Abney level (CV = 13.68%), and clinometer (CV = 21.57%). Estimates of WSS measured in the field with an Abney level and estimated for the same reaches with a GIS used in conjunction with l:24,000-scale topographic maps were significantly correlated (r = 0.86), but there was a tendency for the GIS to overestimate WSS. Detailed accounts of the methods used to measure WSS and recommendations regarding the measurement of WSS are provided.

Proceedings of the 2004 High Spatial Resolution Commercial Imagery Workshop

NASA Technical Reports Server (NTRS)

2006-01-01

Topics covered include: NASA Applied Sciences Program; USGS Land Remote Sensing: Overview; QuickBird System Status and Product Overview; ORBIMAGE Overview; IKONOS 2004 Calibration and Validation Status; OrbView-3 Spatial Characterization; On-Orbit Modulation Transfer Function (MTF) Measurement of QuickBird; Spatial Resolution Characterization for QuickBird Image Products 2003-2004 Season; Image Quality Evaluation of QuickBird Super Resolution and Revisit of IKONOS: Civil and Commercial Application Project (CCAP); On-Orbit System MTF Measurement; QuickBird Post Launch Geopositional Characterization Update; OrbView-3 Geometric Calibration and Geopositional Accuracy; Geopositional Statistical Methods; QuickBird and OrbView-3 Geopositional Accuracy Assessment; Initial On-Orbit Spatial Resolution Characterization of OrbView-3 Panchromatic Images; Laboratory Measurement of Bidirectional Reflectance of Radiometric Tarps; Stennis Space Center Verification and Validation Capabilities; Joint Agency Commercial Imagery Evaluation (JACIE) Team; Adjacency Effects in High Resolution Imagery; Effect of Pulse Width vs. GSD on MTF Estimation; Camera and Sensor Calibration at the USGS; QuickBird Geometric Verification; Comparison of MODTRAN to Heritage-based Results in Vicarious Calibration at University of Arizona; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Estimating Sub-Pixel Proportions of Sagebrush with a Regression Tree; How Do YOU Use the National Land Cover Dataset?; The National Map Hazards Data Distribution System; Recording a Troubled World; What Does This-Have to Do with This?; When Can a Picture Save a Thousand Homes?; InSAR Studies of Alaska Volcanoes; Earth Observing-1 (EO-1) Data Products; Improving Access to the USGS Aerial Film Collections: High Resolution Scanners; Improving Access to the USGS Aerial Film Collections: Phoenix Digitizing System Product Distribution; System and Product Characterization: Issues Approach; Innovative Approaches to Analysis of Lidar Data for the National Map; Changes in Imperviousness near Military Installations; Geopositional Accuracy Evaluations of QuickBird and OrbView-3: Civil and Commercial Applications Project (CCAP); Geometric Accuracy Assessment: OrbView ORTHO Products; QuickBird Radiometric Calibration Update; OrbView-3 Radiometric Calibration; QuickBird Radiometric Characterization; NASA Radiometric Characterization; Establishing and Verifying the Traceability of Remote-Sensing Measurements to International Standards; QuickBird Applications; Airport Mapping and Perpetual Monitoring Using IKONOS; OrbView-3 Relative Accuracy Results and Impacts on Exploitation and Accuracy Improvement; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Applying High-Resolution Satellite Imagery and Remotely Sensed Data to Local Government Applications: Sioux Falls, South Dakota; Automatic Co-Registration of QuickBird Data for Change Detection Applications; Developing Coastal Surface Roughness Maps Using ASTER and QuickBird Data Sources; Automated, Near-Real Time Cloud and Cloud Shadow Detection in High Resolution VNIR Imagery; Science Applications of High Resolution Imagery at the USGS EROS Data Center; Draft Plan for Characterizing Commercial Data Products in Support of Earth Science Research; Atmospheric Correction Prototype Algorithm for High Spatial Resolution Multispectral Earth Observing Imaging Systems; Determining Regional Arctic Tundra Carbon Exchange: A Bottom-Up Approach; Using IKONOS Imagery to Assess Impervious Surface Area, Riparian Buffers and Stream Health in the Mid-Atlantic Region; Commercial Remote Sensing Space Policy Civil Implementation Update; USGS Commercial Remote Sensing Data Contracts (CRSDC); and Commercial Remote Sensing Space Policy (CRSSP): Civil Near-Term Requirements Collection Update.

Predictive model of outcome of targeted nodal assessment in colorectal cancer.

PubMed

Nissan, Aviram; Protic, Mladjan; Bilchik, Anton; Eberhardt, John; Peoples, George E; Stojadinovic, Alexander

2010-02-01

Improvement in staging accuracy is the principal aim of targeted nodal assessment in colorectal carcinoma. Technical factors independently predictive of false negative (FN) sentinel lymph node (SLN) mapping should be identified to facilitate operative decision making. To define independent predictors of FN SLN mapping and to develop a predictive model that could support surgical decisions. Data was analyzed from 2 completed prospective clinical trials involving 278 patients with colorectal carcinoma undergoing SLN mapping. Clinical outcome of interest was FN SLN(s), defined as one(s) with no apparent tumor cells in the presence of non-SLN metastases. To assess the independent predictive effect of a covariate for a nominal response (FN SLN), a logistic regression model was constructed and parameters estimated using maximum likelihood. A probabilistic Bayesian model was also trained and cross validated using 10-fold train-and-test sets to predict FN SLN mapping. Area under the curve (AUC) from receiver operating characteristics curves of these predictions was calculated to determine the predictive value of the model. Number of SLNs (<3; P = 0.03) and tumor-replaced nodes (P < 0.01) independently predicted FN SLN. Cross validation of the model created with Bayesian Network Analysis effectively predicted FN SLN (area under the curve = 0.84-0.86). The positive and negative predictive values of the model are 83% and 97%, respectively. This study supports a minimum threshold of 3 nodes for targeted nodal assessment in colorectal cancer, and establishes sufficient basis to conclude that SLN mapping and biopsy cannot be justified in the presence of clinically apparent tumor-replaced nodes.

Integration of electro-anatomical and imaging data of the left ventricle: An evaluation framework.

PubMed

Soto-Iglesias, David; Butakoff, Constantine; Andreu, David; Fernández-Armenta, Juan; Berruezo, Antonio; Camara, Oscar

2016-08-01

Integration of electrical and structural information for scar characterization in the left ventricle (LV) is a crucial step to better guide radio-frequency ablation therapies, which are usually performed in complex ventricular tachycardia (VT) cases. This integration requires finding a common representation where to map the electrical information from the electro-anatomical map (EAM) surfaces and tissue viability information from delay-enhancement magnetic resonance images (DE-MRI). However, the development of a consistent integration method is still an open problem due to the lack of a proper evaluation framework to assess its accuracy. In this paper we present both: (i) an evaluation framework to assess the accuracy of EAM and imaging integration strategies with simulated EAM data and a set of global and local measures; and (ii) a new integration methodology based on a planar disk representation where the LV surface meshes are quasi-conformally mapped (QCM) by flattening, allowing for simultaneous visualization and joint analysis of the multi-modal data. The developed evaluation framework was applied to estimate the accuracy of the QCM-based integration strategy on a benchmark dataset of 128 synthetically generated ground-truth cases presenting different scar configurations and EAM characteristics. The obtained results demonstrate a significant reduction in global overlap errors (50-100%) with respect to state-of-the-art integration techniques, also better preserving the local topology of small structures such as conduction channels in scars. Data from seventeen VT patients were also used to study the feasibility of the QCM technique in a clinical setting, consistently outperforming the alternative integration techniques in the presence of sparse and noisy clinical data. The proposed evaluation framework has allowed a rigorous comparison of different EAM and imaging data integration strategies, providing useful information to better guide clinical practice in complex cardiac interventions. Copyright © 2016 Elsevier B.V. All rights reserved.

Probabilistic atlas-based segmentation of combined T1-weighted and DUTE MRI for calculation of head attenuation maps in integrated PET/MRI scanners.

PubMed

Poynton, Clare B; Chen, Kevin T; Chonde, Daniel B; Izquierdo-Garcia, David; Gollub, Randy L; Gerstner, Elizabeth R; Batchelor, Tracy T; Catana, Ciprian

2014-01-01

We present a new MRI-based attenuation correction (AC) approach for integrated PET/MRI systems that combines both segmentation- and atlas-based methods by incorporating dual-echo ultra-short echo-time (DUTE) and T1-weighted (T1w) MRI data and a probabilistic atlas. Segmented atlases were constructed from CT training data using a leave-one-out framework and combined with T1w, DUTE, and CT data to train a classifier that computes the probability of air/soft tissue/bone at each voxel. This classifier was applied to segment the MRI of the subject of interest and attenuation maps (μ-maps) were generated by assigning specific linear attenuation coefficients (LACs) to each tissue class. The μ-maps generated with this "Atlas-T1w-DUTE" approach were compared to those obtained from DUTE data using a previously proposed method. For validation of the segmentation results, segmented CT μ-maps were considered to the "silver standard"; the segmentation accuracy was assessed qualitatively and quantitatively through calculation of the Dice similarity coefficient (DSC). Relative change (RC) maps between the CT and MRI-based attenuation corrected PET volumes were also calculated for a global voxel-wise assessment of the reconstruction results. The μ-maps obtained using the Atlas-T1w-DUTE classifier agreed well with those derived from CT; the mean DSCs for the Atlas-T1w-DUTE-based μ-maps across all subjects were higher than those for DUTE-based μ-maps; the atlas-based μ-maps also showed a lower percentage of misclassified voxels across all subjects. RC maps from the atlas-based technique also demonstrated improvement in the PET data compared to the DUTE method, both globally as well as regionally.

Analysis of building deformation in landslide area using multisensor PSInSAR™ technique.

PubMed

Ciampalini, Andrea; Bardi, Federica; Bianchini, Silvia; Frodella, William; Del Ventisette, Chiara; Moretti, Sandro; Casagli, Nicola

2014-12-01

Buildings are sensitive to movements caused by ground deformation. The mapping both of spatial and temporal distribution, and of the degree of building damages represents a useful tool in order to understand the landslide evolution, magnitude and stress distribution. The high spatial resolution of space-borne SAR interferometry can be used to monitor displacements related to building deformations. In particular, PSInSAR technique is used to map and monitor ground deformation with millimeter accuracy. The usefulness of the above mentioned methods was evaluated in San Fratello municipality (Sicily, Italy), which was historically affected by landslides: the most recent one occurred on 14th February 2010. PSInSAR data collected by ERS 1/2, ENVISAT, RADARSAT-1 were used to study the building deformation velocities before the 2010 landslide. The X-band sensors COSMO-SkyMed and TerraSAR-X were used in order to monitor the building deformation after this event. During 2013, after accurate field inspection on buildings and structures, damage assessment map of San Fratello were created and then compared to the building deformation velocity maps. The most interesting results were obtained by the comparison between the building deformation velocity map obtained through COSMO-SkyMed and the damage assessment map. This approach can be profitably used by local and Civil Protection Authorities to manage the post-event phase and evaluate the residual risks.

Unmanned aircraft systems image collection and computer vision image processing for surveying and mapping that meets professional needs

NASA Astrophysics Data System (ADS)

Peterson, James Preston, II

Unmanned Aerial Systems (UAS) are rapidly blurring the lines between traditional and close range photogrammetry, and between surveying and photogrammetry. UAS are providing an economic platform for performing aerial surveying on small projects. The focus of this research was to describe traditional photogrammetric imagery and Light Detection and Ranging (LiDAR) geospatial products, describe close range photogrammetry (CRP), introduce UAS and computer vision (CV), and investigate whether industry mapping standards for accuracy can be met using UAS collection and CV processing. A 120-acre site was selected and 97 aerial targets were surveyed for evaluation purposes. Four UAS flights of varying heights above ground level (AGL) were executed, and three different target patterns of varying distances between targets were analyzed for compliance with American Society for Photogrammetry and Remote Sensing (ASPRS) and National Standard for Spatial Data Accuracy (NSSDA) mapping standards. This analysis resulted in twelve datasets. Error patterns were evaluated and reasons for these errors were determined. The relationship between the AGL, ground sample distance, target spacing and the root mean square error of the targets is exploited by this research to develop guidelines that use the ASPRS and NSSDA map standard as the template. These guidelines allow the user to select the desired mapping accuracy and determine what target spacing and AGL is required to produce the desired accuracy. These guidelines also address how UAS/CV phenomena affect map accuracy. General guidelines and recommendations are presented that give the user helpful information for planning a UAS flight using CV technology.

Accuracy of PDFF estimation by magnitude-based and complex-based MRI in children with MR spectroscopy as a reference.

PubMed

Haufe, William M; Wolfson, Tanya; Hooker, Catherine A; Hooker, Jonathan C; Covarrubias, Yesenia; Schlein, Alex N; Hamilton, Gavin; Middleton, Michael S; Angeles, Jorge E; Hernando, Diego; Reeder, Scott B; Schwimmer, Jeffrey B; Sirlin, Claude B

2017-12-01

To assess and compare the accuracy of magnitude-based magnetic resonance imaging (MRI-M) and complex-based MRI (MRI-C) for estimating hepatic proton density fat fraction (PDFF) in children, using MR spectroscopy (MRS) as the reference standard. A secondary aim was to assess the agreement between MRI-M and MRI-C. This was a HIPAA-compliant, retrospective analysis of data collected in children enrolled in prospective, Institutional Review Board (IRB)-approved studies between 2012 and 2014. Informed consent was obtained from 200 children (ages 8-19 years) who subsequently underwent 3T MR exams that included MRI-M, MRI-C, and T 1 -independent, T 2 -corrected, single-voxel stimulated echo acquisition mode (STEAM) MRS. Both MRI methods acquired six echoes at low flip angles. T2*-corrected PDFF parametric maps were generated. PDFF values were recorded from regions of interest (ROIs) drawn on the maps in each of the nine Couinaud segments and three ROIs colocalized to the MRS voxel location. Regression analyses assessing agreement with MRS were performed to evaluate the accuracy of each MRI method, and Bland-Altman and intraclass correlation coefficient (ICC) analyses were performed to assess agreement between the MRI methods. MRI-M and MRI-C PDFF were accurate relative to the colocalized MRS reference standard, with regression intercepts of 0.63% and -0.07%, slopes of 0.998 and 0.975, and proportion-of-explained-variance values (R 2 ) of 0.982 and 0.979, respectively. For individual Couinaud segments and for the whole liver averages, Bland-Altman biases between MRI-M and MRI-C were small (ranging from 0.04 to 1.11%) and ICCs were high (≥0.978). Both MRI-M and MRI-C accurately estimated hepatic PDFF in children, and high intermethod agreement was observed. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1641-1647. © 2017 International Society for Magnetic Resonance in Medicine.

Build Angle: Does It Influence the Accuracy of 3D-Printed Dental Restorations Using Digital Light-Processing Technology?

PubMed

Osman, Reham B; Alharbi, Nawal; Wismeijer, Daniel

The aim of this study was to evaluate the effect of the build orientation/build angle on the dimensional accuracy of full-coverage dental restorations manufactured using digital light-processing technology (DLP-AM). A full dental crown was digitally designed and 3D-printed using DLP-AM. Nine build angles were used: 90, 120, 135, 150, 180, 210, 225, 240, and 270 degrees. The specimens were digitally scanned using a high-resolution optical surface scanner (IScan D104i, Imetric). Dimensional accuracy was evaluated using the digital subtraction technique. The 3D digital files of the scanned printed crowns (test model) were exported in standard tessellation language (STL) format and superimposed on the STL file of the designed crown [reference model] using Geomagic Studio 2014 (3D Systems). The root mean square estimate (RMSE) values were evaluated, and the deviation patterns on the color maps were further assessed. The build angle influenced the dimensional accuracy of 3D-printed restorations. The lowest RMSE was recorded for the 135-degree and 210-degree build angles. However, the overall deviation pattern on the color map was more favorable with the 135-degree build angle in contrast with the 210-degree build angle where the deviation was observed around the critical marginal area. Within the limitations of this study, the recommended build angle using the current DLP system was 135 degrees. Among the selected build angles, it offers the highest dimensional accuracy and the most favorable deviation pattern. It also offers a self-supporting crown geometry throughout the building process.

Monitoring strip mining and reclamation with LANDSAT data in Belmont County, Ohio

NASA Technical Reports Server (NTRS)

Witt, R. G.; Schaal, G. M.; Bly, B. G.

