Improving the Accuracy of Mapping Urban Vegetation Carbon Density by Combining Shadow Remove, Spectral Unmixing Analysis and Spatial Modeling

NASA Astrophysics Data System (ADS)

Qie, G.; Wang, G.; Wang, M.

2016-12-01

Mixed pixels and shadows due to buildings in urban areas impede accurate estimation and mapping of city vegetation carbon density. In most of previous studies, these factors are often ignored, which thus result in underestimation of city vegetation carbon density. In this study we presented an integrated methodology to improve the accuracy of mapping city vegetation carbon density. Firstly, we applied a linear shadow remove analysis (LSRA) on remotely sensed Landsat 8 images to reduce the shadow effects on carbon estimation. Secondly, we integrated a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), and utilized and compared the integrated models on shadow-removed images to map vegetation carbon density. This methodology was examined in Shenzhen City of Southeast China. A data set from a total of 175 sample plots measured in 2013 and 2014 was used to train the models. The independent variables statistically significantly contributing to improving the fit of the models to the data and reducing the sum of squared errors were selected from a total of 608 variables derived from different image band combinations and transformations. The vegetation fraction from LSUA was then added into the models as an important independent variable. The estimates obtained were evaluated using a cross-validation method. Our results showed that higher accuracies were obtained from the integrated models compared with the ones using traditional methods which ignore the effects of mixed pixels and shadows. This study indicates that the integrated method has great potential on improving the accuracy of urban vegetation carbon density estimation. Key words: Urban vegetation carbon, shadow, spectral unmixing, spatial modeling, Landsat 8 images

Mapping and monitoring changes in vegetation communities of Jasper Ridge, CA, using spectral fractions derived from AVIRIS images

NASA Technical Reports Server (NTRS)

Sabol, Donald E., Jr.; Roberts, Dar A.; Adams, John B.; Smith, Milton O.

1993-01-01

An important application of remote sensing is to map and monitor changes over large areas of the land surface. This is particularly significant with the current interest in monitoring vegetation communities. Most of traditional methods for mapping different types of plant communities are based upon statistical classification techniques (i.e., parallel piped, nearest-neighbor, etc.) applied to uncalibrated multispectral data. Classes from these techniques are typically difficult to interpret (particularly to a field ecologist/botanist). Also, classes derived for one image can be very different from those derived from another image of the same area, making interpretation of observed temporal changes nearly impossible. More recently, neural networks have been applied to classification. Neural network classification, based upon spectral matching, is weak in dealing with spectral mixtures (a condition prevalent in images of natural surfaces). Another approach to mapping vegetation communities is based on spectral mixture analysis, which can provide a consistent framework for image interpretation. Roberts et al. (1990) mapped vegetation using the band residuals from a simple mixing model (the same spectral endmembers applied to all image pixels). Sabol et al. (1992b) and Roberts et al. (1992) used different methods to apply the most appropriate spectral endmembers to each image pixel, thereby allowing mapping of vegetation based upon the the different endmember spectra. In this paper, we describe a new approach to classification of vegetation communities based upon the spectra fractions derived from spectral mixture analysis. This approach was applied to three 1992 AVIRIS images of Jasper Ridge, California to observe seasonal changes in surface composition.

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.

Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image.

PubMed

Rapinel, Sébastien; Clément, Bernard; Magnanon, Sylvie; Sellin, Vanessa; Hubert-Moy, Laurence

2014-11-01

Identification and mapping of natural vegetation are major issues for biodiversity management and conservation. Remotely sensed data with very high spatial resolution are currently used to study vegetation, but most satellite sensors are limited to four spectral bands, which is insufficient to identify some natural vegetation formations. The study objectives are to discriminate natural vegetation and identify natural vegetation formations using a Worldview-2 satellite image. The classification of the Worldview-2 image and ancillary thematic data was performed using a hybrid pixel-based and object-oriented approach. A hierarchical scheme using three levels was implemented, from land cover at a field scale to vegetation formation. This method was applied on a 48 km² site located on the French Atlantic coast which includes a classified NATURA 2000 dune and marsh system. The classification accuracy was very high, the Kappa index varying between 0.90 and 0.74 at land cover and vegetation formation levels respectively. These results show that Wordlview-2 images are suitable to identify natural vegetation. Vegetation maps derived from Worldview-2 images are more detailed than existing ones. They provide a useful medium for environmental management of vulnerable areas. The approach used to map natural vegetation is reproducible for a wider application by environmental managers. Copyright © 2014 Elsevier Ltd. All rights reserved.

SRS 2010 Vegetation Inventory GeoStatistical Mapping Results for Custom Reaction Intensity and Total Dead Fuels.

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

Edwards, Lloyd A.; Paresol, Bernard

This report of the geostatistical analysis results of the fire fuels response variables, custom reaction intensity and total dead fuels is but a part of an SRS 2010 vegetation inventory project. For detailed description of project, theory and background including sample design, methods, and results please refer to USDA Forest Service Savannah River Site internal report “SRS 2010 Vegetation Inventory GeoStatistical Mapping Report”, (Edwards & Parresol 2013).

Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology1

PubMed Central

Cruzan, Mitchell B.; Weinstein, Ben G.; Grasty, Monica R.; Kohrn, Brendan F.; Hendrickson, Elizabeth C.; Arredondo, Tina M.; Thompson, Pamela G.

2016-01-01

Premise of the study: Low-elevation surveys with small aerial drones (micro–unmanned aerial vehicles [UAVs]) may be used for a wide variety of applications in plant ecology, including mapping vegetation over small- to medium-sized regions. We provide an overview of methods and procedures for conducting surveys and illustrate some of these applications. Methods: Aerial images were obtained by flying a small drone along transects over the area of interest. Images were used to create a composite image (orthomosaic) and a digital surface model (DSM). Vegetation classification was conducted manually and using an automated routine. Coverage of an individual species was estimated from aerial images. Results: We created a vegetation map for the entire region from the orthomosaic and DSM, and mapped the density of one species. Comparison of our manual and automated habitat classification confirmed that our mapping methods were accurate. A species with high contrast to the background matrix allowed adequate estimate of its coverage. Discussion: The example surveys demonstrate that small aerial drones are capable of gathering large amounts of information on the distribution of vegetation and individual species with minimal impact to sensitive habitats. Low-elevation aerial surveys have potential for a wide range of applications in plant ecology. PMID:27672518

HOTEX: An Approach for Global Mapping of Human Built-Up and Settlement Extent

NASA Technical Reports Server (NTRS)

Wang, Panshi; Huang, Chengquan; Tilton, James C.; Tan, Bin; Brown De Colstoun, Eric C.

2017-01-01

Understanding the impacts of urbanization requires accurate and updatable urban extent maps. Here we present an algorithm for mapping urban extent at global scale using Landsat data. An innovative hierarchical object-based texture (HOTex) classification approach was designed to overcome spectral confusion between urban and nonurban land cover types. VIIRS nightlights data and MODIS vegetation index datasets are integrated as high-level features under an object-based framework. We applied the HOTex method to the GLS-2010 Landsat images to produce a global map of human built-up and settlement extent. As shown by visual assessments, our method could effectively map urban extent and generate consistent results using images with inconsistent acquisition time and vegetation phenology. Using scene-level cross validation for results in Europe, we assessed the performance of HOTex and achieved a kappa coefficient of 0.91, compared to 0.74 from a baseline method using per-pixel classification using spectral information.

Developing a Method to Mask Trees in Commercial Multispectral Imagery

NASA Astrophysics Data System (ADS)

Becker, S. J.; Daughtry, C. S. T.; Jain, D.; Karlekar, S. S.

2015-12-01

The US Army has an increasing focus on using automated remote sensing techniques with commercial multispectral imagery (MSI) to map urban and peri-urban agricultural and vegetative features; however, similar spectral profiles between trees (i.e., forest canopy) and other vegetation result in confusion between these cover classes. Established vegetation indices, like the Normalized Difference Vegetation Index (NDVI), are typically not effective in reliably differentiating between trees and other vegetation. Previous research in tree mapping has included integration of hyperspectral imagery (HSI) and LiDAR for tree detection and species identification, as well as the use of MSI to distinguish tree crowns from non-vegetated features. This project developed a straightforward method to model and also mask out trees from eight-band WorldView-2 (1.85 meter x 1.85 meter resolution at nadir) satellite imagery at the Beltsville Agricultural Research Center in Beltsville, MD spanning 2012 - 2015. The study site included tree cover, a range of agricultural and vegetative cover types, and urban features. The modeling method exploits the product of the red and red edge bands and defines accurate thresholds between trees and other land covers. Results show this method outperforms established vegetation indices including the NDVI, Soil Adjusted Vegetation Index, Normalized Difference Water Index, Simple Ratio, and Normalized Difference Red Edge Index in correctly masking trees while preserving the other information in the imagery. This method is useful when HSI and LiDAR collection are not possible or when using archived MSI.

A LANDSAT study of ephemeral and perennial rangeland vegetation and soils

NASA Technical Reports Server (NTRS)

Bentley, R. G., Jr. (Principal Investigator); Salmon-Drexler, B. C.; Bonner, W. J.; Vincent, R. K.

1976-01-01

The author has identified the following significant results. Several methods of computer processing were applied to LANDSAT data for mapping vegetation characteristics of perennial rangeland in Montana and ephemeral rangeland in Arizona. The choice of optimal processing technique was dependent on prescribed mapping and site condition. Single channel level slicing and ratioing of channels were used for simple enhancement. Predictive models for mapping percent vegetation cover based on data from field spectra and LANDSAT data were generated by multiple linear regression of six unique LANDSAT spectral ratios. Ratio gating logic and maximum likelihood classification were applied successfully to recognize plant communities in Montana. Maximum likelihood classification did little to improve recognition of terrain features when compared to a single channel density slice in sparsely vegetated Arizona. LANDSAT was found to be more sensitive to differences between plant communities based on percentages of vigorous vegetation than to actual physical or spectral differences among plant species.