1983-01-01

The utility of LANDSAT digital data for mapping and monitoring surface mines in Belmont County, Ohio was investigated. Two data sets from 1976 and 1979 were processed to classify level 1 land covers and three strip mine categories in order to examine change over time and assess reclamation efforts. The two classifications were compared with aerial photographs. Results of the accuracy assessment show that both classifications are approximately 86 per cent correct, and that surface mine change detection (date-to-date comparison) is facilitated by the digital format of LANDSAT data.

Machine learning-based dual-energy CT parametric mapping

NASA Astrophysics Data System (ADS)

Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W.; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Helo, Rose Al; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C.; Rassouli, Negin; Gilkeson, Robert C.; Traughber, Bryan J.; Cheng, Chee-Wai; Muzic, Raymond F., Jr.

2018-06-01

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρ e), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

Machine learning-based dual-energy CT parametric mapping.

PubMed

Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Al Helo, Rose; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C; Rassouli, Negin; Gilkeson, Robert C; Traughber, Bryan J; Cheng, Chee-Wai; Muzic, Raymond F

2018-06-08

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z eff ), relative electron density (ρ e ), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

Accuracy of three-dimensional multislice view Doppler in diagnosis of morbid adherent placenta

PubMed Central

Abdel Moniem, Alaa M.; Ibrahim, Ahmed; Akl, Sherif A.; Aboul-Enen, Loay; Abdelazim, Ibrahim A.

2015-01-01

Objective To detect the accuracy of the three-dimensional multislice view (3D MSV) Doppler in the diagnosis of morbid adherent placenta (MAP). Material and Methods Fifty pregnant women at ≥28 weeks gestation with suspected MAP were included in this prospective study. Two dimensional (2D) trans-abdominal gray-scale ultrasound scan was performed for the subjects to confirm the gestational age, placental location, and findings suggestive of MAP, followed by the 3D power Doppler and then the 3D MSV Doppler to confirm the diagnosis of MAP. Intraoperative findings and histopathology results of removed uteri in cases managed by emergency hysterectomy were compared with preoperative sonographic findings to detect the accuracy of the 3D MSV Doppler in the diagnosis of MAP. Results The 3D MSV Doppler increased the accuracy and predictive values of the diagnostic criteria of MAP compared with the 3D power Doppler. The sensitivity and negative predictive value (NPV) (79.6% and 82.2%, respectively) of crowded vessels over the peripheral sub-placental zone to detect difficult placental separation and considerable intraoperative blood loss in cases of MAP using the 3D power Doppler was increased to 82.6% and 84%, respectively, using the 3D MSV Doppler. In addition, the sensitivity, specificity, and positive predictive value (PPV) (90.9%, 68.8%, and 47%, respectively) of the disruption of the uterine serosa-bladder interface for the detection of emergency hysterectomy in cases of MAP using the 3D power Doppler was increased to 100%, 71.8%, and 50%, respectively, using the 3D MSV Doppler. Conclusion The 3D MSV Doppler is a useful adjunctive tool to the 3D power Doppler or color Doppler to refine the diagnosis of MAP. PMID:26401104

Assessing the Application of a Geographic Presence-Only Model for Land Suitability Mapping

PubMed Central

Heumann, Benjamin W.; Walsh, Stephen J.; McDaniel, Phillip M.

2011-01-01

Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1,300 sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the Maxent model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results. PMID:21860606

Matching soil grid unit resolutions with polygon unit scales for DNDC modelling of regional SOC pool

NASA Astrophysics Data System (ADS)

Zhang, H. D.; Yu, D. S.; Ni, Y. L.; Zhang, L. M.; Shi, X. Z.

2015-03-01

Matching soil grid unit resolution with polygon unit map scale is important to minimize uncertainty of regional soil organic carbon (SOC) pool simulation as their strong influences on the uncertainty. A series of soil grid units at varying cell sizes were derived from soil polygon units at the six map scales of 1:50 000 (C5), 1:200 000 (D2), 1:500 000 (P5), 1:1 000 000 (N1), 1:4 000 000 (N4) and 1:14 000 000 (N14), respectively, in the Tai lake region of China. Both format soil units were used for regional SOC pool simulation with DeNitrification-DeComposition (DNDC) process-based model, which runs span the time period 1982 to 2000 at the six map scales, respectively. Four indices, soil type number (STN) and area (AREA), average SOC density (ASOCD) and total SOC stocks (SOCS) of surface paddy soils simulated with the DNDC, were attributed from all these soil polygon and grid units, respectively. Subjecting to the four index values (IV) from the parent polygon units, the variation of an index value (VIV, %) from the grid units was used to assess its dataset accuracy and redundancy, which reflects uncertainty in the simulation of SOC. Optimal soil grid unit resolutions were generated and suggested for the DNDC simulation of regional SOC pool, matching with soil polygon units map scales, respectively. With the optimal raster resolution the soil grid units dataset can hold the same accuracy as its parent polygon units dataset without any redundancy, when VIV < 1% of all the four indices was assumed as criteria to the assessment. An quadratic curve regression model y = -8.0 × 10-6x2 + 0.228x + 0.211 (R2 = 0.9994, p < 0.05) was revealed, which describes the relationship between optimal soil grid unit resolution (y, km) and soil polygon unit map scale (1:x). The knowledge may serve for grid partitioning of regions focused on the investigation and simulation of SOC pool dynamics at certain map scale.

Rice crop mapping and change prediction using multi-temporal satellite images in the Mekong Delta, Vietnam

NASA Astrophysics Data System (ADS)

Chen, C. R.; Chen, C. F.; Nguyen, S. T.

2014-12-01

The rice cropping systems in the Vietnamese Mekong Delta (VMD) has been undergoing major changes to cope with developing agro-economics, increasing population and changing climate. Information on rice cropping practices and changes in cropping systems is critical for policymakers to devise successful strategies to ensure food security and rice grain exports for the country. The primary objective of this research is to map rice cropping systems and predict future dynamics of rice cropping systems using the MODIS time-series data of 2002, 2006, and 2010. First, a phenology-based classification approach was applied for the classification and assessment of rice cropping systems in study region. Second, the Cellular Automata-Markov (CA-Markov) models was used to simulate the rice-cropping system map of VMD for 2010. The comparisons between the classification maps and the ground reference data indicated satisfactory results with overall accuracies and Kappa coefficients, respectively, of 81.4% and 0.75 for 2002, 80.6% and 0.74 for 2006 and 85.5% and 0.81 for 2010. The simulated map of rice cropping system for 2010 was extrapolated by CA-Markov model based on the trend of rice cropping systems during 2002~2006. The comparison between predicted scenario and classification map for 2010 presents a reasonably closer agreement. In conclusion, the CA-Markov model performs a powerful tool for the dynamic modeling of changes in rice cropping systems, and the results obtained demonstrate that the approach produces satisfactory results in terms of accuracy, quantitative forecast and spatial pattern changes. Meanwhile, the projections of the future changes would provide useful inputs to the agricultural policy for effective management of the rice cropping practices in VMD.

Influence of pansharpening techniques in obtaining accurate vegetation thematic maps

NASA Astrophysics Data System (ADS)

Ibarrola-Ulzurrun, Edurne; Gonzalo-Martin, Consuelo; Marcello-Ruiz, Javier

2016-10-01

In last decades, there have been a decline in natural resources, becoming important to develop reliable methodologies for their management. The appearance of very high resolution sensors has offered a practical and cost-effective means for a good environmental management. In this context, improvements are needed for obtaining higher quality of the information available in order to get reliable classified images. Thus, pansharpening enhances the spatial resolution of the multispectral band by incorporating information from the panchromatic image. The main goal in the study is to implement pixel and object-based classification techniques applied to the fused imagery using different pansharpening algorithms and the evaluation of thematic maps generated that serve to obtain accurate information for the conservation of natural resources. A vulnerable heterogenic ecosystem from Canary Islands (Spain) was chosen, Teide National Park, and Worldview-2 high resolution imagery was employed. The classes considered of interest were set by the National Park conservation managers. 7 pansharpening techniques (GS, FIHS, HCS, MTF based, Wavelet `à trous' and Weighted Wavelet `à trous' through Fractal Dimension Maps) were chosen in order to improve the data quality with the goal to analyze the vegetation classes. Next, different classification algorithms were applied at pixel-based and object-based approach, moreover, an accuracy assessment of the different thematic maps obtained were performed. The highest classification accuracy was obtained applying Support Vector Machine classifier at object-based approach in the Weighted Wavelet `à trous' through Fractal Dimension Maps fused image. Finally, highlight the difficulty of the classification in Teide ecosystem due to the heterogeneity and the small size of the species. Thus, it is important to obtain accurate thematic maps for further studies in the management and conservation of natural resources.

Behavior Analysis of Novel Wearable Indoor Mapping System Based on 3D-SLAM

PubMed Central

Dorado, Iago; Gesto, Manuel; Arias, Pedro; Lorenzo, Henrique

2018-01-01

This paper presents a Wearable Prototype for indoor mapping developed by the University of Vigo. The system is based on a Velodyne LiDAR, acquiring points with 16 rays for a simplistic or low-density 3D representation of reality. With this, a Simultaneous Localization and Mapping (3D-SLAM) method is developed for the mapping and generation of 3D point clouds of scenarios deprived from GNSS signal. The quality of the system presented is validated through the comparison with a commercial indoor mapping system, Zeb-Revo, from the company GeoSLAM and with a terrestrial LiDAR, Faro Focus3D X330. The first is considered as a relative reference with other mobile systems and is chosen due to its use of the same principle for mapping: SLAM techniques based on Robot Operating System (ROS), while the second is taken as ground-truth for the determination of the final accuracy of the system regarding reality. Results show that the accuracy of the system is mainly determined by the accuracy of the sensor, with little increment in the error introduced by the mapping algorithm. PMID:29498715

Assessment of the Relative Accuracy of Hemispheric-Scale Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Kelly, Richard E.; Riggs, George A.; Chang, Alfred T. C.; Foster, James L.; Houser, Paul (Technical Monitor)

2001-01-01

There are several hemispheric-scale satellite-derived snow-cover maps available, but none has been fully validated. For the period October 23 - December 25, 2000, we compare snow maps of North America derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the National Oceanic and Atmospheric Administration (NOAA) National Operational Hydrologic Remote Sensing Center (NOHRSC), which both rely on satellite data from the visible and near-infrared parts of the spectrum; we also compare MODIS and Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) passive-microwave snow maps. The maps derived from visible and near-infrared data are more accurate for mapping snow cover than are the passive-microwave-derived maps, however discrepancies exist as to the location and extent of the snow cover among those maps. The large (approx. 30 km) footprint of the SSM/I data and the difficulty in distinguishing wet and shallow snow from wet or snow-free ground, reveal differences up to 5.32 million sq km in the amount of snow mapped using MODIS versus SSM/I data. Algorithms that utilize both visible and passive-microwave data, which would take advantage of the all-weather mapping ability of the passive-microwave data, will be refined following the launch of the Advanced Microwave Scanning Radiometer (AMSR) in the fall of 2001.

Remote sensing and GIS-based site suitability analysis for tourism development in Gili Indah, East Lombok

NASA Astrophysics Data System (ADS)

Agnes, Debrina; Nandatama, Akbar; Isdyantoko, Bagus Andi; Aditya Nugraha, Fajri; Ghivarry, Giusti; Putra Aghni, Perwira; ChandraWijaya, Renaldi; Widayani, Prima

2016-11-01

Gili Indah area, located in Jerowaru, East Lombok Regency is a region that classified as farm area in spatial layout planning map of West Nusa Tenggara province. Gili Indah area has a potential as a new tourism attraction within its gilis (local term for ‘small island’). Assessment should be done to prevent ecological disturbance and infringement towards spatial layout planning map caused by incorrect landuse. Land suitability assessment will be done using remote sensing approach whilst satellite imagery being used to get information about ocean ecology and land physical spatial distribution that will be the parameter of tourism land suitability, such as water clarity, ocean current, type of beaches’ substrate, and beach typology. Field observation then will evaluate the accuracy of data extraction also as a material to do reinterpretation. The actual physical condition will be pictured after the spatial model built with GIS by tiered qualitative analysis approach. The result of assessment and mapping of tourism land suitability is that parts of Gili Indah Area (GiliMaringkik, Greater GiliBembeq, and Small GiliBembeq) are suitable for archipelago tourism while the others is not.

Chosen Aspects of the Production of the Basic Map Using Uav Imagery

NASA Astrophysics Data System (ADS)

Kedzierski, M.; Fryskowska, A.; Wierzbicki, D.; Nerc, P.

2016-06-01

For several years there has been an increasing interest in the use of unmanned aerial vehicles in acquiring image data from a low altitude. Considering the cost-effectiveness of the flight time of UAVs vs. conventional airplanes, the use of the former is advantageous when generating large scale accurate ortophotos. Through the development of UAV imagery, we can update large-scale basic maps. These maps are cartographic products which are used for registration, economic, and strategic planning. On the basis of these maps other cartographic maps are produced, for example maps used building planning. The article presents an assessesment of the usefulness of orthophotos based on UAV imagery to upgrade the basic map. In the research a compact, non-metric camera, mounted on a fixed wing powered by an electric motor was used. The tested area covered flat, agricultural and woodland terrains. The processing and analysis of orthorectification were carried out with the INPHO UASMaster programme. Due to the effect of UAV instability on low-altitude imagery, the use of non-metric digital cameras and the low-accuracy GPS-INS sensors, the geometry of images is visibly lower were compared to conventional digital aerial photos (large values of phi and kappa angles). Therefore, typically, low-altitude images require large along- and across-track direction overlap - usually above 70 %. As a result of the research orthoimages were obtained with a resolution of 0.06 meters and a horizontal accuracy of 0.10m. Digitized basic maps were used as the reference data. The accuracy of orthoimages vs. basic maps was estimated based on the study and on the available reference sources. As a result, it was found that the geometric accuracy and interpretative advantages of the final orthoimages allow the updating of basic maps. It is estimated that such an update of basic maps based on UAV imagery reduces processing time by approx. 40%.

Earth-Base: testing the temporal congruency of paleontological collections and geologic maps of North America

NASA Astrophysics Data System (ADS)

Heim, N. A.; Kishor, P.; McClennen, M.; Peters, S. E.

2012-12-01

Free and open source software and data facilitate novel research by allowing geoscientists to quickly and easily bring together disparate data that have been independently collected for many different purposes. The Earth-Base project brings together several datasets using a common space-time framework that is managed and analyzed using open source software. Earth-Base currently draws on stratigraphic, paleontologic, tectonic, geodynamic, seismic, botanical, hydrologic and cartographic data. Furthermore, Earth-Base is powered by RESTful data services operating on top of PostgreSQL and MySQL databases and the R programming environment, making much of the functionality accessible to third-parties even though the detailed data schemas are unknown to them. We demonstrate the scientific potential of Earth-Base and other FOSS by comparing the stated age of fossil collections to the age of the bedrock upon which they are geolocated. This analysis makes use of web services for the Paleobiology Database (PaleoDB), Macrostrat, the 2005 Geologic Map of North America (Garrity et al. 2009) and geologic maps of the conterminous United States. This analysis is a way to quickly assess the accuracy of temporal and spatial congruence of the paleontologic and geologic map datasets. We find that 56.1% of the 52,593 PaleoDB collections have temporally consistent ages with the bedrock upon which they are located based on the Geologic Map of North America. Surprisingly, fossil collections within the conterminous United States are more consistently located on bedrock with congruent geological ages, even though the USA maps are spatially and temporally more precise. Approximately 57% of the 37,344 PaleoDB collections in the USA are located on similarly aged geologic map units. Increased accuracy is attributed to the lumping of Pliocene and Quaternary geologic map units along the Atlantic and Gulf coastal plains in the Geologic Map of North America. The abundant Pliocene fossil collections are thus located on geologic map units that have an erroneous age designation of Quaternary. We also demonstrate the power of the R programming environment for performing analyses and making publication-quality maps for visualizing results.

An intelligent and secure system for predicting and preventing Zika virus outbreak using Fog computing

NASA Astrophysics Data System (ADS)

Sareen, Sanjay; Gupta, Sunil Kumar; Sood, Sandeep K.

2017-10-01

Zika virus is a mosquito-borne disease that spreads very quickly in different parts of the world. In this article, we proposed a system to prevent and control the spread of Zika virus disease using integration of Fog computing, cloud computing, mobile phones and the Internet of things (IoT)-based sensor devices. Fog computing is used as an intermediary layer between the cloud and end users to reduce the latency time and extra communication cost that is usually found high in cloud-based systems. A fuzzy k-nearest neighbour is used to diagnose the possibly infected users, and Google map web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each Zika virus (ZikaV)-infected user, mosquito-dense sites and breeding sites on the Google map that help the government healthcare authorities to control such risk-prone areas effectively and efficiently. The proposed system is deployed on Amazon EC2 cloud to evaluate its performance and accuracy using data set for 2 million users. Our system provides high accuracy of 94.5% for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment.