National Park Service Vegetation Mapping Inventory Program: Appalachian National Scenic Trail vegetation mapping project

USGS Publications Warehouse

Hop, Kevin D.; Strassman, Andrew C.; Hall, Mark; Menard, Shannon; Largay, Ery; Sattler, Stephanie; Hoy, Erin E.; Ruhser, Janis; Hlavacek, Enrika; Dieck, Jennifer

2017-01-01

The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program classifies, describes, and maps existing vegetation of national park units for the NPS Natural Resource Inventory and Monitoring (I&M) Program. The NPS VMI Program is managed by the NPS I&M Division and provides baseline vegetation information to the NPS Natural Resource I&M Program. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, NPS Northeast Temperate Network, and NPS Appalachian National Scenic Trail (APPA) have completed vegetation classification and mapping of APPA for the NPS VMI Program.Mappers, ecologists, and botanists collaborated to affirm vegetation types within the U.S. National Vegetation Classification (USNVC) of APPA and to determine how best to map the vegetation types by using aerial imagery. Analyses of data from 1,618 vegetation plots were used to describe USNVC associations of APPA. Data from 289 verification sites were collected to test the field key to vegetation associations and the application of vegetation associations to a sample set of map polygons. Data from 269 validation sites were collected to assess vegetation mapping prior to submitting the vegetation map for accuracy assessment (AA). Data from 3,265 AA sites were collected, of which 3,204 were used to test accuracy of the vegetation map layer. The collective of these datasets affirmed 280 USNVC associations for the APPA vegetation mapping project.To map the vegetation and land cover of APPA, 169 map classes were developed. The 169 map classes consist of 150 that represent natural (including ruderal) vegetation types in the USNVC, 11 that represent cultural (agricultural and developed) vegetation types in the USNVC, 5 that represent natural landscapes with catastrophic disturbance or some other modification to natural vegetation preventing accurate classification in the USNVC, and 3 that represent nonvegetated water (non-USNVC). Features were interpreted from viewing 4-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems (GIS). (Digital aerial imagery was collected each fall during 2009–11 to capture leaf-phenology change of hardwood trees across the latitudinal range of APPA.) The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in GIS. Polygon units were mapped to either a 0.5-hectare (ha) or 0.25-ha minimum mapping unit, depending on vegetation type or scenario; however, polygon units were mapped to 0.1 ha for alpine vegetation.A geodatabase containing various feature-class layers and tables provide locations and support data to USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, validation sites, AA sites, project boundary extent and zones, and aerial image centers and flight lines. The feature-class layer and related tables of the vegetation map layer provide 30,395 polygons of detailed attribute data covering 110,919.7 ha, with an average polygon size of 3.6 ha; the vegetation map coincides closely with the administrative boundary for APPA.Summary reports generated from the vegetation map layer of the map classes representing USNVC natural (including ruderal) vegetation types apply to 28,242 polygons (92.9% of polygons) and cover 106,413.0 ha (95.9%) of the map extent for APPA. The map layer indicates APPA to be 92.4% forest and woodland (102,480.8 ha), 1.7% shrubland (1866.3 ha), and 1.8% herbaceous cover (2,065.9 ha). Map classes representing park-special vegetation (undefined in the USNVC) apply to 58 polygons (0.2% of polygons) and cover 404.3 ha (0.4%) of the map extent. Map classes representing USNVC cultural types apply to 1,777 polygons (5.8% of polygons) and cover 2,516.3 ha (2.3%) of the map extent. Map classes representing nonvegetated water (non-USNVC) apply to 332 polygons (1.1% of polygons) and cover 1,586.2 ha (1.4%) of the map extent.

A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

USGS Publications Warehouse

Tadesse, Tsegaye; Brown, Jesslyn F.; Hayes, M.J.

2005-01-01

Droughts are normal climate episodes, yet they are among the most expensive natural disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the landscape can be incorporated into effective planning and decision-making. In this study, we present a data mining approach to modeling vegetation stress due to drought and mapping its spatial extent during the growing season. Rule-based regression tree models were generated that identify relationships between satellite-derived vegetation conditions, climatic drought indices, and biophysical data, including land-cover type, available soil water capacity, percent of irrigated farm land, and ecological type. The data mining method builds numerical rule-based models that find relationships among the input variables. Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions. Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model. We present prototype maps for the 2002 drought year for Nebraska and South Dakota and discuss potential uses for these maps.

Small unmanned aerial vehicles (micro-UAVs, drones) in plant ecology.

PubMed

Cruzan, Mitchell B; Weinstein, Ben G; Grasty, Monica R; Kohrn, Brendan F; Hendrickson, Elizabeth C; Arredondo, Tina M; Thompson, Pamela G

2016-09-01

Low-elevation surveys with small aerial drones (micro-unmanned aerial vehicles [UAVs]) may be used for a wide variety of applications in plant ecology, including mapping vegetation over small- to medium-sized regions. We provide an overview of methods and procedures for conducting surveys and illustrate some of these applications. Aerial images were obtained by flying a small drone along transects over the area of interest. Images were used to create a composite image (orthomosaic) and a digital surface model (DSM). Vegetation classification was conducted manually and using an automated routine. Coverage of an individual species was estimated from aerial images. We created a vegetation map for the entire region from the orthomosaic and DSM, and mapped the density of one species. Comparison of our manual and automated habitat classification confirmed that our mapping methods were accurate. A species with high contrast to the background matrix allowed adequate estimate of its coverage. The example surveys demonstrate that small aerial drones are capable of gathering large amounts of information on the distribution of vegetation and individual species with minimal impact to sensitive habitats. Low-elevation aerial surveys have potential for a wide range of applications in plant ecology.

Mapping tropical dry forest habitats integrating landsat NDVI, Ikonos imagery, and topographic information in the Caribbean island of Mona.

PubMed

Martinuzzi, Sebastiáin; Gould, William A; Ramos Gonzalez, Olga M; Martinez Robles, Alma; Calle Maldonado, Paulina; Pérez-Buitrago, Néstor; Fumero Caban, José J

2008-06-01

Assessing the status of tropical dry forest habitats using remote sensing technologies is one of the research priorities for Neotropical forests. We developed a simple method for mapping vegetation and habitats in a tropical dry forest reserve, Mona Island, Puerto Rico, by integrating the Normalized Difference Vegetation Index (NDVI) from Landsat, topographic information, and high-resolution Ikonos imagery. The method was practical for identifying vegetation types in areas with a great variety of plant communities and complex relief, and can be adapted to other dry forest habitats of the Caribbean Islands. NDVI was useful for identifying the distribution of forests, woodlands, and shrubland, providing a natural representation of the vegetation patterns on the island. The use of Ikonos imagery allowed increasing the number of land cover classes. As a result, sixteen land-cover types were mapped over the 5500 ha area, with a kappa coefficient of accuracy equal to 79%. This map is a central piece for modeling vertebrate species distribution and biodiversity patterns by the Puerto Rico Gap Analysis Project, and it is of great value for assisting research and management actions in the island.

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

Exploiting differential vegetation phenology for satellite-based mapping of semiarid grass vegetation in the southwestern United States and northern Mexico

USGS Publications Warehouse

Dye, Dennis G.; Middleton, Barry R.; Vogel, John M.; Wu, Zhuoting; Velasco, Miguel G.

2016-01-01

We developed and evaluated a methodology for subpixel discrimination and large-area mapping of the perennial warm-season (C4) grass component of vegetation cover in mixed-composition landscapes of the southwestern United States and northern Mexico. We describe the methodology within a general, conceptual framework that we identify as the differential vegetation phenology (DVP) paradigm. We introduce a DVP index, the Normalized Difference Phenometric Index (NDPI) that provides vegetation type-specific information at the subpixel scale by exploiting differential patterns of vegetation phenology detectable in time-series spectral vegetation index (VI) data from multispectral land imagers. We used modified soil-adjusted vegetation index (MSAVI2) data from Landsat to develop the NDPI, and MSAVI2 data from MODIS to compare its performance relative to one alternate DVP metric (difference of spring average MSAVI2 and summer maximum MSAVI2), and two simple, conventional VI metrics (summer average MSAVI2, summer maximum MSAVI2). The NDPI in a scaled form (NDPIs) performed best in predicting variation in perennial C4 grass cover as estimated from landscape photographs at 92 sites (R2 = 0.76, p < 0.001), indicating improvement over the alternate DVP metric (R2 = 0.73, p < 0.001) and substantial improvement over the two conventional VI metrics (R2 = 0.62 and 0.56, p < 0.001). The results suggest DVP-based methods, and the NDPI in particular, can be effective for subpixel discrimination and mapping of exposed perennial C4 grass cover within mixed-composition landscapes of the Southwest, and potentially for monitoring of its response to drought, climate change, grazing and other factors, including land management. With appropriate adjustments, the method could potentially be used for subpixel discrimination and mapping of grass or other vegetation types in other regions where the vegetation components of the landscape exhibit contrasting seasonal patterns of phenology.

A conceptual method for monitoring locust habitat

USGS Publications Warehouse

Howard, Stephen M.; Loveland, Thomas R.; Ohlen, Donald O.; Moore, Donald G.; Gallo, Kevin P.; Olsson, Jonathon

1987-01-01

A procedure to map and monitor vegetation conditions in near-real time was developed at the United States Geological Survey;s Earth Resources Observation Systems Data Center for use in locust control efforts. Meteorological satellite dat were acquired daily for 3 weeks in October and November 1986 over a 1.4-million-square-kilometer study area centered on Botswana in southern Africa. Advanced Very High Resolution Radiometer data were screened to remove cloud-contaminated data and registered to a 1-kilometer geographic base. Each day the normalized difference vegetation index (NDVI) was calculated to determine the presence and relative amounts of green vegetation in the area. Over a 10-day cycle, subsequent dates of NDVI data were composited to fill in data removed by the cloud-screening process. At any pixel location, the maximum NDVI value was retained. At the end of the 10-day cycle, a composite vegetation-greenness map was produced and another cycle started. Greenness-change maps were produced by comparing two 10-day composite greenness images. Automated map production procedures were used to merge the NDVI image data with cartographic data (boundaries, roads, tick marks) digitized from 1:1,000,000-scale operational navigation charts. The vegetation-greenness map shoes the current distribution of vegetation in the region and can be used to locate potential locust breeding area. The change map shows areas where increases and decreases in greenness have occurred between processing cycles. Significant areas of locust damage in remote regions are characterized by an unexpected decrease in greenness. These maps can be used by locust control teams to efficiently target areas for reconnaissance. In general, the procedures and products have utility for resource managers who are required to monitor vegetation resources over large geographic regions.

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.

Investigation of environmental change pattern in Japan. Investigation of the ecological environment index from observation of the regional vegetation cover and their growing condition

NASA Technical Reports Server (NTRS)

Maruyasu, T. (Principal Investigator); Nakajima, I.

1977-01-01

The author has identified the following significant results. Practical use of recognition results of LANDSAT data as the base map of the field survey or the retouching work of vegetation and land use has the effective benefit to cut down the cost, labor, and time lower than 10% of a conventional method. Correct and detailed vegetation maps were prepared using combined interpretation of repetition of data of different seasons at warm and temperate forested areas.

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.

Evaluating rapid ground sampling and scaling estimated plant cover using UAV imagery up to Landsat for mapping arctic vegetation

NASA Astrophysics Data System (ADS)

Nelson, P.; Paradis, D. P.

2017-12-01

The small stature and spectral diversity of arctic plant taxa presents challenges in mapping arctic vegetation. Mapping vegetation at the appropriate scale is needed to visualize effects of disturbance, directional vegetation change or mapping of specific plant groups for other applications (eg. habitat mapping). Fine spatial grain of remotely sensed data (ca. 10 cm pixels) is often necessary to resolve patches of many arctic plant groups, such as bryophytes and lichens. These groups are also spectrally different from mineral, litter and vascular plants. We sought to explore method to generate high-resolution spatial and spectral data to explore better mapping methods for arctic vegetation. We sampled ground vegetation at seven sites north or west of tree-line in Alaska, four north of Fairbanks and three northwest of Bethel, respectively. At each site, we estimated cover of plant functional types in 1m2 quadrats spaced approximately every 10 m along a 100 m long transect. Each quadrat was also scanned using a field spectroradiometer (PSR+ Spectral Evolution, 400-2500 nm range) and photographed from multiple perspectives. We then flew our small UAV with a RGB camera over the transect and at least 50 m on either side collecting on imagery of the plot, which were used to generate a image mosaic and digital surface model of the plot. We compare plant functional group cover ocular estimated in situ to post-hoc estimation, either automated or using a human observer, using the quadrat photos. We also compare interpolated lichen cover from UAV scenes to estimated lichen cover using a statistical models using Landsat data, with focus on lichens. Light and yellow lichens are discernable in the UAV imagery but certain lichens, especially dark colored lichens or those with spectral signatures similar to graminoid litter, present challenges. Future efforts will focus on integrating UAV-upscaled ground cover estimates to hyperspectral sensors (eg. AVIRIS ng) for better combined spectral and spatial resolution.

Using remote sensing and GIS techniques to estimate discharge and recharge. fluxes for the Death Valley regional groundwater flow system, USA

USGS Publications Warehouse

D'Agnese, F. A.; Faunt, C.C.; Keith, Turner A.

1996-01-01

The recharge and discharge components of the Death Valley regional groundwater flow system were defined by remote sensing and GIS techniques that integrated disparate data types to develop a spatially complex representation of near-surface hydrological processes. Image classification methods were applied to multispectral satellite data to produce a vegetation map. This map provided a basis for subsequent evapotranspiration and infiltration estimations. The vegetation map was combined with ancillary data in a GIS to delineate different types of wetlands, phreatophytes and wet playa areas. Existing evapotranspiration-rate estimates were then used to calculate discharge volumes for these areas. A previously used empirical method of groundwater recharge estimation was modified by GIS methods to incorporate data describing soil-moisture conditions, and a recharge potential map was produced. These discharge and recharge maps were readily converted to data arrays for numerical modelling codes. Inverse parameter estimation techniques also used these data to evaluate the reliability and sensitivity of estimated values.

Deep Learning of Post-Wildfire Vegetation Loss using Bitemporal Synthetic Aperture Radar Images

NASA Astrophysics Data System (ADS)

Chen, Z.; Glasscoe, M. T.; Parker, J. W.

2017-12-01

Wildfire events followed by heavy precipitation have been proven causally related to breakouts of mudflow or debris flow, which, can demand rapid evacuation and threaten residential communities and civil infrastructure. For example, in the case of the city of Glendora, California, it was first afflicted by a severe wildfire in 1968 and then the flooding caused mudslides and debris flow in 1969 killed 34 people. Therefore, burn area or vegetation loss mapping due to wildfire is critical to agencies for preparing for secondary hazards, particularly flooding and flooding induced mudflow. However, rapid post-wildfire mapping of vegetation loss mapping is not readily obtained by regular remote sensing methods, e.g. various optical methods, due to the presence of smoke, haze, and rainy/cloudy conditions that often follow a wildfire event. In this paper, we will introduce and develop a deep learning-based framework that uses Synthetic Aperture Radar images collected prior to and after a wildfire event. A convolutional neural network (CNN) approach will be used that replaces traditional principle component analysis (PCA) based differencing for non-supervised change feature extraction. Using a small sample of human-labeled burned vegetation, normal vegetation, and urban built-up pixels, we will compare the performance of deep learning and PCA-based feature extraction. The 2014 Coby Fire event, which affected the downstream city of Glendora, was used to evaluate the proposed framework. The NASA's UAVSAR data (https://uavsar.jpl.nasa.gov/) will be utilized for mapping the vegetation damage due to the Coby Fire event.

National Park Service Vegetation Mapping Inventory Program: Natchez Trace Parkway vegetation mapping project report

USGS Publications Warehouse

Hop, Kevin D.; Strassman, Andrew C.; Nordman, Carl; Pyne, Milo; White, Rickie; Jakusz, Joseph; Hoy, Erin E.; Dieck, Jennifer

2016-01-01

The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program is an effort to classify, describe, and map existing vegetation of national park units for the NPS Natural Resource Inventory and Monitoring (I&M) Program. The NPS VMI Program is managed by the NPS I&M Division and provides baseline vegetation information to the NPS Natural Resource I&M Program. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, NPS Gulf Coast Network, and NPS Natchez Trace Parkway (NATR; also referred to as Parkway) have completed vegetation classification and mapping of NATR for the NPS VMI Program.Mappers, ecologists, and botanists collaborated to affirm vegetation types within the U.S. National Vegetation Classification (USNVC) of NATR and to determine how best to map them by using aerial imagery. Analyses of data from 589 vegetation plots had been used to describe an initial 99 USNVC associations in the Parkway; this classification work was completed prior to beginning this NATR vegetation mapping project. Data were collected during this project from another eight quick plots to support new vegetation types not previously identified at the Parkway. Data from 120 verification sites were collected to test the field key to vegetation associations and the application of vegetation associations to a sample set of map polygons. Furthermore, data from 900 accuracy assessment (AA) sites were collected (of which 894 were used to test accuracy of the vegetation map layer). The collective of all these datasets resulted in affirming 122 USNVC associations at NATR.To map the vegetation and open water of NATR, 63 map classes were developed. including the following: 54 map classes represent natural (including ruderal) vegetation types in the USNVC, 5 map classes represent cultural (agricultural and developed) vegetation types in the USNVC, 3 map classes represent nonvegetation open-water bodies (non-USNVC), and 1 map class represents landscapes that had received tornado damage a few months prior to the time of aerial imagery collection. Features were interpreted from viewing 4-band digital aerial imagery by means of digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems. (The aerial imagery was collected during mid-October 2011 for the northern reach of the Parkway and mid-November 2011 for the southern reach of the Parkway to capture peak leaf-phenology of trees.) The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in geographic information systems. Polygon units were mapped to either a 0.5 hectare (ha) or 0.25 ha minimum mapping unit, depending on vegetation type or scenario.A geodatabase containing various feature-class layers and tables present the locations of USNVC vegetation types (vegetation map), vegetation plot samples, verification sites, AA sites, project boundary extent, and aerial image centers. The feature-class layer and related tables for the vegetation map provide 13,529 polygons of detailed attribute data covering 21,655.5 ha, with an average polygon size of 1.6 ha; the vegetation map coincides closely with the administrative boundary for NATR.Summary reports generated from the vegetation map layer of the map classes representing USNVC natural (including ruderal) vegetation types apply to 12,648 polygons (93.5% of polygons) and cover 18,542.7 ha (85.6%) of the map extent for NATR. The map layer indicates the Parkway to be 70.5% forest and woodland (15,258.7 ha), 0.3% shrubland (63.0 ha), and 14.9% herbaceous cover (3,221.0 ha). Map classes representing USNVC cultural types apply to 678 polygons (5.0% of polygons) and cover 2,413.9 ha (11.1%) of the map extent.

Prior-knowledge-based spectral mixture analysis for impervious surface mapping

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

Zhang, Jinshui; He, Chunyang; Zhou, Yuyu

2014-01-03

In this study, we developed a prior-knowledge-based spectral mixture analysis (PKSMA) to map impervious surfaces by using endmembers derived separately for high- and low-density urban regions. First, an urban area was categorized into high- and low-density urban areas, using a multi-step classification method. Next, in high-density urban areas that were assumed to have only vegetation and impervious surfaces (ISs), the Vegetation-Impervious model (V-I) was used in a spectral mixture analysis (SMA) with three endmembers: vegetation, high albedo, and low albedo. In low-density urban areas, the Vegetation-Impervious-Soil model (V-I-S) was used in an SMA analysis with four endmembers: high albedo, lowmore » albedo, soil, and vegetation. The fraction of IS with high and low albedo in each pixel was combined to produce the final IS map. The root mean-square error (RMSE) of the IS map produced using PKSMA was about 11.0%, compared to 14.52% using four-endmember SMA. Particularly in high-density urban areas, PKSMA (RMSE = 6.47%) showed better performance than four-endmember (15.91%). The results indicate that PKSMA can improve IS mapping compared to traditional SMA by using appropriately selected endmembers and is particularly strong in high-density urban areas.« less

Fuel loads and fuel type mapping

USGS Publications Warehouse

Chuvieco, Emilio; Riaño, David; Van Wagtendonk, Jan W.; Morsdof, Felix; Chuvieco, Emilio

2003-01-01

Correct description of fuel properties is critical to improve fire danger assessment and fire behaviour modeling, since they guide both fire ignition and fire propagation. This chapter deals with properties of fuel that can be considered static in short periods of time: biomass loads, plant geometry, compactness, etc. Mapping these properties require a detail knowledge of vegetation vertical and horizontal structure. Several systems to classify the great diversity of vegetation characteristics in few fuel types are described, as well as methods for mapping them with special emphasis on those based on remote sensing images.