An evaluation of rapid methods for monitoring vegetation characteristics of wetland bird habitat

USGS Publications Warehouse

Tavernia, Brian G.; Lyons, James E.; Loges, Brian W.; Wilson, Andrew; Collazo, Jaime A.; Runge, Michael C.

2016-01-01

Wetland managers benefit from monitoring data of sufficient precision and accuracy to assess wildlife habitat conditions and to evaluate and learn from past management decisions. For large-scale monitoring programs focused on waterbirds (waterfowl, wading birds, secretive marsh birds, and shorebirds), precision and accuracy of habitat measurements must be balanced with fiscal and logistic constraints. We evaluated a set of protocols for rapid, visual estimates of key waterbird habitat characteristics made from the wetland perimeter against estimates from (1) plots sampled within wetlands, and (2) cover maps made from aerial photographs. Estimated percent cover of annuals and perennials using a perimeter-based protocol fell within 10 percent of plot-based estimates, and percent cover estimates for seven vegetation height classes were within 20 % of plot-based estimates. Perimeter-based estimates of total emergent vegetation cover did not differ significantly from cover map estimates. Post-hoc analyses revealed evidence for observer effects in estimates of annual and perennial covers and vegetation height. Median time required to complete perimeter-based methods was less than 7 percent of the time needed for intensive plot-based methods. Our results show that rapid, perimeter-based assessments, which increase sample size and efficiency, provide vegetation estimates comparable to more intensive methods.

Simultaneous Quantitative MRI Mapping of T1, T2* and Magnetic Susceptibility with Multi-Echo MP2RAGE

PubMed Central

Kober, Tobias; Möller, Harald E.; Schäfer, Andreas

2017-01-01

The knowledge of relaxation times is essential for understanding the biophysical mechanisms underlying contrast in magnetic resonance imaging. Quantitative experiments, while offering major advantages in terms of reproducibility, may benefit from simultaneous acquisitions. In this work, we demonstrate the possibility of simultaneously recording relaxation-time and susceptibility maps with a prototype Multi-Echo (ME) Magnetization-Prepared 2 RApid Gradient Echoes (MP2RAGE) sequence. T1 maps can be obtained using the MP2RAGE sequence, which is relatively insensitive to inhomogeneities of the radio-frequency transmit field, B1+. As an extension, multiple gradient echoes can be acquired in each of the MP2RAGE readout blocks, which permits the calculation of T2* and susceptibility maps. We used computer simulations to explore the effects of the parameters on the precision and accuracy of the mapping. In vivo parameter maps up to 0.6 mm nominal resolution were acquired at 7 T in 19 healthy volunteers. Voxel-by-voxel correlations and the test-retest reproducibility were used to assess the reliability of the results. When using optimized paramenters, T1 maps obtained with ME-MP2RAGE and standard MP2RAGE showed excellent agreement for the whole range of values found in brain tissues. Simultaneously obtained T2* and susceptibility maps were of comparable quality as Fast Low-Angle SHot (FLASH) results. The acquisition times were more favorable for the ME-MP2RAGE (≈ 19 min) sequence as opposed to the sum of MP2RAGE (≈ 12 min) and FLASH (≈ 10 min) acquisitions. Without relevant sacrifice in accuracy, precision or flexibility, the multi-echo version may yield advantages in terms of reduced acquisition time and intrinsic co-registration, provided that an appropriate optimization of the acquisition parameters is performed. PMID:28081157

Comparing Selections of Environmental Variables for Ecological Studies: A Focus on Terrain Attributes.

PubMed

Lecours, Vincent; Brown, Craig J; Devillers, Rodolphe; Lucieer, Vanessa L; Edinger, Evan N

2016-01-01

Selecting appropriate environmental variables is a key step in ecology. Terrain attributes (e.g. slope, rugosity) are routinely used as abiotic surrogates of species distribution and to produce habitat maps that can be used in decision-making for conservation or management. Selecting appropriate terrain attributes for ecological studies may be a challenging process that can lead users to select a subjective, potentially sub-optimal combination of attributes for their applications. The objective of this paper is to assess the impacts of subjectively selecting terrain attributes for ecological applications by comparing the performance of different combinations of terrain attributes in the production of habitat maps and species distribution models. Seven different selections of terrain attributes, alone or in combination with other environmental variables, were used to map benthic habitats of German Bank (off Nova Scotia, Canada). 29 maps of potential habitats based on unsupervised classifications of biophysical characteristics of German Bank were produced, and 29 species distribution models of sea scallops were generated using MaxEnt. The performances of the 58 maps were quantified and compared to evaluate the effectiveness of the various combinations of environmental variables. One of the combinations of terrain attributes-recommended in a related study and that includes a measure of relative position, slope, two measures of orientation, topographic mean and a measure of rugosity-yielded better results than the other selections for both methodologies, confirming that they together best describe terrain properties. Important differences in performance (up to 47% in accuracy measurement) and spatial outputs (up to 58% in spatial distribution of habitats) highlighted the importance of carefully selecting variables for ecological applications. This paper demonstrates that making a subjective choice of variables may reduce map accuracy and produce maps that do not adequately represent habitats and species distributions, thus having important implications when these maps are used for decision-making.

Spectral features based tea garden extraction from digital orthophoto maps

NASA Astrophysics Data System (ADS)

Jamil, Akhtar; Bayram, Bulent; Kucuk, Turgay; Zafer Seker, Dursun

2018-05-01

The advancements in the photogrammetry and remote sensing technologies has made it possible to extract useful tangible information from data which plays a pivotal role in various application such as management and monitoring of forests and agricultural lands etc. This study aimed to evaluate the effectiveness of spectral signatures for extraction of tea gardens from 1 : 5000 scaled digital orthophoto maps obtained from Rize city in Turkey. First, the normalized difference vegetation index (NDVI) was derived from the input images to suppress the non-vegetation areas. NDVI values less than zero were discarded and the output images was normalized in the range 0-255. Individual pixels were then mapped into meaningful objects using global region growing technique. The resulting image was filtered and smoothed to reduce the impact of noise. Furthermore, geometrical constraints were applied to remove small objects (less than 500 pixels) followed by morphological opening operator to enhance the results. These objects served as building blocks for further image analysis. Finally, for the classification stage, a range of spectral values were empirically calculated for each band and applied on candidate objects to extract tea gardens. For accuracy assessment, we employed an area based similarity metric by overlapping obtained tea garden boundaries with the manually digitized tea garden boundaries created by experts of photogrammetry. The overall accuracy of the proposed method scored 89 % for tea gardens from 10 sample orthophoto maps. We concluded that exploiting the spectral signatures using object based analysis is an effective technique for extraction of dominant tree species from digital orthophoto maps.

Demonstration of wetland vegetation mapping in Florida from computer-processed satellite and aircraft multispectral scanner data

NASA Technical Reports Server (NTRS)

Butera, M. K.

1979-01-01

The success of remotely mapping wetland vegetation of the southwestern coast of Florida is examined. A computerized technique to process aircraft and LANDSAT multispectral scanner data into vegetation classification maps was used. The cost effectiveness of this mapping technique was evaluated in terms of user requirements, accuracy, and cost. Results indicate that mangrove communities are classified most cost effectively by the LANDSAT technique, with an accuracy of approximately 87 percent and with a cost of approximately 3 cent per hectare compared to $46.50 per hectare for conventional ground survey methods.

Remote sensing change detection methods to track deforestation and growth in threatened rainforests in Madre de Dios, Peru

USGS Publications Warehouse

Shermeyer, Jacob S.; Haack, Barry N.

2015-01-01

Two forestry-change detection methods are described, compared, and contrasted for estimating deforestation and growth in threatened forests in southern Peru from 2000 to 2010. The methods used in this study rely on freely available data, including atmospherically corrected Landsat 5 Thematic Mapper and Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation continuous fields (VCF). The two methods include a conventional supervised signature extraction method and a unique self-calibrating method called MODIS VCF guided forest/nonforest (FNF) masking. The process chain for each of these methods includes a threshold classification of MODIS VCF, training data or signature extraction, signature evaluation, k-nearest neighbor classification, analyst-guided reclassification, and postclassification image differencing to generate forest change maps. Comparisons of all methods were based on an accuracy assessment using 500 validation pixels. Results of this accuracy assessment indicate that FNF masking had a 5% higher overall accuracy and was superior to conventional supervised classification when estimating forest change. Both methods succeeded in classifying persistently forested and nonforested areas, and both had limitations when classifying forest change.

Middle-School Students' Map Construction: Understanding Complex Spatial Displays.

ERIC Educational Resources Information Center

Bausmith, Jennifer Merriman; Leinhardt, Gaea

1998-01-01

Examines the map-making process of middle-school students to determine which actions influence their accuracy, how prior knowledge helps their map construction, and what lessons can be learned from map making. Indicates that instruction that focuses on recognition of interconnections between map elements can promote map reasoning skills. (DSK)

Performance of Automated Software in the Assessment of Segmental Left Ventricular Function in Cardiac CT: Comparison with Cardiac Magnetic Resonance.

PubMed

Wang, Rui; Meinel, Felix G; Schoepf, U Joseph; Canstein, Christian; Spearman, James V; De Cecco, Carlo N

2015-12-01

To evaluate the accuracy, reliability and time saving potential of a novel cardiac CT (CCT)-based, automated software for the assessment of segmental left ventricular function compared to visual and manual quantitative assessment of CCT and cardiac magnetic resonance (CMR). Forty-seven patients with suspected or known coronary artery disease (CAD) were enrolled in the study. Wall thickening was calculated. Segmental LV wall motion was automatically calculated and shown as a colour-coded polar map. Processing time for each method was recorded. Mean wall thickness in both systolic and diastolic phases on polar map, CCT, and CMR was 9.2 ± 0.1 mm and 14.9 ± 0.2 mm, 8.9 ± 0.1 mm and 14.5 ± 0.1 mm, 8.3 ± 0.1 mm and 13.6 ± 0.1 mm, respectively. Mean wall thickening was 68.4 ± 1.5 %, 64.8 ± 1.4 % and 67.1 ± 1.4 %, respectively. Agreement for the assessment of LV wall motion between CCT, CMR and polar maps was good. Bland-Altman plots and ICC indicated good agreement between CCT, CMR and automated polar maps of the diastolic and systolic segmental wall thickness and thickening. The processing time using polar map was significantly decreased compared with CCT and CMR. Automated evaluation of segmental LV function with polar maps provides similar measurements to manual CCT and CMR evaluation, albeit with substantially reduced analysis time. • Cardiac computed tomography (CCT) can accurately assess segmental left ventricular wall function. • A novel automated software permits accurate and fast evaluation of wall function. • The software may improve the clinical implementation of segmental functional analysis.

A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data

PubMed Central

Iannelli, Gianni Cristian; Torres, Marco A.

2018-01-01

Cash crops are agricultural crops intended to be sold for profit as opposed to subsistence crops, meant to support the producer, or to support livestock. Since cash crops are intended for future sale, they translate into large financial value when considered on a wide geographical scale, so their production directly involves financial risk. At a national level, extreme weather events including destructive rain or hail, as well as drought, can have a significant impact on the overall economic balance. It is thus important to map such crops in order to set up insurance and mitigation strategies. Using locally generated data—such as municipality-level records of crop seeding—for mapping purposes implies facing a series of issues like data availability, quality, homogeneity, etc. We thus opted for a different approach relying on global datasets. Global datasets ensure homogeneity and availability of data, although sometimes at the expense of precision and accuracy. A typical global approach makes use of spaceborne remote sensing, for which different land cover classification strategies are available in literature at different levels of cost and accuracy. We selected the optimal strategy in the perspective of a global processing chain. Thanks to a specifically developed strategy for fusing unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data, we managed to obtain good classification results despite the constraints placed. The overall production process was composed using “good-enough" algorithms at each step, ensuring that the precision, accuracy, and data-hunger of each algorithm was commensurate to the precision, accuracy, and amount of data available. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy which is believed to be profitably reusable in other, similar contexts. The paper presents the problem, the constraints and the adopted solutions; it then summarizes the main findings including that efforts and costs can be saved on the side of Earth Observation data processing when additional ground-based data are available to support the mapping task. PMID:29443919

A Novel Strategy for Very-Large-Scale Cash-Crop Mapping in the Context of Weather-Related Risk Assessment, Combining Global Satellite Multispectral Datasets, Environmental Constraints, and In Situ Acquisition of Geospatial Data.

PubMed

Dell'Acqua, Fabio; Iannelli, Gianni Cristian; Torres, Marco A; Martina, Mario L V

2018-02-14

Cash crops are agricultural crops intended to be sold for profit as opposed to subsistence crops, meant to support the producer, or to support livestock. Since cash crops are intended for future sale, they translate into large financial value when considered on a wide geographical scale, so their production directly involves financial risk. At a national level, extreme weather events including destructive rain or hail, as well as drought, can have a significant impact on the overall economic balance. It is thus important to map such crops in order to set up insurance and mitigation strategies. Using locally generated data-such as municipality-level records of crop seeding-for mapping purposes implies facing a series of issues like data availability, quality, homogeneity, etc. We thus opted for a different approach relying on global datasets. Global datasets ensure homogeneity and availability of data, although sometimes at the expense of precision and accuracy. A typical global approach makes use of spaceborne remote sensing, for which different land cover classification strategies are available in literature at different levels of cost and accuracy. We selected the optimal strategy in the perspective of a global processing chain. Thanks to a specifically developed strategy for fusing unsupervised classification results with environmental constraints and other geospatial inputs including ground-based data, we managed to obtain good classification results despite the constraints placed. The overall production process was composed using "good-enough" algorithms at each step, ensuring that the precision, accuracy, and data-hunger of each algorithm was commensurate to the precision, accuracy, and amount of data available. This paper describes the tailored strategy developed on the occasion as a cooperation among different groups with diverse backgrounds, a strategy which is believed to be profitably reusable in other, similar contexts. The paper presents the problem, the constraints and the adopted solutions; it then summarizes the main findings including that efforts and costs can be saved on the side of Earth Observation data processing when additional ground-based data are available to support the mapping task.

2pBAb5. Validation of three-dimensional strain tracking by volumetric ultrasound image correlation in a pubovisceral muscle model

PubMed Central

Nagle, Anna S.; Nageswaren, Ashok R.; Haridas, Balakrishna; Mast, T. D.

2014-01-01

Little is understood about the biomechanical changes leading to pelvic floor disorders such as stress urinary incontinence. In order to measure regional biomechanical properties of the pelvic floor muscles in vivo, a three dimensional (3D) strain tracking technique employing correlation of volumetric ultrasound images has been implemented. In this technique, local 3D displacements are determined as a function of applied stress and then converted to strain maps. To validate this approach, an in vitro model of the pubovisceral muscle, with a hemispherical indenter emulating the downward stress caused by intra-abdominal pressure, was constructed. Volumetric B-scan images were recorded as a function of indenter displacement while muscle strain was measured independently by a sonomicrometry system (Sonometrics). Local strains were computed by ultrasound image correlation and compared with sonomicrometry-measured strains to assess strain tracking accuracy. Image correlation by maximizing an exponential likelihood function was found more reliable than the Pearson correlation coefficient. Strain accuracy was dependent on sizes of the subvolumes used for image correlation, relative to characteristic speckle length scales of the images. Decorrelation of echo signals was mapped as a function of indenter displacement and local tissue orientation. Strain measurement accuracy was weakly related to local echo decorrelation. PMID:24900165

Navigation assistance: a trade-off between wayfinding support and configural learning support.

PubMed

Münzer, Stefan; Zimmer, Hubert D; Baus, Jörg

2012-03-01

Current GPS-based mobile navigation assistance systems support wayfinding, but they do not support learning about the spatial configuration of an environment. The present study examined effects of visual presentation modes for navigation assistance on wayfinding accuracy, route learning, and configural learning. Participants (high-school students) visited a university campus for the first time and took a predefined assisted tour. In Experiment 1 (n = 84, 42 females), a presentation mode showing wayfinding information from eye-level was contrasted with presentation modes showing wayfinding information included in views that provided comprehensive configural information. In Experiment 2 (n = 48, 24 females), wayfinding information was included in map fragments. A presentation mode which always showed north on top of the device was compared with a mode which rotated according to the orientation of the user. Wayfinding accuracy (deviations from the route), route learning, and configural learning (direction estimates, sketch maps) were assessed. Results indicated a trade-off between wayfinding and configural learning: Presentation modes providing comprehensive configural information supported the acquisition of configural knowledge at the cost of accurate wayfinding. The route presentation mode supported wayfinding at the cost of configural knowledge acquisition. Both presentation modes based on map fragments supported wayfinding. Individual differences in visual-spatial working memory capacity explained a considerable portion of the variance in wayfinding accuracy, route learning, and configural learning. It is concluded that learning about an unknown environment during assisted navigation is based on the integration of spatial information from multiple sources and can be supported by appropriate visualization. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Regulations in the field of Geo-Information

NASA Astrophysics Data System (ADS)

Felus, Y.; Keinan, E.; Regev, R.