Detection and mapping of hydrothermally altered rocks in the vicinity of the comstock lode, Virginia Range, Nevada, using enhanced LANDSAT images

NASA Technical Reports Server (NTRS)

Ashley, R. P. (Principal Investigator); Goetz, A. F. H.; Rowan, L. C.; Abrams, M. J.

1979-01-01

The author has identified the following significant results. LANDSAT images enhanced by the band-ratioing method can be used for reconnaissance alteration mapping in moderately heavily vegetated semiarid terrain as well as in sparsely vegetated to semiarid terrain where the technique was originally developed. Significant vegetation cover in a scene, however, requires the use of MSS ratios 4/5, 4/6, and 6/7 rather than 4/5, 5/6, and 6/7, and requires careful interpretation of the results. Supplemental information suitable to vegetation identification and cover estimates, such as standard LANDSAT false-color composites and low altitude aerial photographs of selected areas is desirable.

Automatically Generated Vegetation Density Maps with LiDAR Survey for Orienteering Purpose

NASA Astrophysics Data System (ADS)

Petrovič, Dušan

2018-05-01

The focus of our research was to automatically generate the most adequate vegetation density maps for orienteering purpose. Application Karttapullatuin was used for automated generation of vegetation density maps, which requires LiDAR data to process an automatically generated map. A part of the orienteering map in the area of Kazlje-Tomaj was used to compare the graphical display of vegetation density. With different settings of parameters in the Karttapullautin application we changed the way how vegetation density of automatically generated map was presented, and tried to match it as much as possible with the orienteering map of Kazlje-Tomaj. Comparing more created maps of vegetation density the most suitable parameter settings to automatically generate maps on other areas were proposed, too.

VEGETATION COVER ANALYSIS OF HAZARDOUS WASTE SITES IN UTAH AND ARIZONA USING HYPERSPECTRAL REMOTE SENSING

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

Serrato, M.; Jungho, I.; Jensen, J.

2012-01-17

Remote sensing technology can provide a cost-effective tool for monitoring hazardous waste sites. This study investigated the usability of HyMap airborne hyperspectral remote sensing data (126 bands at 2.3 x 2.3 m spatial resolution) to characterize the vegetation at U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using threemore » different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. Regression trees resulted in the best calibration performance of LAI estimation (R{sup 2} > 0.80). The use of REPs failed to accurately predict LAI (R{sup 2} < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of 1 higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (< 1m) found on the sites.« less

A new world natural vegetation map for global change studies.

PubMed

Lapola, David M; Oyama, Marcos D; Nobre, Carlos A; Sampaio, Gilvan

2008-06-01

We developed a new world natural vegetation map at 1 degree horizontal resolution for use in global climate models. We used the Dorman and Sellers vegetation classification with inclusion of a new biome: tropical seasonal forest, which refers to both deciduous and semi-deciduous tropical forests. SSiB biogeophysical parameters values for this new biome type are presented. Under this new vegetation classification we obtained a consensus map between two global natural vegetation maps widely used in climate studies. We found that these two maps assign different biomes in ca. 1/3 of the continental grid points. To obtain a new global natural vegetation map, non-consensus areas were filled according to regional consensus based on more than 100 regional maps available on the internet. To minimize the risk of using poor quality information, the regional maps were obtained from reliable internet sources, and the filling procedure was based on the consensus among several regional maps obtained from independent sources. The new map was designed to reproduce accurately both the large-scale distribution of the main vegetation types (as it builds on two reliable global natural vegetation maps) and the regional details (as it is based on the consensus of regional maps).

Effects of experimental protocol on global vegetation model accuracy: a comparison of simulated and observed vegetation patterns for Asia

USGS Publications Warehouse

Tang, Guoping; Shafer, Sarah L.; Barlein, Patrick J.; Holman, Justin O.

2009-01-01

Prognostic vegetation models have been widely used to study the interactions between environmental change and biological systems. This study examines the sensitivity of vegetation model simulations to: (i) the selection of input climatologies representing different time periods and their associated atmospheric CO2 concentrations, (ii) the choice of observed vegetation data for evaluating the model results, and (iii) the methods used to compare simulated and observed vegetation. We use vegetation simulated for Asia by the equilibrium vegetation model BIOME4 as a typical example of vegetation model output. BIOME4 was run using 19 different climatologies and their associated atmospheric CO2 concentrations. The Kappa statistic, Fuzzy Kappa statistic and a newly developed map-comparison method, the Nomad index, were used to quantify the agreement between the biomes simulated under each scenario and the observed vegetation from three different global land- and tree-cover data sets: the global Potential Natural Vegetation data set (PNV), the Global Land Cover Characteristics data set (GLCC), and the Global Land Cover Facility data set (GLCF). The results indicate that the 30-year mean climatology (and its associated atmospheric CO2 concentration) for the time period immediately preceding the collection date of the observed vegetation data produce the most accurate vegetation simulations when compared with all three observed vegetation data sets. The study also indicates that the BIOME4-simulated vegetation for Asia more closely matches the PNV data than the other two observed vegetation data sets. Given the same observed data, the accuracy assessments of the BIOME4 simulations made using the Kappa, Fuzzy Kappa and Nomad index map-comparison methods agree well when the compared vegetation types consist of a large number of spatially continuous grid cells. The results of this analysis can assist model users in designing experimental protocols for simulating vegetation.

Application of automated multispectral analysis to Delaware's coastal vegetation mapping

NASA Technical Reports Server (NTRS)

Klemas, V. (Principal Investigator); Daiber, D.; Bartlett, D. S.; Crichton, O. W.; Fornes, A. O.

1973-01-01

There are no author-identified significant results in this report. Overlay maps of Delaware's wetlands have been prepared, showing the dominant species or group of species of vegetation present. Five such categories of vegetation were used indicating marshes dominated by: (1) salt marsh cord grass; (2) salt marsh hay and spike grass; (3) reed grass; (4) high tide bush and sea myrtle; and (5) a group of fresh water species found in impounded areas built to attract water fowl. Fifteen such maps cover Delaware's wetlands from the Pennsylvania to the Maryland borders. The mapping technique employed utilizes the General Electric multispectral data processing system. This system is a hybrid analog-digital system designed as an analysis tool to be used by an operator whose own judgment and knowledge of ground truth can be incorporated at any time into the analyzing process. The result is a high speed, cost effective method for producing enhanced photomaps showing a number of spectral classes, each enhanced spectral class being representative of a vegetative species or group of species.

Digital soil mapping using remote sensing indices, terrain attributes, and vegetation features in the rangelands of northeastern Iran.

PubMed

Mahmoudabadi, Ebrahim; Karimi, Alireza; Haghnia, Gholam Hosain; Sepehr, Adel

2017-09-11

Digital soil mapping has been introduced as a viable alternative to the traditional mapping methods due to being fast and cost-effective. The objective of the present study was to investigate the capability of the vegetation features and spectral indices as auxiliary variables in digital soil mapping models to predict soil properties. A region with an area of 1225 ha located in Bajgiran rangelands, Khorasan Razavi province, northeastern Iran, was chosen. A total of 137 sampling sites, each containing 3-5 plots with 10-m interval distance along a transect established based on randomized-systematic method, were investigated. In each plot, plant species names and numbers as well as vegetation cover percentage (VCP) were recorded, and finally one composite soil sample was taken from each transect at each site (137 soil samples in total). Terrain attributes were derived from a digital elevation model, different bands and spectral indices were obtained from the Landsat7 ETM+ images, and vegetation features were calculated in the plots, all of which were used as auxiliary variables to predict soil properties using artificial neural network, gene expression programming, and multivariate linear regression models. According to R 2 RMSE and MBE values, artificial neutral network was obtained as the most accurate soil properties prediction function used in scorpan model. Vegetation features and indices were more effective than remotely sensed data and terrain attributes in predicting soil properties including calcium carbonate equivalent, clay, bulk density, total nitrogen, carbon, sand, silt, and saturated moisture capacity. It was also shown that vegetation indices including NDVI, SAVI, MSAVI, SARVI, RDVI, and DVI were more effective in estimating the majority of soil properties compared to separate bands and even some soil spectral indices.

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.

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).

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

USGS Publications Warehouse

Hansen, M.C.; Reed, B.

2000-01-01

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.

Vegetation mapping of the Mond Protected Area of Bushehr Province (south-west Iran).

PubMed

Mehrabian, Ahmadreza; Naqinezhad, Alireza; Mahiny, Abdolrassoul Salman; Mostafavi, Hossein; Liaghati, Homan; Kouchekzadeh, Mohsen

2009-03-01

Arid regions of the world occupy up to 35% of the earth's surface, the basis of various definitions of climatic conditions, vegetation types or potential for food production. Due to their high ecological value, monitoring of arid regions is necessary and modern vegetation studies can help in the conservation and management of these areas. The use of remote sensing for mapping of desert vegetation is difficult due to mixing of the spectral reflectance of bright desert soils with the weak spectral response of sparse vegetation. We studied the vegetation types in the semiarid to arid region of Mond Protected Area, south-west Iran, based on unsupervised classification of the Spot XS bands and then produced updated maps. Sixteen map units covering 12 vegetation types were recognized in the area based on both field works and satellite mapping. Halocnemum strobilaceum and Suaeda fruticosa vegetation types were the dominant types and Ephedra foliata, Salicornia europaea-Suaeda heterophylla vegetation types were the smallest. Vegetation coverage decreased sharply with the increase in salinity towards the coastal areas of the Persian Gulf. The highest vegetation coverage belonged to the riparian vegetation along the Mond River, which represents the northern boundary of the protected area. The location of vegetation types was studied on the separate soil and habitat diversity maps of the study area, which helped in final refinements of the vegetation map produced.