2013-10-01

The geomatics profession has gone through a major revolution during the last two decades with the emergence of advanced GNSS, GIS and Remote Sensing technologies. These technologies have changed the core principles and working procedures of geomatics professionals. For this reason, surveying and mapping regulations, standards and specifications should be updated to reflect these changes. In Israel, the "Survey Regulations" is the principal document that regulates the professional activities in four key areas geodetic control, mapping, cadastre and Georaphic information systems. Licensed Surveyors and mapping professionals in Israel are required to work according to those regulations. This year a new set of regulations have been published and include a few major amendments as follows: In the Geodesy chapter, horizontal control is officially based on the Israeli network of Continuously Operating GNSS Reference Stations (CORS). The regulations were phrased in a manner that will allow minor datum changes to the CORS stations due to Earth Crustal Movements. Moreover, the regulations permit the use of GNSS for low accuracy height measurements. In the Cadastre chapter, the most critical change is the move to Coordinate Based Cadastre (CBC). Each parcel corner point is ranked according to its quality (accuracy and clarity of definition). The highest ranking for a parcel corner is 1. A point with a rank of 1 is defined by its coordinates alone. Any other contradicting evidence is inferior to the coordinates values. Cadastral Information is stored and managed via the National Cadastral Databases. In the Mapping and GIS chapter; the traditional paper maps (ranked by scale) are replaced by digital maps or spatial databases. These spatial databases are ranked by their quality level. Quality level is determined (similar to the ISO19157 Standard) by logical consistency, completeness, positional accuracy, attribute accuracy, temporal accuracy and usability. Metadata is another critical component of any spatial database. Every component in a map should have a metadata identification, even if the map was compiled from multiple resources. The regulations permit the use of advanced sensors and mapping techniques including LIDAR and digita l cameras that have been certified and meet the defined criteria. The article reviews these new regulations and the decision that led to them.

Road Extraction from AVIRIS Using Spectral Mixture and Q-Tree Filter Techniques

NASA Technical Reports Server (NTRS)

Gardner, Margaret E.; Roberts, Dar A.; Funk, Chris; Noronha, Val

2001-01-01

Accurate road location and condition information are of primary importance in road infrastructure management. Additionally, spatially accurate and up-to-date road networks are essential in ambulance and rescue dispatch in emergency situations. However, accurate road infrastructure databases do not exist for vast areas, particularly in areas with rapid expansion. Currently, the US Department of Transportation (USDOT) extends great effort in field Global Positioning System (GPS) mapping and condition assessment to meet these informational needs. This methodology, though effective, is both time-consuming and costly, because every road within a DOT's jurisdiction must be field-visited to obtain accurate information. Therefore, the USDOT is interested in identifying new technologies that could help meet road infrastructure informational needs more effectively. Remote sensing provides one means by which large areas may be mapped with a high standard of accuracy and is a technology with great potential in infrastructure mapping. The goal of our research is to develop accurate road extraction techniques using high spatial resolution, fine spectral resolution imagery. Additionally, our research will explore the use of hyperspectral data in assessing road quality. Finally, this research aims to define the spatial and spectral requirements for remote sensing data to be used successfully for road feature extraction and road quality mapping. Our findings will facilitate the USDOT in assessing remote sensing as a new resource in infrastructure studies.

Strategy for reliable strain measurement in InAs/GaAs materials from high-resolution Z-contrast STEM images

NASA Astrophysics Data System (ADS)

Vatanparast, Maryam; Vullum, Per Erik; Nord, Magnus; Zuo, Jian-Min; Reenaas, Turid W.; Holmestad, Randi

2017-09-01

Geometric phase analysis (GPA), a fast and simple Fourier space method for strain analysis, can give useful information on accumulated strain and defect propagation in multiple layers of semiconductors, including quantum dot materials. In this work, GPA has been applied to high resolution Z-contrast scanning transmission electron microscopy (STEM) images. Strain maps determined from different g vectors of these images are compared to each other, in order to analyze and assess the GPA technique in terms of accuracy. The SmartAlign tool has been used to improve the STEM image quality getting more reliable results. Strain maps from template matching as a real space approach are compared with strain maps from GPA, and it is discussed that a real space analysis is a better approach than GPA for aberration corrected STEM images.

Myocardial Tissue Characterization by Magnetic Resonance Imaging

PubMed Central

Ferreira, Vanessa M.; Piechnik, Stefan K.; Robson, Matthew D.; Neubauer, Stefan

2014-01-01

Cardiac magnetic resonance (CMR) imaging is a well-established noninvasive imaging modality in clinical cardiology. Its unsurpassed accuracy in defining cardiac morphology and function and its ability to provide tissue characterization make it well suited for the study of patients with cardiac diseases. Late gadolinium enhancement was a major advancement in the development of tissue characterization techniques, allowing the unique ability of CMR to differentiate ischemic heart disease from nonischemic cardiomyopathies. Using T2-weighted techniques, areas of edema and inflammation can be identified in the myocardium. A new generation of myocardial mapping techniques are emerging, enabling direct quantitative assessment of myocardial tissue properties in absolute terms. This review will summarize recent developments involving T1-mapping and T2-mapping techniques and focus on the clinical applications and future potential of these evolving CMR methodologies. PMID:24576837

Exploring Capabilities of SENTINEL-2 for Vegetation Mapping Using Random Forest

NASA Astrophysics Data System (ADS)

Saini, R.; Ghosh, S. K.

2018-04-01

Accurate vegetation mapping is essential for monitoring crop and sustainable agricultural practice. This study aims to explore the capabilities of Sentinel-2 data over Landsat-8 Operational Land Imager (OLI) data for vegetation mapping. Two combination of Sentinel-2 dataset have been considered, first combination is 4-band dataset at 10m resolution which consists of NIR, R, G and B bands, while second combination is generated by stacking 4 bands having 10 m resolution along with other six sharpened bands using Gram-Schmidt algorithm. For Landsat-8 OLI dataset, six multispectral bands have been pan-sharpened to have a spatial resolution of 15 m using Gram-Schmidt algorithm. Random Forest (RF) and Maximum Likelihood classifier (MLC) have been selected for classification of images. It is found that, overall accuracy achieved by RF for 4-band, 10-band dataset of Sentinel-2 and Landsat-8 OLI are 88.38 %, 90.05 % and 86.68 % respectively. While, MLC give an overall accuracy of 85.12 %, 87.14 % and 83.56 % for 4-band, 10-band Sentinel and Landsat-8 OLI respectively. Results shown that 10-band Sentinel-2 dataset gives highest accuracy and shows a rise of 3.37 % for RF and 3.58 % for MLC compared to Landsat-8 OLI. However, all the classes show significant improvement in accuracy but a major rise in accuracy is observed for Sugarcane, Wheat and Fodder for Sentinel 10-band imagery. This study substantiates the fact that Sentinel-2 data can be utilized for mapping of vegetation with a good degree of accuracy when compared to Landsat-8 OLI specifically when objective is to map a sub class of vegetation.

Mapping Mangrove Density from Rapideye Data in Central America

NASA Astrophysics Data System (ADS)

Son, Nguyen-Thanh; Chen, Chi-Farn; Chen, Cheng-Ru

2017-06-01

Mangrove forests provide a wide range of socioeconomic and ecological services for coastal communities. Extensive aquaculture development of mangrove waters in many developing countries has constantly ignored services of mangrove ecosystems, leading to unintended environmental consequences. Monitoring the current status and distribution of mangrove forests is deemed important for evaluating forest management strategies. This study aims to delineate the density distribution of mangrove forests in the Gulf of Fonseca, Central America with Rapideye data using the support vector machines (SVM). The data collected in 2012 for density classification of mangrove forests were processed based on four different band combination schemes: scheme-1 (bands 1-3, 5 excluding the red-edge band 4), scheme-2 (bands 1-5), scheme-3 (bands 1-3, 5 incorporating with the normalized difference vegetation index, NDVI), and scheme-4 (bands 1-3, 5 incorporating with the normalized difference red-edge index, NDRI). We also hypothesized if the obvious contribution of Rapideye red-edge band could improve the classification results. Three main steps of data processing were employed: (1), data pre-processing, (2) image classification, and (3) accuracy assessment to evaluate the contribution of red-edge band in terms of the accuracy of classification results across these four schemes. The classification maps compared with the ground reference data indicated the slightly higher accuracy level observed for schemes 2 and 4. The overall accuracies and Kappa coefficients were 97% and 0.95 for scheme-2 and 96.9% and 0.95 for scheme-4, respectively.

Establishing a baseline for regional scale monitoring of eelgrass (Zostera marina) habitat on the lower Alaska Peninsula

USGS Publications Warehouse

Hogrefe, Kyle R.; Ward, David H.; Donnelly, Tyrone F.; Dau, Niels

2014-01-01

Seagrass meadows, one of the world’s most widespread and productive ecosystems, provide a wide range of services with real economic value. Worldwide declines in the distribution and abundance of seagrasses and increased threats to coastal ecosystems from climate change have prompted a need to acquire baseline data for monitoring and protecting these important habitats. We assessed the distribution and abundance of eelgrass (Zostera marina) along nearly 1200 km of shoreline on the lower Alaska Peninsula, a region of expansive eelgrass meadows whose status and trends are poorly understood. We demonstrate the effectiveness of a multi-scale approach by using Landsat satellite imagery to map the total areal extent of eelgrass while integrating field survey data to improve map accuracy and describe the physical and biological condition of the meadows. Innovative use of proven methods and processing tools was used to address challenges inherent to remote sensing in high latitude, coastal environments. Eelgrass was estimated to cover ~31,000 ha, 91% of submerged aquatic vegetation on the lower Alaska Peninsula, nearly doubling the known spatial extent of eelgrass in the region. Mapping accuracy was 80%–90% for eelgrass distribution at locations containing adequate field survey data for error analysis.

An integrated approach to flood hazard assessment on alluvial fans using numerical modeling, field mapping, and remote sensing

USGS Publications Warehouse

Pelletier, J.D.; Mayer, L.; Pearthree, P.A.; House, P.K.; Demsey, K.A.; Klawon, J.K.; Vincent, K.R.

2005-01-01

Millions of people in the western United States live near the dynamic, distributary channel networks of alluvial fans where flood behavior is complex and poorly constrained. Here we test a new comprehensive approach to alluvial-fan flood hazard assessment that uses four complementary methods: two-dimensional raster-based hydraulic modeling, satellite-image change detection, fieldbased mapping of recent flood inundation, and surficial geologic mapping. Each of these methods provides spatial detail lacking in the standard method and each provides critical information for a comprehensive assessment. Our numerical model simultaneously solves the continuity equation and Manning's equation (Chow, 1959) using an implicit numerical method. It provides a robust numerical tool for predicting flood flows using the large, high-resolution Digital Elevation Models (DEMs) necessary to resolve the numerous small channels on the typical alluvial fan. Inundation extents and flow depths of historic floods can be reconstructed with the numerical model and validated against field- and satellite-based flood maps. A probabilistic flood hazard map can also be constructed by modeling multiple flood events with a range of specified discharges. This map can be used in conjunction with a surficial geologic map to further refine floodplain delineation on fans. To test the accuracy of the numerical model, we compared model predictions of flood inundation and flow depths against field- and satellite-based flood maps for two recent extreme events on the southern Tortolita and Harquahala piedmonts in Arizona. Model predictions match the field- and satellite-based maps closely. Probabilistic flood hazard maps based on the 10 yr, 100 yr, and maximum floods were also constructed for the study areas using stream gage records and paleoflood deposits. The resulting maps predict spatially complex flood hazards that strongly reflect small-scale topography and are consistent with surficial geology. In contrast, FEMA Flood Insurance Rate Maps (FIRMs) based on the FAN model predict uniformly high flood risk across the study areas without regard for small-scale topography and surficial geology. ?? 2005 Geological Society of America.

Hotspot detection in pancreatic neuroendocrine tumors: density approximation by α-shape maps

NASA Astrophysics Data System (ADS)

Niazi, M. K. K.; Hartman, Douglas J.; Pantanowitz, Liron; Gurcan, Metin N.

2016-03-01

The grading of neuroendocrine tumors of the digestive system is dependent on accurate and reproducible assessment of the proliferation with the tumor, either by counting mitotic figures or counting Ki-67 positive nuclei. At the moment, most pathologists manually identify the hotspots, a practice which is tedious and irreproducible. To better help pathologists, we present an automatic method to detect all potential hotspots in neuroendocrine tumors of the digestive system. The method starts by segmenting Ki-67 positive nuclei by entropy based thresholding, followed by detection of centroids for all Ki-67 positive nuclei. Based on geodesic distance, approximated by the nuclei centroids, we compute two maps: an amoeba map and a weighted amoeba map. These maps are later combined to generate the heat map, the segmentation of which results in the hotspots. The method was trained on three and tested on nine whole slide images of neuroendocrine tumors. When evaluated by two expert pathologists, the method reached an accuracy of 92.6%. The current method does not discriminate between tumor, stromal and inflammatory nuclei. The results show that α-shape maps may represent how hotspots are perceived.

Mapping forest canopy gaps using air-photo interpretation and ground surveys

USGS Publications Warehouse

Fox, T.J.; Knutson, M.G.; Hines, R.K.

2000-01-01

Canopy gaps are important structural components of forested habitats for many wildlife species. Recent improvements in the spatial accuracy of geographic information system tools facilitate accurate mapping of small canopy features such as gaps. We compared canopy-gap maps generated using ground survey methods with those derived from air-photo interpretation. We found that maps created from high-resolution air photos were more accurate than those created from ground surveys. Errors of omission were 25.6% for the ground-survey method and 4.7% for the air-photo method. One variable of inter est in songbird research is the distance from nests to gap edges. Distances from real and simulated nests to gap edges were longer using the ground-survey maps versus the air-photo maps, indicating that gap omission could potentially bias the assessment of spatial relationships. If research or management goals require location and size of canopy gaps and specific information about vegetation structure, we recommend a 2-fold approach. First, canopy gaps can be located and the perimeters defined using 1:15,000-scale or larger aerial photographs and the methods we describe. Mapped gaps can then be field-surveyed to obtain detailed vegetation data.

Surficial Geologic Map of the Ashby-Lowell-Sterling-Billerica 11-Quadrangle Area in Northeast-Central Massachusetts

USGS Publications Warehouse

Stone, Byron D.; Stone, Janet R.

2007-01-01

The surficial geologic map shows the distribution of nonlithified earth materials at land surface in an area of eleven 7.5-minute quadrangles (total 505 mi2) in northeast-central Massachusetts. The geologic map differentiates surficial materials of Quaternary age on the basis of their lithologic characteristics (such as grain size and sedimentary structures), constructional geomorphic features, stratigraphic relationships, and age. Surficial earth materials significantly affect human use of the land, and an accurate description of their distribution is particularly important for water resources, construction aggregate resources, earth-surface hazards assessments, and land-use decisions. This compilation of surficial geologic materials is an interim product that defines the areas of exposed bedrock, and the boundaries between glacial till, glacial stratified deposits, and overlying postglacial deposits. This work is part of a comprehensive study to produce a statewide digital map of the surficial geology at a 1:24,000-scale level of accuracy. This report includes explanatory text (PDF), a regional map at 1:50,000 scale (PDF), quadrangle maps at 1:24,000 scale (PDF files), GIS data layers (ArcGIS shapefiles), metadata for the GIS layers, scanned topographic base maps (TIF), and a readme.txt file.

Surficial Geologic Map of the Salem Depot-Newburyport East-Wilmington-Rockport 16-Quadrangle Area in Northeast Massachusetts

USGS Publications Warehouse

Stone, Byron D.; Stone, Janet Radway; DiGiacomo-Cohen, Mary L.