Approaches to vegetation mapping and ecophysiological hypothesis testing using combined information from TIMS, AVIRIS, and AIRSAR

NASA Technical Reports Server (NTRS)

Oren, R.; Vane, G.; Zimmermann, R.; Carrere, V.; Realmuto, V.; Zebker, Howard A.; Schoeneberger, P.; Schoeneberger, M.

1991-01-01

The Tropical Rainforest Ecology Experiment (TREE) had two primary objectives: (1) to design a method for mapping vegetation in tropical regions using remote sensing and determine whether the result improves on available vegetation maps; and (2) to test a specific hypothesis on plant/water relations. Both objectives were thought achievable with the combined information from the Thermal Infrared Multispectral Scanner (TIMS), Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and Airborne Synthetic Aperture Radar (AIRSAR). Implicitly, two additional objectives were: (1) to ascertain that the range within each variable potentially measurable with the three instruments is large enough in the site, relative to the sensitivity of the instruments, so that differences between ecological groups may be detectable; and (2) to determine the ability of the three systems to quantify different variables and sensitivities. We found that the ranges in values of foliar nitrogen concentration, water availability, stand structure and species composition, and plant/water relations were large, even within the upland broadleaf vegetation type. The range was larger when other vegetation types were considered. Unfortunately, cloud cover and navigation errors compromised the utility of the TIMS and AVIRIS data. Nevertheless, the AIRSAR data alone appear to have improved on the available vegetation map for the study area. An example from an area converted to a farm is given to demonstrate how the combined information from AIRSAR, TIMS, and AVIRIS can uniquely identify distinct classes of land use. The example alludes to the potential utility of the three instruments for identifying vegetation at an ecological scale finer than vegetation types.

Biotechnology of trees: Chestnut

Treesearch

C.D. Nelson; W.A. Powell; S.A. Merkle; J.E. Carlson; F.V. Hebard; N Islam-Faridi; M.E. Staton; L. Georgi

2014-01-01

Biotechnology has been practiced on chestnuts (Castanea spp.) for many decades, including vegetative propagation, controlled crossing followed by testing and selection, genetic and cytogenetic mapping, genetic modifi cation, and gene and genome sequencing. Vegetative propagation methods have ranged from grafting and rooting to somatic embryogenesis, often in...

(abstract) Studies of Interferometric Penetration into Vegetation Canopies using Multifrequency Interferometry Data at JPL

NASA Technical Reports Server (NTRS)

Hensley, Scott; Rodriguez, Ernesto; Truhafft, Bob; van Zyl, Jakob; Rosen, Paul; Werner, Charles; Madsen, Sren; Chapin, Elaine

1997-01-01

Radar interferometric observations both from spaceborne and airborne platforms have been used to generate accurate topographic maps, measure milimeter level displacements from earthquakes and volcanoes, and for making land cover classification and land cover change maps. Interferometric observations have two basic measurements, interferometric phase, which depends upon the path difference between the two antennas and the correlation. One of the key questions concerning interferometric observations of vegetated regions is where in the canopy does the interferometric phase measure the height. Results for two methods of extracting tree heights and other vegetation parameters based upon the amount of volumetric decorrelation will be presented.

Vegetation Change in Interior Alaska Over the Last Four Decades

NASA Astrophysics Data System (ADS)

Huhman, H.; Dewitz, J.; Cristobal, J.; Prakash, A.

2017-12-01

The Arctic has become a generally warmer place over the past decades leading to earlier snowmelt, permafrost degradation and changing plant communities. One area in particular, vegetation change, is responding relatively rapidly to climate change, impacting the surrounding environment with changes to forest fire regime, forest type, forest resiliency, habitat availability for subsistence flora and fauna, hydrology, among others. To quantify changes in vegetation in the interior Alaska boreal forest over the last four decades, this study uses the National Land Cover Database (NLCD) decision-tree based classification methods, using both C5 and ERDAS Imagine software, to classify Landsat Surface Reflectance Images into the following NLCD-consistent vegetation classes: planted, herbaceous, shrubland, and forest (deciduous, evergreen and mixed). The results of this process are a total of four vegetation cover maps, that are freely accessible to the public, one for each decade in the 1980's, 1990's, 2000's, and a current map for 2017. These maps focus on Fairbanks, Alaska and the surrounding area covering approximately 36,140 square miles. The maps are validated with over 4,000 ground truth points collected through organizations such as the Landfire Project and the Long Term Ecological Research Network, as well as vegetation and soil spectra collected from the study area concurrent with the Landsat satellite over-passes with a Spectral Evolution PSR+ 3500 spectro-radiometer (0.35 - 2.5 μm). We anticipate these maps to be viewed by a wide user-community and may aid in preparing the residents of Alaska for changes in their subsistence food sources and will contribute to the scientific community in understanding the variety of changes that can occur in response to changing vegetation.

Multi-scale functional mapping of tidal marsh vegetation for restoration monitoring

NASA Astrophysics Data System (ADS)

Tuxen Bettman, Karin

2007-12-01

Nearly half of the world's natural wetlands have been destroyed or degraded, and in recent years, there have been significant endeavors to restore wetland habitat throughout the world. Detailed mapping of restoring wetlands can offer valuable information about changes in vegetation and geomorphology, which can inform the restoration process and ultimately help to improve chances of restoration success. I studied six tidal marshes in the San Francisco Estuary, CA, US, between 2003 and 2004 in order to develop techniques for mapping tidal marshes at multiple scales by incorporating specific restoration objectives for improved longer term monitoring. I explored a "pixel-based" remote sensing image analysis method for mapping vegetation in restored and natural tidal marshes, describing the benefits and limitations of this type of approach (Chapter 2). I also performed a multi-scale analysis of vegetation pattern metrics for a recently restored tidal marsh in order to target the metrics that are consistent across scales and will be robust measures of marsh vegetation change (Chapter 3). Finally, I performed an "object-based" image analysis using the same remotely sensed imagery, which maps vegetation type and specific wetland functions at multiple scales (Chapter 4). The combined results of my work highlight important trends and management implications for monitoring wetland restoration using remote sensing, and will better enable restoration ecologists to use remote sensing for tidal marsh monitoring. Several findings important for tidal marsh restoration monitoring were made. Overall results showed that pixel-based methods are effective at quantifying landscape changes in composition and diversity in recently restored marshes, but are limited in their use for quantifying smaller, more fine-scale changes. While pattern metrics can highlight small but important changes in vegetation composition and configuration across years, scientists should exercise caution when using metrics in their studies or to validate restoration management decisions, and multi-scale analyses should be performed before metrics are used in restoration science for important management decisions. Lastly, restoration objectives, ecosystem function, and scale can each be integrated into monitoring techniques using remote sensing for improved restoration monitoring.

Special-Status Plant Species Surveys and Vegetation Mapping at Lawrence Livermore National Laboratory

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

Preston, R E

This report presents the results of Jones & Stokes special-status plant surveys and vegetation mapping for the University of California, Lawrence Livermore National Laboratory (LLNL). Special-status plant surveys were conducted at Site 300 in April to May 1997 and in March to April 2002. Eight special-status plants were identified at Site 300: large-flowered fiddleneck, big tarplant, diamond-petaled poppy, round-leaved filaree, gypsum-loving larkspur, California androsace, stinkbells, and hogwallow starfish. Maps identifying the locations of these species, a discussion of the occurrence of these species at Site 300, and a checklist of the flora of Site 300 are presented. A reconnaissance surveymore » of the LLNL Livermore Site was conducted in June 2002. This survey concluded that no special-status plants occur at the Livermore Site. Vegetation mapping was conducted in 2001 at Site 300 to update a previous vegetation study done in 1986. The purpose of the vegetation mapping was to update and to delineate more precisely the boundaries between vegetation types and to map vegetation types that previously were not mapped. The vegetation map is presented with a discussion of the vegetation classification used.« less

Habitat mapping using hyperspectral images in the vicinity of Hekla volcano in Iceland

NASA Astrophysics Data System (ADS)

Vilmundardóttir, Olga K.; Sigurmundsson, Friðþór S.; Pedersen, Gro B. M.; Falco, Nicola; Rustowicz, Rose; Gísladóttir, Guðrún; Benediktsson, Jón A.

2016-04-01

Hekla, one of the most active volcanoes in Iceland, has created a diverse volcanic landscape with lava flows, hyaloclastite and tephra fields. The variety of geological formations and different times of formation create diverse vegetation within Hekla's vicinity. The region is subjected to extensive loss of vegetation cover and soil erosion due to human utilization of woodlands and ongoing sheep grazing. The eolian activity and frequent tephra deposition has created vast areas of sparse vegetation cover. Over the 20th century, many activities have centered on preventing further loss of vegetated land and restoring ecosystems. The benefit of these activities is now noticeable in the increased vegetation and woodland cover although erosion is still active within the area. For mapping and monitoring this highly dynamic environment remote sensing techniques are extremely useful. One of the principal goals of the project 'Environmental Mapping and Monitoring of Iceland with Remote Sensing' (EMMIRS) is to use hyperspectral images and LiDAR data to classify and map the vegetation within the Hekla area. The data was collected in an aerial survey in summer 2015 by the Natural Environment Research Council (NERC), UK. The habitat type classification, currently being developed at the Icelandic Institute of Natural History and follows the structure of the EUNIS classification system, will be used for classifying the vegetation. The habitat map created by this new technique's outcome will be compared to the existent vegetation maps made by the conventional vegetation mapping method and the multispectral image classification techniques. In the field, vegetation cover, soil properties and spectral reflectance were measured within different habitat types. Special emphasis was on collecting data on vegetation and soil in the historical lavas from Hekla for assessing habitats forming over the millennia. A lava-chronosequence was established by measuring vegetation and soil in lavas formed in 2000, 1991, 1980-81, 1970, 1947, 1913, 1878, 1845, 1766-68, 1693, 1554, 1389-90, 1300, and 1206, representing surfaces of age 15-809 years. Results showed that vegetation cover established rather quickly on the lavas where mosses and lichens already created a full cover after 24 years. The cover remained stable and mosses were the dominant plant group for centuries, unless where tephra fall had occurred or where eolian deposition prevailed. The colonization of vascular plants on the lava was slow except at sites of eolian deposition and tephra fall. Dwarf shrubs and shrubs were rare or even absent on the lavas formed during the last century but their cover increased with increasing age of the lava fields. The older lava fields featured a variety of vegetation classes, indicating different rates and pathways of succession depending on altitude, proximity to eolian sources, land use and other factors. The many similarities yet big contrasts in the habitats featured within the Hekla region pose a challenge for creating a habitat map of the area, testing the potency of the hyperspectral data and classification techniques to the fullest.