2006-01-01

The surficial geologic map shows the distribution of nonlithified earth materials at land surface in an area of 16 7.5-minute quadrangles (total 658 mi2) in northeast Massachusetts. The geologic map differentiates surficial materials of Quaternary age on the basis of their lithologic characteristics (grain size, sedimentary structures, mineral and rock-particle composition), constructional geomorphic features, stratigraphic relationships, and age. Surficial earth materials significantly affect human use of the land, and an accurate description of their distribution is particularly important for water resources, construction aggregate resources, earth-surface hazards assessments, and land-use decisions. This compilation of surficial geologic materials is an interim product that defines the areas of exposed bedrock, and the boundaries between glacial till, glacial stratified deposits, and overlying postglacial deposits. This work is part of a comprehensive study to produce a statewide digital map of the surficial geology at a 1:24,000-scale level of accuracy. This report includes explanatory text (PDF), a regional map at 1:50,000 scale (PDF), quadrangle maps at 1:24,000 scale (PDF files), GIS data layers (ArcGIS shapefiles), metadata for the GIS layers, scanned topographic base maps (TIF), and a readme.txt file.

Mapping of the Seagrass Cover Along the Mediterranean Coast of Turkey Using Landsat 8 Oli Images

NASA Astrophysics Data System (ADS)

Bakirman, T.; Gumusay, M. U.; Tuney, I.

2016-06-01

Benthic habitat is defined as ecological environment where marine animals, plants and other organisms live in. Benthic habitat mapping is defined as plotting the distribution and extent of habitats to create a map with complete coverage of the seabed showing distinct boundaries separating adjacent habitats or the use of spatially continuous environmental data sets to represent and predict biological patterns on the seafloor. Seagrass is an essential endemic marine species that prevents coast erosion and regulates carbon dioxide absorption in both undersea and atmosphere. Fishing, mining, pollution and other human activities cause serious damage to seabed ecosystems and reduce benthic biodiversity. According to the latest studies, only 5-10% of the seafloor is mapped, therefore it is not possible to manage resources effectively, protect ecologically important areas. In this study, it is aimed to map seagrass cover using Landsat 8 OLI images in the northern part of Mediterranean coast of Turkey. After pre-processing (e.g. radiometric, atmospheric, water depth correction) of Landsat images, coverage maps are produced with supervised classification using in-situ data which are underwater photos and videos. Result maps and accuracy assessment are presented and discussed.

An interpolation method for stream habitat assessments

USGS Publications Warehouse

Sheehan, Kenneth R.; Welsh, Stuart A.

2015-01-01

Interpolation of stream habitat can be very useful for habitat assessment. Using a small number of habitat samples to predict the habitat of larger areas can reduce time and labor costs as long as it provides accurate estimates of habitat. The spatial correlation of stream habitat variables such as substrate and depth improves the accuracy of interpolated data. Several geographical information system interpolation methods (natural neighbor, inverse distance weighted, ordinary kriging, spline, and universal kriging) were used to predict substrate and depth within a 210.7-m2 section of a second-order stream based on 2.5% and 5.0% sampling of the total area. Depth and substrate were recorded for the entire study site and compared with the interpolated values to determine the accuracy of the predictions. In all instances, the 5% interpolations were more accurate for both depth and substrate than the 2.5% interpolations, which achieved accuracies up to 95% and 92%, respectively. Interpolations of depth based on 2.5% sampling attained accuracies of 49–92%, whereas those based on 5% percent sampling attained accuracies of 57–95%. Natural neighbor interpolation was more accurate than that using the inverse distance weighted, ordinary kriging, spline, and universal kriging approaches. Our findings demonstrate the effective use of minimal amounts of small-scale data for the interpolation of habitat over large areas of a stream channel. Use of this method will provide time and cost savings in the assessment of large sections of rivers as well as functional maps to aid the habitat-based management of aquatic species.

Mapping Vegetation Community Types in a Highly-Disturbed Landscape: Integrating Hiearchical Object-Based Image Analysis with Digital Surface Models

NASA Astrophysics Data System (ADS)

Snavely, Rachel A.

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island, California, this research tests the effectiveness of incorporating a hierarchal object-based image analysis (OBIA) approach with high-spatial resolution imagery and light detection and range (LiDAR) derived canopy height surfaces for mapping vegetation communities. The study is part of a large-scale research effort conducted by researchers at San Diego State University's (SDSU) Center for Earth Systems Analysis Research (CESAR) and Soil Ecology and Restoration Group (SERG), to develop an updated vegetation community map which will support both conservation and management decisions on Naval Auxiliary Landing Field (NALF) San Clemente Island. Trimble's eCognition Developer software was used to develop and generate vegetation community maps for two study sites, with and without vegetation height data as input. Overall and class-specific accuracies were calculated and compared across the two classifications. The highest overall accuracy (approximately 80%) was observed with the classification integrating airborne visible and near infrared imagery having very high spatial resolution with a LiDAR derived canopy height model. Accuracies for individual vegetation classes differed between both classification methods, but were highest when incorporating the LiDAR digital surface data. The addition of a canopy height model, however, yielded little difference in classification accuracies for areas of very dense shrub cover. Overall, the results show the utility of the OBIA approach for mapping vegetation with high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accuracy characterizing highly disturbed landscapes. The integrated imagery and digital canopy height model approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping vegetation communities.

Low Altitude AVIRIS Data for Mapping Landform Types on West Ship Island, Mississippi

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Otvos, Ervin; Giardino, Marco

2002-01-01

A chain of barrier islands provides protection against hurricanes and severe storms along the south and southeastern shores of the United States. Barrier island landform types can be spectrally similar and as small as a few meters across, making highly detailed maps difficult to produce. To determine whether high-resolution airborne hyperspectral imagery could provide detailed maps of barrier island landform types, we used low-altitude hyperspectral and multispectral imagery to map surface environments of West Ship Island, Mississippi. We employed 3.4-meter AVIRIS hyperspectral imagery acquired in July 1999 and 0.5-meter ADAR multispectral data acquired in November 1997. The data were co-registered to digital ortho aerial imagery, and the AVIRIS data was scaled to ground reflectance using ATREM software. Unsupervised classification of AVIRIS and ADAR data proceeded using ISODATA clustering techniques. The resulting landform maps were field-checked and compared to aerial photography and digital elevation maps. Preliminary analyses indicated that the AVIRIS classification mapped more landform types, while the ADAR-based map enabled smaller patches to be identified. Used together, these maps provided a means to assess landform distributions of West Ship Island before and after Hurricane Gorges. Classification accuracy is being addressed through photo-interpretation and field surveys of sample areas selected with stratified random sampling.

Regional mapping of soil parent material by machine learning based on point data

NASA Astrophysics Data System (ADS)

Lacoste, Marine; Lemercier, Blandine; Walter, Christian

2011-10-01

A machine learning system (MART) has been used to predict soil parent material (SPM) at the regional scale with a 50-m resolution. The use of point-specific soil observations as training data was tested as a replacement for the soil maps introduced in previous studies, with the aim of generating a more even distribution of training data over the study area and reducing information uncertainty. The 27,020-km 2 study area (Brittany, northwestern France) contains mainly metamorphic, igneous and sedimentary substrates. However, superficial deposits (aeolian loam, colluvial and alluvial deposits) very often represent the actual SPM and are typically under-represented in existing geological maps. In order to calibrate the predictive model, a total of 4920 point soil descriptions were used as training data along with 17 environmental predictors (terrain attributes derived from a 50-m DEM, as well as emissions of K, Th and U obtained by means of airborne gamma-ray spectrometry, geological variables at the 1:250,000 scale and land use maps obtained by remote sensing). Model predictions were then compared: i) during SPM model creation to point data not used in model calibration (internal validation), ii) to the entire point dataset (point validation), and iii) to existing detailed soil maps (external validation). The internal, point and external validation accuracy rates were 56%, 81% and 54%, respectively. Aeolian loam was one of the three most closely predicted substrates. Poor prediction results were associated with uncommon materials and areas with high geological complexity, i.e. areas where existing maps used for external validation were also imprecise. The resultant predictive map turned out to be more accurate than existing geological maps and moreover indicated surface deposits whose spatial coverage is consistent with actual knowledge of the area. This method proves quite useful in predicting SPM within areas where conventional mapping techniques might be too costly or lengthy or where soil maps are insufficient for use as training data. In addition, this method allows producing repeatable and interpretable results, whose accuracy can be assessed objectively.

TAxonomy of Self-reported Sedentary behaviour Tools (TASST) framework for development, comparison and evaluation of self-report tools: content analysis and systematic review.

PubMed

Dall, P M; Coulter, E H; Fitzsimons, C F; Skelton, D A; Chastin, Sfm

2017-04-08

Sedentary behaviour (SB) has distinct deleterious health outcomes, yet there is no consensus on best practice for measurement. This study aimed to identify the optimal self-report tool for population surveillance of SB, using a systematic framework. A framework, TAxonomy of Self-reported Sedentary behaviour Tools (TASST), consisting of four domains (type of assessment, recall period, temporal unit and assessment period), was developed based on a systematic inventory of existing tools. The inventory was achieved through a systematic review of studies reporting SB and tracing back to the original description. A systematic review of the accuracy and sensitivity to change of these tools was then mapped against TASST domains. Systematic searches were conducted via EBSCO, reference lists and expert opinion. The inventory included tools measuring SB in adults that could be self-completed at one sitting, and excluded tools measuring SB in specific populations or contexts. The systematic review included studies reporting on the accuracy against an objective measure of SB and/or sensitivity to change of a tool in the inventory. The systematic review initially identified 32 distinct tools (141 questions), which were used to develop the TASST framework. Twenty-two studies evaluated accuracy and/or sensitivity to change representing only eight taxa. Assessing SB as a sum of behaviours and using a previous day recall were the most promising features of existing tools. Accuracy was poor for all existing tools, with underestimation and overestimation of SB. There was a lack of evidence about sensitivity to change. Despite the limited evidence, mapping existing SB tools onto the TASST framework has enabled informed recommendations to be made about the most promising features for a surveillance tool, identified aspects on which future research and development of SB surveillance tools should focus. International prospective register of systematic reviews (PROPSPERO)/CRD42014009851. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Mapping analysis and planning system for the John F. Kennedy Space Center

NASA Technical Reports Server (NTRS)

Hall, C. R.; Barkaszi, M. J.; Provancha, M. J.; Reddick, N. A.; Hinkle, C. R.; Engel, B. A.; Summerfield, B. R.

1994-01-01

Environmental management, impact assessment, research and monitoring are multidisciplinary activities which are ideally suited to incorporate a multi-media approach to environmental problem solving. Geographic information systems (GIS), simulation models, neural networks and expert-system software are some of the advancing technologies being used for data management, query, analysis and display. At the 140,000 acre John F. Kennedy Space Center, the Advanced Software Technology group has been supporting development and implementation of a program that integrates these and other rapidly evolving hardware and software capabilities into a comprehensive Mapping, Analysis and Planning System (MAPS) based in a workstation/local are network environment. An expert-system shell is being developed to link the various databases to guide users through the numerous stages of a facility siting and environmental assessment. The expert-system shell approach is appealing for its ease of data access by management-level decision makers while maintaining the involvement of the data specialists. This, as well as increased efficiency and accuracy in data analysis and report preparation, can benefit any organization involved in natural resources management.

Comparative Accuracy Evaluation of Fine-Scale Global and Local Digital Surface Models: The Tshwane Case Study I

NASA Astrophysics Data System (ADS)

Breytenbach, A.

2016-10-01

Conducted in the City of Tshwane, South Africa, this study set about to test the accuracy of DSMs derived from different remotely sensed data locally. VHR digital mapping camera stereo-pairs, tri-stereo imagery collected by a Pléiades satellite and data detected from the Tandem-X InSAR satellite configuration were fundamental in the construction of seamless DSM products at different postings, namely 2 m, 4 m and 12 m. The three DSMs were sampled against independent control points originating from validated airborne LiDAR data. The reference surfaces were derived from the same dense point cloud at grid resolutions corresponding to those of the samples. The absolute and relative positional accuracies were computed using well-known DEM error metrics and accuracy statistics. Overall vertical accuracies were also assessed and compared across seven slope classes and nine primary land cover classes. Although all three DSMs displayed significantly more vertical errors where solid waterbodies, dense natural and/or alien woody vegetation and, in a lesser degree, urban residential areas with significant canopy cover were encountered, all three surpassed their expected positional accuracies overall.

Implications of allometric model selection for county-level biomass mapping

Treesearch

Laura Duncanson; Wenli Huang; Kristofer Johnson; Anu Swatantran; Ronald E. McRoberts; Ralph Dubayah

2017-01-01

Background: Carbon accounting in forests remains a large area of uncertainty in the global carbon cycle. Forest aboveground biomass is therefore an attribute of great interest for the forest management community, but the accuracy of aboveground biomass maps depends on the accuracy of the underlying field estimates used to calibrate models. These field estimates depend...

EnviroAtlas - Minneapolis/St. Paul, MN - One Meter Resolution Urban Area Land Cover Map (MULC) (2010)

EPA Pesticide Factsheets

The Minneapolis-St. Paul, MN EnviroAtlas Meter-scale Urban Land Cover (MULC) data were generated from four-band (red, green, blue, and near infrared) aerial photography provided by the United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP). The NAIP imagery for the state of Minnesota was collected during the summer and fall of 2010. Lidar data and relevant ancillary datasets contributed to the classification. Eight land cover types were classified: water, impervious surface, soil and barren land, trees and forest, grass and herbaceous, agriculture, woody wetland, and emergent wetland. An accuracy assessment of 644 completely random and 62 stratified random photointerpreted reference points yielded an overall User's Accuracy of 83 percent. The boundary of this data layer is delineated by the US Census Bureau's 2010 Urban Statistical Area for Minneapolis-St. Paul, MN plus a 1-km buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associat

A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing

USGS Publications Warehouse

Biradar, C.M.; Thenkabail, P.S.; Noojipady, P.; Li, Y.; Dheeravath, V.; Turral, H.; Velpuri, M.; Gumma, M.K.; Gangalakunta, O.R.P.; Cai, X.L.; Xiao, X.; Schull, M.A.; Alankara, R.D.; Gunasinghe, S.; Mohideen, S.

2009-01-01

The overarching goal of this study was to produce a global map of rainfed cropland areas (GMRCA) and calculate country-by-country rainfed area statistics using remote sensing data. A suite of spatial datasets, methods and protocols for mapping GMRCA were described. These consist of: (a) data fusion and composition of multi-resolution time-series mega-file data-cube (MFDC), (b) image segmentation based on precipitation, temperature, and elevation zones, (c) spectral correlation similarity (SCS), (d) protocols for class identification and labeling through uses of SCS R2-values, bi-spectral plots, space-time spiral curves (ST-SCs), rich source of field-plot data, and zoom-in-views of Google Earth (GE), and (e) techniques for resolving mixed classes by decision tree algorithms, and spatial modeling. The outcome was a 9-class GMRCA from which country-by-country rainfed area statistics were computed for the end of the last millennium. The global rainfed cropland area estimate from the GMRCA 9-class map was 1.13 billion hectares (Bha). The total global cropland areas (rainfed plus irrigated) was 1.53 Bha which was close to national statistics compiled by FAOSTAT (1.51 Bha). The accuracies and errors of GMRCA were assessed using field-plot and Google Earth data points. The accuracy varied between 92 and 98% with kappa value of about 0.76, errors of omission of 2-8%, and the errors of commission of 19-36%. ?? 2008 Elsevier B.V.

Decadal Trend in Agricultural Abandonment and Woodland Expansion in an Agro-Pastoral Transition Band in Northern China.