Vegetation burn severity mapping using Landsat-8 and WorldView-2

USGS Publications Warehouse

Wu, Zhuoting; Middleton, Barry R.; Hetzler, Robert; Vogel, John M.; Dye, Dennis G.

2015-01-01

We used remotely sensed data from the Landsat-8 and WorldView-2 satellites to estimate vegetation burn severity of the Creek Fire on the San Carlos Apache Reservation, where wildfire occurrences affect the Tribe's crucial livestock and logging industries. Accurate pre- and post-fire canopy maps at high (0.5-meter) resolution were created from World- View-2 data to generate canopy loss maps, and multiple indices from pre- and post-fire Landsat-8 images were used to evaluate vegetation burn severity. Normalized difference vegetation index based vegetation burn severity map had the highest correlation coefficients with canopy loss map from WorldView-2. Two distinct approaches - canopy loss mapping from WorldView-2 and spectral index differencing from Landsat-8 - agreed well with the field-based burn severity estimates and are both effective for vegetation burn severity mapping. Canopy loss maps created with WorldView-2 imagery add to a short list of accurate vegetation burn severity mapping techniques that can help guide effective management of forest resources on the San Carlos Apache Reservation, and the broader fire-prone regions of the Southwest.

SENTINEL-1 and SENTINEL-2 Data Fusion for Wetlands Mapping: Balikdami, Turkey

NASA Astrophysics Data System (ADS)

Kaplan, G.; Avdan, U.

2018-04-01

Wetlands provide a number of environmental and socio-economic benefits such as their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Remote sensing technology has proven to be a useful and frequent application in monitoring and mapping wetlands. Combining optical and microwave satellite data can help with mapping and monitoring the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing radar and optical remote sensing data can increase the wetland classification accuracy. In this paper, data from the fine spatial resolution optical satellite, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, were fused for mapping wetlands. Both Sentinel-1 and Sentinel-2 images were pre-processed. After the pre-processing, vegetation indices were calculated using the Sentinel-2 bands and the results were included in the fusion data set. For the classification of the fused data, three different classification approaches were used and compared. The results showed significant improvement in the wetland classification using both multispectral and microwave data. Also, the presence of the red edge bands and the vegetation indices used in the data set showed significant improvement in the discrimination between wetlands and other vegetated areas. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, showing an overall classification accuracy of approximately 90 % in the object-based classification method. For future research, we recommend multi-temporal image use, terrain data collection, as well as a comparison of the used method with the traditional image fusion techniques.

THERMAL-INERTIA MAPPING IN VEGETATED TERRAIN FROM HEAT CAPACITY MAPPING MISSION SATELLITE DATA.

USGS Publications Warehouse

Watson, Ken; Hummer-Miller, Susanne

1984-01-01

Thermal-inertia data, derived from the Heat Capacity Mapping Mission (HCMM) satellite, were analyzed in areas of varying amounts of vegetation cover. Thermal differences which appear to correlate with lithologic differences have been observed previously in areas of substantial vegetation cover. However, the energy exchange occurring within the canopy is much more complex than that used to develop the methods employed to produce thermal-inertia images. Because adequate models are lacking at present, the interpretation is largely dependent on comparison, correlation, and inference. Two study areas were selected in the western United States: the Richfield, Utah and the Silver City, Arizona-New Mexico, 1 degree multiplied by 2 degree quadrangles. Many thermal-inertia highs were found to be associated with geologic-unit boundaries, faults, and ridges. Lows occur in valleys with residual soil cover.

Fourth international circumpolar arctic vegetation mapping workshop

USGS Publications Warehouse

Raynolds, Martha K.; Markon, C.J.

2002-01-01

During the week of April 10, 2001, the Fourth International Circumpolar Arctic Vegetation Mapping Workshop was held in Moscow, Russia. The purpose of this meeting was to bring together the vegetation scientists working on the Circumpolar Arctic Vegetation Map (CAVM) to (1) review the progress of current mapping activities, (2) discuss and agree upon a standard set of arctic tundra subzones, (3) plan for the production and dissemination of a draft map, and (4) begin work on a legend for the final map.

An integrated Landsat/ancillary data classification of desert rangeland

NASA Technical Reports Server (NTRS)

Price, K. P.; Ridd, M. K.; Merola, J. A.

1985-01-01

Range inventorying methods using Landsat MSS data, coupled with ancillary data were examined. The study area encompassed nearly 20,000 acres in Rush Valley, UT. The vegetation is predominately desert shrub and annual grasses, with same annual forbs. Three Landsat scenes were evaluated using a Kauth-Thomas brightness/greenness data transformation (May, June, and August dates). The data was classified using a four-band maximum-likelihood classifier. A print map was taken into the field to determine the relationship between print symbols and vegetation. It was determined that classification confusion could be greatly reduced by incorporating geomorphic units and soil texture (coarse vs fine) into the classification. Spectral data, geomorphic units, and soil texture were combined in a GIS format to produce a final vegetation map identifying 12 vegetation types.

An integrated LANDSAT/ancillary data classification of desert rangeland

NASA Technical Reports Server (NTRS)

Price, K. P.; Ridd, M. K.; Merola, J. A.

1984-01-01

Range inventorying methods using LANDSAT MSS data, coupled with ancillary data were examined. The study area encompassed nearly 20,000 acres in Rush Valley, Utah. The vegetation is predominately desert shrub and annual grasses, with some annual forbs. Three LANDSAT scenes were evaluated using a Kauth-Thomas brightness/greenness data transformation (May, June, and August dates). The data was classified using a four-band maximum-likelihood classifier. A print map was taken into the field to determine the relationship between print symbols and vegetation. It was determined that classification confusion could be greatly reduced by incorporating geomorphic units and soil texture (coarse vs fine) into the classification. Spectral data, geomorphic units, and soil texture were combined in a GIS format to produce a final vegetation map identifying 12 vegetation types.

Phreatophytic land-cover map of the northern and central Great Basin Ecoregion: California, Idaho, Nevada, Utah, Oregon, and Wyoming

USGS Publications Warehouse

Mathie, Amy M.; Welborn, Toby L.; Susong, David D.; Tumbusch, Mary L.

2011-01-01

Increasing water use and changing climate in the Great Basin of the western United States are likely affecting the distribution of phreatophytic vegetation in the region. Phreatophytic plant communities that depend on groundwater are susceptible to natural and anthropogenic changes to hydrologic flow systems. The purpose of this report is to document the methods used to create the accompanying map that delineates areas of the Great Basin that have the greatest potential to support phreatophytic vegetation. Several data sets were used to develop the data displayed on the map, including Shrub Map (a land-cover data set derived from the Regional Gap Analysis Program) and Gap Analysis Program (GAP) data sets for California and Wyoming. In addition, the analysis used the surface landforms from the U.S. Geological Survey (USGS) Global Ecosystems Mapping Project data to delineate regions of the study area based on topographic relief that are most favorable to support phreatophytic vegetation. Using spatial analysis techniques in a GIS, phreatophytic vegetation classes identified within Shrub Map and GAP were selected and compared to the spatial distribution of selected landforms in the study area to delineate areas of phreatophyte vegetation. Results were compared to more detailed studies conducted in selected areas. A general qualitative description of the data and the limitations of the base data determined that these results provide a regional overview but are not intended for localized studies or as a substitute for detailed field analysis. The map is intended as a decision-support aide for land managers to better understand, anticipate, and respond to ecosystem changes in the Great Basin.

Mapping ecological systems with a random foret model: tradeoffs between errors and bias

Treesearch

Emilie Grossmann; Janet Ohmann; James Kagan; Heather May; Matthew Gregory

2010-01-01

New methods for predictive vegetation mapping allow improved estimations of plant community composition across large regions. Random Forest (RF) models limit over-fitting problems of other methods, and are known for making accurate classification predictions from noisy, nonnormal data, but can be biased when plot samples are unbalanced. We developed two contrasting...

Lidar and Hyperspectral Remote Sensing for the Analysis of Coniferous Biomass Stocks and Fluxes

NASA Astrophysics Data System (ADS)

Halligan, K. Q.; Roberts, D. A.