PubMed

Wang, Chao; Gao, Qiong; Wang, Xian; Yu, Mei

2015-01-01

Land use land cover (LULC) changes frequently in ecotones due to the large climate and soil gradients, and complex landscape composition and configuration. Accurate mapping of LULC changes in ecotones is of great importance for assessment of ecosystem functions/services and policy-decision support. Decadal or sub-decadal mapping of LULC provides scenarios for modeling biogeochemical processes and their feedbacks to climate, and evaluating effectiveness of land-use policies, e.g. forest conversion. However, it remains a great challenge to produce reliable LULC maps in moderate resolution and to evaluate their uncertainties over large areas with complex landscapes. In this study we developed a robust LULC classification system using multiple classifiers based on MODIS (Moderate Resolution Imaging Spectroradiometer) data and posterior data fusion. Not only does the system create LULC maps with high statistical accuracy, but also it provides pixel-level uncertainties that are essential for subsequent analyses and applications. We applied the classification system to the Agro-pasture transition band in northern China (APTBNC) to detect the decadal changes in LULC during 2003-2013 and evaluated the effectiveness of the implementation of major Key Forestry Programs (KFPs). In our study, the random forest (RF), support vector machine (SVM), and weighted k-nearest neighbors (WKNN) classifiers outperformed the artificial neural networks (ANN) and naive Bayes (NB) in terms of high classification accuracy and low sensitivity to training sample size. The Bayesian-average data fusion based on the results of RF, SVM, and WKNN achieved the 87.5% Kappa statistics, higher than any individual classifiers and the majority-vote integration. The pixel-level uncertainty map agreed with the traditional accuracy assessment. However, it conveys spatial variation of uncertainty. Specifically, it pinpoints the southwestern area of APTBNC has higher uncertainty than other part of the region, and the open shrubland is likely to be misclassified to the bare ground in some locations. Forests, closed shrublands, and grasslands in APTBNC expanded by 23%, 50%, and 9%, respectively, during 2003-2013. The expansion of these land cover types is compensated with the shrinkages in croplands (20%), bare ground (15%), and open shrublands (30%). The significant decline in agricultural lands is primarily attributed to the KFPs implemented in the end of last century and the nationwide urbanization in recent decade. The increased coverage of grass and woody plants would largely reduce soil erosion, improve mitigation of climate change, and enhance carbon sequestration in this region.

Increased genomic prediction accuracy in wheat breeding using a large Australian panel.

PubMed

Norman, Adam; Taylor, Julian; Tanaka, Emi; Telfer, Paul; Edwards, James; Martinant, Jean-Pierre; Kuchel, Haydn

2017-12-01

Genomic prediction accuracy within a large panel was found to be substantially higher than that previously observed in smaller populations, and also higher than QTL-based prediction. In recent years, genomic selection for wheat breeding has been widely studied, but this has typically been restricted to population sizes under 1000 individuals. To assess its efficacy in germplasm representative of commercial breeding programmes, we used a panel of 10,375 Australian wheat breeding lines to investigate the accuracy of genomic prediction for grain yield, physical grain quality and other physiological traits. To achieve this, the complete panel was phenotyped in a dedicated field trial and genotyped using a custom Axiom TM Affymetrix SNP array. A high-quality consensus map was also constructed, allowing the linkage disequilibrium present in the germplasm to be investigated. Using the complete SNP array, genomic prediction accuracies were found to be substantially higher than those previously observed in smaller populations and also more accurate compared to prediction approaches using a finite number of selected quantitative trait loci. Multi-trait genetic correlations were also assessed at an additive and residual genetic level, identifying a negative genetic correlation between grain yield and protein as well as a positive genetic correlation between grain size and test weight.

Accuracy assessment of ALOS optical instruments: PRISM and AVNIR-2

NASA Astrophysics Data System (ADS)

Tadono, Takeo; Shimada, Masanobu; Iwata, Takanori; Takaku, Junichi; Kawamoto, Sachi

2017-11-01

This paper describes the updated results of calibration and validation to assess the accuracies for optical instruments onboard the Advanced Land Observing Satellite (ALOS, nicknamed "Daichi"), which was successfully launched on January 24th, 2006 and it is continuously operating very well. ALOS has an L-band Synthetic Aperture Radar called PALSAR and two optical instruments i.e. the Panchromatic Remotesensing Instrument for Stereo Mapping (PRISM) and the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2). PRISM consists of three radiometers and is used to derive a digital surface model (DSM) with high spatial resolution that is an objective of the ALOS mission. Therefore, geometric calibration is important in generating a precise DSM with stereo pair images of PRISM. AVNIR-2 has four radiometric bands from blue to near infrared and uses for regional environment and disaster monitoring etc. The radiometric calibration and image quality evaluation are also important for AVNIR-2 as well as PRISM. This paper describes updated results of geometric calibration including geolocation determination accuracy evaluations of PRISM and AVNIR-2, image quality evaluation of PRISM, and validation of generated PRISM DSM. These works will be done during the ALOS mission life as an operational calibration to keep absolute accuracies of the standard products.

Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping

NASA Astrophysics Data System (ADS)

Park, Inhye; Choi, Jaewon; Jin Lee, Moung; Lee, Saro

2012-11-01

We constructed hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok City, Korea, using an adaptive neuro-fuzzy inference system (ANFIS) and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, and ground subsidence maps. An attribute database was also constructed from field investigations and reports on existing ground subsidence areas at the study site. Five major factors causing ground subsidence were extracted: (1) depth of drift; (2) distance from drift; (3) slope gradient; (4) geology; and (5) land use. The adaptive ANFIS model with different types of membership functions (MFs) was then applied for ground subsidence hazard mapping in the study area. Two ground subsidence hazard maps were prepared using the different MFs. Finally, the resulting ground subsidence hazard maps were validated using the ground subsidence test data which were not used for training the ANFIS. The validation results showed 95.12% accuracy using the generalized bell-shaped MF model and 94.94% accuracy using the Sigmoidal2 MF model. These accuracy results show that an ANFIS can be an effective tool in ground subsidence hazard mapping. Analysis of ground subsidence with the ANFIS model suggests that quantitative analysis of ground subsidence near AUCMs is possible.

Cadastral Positioning Accuracy Improvement: a Case Study in Malaysia

NASA Astrophysics Data System (ADS)

Hashim, N. M.; Omar, A. H.; Omar, K. M.; Abdullah, N. M.; Yatim, M. H. M.

2016-09-01

Cadastral map is a parcel-based information which is specifically designed to define the limitation of boundaries. In Malaysia, the cadastral map is under authority of the Department of Surveying and Mapping Malaysia (DSMM). With the growth of spatial based technology especially Geographical Information System (GIS), DSMM decided to modernize and reform its cadastral legacy datasets by generating an accurate digital based representation of cadastral parcels. These legacy databases usually are derived from paper parcel maps known as certified plan. The cadastral modernization will result in the new cadastral database no longer being based on single and static parcel paper maps, but on a global digital map. Despite the strict process of the cadastral modernization, this reform has raised unexpected queries that remain essential to be addressed. The main focus of this study is to review the issues that have been generated by this transition. The transformed cadastral database should be additionally treated to minimize inherent errors and to fit them to the new satellite based coordinate system with high positional accuracy. This review result will be applied as a foundation for investigation to study the systematic and effectiveness method for Positional Accuracy Improvement (PAI) in cadastral database modernization.

Kalman/Map filtering-aided fast normalized cross correlation-based Wi-Fi fingerprinting location sensing.

PubMed

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-11-13

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

PubMed Central

Sun, Yongliang; Xu, Yubin; Li, Cheng; Ma, Lin

2013-01-01

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results. PMID:24233027

Photo-guided sentinel node mapping in breast cancer using marker-free photo-gamma fusion lymphoscintigraphy.

PubMed

Lee, Eun Seong; Chun, In Kook; Ha, Seunggyun; Yoon, Hai-Jeon; Jung, So-Youn; Lee, Seeyoun; Kim, Seok Won; Lee, Eun Sook; Kim, Taeyoon; Kim, Kwang Gi; Lee, Byung Il; Kim, Tae Sung; Kim, Seok-Ki

2013-03-01

Photo-gamma fusion lymphoscintigraphy (PGFLS) was developed by overlying a conventional planar gamma image on a photograph for the guidance of sentinel node biopsy. The feasibility and accuracy of PGFLS was assessed in breast cancer patients. A digital camera and a gamma camera were coordinated to obtain photograph and gamma images from the same angle. Using the distance to the object and calibration acquisition with a flat phantom and radioactive markers, PGFLS was performed both in phantom and in patients without fiducial markers. Marker-free PGFLS was verified using flat phantom, anthropomorphic phantom with markers simulating sentinel nodes and breast cancer patients. In addition, the depth of the radioactive marker or sentinel node was calculated using two gamma images taken at right angles. The feasibility and accuracy of PGFLS were assessed in terms of mismatch errors of co-registration and depth with reference to the data from SPECT/CT. The mismatch error was less than 6 mm in the flat phantom image at a distance from 50 to 62 cm without misalignment. In the anthropomorphic phantom study, co-registration error was 0.42 ± 0.29 cm; depth error was 0.51 ± 0.37 cm, which was well correlated with the reference value on SPECT/CT (x scale: R(2) = 0.99, p < 0.01; y scale: R(2) = 0.99, p < 0.01; depth: R(2) = 0.99, p < 0.01). In ten patients with breast cancer referred for lympho-SPECT/CT, PGFSL enabled photo-guided sentinel lymph node mapping with acceptable accuracy (co-registration error, 0.47 ± 0.24 cm; depth error, 1.20 ±0.41 cm). The results from PGFSL showed close correlation with those from SPECT/CT (x scale: R(2) = 0.99, p < 0.01; y scale: R(2) = 0.98, p < 0.01; depth: R(2) = 0.77, p < 0.01). The novel and convenient PGFLS technique is clinically feasible, showing acceptable accuracy and providing additional visual and quantitative information for sentinel lymph node mapping. This approach will facilitate photo-guided sentinel lymph node dissection in breast cancer.

A method to correct coordinate distortion in EBSD maps

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Y.B., E-mail: yubz@dtu.dk; Elbrønd, A.; Lin, F.X.

2014-10-15

Drift during electron backscatter diffraction mapping leads to coordinate distortions in resulting orientation maps, which affects, in some cases significantly, the accuracy of analysis. A method, thin plate spline, is introduced and tested to correct such coordinate distortions in the maps after the electron backscatter diffraction measurements. The accuracy of the correction as well as theoretical and practical aspects of using the thin plate spline method is discussed in detail. By comparing with other correction methods, it is shown that the thin plate spline method is most efficient to correct different local distortions in the electron backscatter diffraction maps. -more » Highlights: • A new method is suggested to correct nonlinear spatial distortion in EBSD maps. • The method corrects EBSD maps more precisely than presently available methods. • Errors less than 1–2 pixels are typically obtained. • Direct quantitative analysis of dynamic data are available after this correction.« less

Using High Spatial Resolution Satellite Imagery to Map Forest Burn Severity Across Spatial Scales in a Pine Barrens Ecosystem

NASA Technical Reports Server (NTRS)

Meng, Ran; Wu, Jin; Schwager, Kathy L.; Zhao, Feng; Dennison, Philip E.; Cook, Bruce D.; Brewster, Kristen; Green, Timothy M.; Serbin, Shawn P.

2017-01-01

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (less than or equal to 5 m) from very-high-resolution (VHR) data. We assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal - pre- and post-fire event - WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). This work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the less than 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.

Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meng, Ran; Wu, Jin; Schwager, Kathy L.

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. Here we assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severitymore » was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). Lastly, this work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.« less

Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

DOE PAGES

Meng, Ran; Wu, Jin; Schwager, Kathy L.; ...

2017-01-21

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. Here we assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severitymore » was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). Lastly, this work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.« less

Assessing the external validity of algorithms to estimate EQ-5D-3L from the WOMAC.

PubMed

Kiadaliri, Aliasghar A; Englund, Martin

2016-10-04

The use of mapping algorithms have been suggested as a solution to predict health utilities when no preference-based measure is included in the study. However, validity and predictive performance of these algorithms are highly variable and hence assessing the accuracy and validity of algorithms before use them in a new setting is of importance. The aim of the current study was to assess the predictive accuracy of three mapping algorithms to estimate the EQ-5D-3L from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) among Swedish people with knee disorders. Two of these algorithms developed using ordinary least squares (OLS) models and one developed using mixture model. The data from 1078 subjects mean (SD) age 69.4 (7.2) years with frequent knee pain and/or knee osteoarthritis from the Malmö Osteoarthritis study in Sweden were used. The algorithms' performance was assessed using mean error, mean absolute error, and root mean squared error. Two types of prediction were estimated for mixture model: weighted average (WA), and conditional on estimated component (CEC). The overall mean was overpredicted by an OLS model and underpredicted by two other algorithms (P < 0.001). All predictions but the CEC predictions of mixture model had a narrower range than the observed scores (22 to 90 %). All algorithms suffered from overprediction for severe health states and underprediction for mild health states with lesser extent for mixture model. While the mixture model outperformed OLS models at the extremes of the EQ-5D-3D distribution, it underperformed around the center of the distribution. While algorithm based on mixture model reflected the distribution of EQ-5D-3L data more accurately compared with OLS models, all algorithms suffered from systematic bias. This calls for caution in applying these mapping algorithms in a new setting particularly in samples with milder knee problems than original sample. Assessing the impact of the choice of these algorithms on cost-effectiveness studies through sensitivity analysis is recommended.

A study of atmospheric effects on pattern recognition devices. [Sacramento Valley, California

NASA Technical Reports Server (NTRS)

Thomson, F. J. (Principal Investigator); Sadowski, F. G.

1975-01-01

The author has identified the following significant results. ERTS-1 imagery can be applied in the broadscale assessment of forest resources as a supplement to aerial photography and field survey. There was no application to inventory of crop and pasture diseases mainly because of poor quality and low resolution, and unreliability of image acquisition. Inventory of soil erosion was satisfactory in humid eastern New South Wales, but not in semi-arid areas. Patterns of snow cover, areas of water in natural and artificial water bodies, extent of bushfires, and location of coastal mobile sand bodies were readily apparent. ERTS-1 imagery was judged to be a valuable addition to conventional techniques of regional small scale geological mapping. ERTS data was successfully used to map flooding and flood progression. The imagery was found suitable for mapping at 1:1,000,000 scale both on the mainland and in Antarctica, but did not meet accuracy specifications for 1:250,000 mapping.

Object Based Image Analysis Combining High Spatial Resolution Imagery and Laser Point Clouds for Urban Land Cover

NASA Astrophysics Data System (ADS)

Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

2016-06-01

With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.

Black-blood native T1 mapping: Blood signal suppression for reduced partial voluming in the myocardium.

PubMed

Weingärtner, Sebastian; Meßner, Nadja M; Zöllner, Frank G; Akçakaya, Mehmet; Schad, Lothar R

2017-08-01

To study the feasibility of black-blood contrast in native T 1 mapping for reduction of partial voluming at the blood-myocardium interface. A saturation pulse prepared heart-rate-independent inversion recovery (SAPPHIRE) T 1 mapping sequence was combined with motion-sensitized driven-equilibrium (MSDE) blood suppression for black-blood T 1 mapping at 3 Tesla. Phantom scans were performed to assess the T 1 time accuracy. In vivo black-blood and conventional SAPPHIRE T 1 mapping was performed in eight healthy subjects and analyzed for T 1 times, precision, and inter- and intraobserver variability. Furthermore, manually drawn regions of interest (ROIs) in all T 1 maps were dilated and eroded to analyze the dependence of septal T 1 times on the ROI thickness. Phantom results and in vivo myocardial T 1 times show comparable accuracy with black-blood compared to conventional SAPPHIRE (in vivo: black-blood: 1562 ± 56 ms vs. conventional: 1583 ± 58 ms, P = 0.20); Using black-blood SAPPHIRE precision was significantly lower (standard deviation: 133.9 ± 24.6 ms vs. 63.1 ± 6.4 ms, P < .0001), and blood T 1 time measurement was not possible. Significantly increased interobserver interclass correlation coefficient (ICC) (0.996 vs. 0.967, P = 0.011) and similar intraobserver ICC (0.979 vs. 0.939, P = 0.11) was obtained with the black-blood sequence. Conventional SAPPHIRE showed strong dependence on the ROI thickness (R 2 = 0.99). No such trend was observed using the black-blood approach (R 2 = 0.29). Black-blood SAPPHIRE successfully eliminates partial voluming at the blood pool in native myocardial T 1 mapping while providing accurate T 1 times, albeit at a reduced precision. Magn Reson Med 78:484-493, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Accurate model annotation of a near-atomic resolution cryo-EM map

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hryc, Corey F.; Chen, Dong-Hua; Afonine, Pavel V.

Electron cryomicroscopy (cryo-EM) has been used to determine the atomic coordinates (models) from density maps of biological assemblies. These models can be assessed by their overall fit to the experimental data and stereochemical information. However, these models do not annotate the actual density values of the atoms nor their positional uncertainty. Here, we introduce a computational procedure to derive an atomic model from a cryo- EM map with annotated metadata. The accuracy of such a model is validated by a faithful replication of the experimental cryo-EM map computed using the coordinates and associated metadata. The functional interpretation of any structuralmore » features in the model and its utilization for future studies can be made in the context of its measure of uncertainty. We applied this protocol to the 3.3-Å map of the mature P22 bacteriophage capsid, a large and complex macromolecular assembly. With this protocol, we identify and annotate previously undescribed molecular interactions between capsid subunits that are crucial to maintain stability in the absence of cementing proteins or cross-linking, as occur in other bacteriophages.« less

Accurate model annotation of a near-atomic resolution cryo-EM map.

PubMed

Hryc, Corey F; Chen, Dong-Hua; Afonine, Pavel V; Jakana, Joanita; Wang, Zhao; Haase-Pettingell, Cameron; Jiang, Wen; Adams, Paul D; King, Jonathan A; Schmid, Michael F; Chiu, Wah

2017-03-21

Electron cryomicroscopy (cryo-EM) has been used to determine the atomic coordinates (models) from density maps of biological assemblies. These models can be assessed by their overall fit to the experimental data and stereochemical information. However, these models do not annotate the actual density values of the atoms nor their positional uncertainty. Here, we introduce a computational procedure to derive an atomic model from a cryo-EM map with annotated metadata. The accuracy of such a model is validated by a faithful replication of the experimental cryo-EM map computed using the coordinates and associated metadata. The functional interpretation of any structural features in the model and its utilization for future studies can be made in the context of its measure of uncertainty. We applied this protocol to the 3.3-Å map of the mature P22 bacteriophage capsid, a large and complex macromolecular assembly. With this protocol, we identify and annotate previously undescribed molecular interactions between capsid subunits that are crucial to maintain stability in the absence of cementing proteins or cross-linking, as occur in other bacteriophages.

Accurate model annotation of a near-atomic resolution cryo-EM map

PubMed Central

Hryc, Corey F.; Chen, Dong-Hua; Afonine, Pavel V.; Jakana, Joanita; Wang, Zhao; Haase-Pettingell, Cameron; Jiang, Wen; Adams, Paul D.; King, Jonathan A.; Schmid, Michael F.; Chiu, Wah

2017-01-01

Electron cryomicroscopy (cryo-EM) has been used to determine the atomic coordinates (models) from density maps of biological assemblies. These models can be assessed by their overall fit to the experimental data and stereochemical information. However, these models do not annotate the actual density values of the atoms nor their positional uncertainty. Here, we introduce a computational procedure to derive an atomic model from a cryo-EM map with annotated metadata. The accuracy of such a model is validated by a faithful replication of the experimental cryo-EM map computed using the coordinates and associated metadata. The functional interpretation of any structural features in the model and its utilization for future studies can be made in the context of its measure of uncertainty. We applied this protocol to the 3.3-Å map of the mature P22 bacteriophage capsid, a large and complex macromolecular assembly. With this protocol, we identify and annotate previously undescribed molecular interactions between capsid subunits that are crucial to maintain stability in the absence of cementing proteins or cross-linking, as occur in other bacteriophages. PMID:28270620

Accurate model annotation of a near-atomic resolution cryo-EM map

DOE PAGES

Hryc, Corey F.; Chen, Dong-Hua; Afonine, Pavel V.; ...

2017-03-07

Electron cryomicroscopy (cryo-EM) has been used to determine the atomic coordinates (models) from density maps of biological assemblies. These models can be assessed by their overall fit to the experimental data and stereochemical information. However, these models do not annotate the actual density values of the atoms nor their positional uncertainty. Here, we introduce a computational procedure to derive an atomic model from a cryo- EM map with annotated metadata. The accuracy of such a model is validated by a faithful replication of the experimental cryo-EM map computed using the coordinates and associated metadata. The functional interpretation of any structuralmore » features in the model and its utilization for future studies can be made in the context of its measure of uncertainty. We applied this protocol to the 3.3-Å map of the mature P22 bacteriophage capsid, a large and complex macromolecular assembly. With this protocol, we identify and annotate previously undescribed molecular interactions between capsid subunits that are crucial to maintain stability in the absence of cementing proteins or cross-linking, as occur in other bacteriophages.« less

Land use/land cover mapping using multi-scale texture processing of high resolution data

NASA Astrophysics Data System (ADS)

Wong, S. N.; Sarker, M. L. R.

2014-02-01

Land use/land cover (LULC) maps are useful for many purposes, and for a long time remote sensing techniques have been used for LULC mapping using different types of data and image processing techniques. In this research, high resolution satellite data from IKONOS was used to perform land use/land cover mapping in Johor Bahru city and adjacent areas (Malaysia). Spatial image processing was carried out using the six texture algorithms (mean, variance, contrast, homogeneity, entropy, and GLDV angular second moment) with five difference window sizes (from 3×3 to 11×11). Three different classifiers i.e. Maximum Likelihood Classifier (MLC), Artificial Neural Network (ANN) and Supported Vector Machine (SVM) were used to classify the texture parameters of different spectral bands individually and all bands together using the same training and validation samples. Results indicated that texture parameters of all bands together generally showed a better performance (overall accuracy = 90.10%) for land LULC mapping, however, single spectral band could only achieve an overall accuracy of 72.67%. This research also found an improvement of the overall accuracy (OA) using single-texture multi-scales approach (OA = 89.10%) and single-scale multi-textures approach (OA = 90.10%) compared with all original bands (OA = 84.02%) because of the complementary information from different bands and different texture algorithms. On the other hand, all of the three different classifiers have showed high accuracy when using different texture approaches, but SVM generally showed higher accuracy (90.10%) compared to MLC (89.10%) and ANN (89.67%) especially for the complex classes such as urban and road.

Multi-Scale Approach for Predicting Fish Species Distributions across Coral Reef Seascapes

PubMed Central

Pittman, Simon J.; Brown, Kerry A.

2011-01-01

Two of the major limitations to effective management of coral reef ecosystems are a lack of information on the spatial distribution of marine species and a paucity of data on the interacting environmental variables that drive distributional patterns. Advances in marine remote sensing, together with the novel integration of landscape ecology and advanced niche modelling techniques provide an unprecedented opportunity to reliably model and map marine species distributions across many kilometres of coral reef ecosystems. We developed a multi-scale approach using three-dimensional seafloor morphology and across-shelf location to predict spatial distributions for five common Caribbean fish species. Seascape topography was quantified from high resolution bathymetry at five spatial scales (5–300 m radii) surrounding fish survey sites. Model performance and map accuracy was assessed for two high performing machine-learning algorithms: Boosted Regression Trees (BRT) and Maximum Entropy Species Distribution Modelling (MaxEnt). The three most important predictors were geographical location across the shelf, followed by a measure of topographic complexity. Predictor contribution differed among species, yet rarely changed across spatial scales. BRT provided ‘outstanding’ model predictions (AUC = >0.9) for three of five fish species. MaxEnt provided ‘outstanding’ model predictions for two of five species, with the remaining three models considered ‘excellent’ (AUC = 0.8–0.9). In contrast, MaxEnt spatial predictions were markedly more accurate (92% map accuracy) than BRT (68% map accuracy). We demonstrate that reliable spatial predictions for a range of key fish species can be achieved by modelling the interaction between the geographical location across the shelf and the topographic heterogeneity of seafloor structure. This multi-scale, analytic approach is an important new cost-effective tool to accurately delineate essential fish habitat and support conservation prioritization in marine protected area design, zoning in marine spatial planning, and ecosystem-based fisheries management. PMID:21637787

Multi-scale approach for predicting fish species distributions across coral reef seascapes.

PubMed

Pittman, Simon J; Brown, Kerry A

2011-01-01

Two of the major limitations to effective management of coral reef ecosystems are a lack of information on the spatial distribution of marine species and a paucity of data on the interacting environmental variables that drive distributional patterns. Advances in marine remote sensing, together with the novel integration of landscape ecology and advanced niche modelling techniques provide an unprecedented opportunity to reliably model and map marine species distributions across many kilometres of coral reef ecosystems. We developed a multi-scale approach using three-dimensional seafloor morphology and across-shelf location to predict spatial distributions for five common Caribbean fish species. Seascape topography was quantified from high resolution bathymetry at five spatial scales (5-300 m radii) surrounding fish survey sites. Model performance and map accuracy was assessed for two high performing machine-learning algorithms: Boosted Regression Trees (BRT) and Maximum Entropy Species Distribution Modelling (MaxEnt). The three most important predictors were geographical location across the shelf, followed by a measure of topographic complexity. Predictor contribution differed among species, yet rarely changed across spatial scales. BRT provided 'outstanding' model predictions (AUC = >0.9) for three of five fish species. MaxEnt provided 'outstanding' model predictions for two of five species, with the remaining three models considered 'excellent' (AUC = 0.8-0.9). In contrast, MaxEnt spatial predictions were markedly more accurate (92% map accuracy) than BRT (68% map accuracy). We demonstrate that reliable spatial predictions for a range of key fish species can be achieved by modelling the interaction between the geographical location across the shelf and the topographic heterogeneity of seafloor structure. This multi-scale, analytic approach is an important new cost-effective tool to accurately delineate essential fish habitat and support conservation prioritization in marine protected area design, zoning in marine spatial planning, and ecosystem-based fisheries management.

Multi-phenology WorldView-2 imagery improves remote sensing of savannah tree species

NASA Astrophysics Data System (ADS)

Madonsela, Sabelo; Cho, Moses Azong; Mathieu, Renaud; Mutanga, Onisimo; Ramoelo, Abel; Kaszta, Żaneta; Kerchove, Ruben Van De; Wolff, Eléonore

2017-06-01

Biodiversity mapping in African savannah is important for monitoring changes and ensuring sustainable use of ecosystem resources. Biodiversity mapping can benefit from multi-spectral instruments such as WorldView-2 with very high spatial resolution and a spectral configuration encompassing important spectral regions not previously available for vegetation mapping. This study investigated i) the benefits of the eight-band WorldView-2 (WV-2) spectral configuration for discriminating tree species in Southern African savannah and ii) if multiple-images acquired at key points of the typical phenological development of savannahs (peak productivity, transition to senescence) improve on tree species classifications. We first assessed the discriminatory power of WV-2 bands using interspecies-Spectral Angle Mapper (SAM) via Band Add-On procedure and tested the spectral capability of WorldView-2 against simulated IKONOS for tree species classification. The results from interspecies-SAM procedure identified the yellow and red bands as the most statistically significant bands (p = 0.000251 and p = 0.000039 respectively) in the discriminatory power of WV-2 during the transition from wet to dry season (April). Using Random Forest classifier, the classification scenarios investigated showed that i) the 8-bands of the WV-2 sensor achieved higher classification accuracy for the April date (transition from wet to dry season, senescence) compared to the March date (peak productivity season) ii) the WV-2 spectral configuration systematically outperformed the IKONOS sensor spectral configuration and iii) the multi-temporal approach (March and April combined) improved the discrimination of tress species and produced the highest overall accuracy results at 80.4%. Consistent with the interspecies-SAM procedure, the yellow (605 nm) band also showed a statistically significant contribution in the improved classification accuracy from WV-2. These results highlight the mapping opportunities presented by WV-2 data for monitoring the distribution status of e.g. species often harvested by local communities (e.g. Sclerocharya birrea), encroaching species, or species-specific tree losses induced by elephants.

Forest and range mapping in the Houston area with ERTS-1

NASA Technical Reports Server (NTRS)

Heath, G. R.; Parker, H. D.

1973-01-01

ERTS-1 data acquired over the Houston area has been analyzed for applications to forest and range mapping. In the field of forestry the Sam Houston National Forest (Texas) was chosen as a test site, (Scene ID 1037-16244). Conventional imagery interpretation as well as computer processing methods were used to make classification maps of timber species, condition and land-use. The results were compared with timber stand maps which were obtained from aircraft imagery and checked in the field. The preliminary investigations show that conventional interpretation techniques indicated an accuracy in classification of 63 percent. The computer-aided interpretations made by a clustering technique gave 70 percent accuracy. Computer-aided and conventional multispectral analysis techniques were applied to range vegetation type mapping in the gulf coast marsh. Two species of salt marsh grasses were mapped.

Generation of a U.S. national urban land use product

USGS Publications Warehouse

Falcone, James A.; Homer, Collin G.

2012-01-01

Characterization of urban land uses is essential for many applications. However, differentiating among thematically-detailed urban land uses (residential, commercial, industrial, institutional, recreational, etc.) over broad areas is challenging, in part because image-based solutions are not ideal for establishing the contextual basis for identifying economic function and use. At present no current United States national-scale mapping exists for urban land uses similar to the classical Anderson Level II classification. This paper describes a product that maps urban land uses, and is linked to and corresponds with the National Land Cover Database (NLCD) 2006. In this product, NLCD urban pixels, in addition to their current imperviousness intensity classification, are assigned one of nine urban use classes based on information drawn from multiple data sources. These sources include detailed infrastructure information, population characteristics, and historical land use. The result is a method for creating a 30 m national-scale grid providing thematically-detailed urban land use information which complements the NLCD. Initial results for 10 major metropolitan areas are provided as an on-line link. Accuracy assessment of initial products yielded an overall accuracy of 81.6 percent.

The Feasibility of 3d Point Cloud Generation from Smartphones

NASA Astrophysics Data System (ADS)

Alsubaie, N.; El-Sheimy, N.

2016-06-01

This paper proposes a new technique for increasing the accuracy of direct geo-referenced image-based 3D point cloud generated from low-cost sensors in smartphones. The smartphone's motion sensors are used to directly acquire the Exterior Orientation Parameters (EOPs) of the captured images. These EOPs, along with the Interior Orientation Parameters (IOPs) of the camera/ phone, are used to reconstruct the image-based 3D point cloud. However, because smartphone motion sensors suffer from poor GPS accuracy, accumulated drift and high signal noise, inaccurate 3D mapping solutions often result. Therefore, horizontal and vertical linear features, visible in each image, are extracted and used as constraints in the bundle adjustment procedure. These constraints correct the relative position and orientation of the 3D mapping solution. Once the enhanced EOPs are estimated, the semi-global matching algorithm (SGM) is used to generate the image-based dense 3D point cloud. Statistical analysis and assessment are implemented herein, in order to demonstrate the feasibility of 3D point cloud generation from the consumer-grade sensors in smartphones.

Binocular Goggle Augmented Imaging and Navigation System provides real-time fluorescence image guidance for tumor resection and sentinel lymph node mapping

NASA Astrophysics Data System (ADS)

B. Mondal, Suman; Gao, Shengkui; Zhu, Nan; Sudlow, Gail P.; Liang, Kexian; Som, Avik; Akers, Walter J.; Fields, Ryan C.; Margenthaler, Julie; Liang, Rongguang; Gruev, Viktor; Achilefu, Samuel

2015-07-01

The inability to identify microscopic tumors and assess surgical margins in real-time during oncologic surgery leads to incomplete tumor removal, increases the chances of tumor recurrence, and necessitates costly repeat surgery. To overcome these challenges, we have developed a wearable goggle augmented imaging and navigation system (GAINS) that can provide accurate intraoperative visualization of tumors and sentinel lymph nodes in real-time without disrupting normal surgical workflow. GAINS projects both near-infrared fluorescence from tumors and the natural color images of tissue onto a head-mounted display without latency. Aided by tumor-targeted contrast agents, the system detected tumors in subcutaneous and metastatic mouse models with high accuracy (sensitivity = 100%, specificity = 98% ± 5% standard deviation). Human pilot studies in breast cancer and melanoma patients using a near-infrared dye show that the GAINS detected sentinel lymph nodes with 100% sensitivity. Clinical use of the GAINS to guide tumor resection and sentinel lymph node mapping promises to improve surgical outcomes, reduce rates of repeat surgery, and improve the accuracy of cancer staging.

A multidisciplinary study of earth resources imagery of Australia, Antarctica and Papua, New Guinea

NASA Technical Reports Server (NTRS)

Fisher, N. H. (Principal Investigator)

1975-01-01

The author has identified the following significant results. A thirteen category recognition map was prepared, showing forest, water, grassland, and exposed rock types. Preliminary assessment of classification accuracies showed that water, forest, meadow, and Niobrara shale were the most accurately mapped classes. Unsatisfactory results, were obtained in an attempt to discrimate sparse forest cover over different substrates. As base elevation varied from 7,000 to 13,000 ft, with an atmospheric visibility of 48 km, no changes in water and forest recognition were observed. Granodiorite recognition accuracy decreased monotonically as base elevation increased, even though the training set location was at 10,000 ft elevation. For snow varying in base elevation from 9400 to 8420 ft, recognition decreases from 99% at the 9400 ft training set elevation to 88% at 8420 ft. Calculations of expected radiance at the ERTS sensor from snow reflectance measured at the site and from Turner model calculations of irradiance, transmission and path radiance, reveal that snow signals should not be clipped, assuming that calculations and ERTS calibration constants were correct.

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce.

PubMed

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.