2006-12-01

Airborne lidar and hyperspectral data can improve estimates of aboveground carbon stocks and fluxes through their complimentary responses to vegetation structure and biochemistry. While strong relationships have been demonstrated between lidar-estimated vegetation structural parameters and field data, research is needed to explore the portability of these methods across a range of topographic conditions, disturbance histories, vegetation type and climate. Additionally, research is needed to evaluate contributions of hyperspectral data in refining biomass estimates and determination of fluxes. To address these questions we are a conducting study of lidar and hyperspectral remote sensing data across sites including coniferous forests, broadleaf deciduous forests and a tropical rainforest. Here we focus on a single study site, Yellowstone National Park, where tree heights, stem locations, above ground biomass and basal area were mapped using first-return small-footprint lidar data. A new method using lidar intensity data was developed for separating the terrain and vegetation components in lidar data using a two-scale iterative local minima filter. Resulting Digital Terrain Models (DTM) and Digital Canopy Models (DCM) were then processed to retrieve a diversity of vertical and horizontal structure metrics. Univariate linear models were used to estimate individual tree heights while stepwise linear regression was used to estimate aboveground biomass and basal area. Three small-area field datasets were compared for their utility in model building and validation of vegetation structure parameters. All structural parameters were linearly correlated with lidar-derived metrics, with higher accuracies obtained where field and imagery data were precisely collocated . Initial analysis of hyperspectral data suggests that vegetation health metrics including measures of live and dead vegetation and stress indices may provide good indicators of carbon flux by mapping vegetation vigor or senescence. Additionally, the strength of hyperspectral data for vegetation classification suggests these data have additional utility for modeling carbon flux dynamics by allowing more accurate plant functional type mapping.

Mapping vegetation heights in China using slope correction ICESat data, SRTM, MODIS-derived and climate data

NASA Astrophysics Data System (ADS)

Huang, Huabing; Liu, Caixia; Wang, Xiaoyi; Biging, Gregory S.; Chen, Yanlei; Yang, Jun; Gong, Peng

2017-07-01

Vegetation height is an important parameter for biomass assessment and vegetation classification. However, vegetation height data over large areas are difficult to obtain. The existing vegetation height data derived from the Ice, Cloud and land Elevation Satellite (ICESat) data only include laser footprints in relatively flat forest regions (<5°). Thus, a large portion of ICESat data over sloping areas has not been used. In this study, we used a new slope correction method to improve the accuracy of estimates of vegetation heights for regions where slopes fall between 5° and 15°. The new method enabled us to use more than 20% additional laser data compared with the existing vegetation height data which only uses ICESat data in relatively flat areas (slope < 5°) in China. With the vegetation height data extracted from ICESat footprints and ancillary data including Moderate Resolution Imaging Spectroradiometer (MODIS) derived data (canopy cover, reflectances and leaf area index), climate data, and topographic data, we developed a wall to wall vegetation height map of China using the Random Forest algorithm. We used the data from 416 field measurements to validate the new vegetation height product. The coefficient of determination (R2) and RMSE of the new vegetation height product were 0.89 and 4.73 m respectively. The accuracy of the product is significantly better than that of the two existing global forest height products produced by Lefsky (2010) and Simard et al. (2011), when compared with the data from 227 field measurements in our study area. The new vegetation height data demonstrated clear distinctions among forest, shrub and grassland, which is promising for improving the classification of vegetation and above-ground forest biomass assessment in China.

Islands of biogeodiversity in arid lands on a polygons map study: Detecting scale invariance patterns from natural resources maps.

PubMed

Ibáñez, J J; Pérez-Gómez, R; Brevik, Eric C; Cerdà, A

2016-12-15

Many maps (geology, hydrology, soil, vegetation, etc.) are created to inventory natural resources. Each of these resources is mapped using a unique set of criteria, including scales and taxonomies. Past research indicates that comparing results of related maps (e.g., soil and geology maps) may aid in identifying mapping deficiencies. Therefore, this study was undertaken in Almeria Province, Spain to (i) compare the underlying map structures of soil and vegetation maps and (ii) investigate if a vegetation map can provide useful soil information that was not shown on a soil map. Soil and vegetation maps were imported into ArcGIS 10.1 for spatial analysis, and results then exported to Microsoft Excel worksheets for statistical analyses to evaluate fits to linear and power law regression models. Vegetative units were grouped according to the driving forces that determined their presence or absence: (i) climatophilous (ii) lithologic-climate; and (iii) edaphophylous. The rank abundance plots for both the soil and vegetation maps conformed to Willis or Hollow Curves, meaning the underlying structures of both maps were the same. Edaphophylous map units, which represent 58.5% of the vegetation units in the study area, did not show a good correlation with the soil map. Further investigation revealed that 87% of the edaphohygrophilous units were found in ramblas, ephemeral riverbeds that are not typically classified and mapped as soils in modern systems, even though they meet the definition of soil given by the most commonly used and most modern soil taxonomic systems. Furthermore, these edaphophylous map units tend to be islands of biodiversity that are threatened by anthropogenic activity in the region. Therefore, this study revealed areas that need to be revisited and studied pedologically. The vegetation mapped in these areas and the soils that support it are key components of the earth's critical zone that must be studied, understood, and preserved. Copyright © 2016 Elsevier B.V. All rights reserved.

Mapping northern Atlantic coastal marshlands, Maryland-Virginia, using ERTS imagery

NASA Technical Reports Server (NTRS)

Anderson, R. R. (Principal Investigator); Carter, V. L.; Mcginness, J. W., Jr.

1973-01-01

The author has identified the following significant results. ERTS-1 data provides repetitive synoptic coverage for DC 00000 of wetland ecology, detection of change, and mapping or inventory of wetland boundaries and plant communities. ERTS-1 positive transparencies of Atlantic Coastal wetlands were enlarged to different scales and mapped using a variety of methods. Results of analysis indicate: (1) mapping of wetland boundaries and vegetative communities from imagery at a scale of 1:1,000,000 is impractical because small details are difficult to illustrate; (2) mapping to a scale of 1:250,000 is practical for defining land-water interface, upper wetland boundary, gross vegetative communities, and spoil disposal/dredge and fill operations; (3) 1:125,000 enlargements provide additional information on transition zones, smaller plant communities, and drainage or mosquito ditching. Overlays may be made directly from prints.

Unsupervised classification of lidar-based vegetation structure metrics at Jean Lafitte National Historical Park and Preserve

USGS Publications Warehouse

Kranenburg, Christine J.; Palaseanu-Lovejoy, Monica; Nayegandhi, Amar; Brock, John; Woodman, Robert

2012-01-01

Traditional vegetation maps capture the horizontal distribution of various vegetation properties, for example, type, species and age/senescence, across a landscape. Ecologists have long known, however, that many important forest properties, for example, interior microclimate, carbon capacity, biomass and habitat suitability, are also dependent on the vertical arrangement of branches and leaves within tree canopies. The objective of this study was to use a digital elevation model (DEM) along with tree canopy-structure metrics derived from a lidar survey conducted using the Experimental Advanced Airborne Research Lidar (EAARL) to capture a three-dimensional view of vegetation communities in the Barataria Preserve unit of Jean Lafitte National Historical Park and Preserve, Louisiana. The EAARL instrument is a raster-scanning, full waveform-resolving, small-footprint, green-wavelength (532-nanometer) lidar system designed to map coastal bathymetry, topography and vegetation structure simultaneously. An unsupervised clustering procedure was then applied to the 3-dimensional-based metrics and DEM to produce a vegetation map based on the vertical structure of the park's vegetation, which includes a flotant marsh, scrub-shrub wetland, bottomland hardwood forest, and baldcypress-tupelo swamp forest. This study was completed in collaboration with the National Park Service Inventory and Monitoring Program's Gulf Coast Network. The methods presented herein are intended to be used as part of a cost-effective monitoring tool to capture change in park resources.

A vegetation mapping strategy for conifer forests by combining airborne LiDAR data and aerial imagery

Treesearch

Yanjun Su; Qinghua Guo; Danny L. Fry; Brandon M. Collins; Maggi Kelly; Jacob P. Flanagan; John J. Battles

2016-01-01

Abstract. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure...

Application of Heat Capacity Mapping Mission data to regional geologic analysis for mineral and energy resource evaluation

NASA Technical Reports Server (NTRS)

Watson, K. (Principal Investigator); Hummer-Miller, S.; Knepper, D. H., Jr.; Krohn, M. D.; Podwysocki, M. H.; Pohn, H. H.; Raines, G. L.; Rowan, L. C.

1983-01-01

Heat Capacity Mapping Mission thermal-inertia images of a diversity of terrains and geologic settings were examined in conjunction with topographic, geologic, geophysical, and LANDSAT data. The images were found to have attributes similar to bedrock maps. In the Cascades region, two new features were identified and a method was developed to characterize regional terranes using linear feature data. Two northeast-trending Lineaments were discovered in the Overthrust Belt of Montana and Idaho. The longer of the two extends from the Idaho-Oregon border, through the Idaho batholith and across the Lewis thrust. It coincides, along segments, with mapped faults and an aeromagnetic pattern change. A major lineament crossing the Colorado Plateau and the Southern Rocky Mountians was detected on several thermal-inertial images and evidence was found for the existence of a geologic discontinuity. Vegetation-covered areas in Richfield and the Silver City quadrangle (Arizona and New Mexico) displayed thermal-inertia differences within heavily vegetation areas although no apreciable correlation was found between vegetation cover and thermal inertia. Resistant ridges and knolls have high thermal inertias and thermal-inertia contrasts occurred at lithologic and fault contacts. In the heavy vegetated Pinaleno Mountains, Arizona, a Lithologic unit obscured on LANDSAT MSS data due to the vegetation cover, exhibited a thermal-inertia contrast with its surroundings.

The Circumpolar Arctic Vegetation Map: AVHRR-derived base maps, environmental controls, and integrated mapping procedures

Treesearch

D. A. WALKER; W. A. GOULD; MAIERH. A.; M. K. RAYNOLDS

2002-01-01

A new false-colour-infrared image derived from biweekly 1993 and 1995 Advanced Very High Resolution Radiometer (AVHRR) data provides a snow-free and cloud-free base image for the interpretation of vegetation as part of a 1:7.5M-scale Circumpolar Arctic Vegetation Map (CAVM). A maximum-NDVI (Normalized DiVerence Vegetation Index) image prepared from the same data...

A fully traits-based approach to modeling global vegetation distribution.