Object-Based Land Use Classification of Agricultural Land by Coupling Multi-Temporal Spectral Characteristics and Phenological Events in Germany

NASA Astrophysics Data System (ADS)

Knoefel, Patrick; Loew, Fabian; Conrad, Christopher

2015-04-01

Crop maps based on classification of remotely sensed data are of increased attendance in agricultural management. This induces a more detailed knowledge about the reliability of such spatial information. However, classification of agricultural land use is often limited by high spectral similarities of the studied crop types. More, spatially and temporally varying agro-ecological conditions can introduce confusion in crop mapping. Classification errors in crop maps in turn may have influence on model outputs, like agricultural production monitoring. One major goal of the PhenoS project ("Phenological structuring to determine optimal acquisition dates for Sentinel-2 data for field crop classification"), is the detection of optimal phenological time windows for land cover classification purposes. Since many crop species are spectrally highly similar, accurate classification requires the right selection of satellite images for a certain classification task. In the course of one growing season, phenological phases exist where crops are separable with higher accuracies. For this purpose, coupling of multi-temporal spectral characteristics and phenological events is promising. The focus of this study is set on the separation of spectrally similar cereal crops like winter wheat, barley, and rye of two test sites in Germany called "Harz/Central German Lowland" and "Demmin". However, this study uses object based random forest (RF) classification to investigate the impact of image acquisition frequency and timing on crop classification uncertainty by permuting all possible combinations of available RapidEye time series recorded on the test sites between 2010 and 2014. The permutations were applied to different segmentation parameters. Then, classification uncertainty was assessed and analysed, based on the probabilistic soft-output from the RF algorithm at the per-field basis. From this soft output, entropy was calculated as a spatial measure of classification uncertainty. The results indicate that uncertainty estimates provide a valuable addition to traditional accuracy assessments and helps the user to allocate error in crop maps.

18F-FDG PET/MRI fusion in characterizing pancreatic tumors: comparison to PET/CT.

PubMed

Tatsumi, Mitsuaki; Isohashi, Kayako; Onishi, Hiromitsu; Hori, Masatoshi; Kim, Tonsok; Higuchi, Ichiro; Inoue, Atsuo; Shimosegawa, Eku; Takeda, Yutaka; Hatazawa, Jun

2011-08-01

To demonstrate that positron emission tomography (PET)/magnetic resonance imaging (MRI) fusion was feasible in characterizing pancreatic tumors (PTs), comparing MRI and computed tomography (CT) as mapping images for fusion with PET as well as fused PET/MRI and PET/CT. We retrospectively reviewed 47 sets of (18)F-fluorodeoxyglucose ((18)F -FDG) PET/CT and MRI examinations to evaluate suspected or known pancreatic cancer. To assess the ability of mapping images for fusion with PET, CT (of PET/CT), T1- and T2-weighted (w) MR images (all non-contrast) were graded regarding the visibility of PT (5-point confidence scale). Fused PET/CT, PET/T1-w or T2-w MR images of the upper abdomen were evaluated to determine whether mapping images provided additional diagnostic information to PET alone (3-point scale). The overall quality of PET/CT or PET/MRI sets in diagnosis was also assessed (3-point scale). These PET/MRI-related scores were compared to PET/CT-related scores and the accuracy in characterizing PTs was compared. Forty-three PTs were visualized on CT or MRI, including 30 with abnormal FDG uptake and 13 without. The confidence score for the visibility of PT was significantly higher on T1-w MRI than CT. The scores for additional diagnostic information to PET and overall quality of each image set in diagnosis were significantly higher on the PET/T1-w MRI set than the PET/CT set. The diagnostic accuracy was higher on PET/T1-w or PET/T2-w MRI (93.0 and 90.7%, respectively) than PET/CT (88.4%), but statistical significance was not obtained. PET/MRI fusion, especially PET with T1-w MRI, was demonstrated to be superior to PET/CT in characterizing PTs, offering better mapping and fusion image quality.

Accuracy Performance Evaluation of Beidou Navigation Satellite System

NASA Astrophysics Data System (ADS)

Wang, W.; Hu, Y. N.

2017-03-01

Accuracy is one of the key elements of the regional Beidou Navigation Satellite System (BDS) performance standard. In this paper, we review the definition specification and evaluation standard of the BDS accuracy. Current accuracy of the regional BDS is analyzed through the ground measurements and compared with GPS in terms of dilution of precision (DOP), signal-in-space user range error (SIS URE), and positioning accuracy. The Positioning DOP (PDOP) map of BDS around Chinese mainland is compared with that of GPS. The GPS PDOP is between 1.0-2.0 and does not vary with the user latitude and longitude, while the BDS PDOP varies between 1.5-5.0, and increases as the user latitude increases, and as the user longitude apart from 118°. The accuracies of the broadcast orbits of BDS are assessed by taking the precise orbits from International GNSS Service (IGS) as the reference, and by making satellite laser ranging (SLR) residuals. The radial errors of the BDS inclined geosynchronous orbit (IGSO) and medium orbit (MEO) satellites broadcast orbits are at the 0.5m level, which are larger than those of GPS satellites at the 0.2m level. The SLR residuals of geosynchronous orbit (GEO) satellites are 65.0cm, which are larger than those of IGSO, and MEO satellites, at the 50.0cm level. The accuracy of broadcast clock offset parameters of BDS is computed by taking the clock measurements of Two-way Satellite Radio Time Frequency Transfer as the reference. Affected by the age of broadcast clock parameters, the error of the broadcast clock offset parameters of the MEO satellites is the largest, at the 0.80m level. Finally, measurements of the multi-GNSS (MGEX) receivers are used for positioning accuracy assessment of BDS and GPS. It is concluded that the positioning accuracy of regional BDS is better than 10m at the horizontal component and the vertical component. The combined positioning accuracy of both systems is better than one specific system.

Comparing the performance of various digital soil mapping approaches to map physical soil properties

NASA Astrophysics Data System (ADS)

Laborczi, Annamária; Takács, Katalin; Pásztor, László

2015-04-01

Spatial information on physical soil properties is intensely expected, in order to support environmental related and land use management decisions. One of the most widely used properties to characterize soils physically is particle size distribution (PSD), which determines soil water management and cultivability. According to their size, different particles can be categorized as clay, silt, or sand. The size intervals are defined by national or international textural classification systems. The relative percentage of sand, silt, and clay in the soil constitutes textural classes, which are also specified miscellaneously in various national and/or specialty systems. The most commonly used is the classification system of the United States Department of Agriculture (USDA). Soil texture information is essential input data in meteorological, hydrological and agricultural prediction modelling. Although Hungary has a great deal of legacy soil maps and other relevant soil information, it often occurs, that maps do not exist on a certain characteristic with the required thematic and/or spatial representation. The recent developments in digital soil mapping (DSM), however, provide wide opportunities for the elaboration of object specific soil maps (OSSM) with predefined parameters (resolution, accuracy, reliability etc.). Due to the simultaneous richness of available Hungarian legacy soil data, spatial inference methods and auxiliary environmental information, there is a high versatility of possible approaches for the compilation of a given soil map. This suggests the opportunity of optimization. For the creation of an OSSM one might intend to identify the optimum set of soil data, method and auxiliary co-variables optimized for the resources (data costs, computation requirements etc.). We started comprehensive analysis of the effects of the various DSM components on the accuracy of the output maps on pilot areas. The aim of this study is to compare and evaluate different digital soil mapping methods and sets of ancillary variables for producing the most accurate spatial prediction of texture classes in a given area of interest. Both legacy and recently collected data on PSD were used as reference information. The predictor variable data set consisted of digital elevation model and its derivatives, lithology, land use maps as well as various bands and indices of satellite images. Two conceptionally different approaches can be applied in the mapping process. Textural classification can be realized after particle size data were spatially extended by proper geostatistical method. Alternatively, the textural classification is carried out first, followed by the spatial extension through suitable data mining method. According to the first approach, maps of sand, silt and clay percentage have been computed through regression kriging (RK). Since the three maps are compositional (their sum must be 100%), we applied Additive Log-Ratio (alr) transformation, instead of kriging them independently. Finally, the texture class map has been compiled according to the USDA categories from the three maps. Different combinations of reference and training soil data and auxiliary covariables resulted several different maps. On the basis of the other way, the PSD were classified firstly into the USDA categories, then the texture class maps were compiled directly by data mining methods (classification trees and random forests). The various results were compared to each other as well as to the RK maps. The performance of the different methods and data sets has been examined by testing the accuracy of the geostatistically computed and the directly classified results to assess the most predictive and accurate method. Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Spatio-temporal Change Patterns of Tropical Forests from 2000 to 2014 Using MOD09A1 Dataset

NASA Astrophysics Data System (ADS)

Qin, Y.; Xiao, X.; Dong, J.

2016-12-01

Large-scale deforestation and forest degradation in the tropical region have resulted in extensive carbon emissions and biodiversity loss. However, restricted by the availability of good-quality observations, large uncertainty exists in mapping the spatial distribution of forests and their spatio-temporal changes. In this study, we proposed a pixel- and phenology-based algorithm to identify and map annual tropical forests from 2000 to 2014, using the 8-day, 500-m MOD09A1 (v005) product, under the support of Google cloud computing (Google Earth Engine). A temporal filter was applied to reduce the random noises and to identify the spatio-temporal changes of forests. We then built up a confusion matrix and assessed the accuracy of the annual forest maps based on the ground reference interpreted from high spatial resolution images in Google Earth. The resultant forest maps showed the consistent forest/non-forest, forest loss, and forest gain in the pan-tropical zone during 2000 - 2014. The proposed algorithm showed the potential for tropical forest mapping and the resultant forest maps are important for the estimation of carbon emission and biodiversity loss.

High resolution critical habitat mapping and classification of tidal freshwater wetlands in the ACE Basin

NASA Astrophysics Data System (ADS)

Strickland, Melissa Anne

In collaboration with the South Carolina Department of Natural Resources ACE Basin National Estuarine Research Reserve (ACE Basin NERR), the tidal freshwater ecosystems along the South Edisto River in the ACE Basin are being accurately mapped and classified using a LIDAR-Remote Sensing Fusion technique that integrates LAS LIDAR data into texture images and then merges the elevation textures and multispectral imagery for very high resolution mapping. This project discusses the development and refinement of an ArcGIS Toolbox capable of automating protocols and procedures for marsh delineation and microhabitat identification. The result is a high resolution habitat and land use map used for the identification of threatened habitat. Tidal freshwater wetlands are also a critical habitat for colonial wading birds and an accurate assessment of community diversity and acreage of this habitat type in the ACE Basin will support SCDNR's conservation and protection efforts. The maps developed by this study will be used to better monitor the freshwater/saltwater interface and establish a baseline for an ACE NERR monitoring program to track the rates and extent of alterations due to projected environmental stressors. Preliminary ground-truthing in the field will provide information about the accuracy of the mapping tool.

Sea ice type maps from Alaska synthetic aperture radar facility imagery: An assessment

NASA Technical Reports Server (NTRS)

Fetterer, Florence M.; Gineris, Denise; Kwok, Ronald

1994-01-01

Synthetic aperture radar (SAR) imagery received at the Alaskan SAR Facility is routinely and automatically classified on the Geophysical Processor System (GPS) to create ice type maps. We evaluated the wintertime performance of the GPS classification algorithm by comparing ice type percentages from supervised classification with percentages from the algorithm. The root mean square (RMS) difference for multiyear ice is about 6%, while the inconsistency in supervised classification is about 3%. The algorithm separates first-year from multiyear ice well, although it sometimes fails to correctly classify new ice and open water owing to the wide distribution of backscatter for these classes. Our results imply a high degree of accuracy and consistency in the growing archive of multiyear and first-year ice distribution maps. These results have implications for heat and mass balance studies which are furthered by the ability to accurately characterize ice type distributions over a large part of the Arctic.

Enhanced systems for measuring and monitoring REDD+: Opportunities to improve the accuracy of emission factor and activity data in Indonesia

NASA Astrophysics Data System (ADS)

Solichin

The importance of accurate measurement of forest biomass in Indonesia has been growing ever since climate change mitigation schemes, particularly the reduction of emissions from deforestation and forest degradation scheme (known as REDD+), were constitutionally accepted by the government of Indonesia. The need for an accurate system of historical and actual forest monitoring has also become more pronounced, as such a system would afford a better understanding of the role of forests in climate change and allow for the quantification of the impact of activities implemented to reduce greenhouse gas emissions. The aim of this study was to enhance the accuracy of estimations of carbon stocks and to monitor emissions in tropical forests. The research encompassed various scales (from trees and stands to landscape-sized scales) and a wide range of aspects, from evaluation and development of allometric equations to exploration of the potential of existing forest inventory databases and evaluation of cutting-edge technology for non-destructive sampling and accurate forest biomass mapping over large areas. In this study, I explored whether accuracy--especially regarding the identification and reduction of bias--of forest aboveground biomass (AGB) estimates in Indonesia could be improved through (1) development and refinement of allometric equations for major forest types, (2) integration of existing large forest inventory datasets, (3) assessing nondestructive sampling techniques for tree AGB measurement, and (4) landscape-scale mapping of AGB and forest cover using lidar. This thesis provides essential foundations to improve the estimation of forest AGB at tree scale through development of new AGB equations for several major forest types in Indonesia. I successfully developed new allometric equations using large datasets from various forest types that enable us to estimate tree aboveground biomass for both forest type specific and generic equations. My models outperformed the existing local equations, with lower bias and higher precision of the AGB estimates. This study also highlights the potential advantages and challenges of using terrestrial lidar and the acoustic velocity tool for non-destructive sampling of tree biomass to enable more sample collection without the felling of trees. Further, I explored whether existing forest inventories and permanent sample plot datasets can be integrated into Indonesia's existing carbon accounting system. My investigation of these existing datasets found that through quality assurance tests these datasets are essential to be integrated into national and provincial forest monitoring and carbon accounting systems. Integration of this information would eventually improve the accuracy of the estimates of forest carbon stocks, biomass growth, mortality and emission factors from deforestation and forest degradation. At landscape scale, this study demonstrates the capability of airborne lidar for forest monitoring and forest cover classification in tropical peat swamp ecosystems. The mapping application using airborne lidar showed a more accurate and precise classification of land and forest cover when compared with mapping using optical and active sensors. To reduce the cost of lidar acquisition, this study assessed the optimum lidar return density for forest monitoring. I found that the density of lidar return could be reduced to at least 1 return per 4 m2. Overall, this study provides essential scientific background to improve the accuracy of forest AGB estimates. Therefore, the described results and techniques should be integrated into the existing monitoring systems to assess emission reduction targets and the impact of REDD+ implementation.

Can color-coded parametric maps improve dynamic enhancement pattern analysis in MR mammography?

PubMed

Baltzer, P A; Dietzel, M; Vag, T; Beger, S; Freiberg, C; Herzog, A B; Gajda, M; Camara, O; Kaiser, W A

2010-03-01

Post-contrast enhancement characteristics (PEC) are a major criterion for differential diagnosis in MR mammography (MRM). Manual placement of regions of interest (ROIs) to obtain time/signal intensity curves (TSIC) is the standard approach to assess dynamic enhancement data. Computers can automatically calculate the TSIC in every lesion voxel and combine this data to form one color-coded parametric map (CCPM). Thus, the TSIC of the whole lesion can be assessed. This investigation was conducted to compare the diagnostic accuracy (DA) of CCPM with TSIC for the assessment of PEC. 329 consecutive patients with 469 histologically verified lesions were examined. MRM was performed according to a standard protocol (1.5 T, 0.1 mmol/kgbw Gd-DTPA). ROIs were drawn manually within any lesion to calculate the TSIC. CCPMs were created in all patients using dedicated software (CAD Sciences). Both methods were rated by 2 observers in consensus on an ordinal scale. Receiver operating characteristics (ROC) analysis was used to compare both methods. The area under the curve (AUC) was significantly (p=0.026) higher for CCPM (0.829) than TSIC (0.749). The sensitivity was 88.5% (CCPM) vs. 82.8% (TSIC), whereas equal specificity levels were found (CCPM: 63.7%, TSIC: 63.0%). The color-coded parametric maps (CCPMs) showed a significantly higher DA compared to TSIC, in particular the sensitivity could be increased. Therefore, the CCPM method is a feasible approach to assessing dynamic data in MRM and condenses several imaging series into one parametric map. © Georg Thieme Verlag KG Stuttgart · New York.

Non-supervised method for early forest fire detection and rapid mapping

NASA Astrophysics Data System (ADS)

Artés, Tomás; Boca, Roberto; Liberta, Giorgio; San-Miguel, Jesús

2017-09-01

Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a non-supervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.