PubMed

van Bodegom, Peter M; Douma, Jacob C; Verheijen, Lieneke M

2014-09-23

Dynamic Global Vegetation Models (DGVMs) are indispensable for our understanding of climate change impacts. The application of traits in DGVMs is increasingly refined. However, a comprehensive analysis of the direct impacts of trait variation on global vegetation distribution does not yet exist. Here, we present such analysis as proof of principle. We run regressions of trait observations for leaf mass per area, stem-specific density, and seed mass from a global database against multiple environmental drivers, making use of findings of global trait convergence. This analysis explained up to 52% of the global variation of traits. Global trait maps, generated by coupling the regression equations to gridded soil and climate maps, showed up to orders of magnitude variation in trait values. Subsequently, nine vegetation types were characterized by the trait combinations that they possess using Gaussian mixture density functions. The trait maps were input to these functions to determine global occurrence probabilities for each vegetation type. We prepared vegetation maps, assuming that the most probable (and thus, most suited) vegetation type at each location will be realized. This fully traits-based vegetation map predicted 42% of the observed vegetation distribution correctly. Our results indicate that a major proportion of the predictive ability of DGVMs with respect to vegetation distribution can be attained by three traits alone if traits like stem-specific density and seed mass are included. We envision that our traits-based approach, our observation-driven trait maps, and our vegetation maps may inspire a new generation of powerful traits-based DGVMs.

Atmospheric Correction of High-Spatial-Resolution Commercial Satellite Imagery Products Using MODIS Atmospheric Products

NASA Technical Reports Server (NTRS)

Pagnutti, Mary; Holekamp, Kara; Ryan, Robert E.; Vaughan, Ronald; Russell, Jeffrey A.; Prados, Don; Stanley, Thomas

2005-01-01

Remotely sensed ground reflectance is the basis for many inter-sensor interoperability or change detection techniques. Satellite inter-comparisons and accurate vegetation indices such as the Normalized Difference Vegetation Index, which is used to describe or to imply a wide variety of biophysical parameters and is defined in terms of near-infrared and redband reflectance, require the generation of accurate reflectance maps. This generation relies upon the removal of solar illumination, satellite geometry, and atmospheric effects and is generally referred to as atmospheric correction. Atmospheric correction of remotely sensed imagery to ground reflectance, however, has been widely applied to only a few systems. In this study, we atmospherically corrected commercially available, high spatial resolution IKONOS and QuickBird imagery using several methods to determine the accuracy of the resulting reflectance maps. We used extensive ground measurement datasets for nine IKONOS and QuickBird scenes acquired over a two-year period to establish reflectance map accuracies. A correction approach using atmospheric products derived from Moderate Resolution Imaging Spectrometer data created excellent reflectance maps and demonstrated a reliable, effective method for reflectance map generation.

[Object-oriented aquatic vegetation extracting approach based on visible vegetation indices.

PubMed

Jing, Ran; Deng, Lei; Zhao, Wen Ji; Gong, Zhao Ning

2016-05-01

Using the estimation of scale parameters (ESP) image segmentation tool to determine the ideal image segmentation scale, the optimal segmented image was created by the multi-scale segmentation method. Based on the visible vegetation indices derived from mini-UAV imaging data, we chose a set of optimal vegetation indices from a series of visible vegetation indices, and built up a decision tree rule. A membership function was used to automatically classify the study area and an aquatic vegetation map was generated. The results showed the overall accuracy of image classification using the supervised classification was 53.7%, and the overall accuracy of object-oriented image analysis (OBIA) was 91.7%. Compared with pixel-based supervised classification method, the OBIA method improved significantly the image classification result and further increased the accuracy of extracting the aquatic vegetation. The Kappa value of supervised classification was 0.4, and the Kappa value based OBIA was 0.9. The experimental results demonstrated that using visible vegetation indices derived from the mini-UAV data and OBIA method extracting the aquatic vegetation developed in this study was feasible and could be applied in other physically similar areas.

Applying Lidar and High-Resolution Multispectral Imagery for Improved Quantification and Mapping of Tundra Vegetation Structure and Distribution in the Alaskan Arctic

NASA Astrophysics Data System (ADS)

Greaves, Heather E.

Climate change is disproportionately affecting high northern latitudes, and the extreme temperatures, remoteness, and sheer size of the Arctic tundra biome have always posed challenges that make application of remote sensing technology especially appropriate. Advances in high-resolution remote sensing continually improve our ability to measure characteristics of tundra vegetation communities, which have been difficult to characterize previously due to their low stature and their distribution in complex, heterogeneous patches across large landscapes. In this work, I apply terrestrial lidar, airborne lidar, and high-resolution airborne multispectral imagery to estimate tundra vegetation characteristics for a research area near Toolik Lake, Alaska. Initially, I explored methods for estimating shrub biomass from terrestrial lidar point clouds, finding that a canopy-volume based algorithm performed best. Although shrub biomass estimates derived from airborne lidar data were less accurate than those from terrestrial lidar data, algorithm parameters used to derive biomass estimates were similar for both datasets. Additionally, I found that airborne lidar-based shrub biomass estimates were just as accurate whether calibrated against terrestrial lidar data or harvested shrub biomass--suggesting that terrestrial lidar potentially could replace destructive biomass harvest. Along with smoothed Normalized Differenced Vegetation Index (NDVI) derived from airborne imagery, airborne lidar-derived canopy volume was an important predictor in a Random Forest model trained to estimate shrub biomass across the 12.5 km2 covered by our lidar and imagery data. The resulting 0.80 m resolution shrub biomass maps should provide important benchmarks for change detection in the Toolik area, especially as deciduous shrubs continue to expand in tundra regions. Finally, I applied 33 lidar- and imagery-derived predictor layers in a validated Random Forest modeling approach to map vegetation community distribution at 20 cm resolution across the data collection area, creating maps that will enable validation of coarser maps, as well as study of fine-scale ecological processes in the area. These projects have pushed the limits of what can be accomplished for vegetation mapping using airborne remote sensing in a challenging but important region; it is my hope that the methods explored here will illuminate potential paths forward as landscapes and technologies inevitably continue to change.

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.

Aerial photo interpretation of understories in two Oregon oak stands.

Treesearch

H. Gyde Lund; George R. Fahnestock; John F. Wear

1967-01-01

Aerial color photography has shown promise for evaluating understory vegetation as a forest-fire fuel. Mapping understory vegetation from special aerial photography produced results reasonably similar to those obtained by an independent ground check. Differences in the methods used in the exploratory work prevented strict comparability, but agreement was close enough...

Using Land Surface Phenology as the Basis for a National Early Warning System for Forest Disturbances

NASA Astrophysics Data System (ADS)

Hargrove, W. W.; Spruce, J.; Norman, S. P.; Hoffman, F. M.

2011-12-01

The National Early Warning System (EWS) provides an 8-day coast-to-coast snapshot of potentially disturbed forests across the U.S.. A prototype system has produced national maps of potential forest disturbances every eight days since January 2010, identifying locations that may require further investigation. Through phenology, the system shows both early and delayed vegetation development and detects all types of unexpected forest disturbances, including insects, disease, wildfires, frost and ice damage, tornadoes, hurricanes, blowdowns, harvest, urbanization, landslides, drought, flood, and climate change. The USDA Forest Service Eastern Forest Environmental Threat Assessment Center is collaborating with NASA Stennis Space Center and the Western Wildland Environmental Threat Assessment Center to develop the tool. The EWS uses differences in phenological responses between an expectation based on historical data and a current view to strategically identify potential forest disturbances and direct attention to locations where forest behavior seems unusual. Disturbance maps are available via the Forest Change Assessment Viewer (FCAV) (http://ews.forestthreats.org/gis), which allows resource managers and other users to see the most current national disturbance maps as soon as they are available. Phenology-based detections show not only vegetation disturbances in the classical sense, but all departures from normal seasonal vegetation behavior. In 2010, the EWS detected a repeated late-frost event at high elevations in North Carolina, USA, that resulted in delayed seasonal development, contrasting with an early spring development at lower elevations, all within close geographic proximity. Throughout 2011, there was a high degree of correspondence between the National Climatic Data Center's North American Drought Monitor maps and EWS maps of phenological drought disturbance in forests. Urban forests showed earlier and more severe phenological drought disturbance than surrounding non-urban forests. An EWS news page (http://www.geobabbble.org/~hnw/EWSNews) highlights disturbances the system has detected during the 2011 season. Unsupervised statistical multivariate clustering of smoothed phenology data every 8 days over an 11-year period produces a detailed map of national vegetation types, including major disturbances. Examining the constancy of these phenological classifications at a particular location from year to year produces a national map showing the persistence of vegetation, regardless of vegetation type. Using spectral unmixing methods, national maps of evergreen decline can be produced which are a composite of insect, disease, and anthropogenic factors causing chronic decline in these forests, including hemlock wooly adelgid, mountain pine beetle, wildfire, tree harvest, and urbanization. Because phenology shows vegetation responses, all disturbance and recovery events detected by the EWS are viewed through the lens of the vegetation.

Field Guide to the Plant Community Types of Voyageurs National Park

USGS Publications Warehouse

Faber-Langendoen, Don; Aaseng, Norman; Hop, Kevin; Lew-Smith, Michael

2007-01-01

INTRODUCTION The objective of the U.S. Geological Survey-National Park Service Vegetation Mapping Program is to classify, describe, and map vegetation for most of the park units within the National Park Service (NPS). The program was created in response to the NPS Natural Resources Inventory and Monitoring Guidelines issued in 1992. Products for each park include digital files of the vegetation map and field data, keys and descriptions to the plant communities, reports, metadata, map accuracy verification summaries, and aerial photographs. Interagency teams work in each park and, following standardized mapping and field sampling protocols, develop products and vegetation classification standards that document the various vegetation types found in a given park. The use of a standard national vegetation classification system and mapping protocol facilitate effective resource stewardship by ensuring compatibility and widespread use of the information throughout the NPS as well as by other Federal and state agencies. These vegetation classifications and maps and associated information support a wide variety of resource assessment, park management, and planning needs, and provide a structure for framing and answering critical scientific questions about plant communities and their relation to environmental processes across the landscape. This field guide is intended to make the classification accessible to park visitors and researchers at Voyageurs National Park, allowing them to identify any stand of natural vegetation and showing how the classification can be used in conjunction with the vegetation map (Hop and others, 2001).

Wetland mapping from digitized aerial photography. [Sheboygen Marsh, Sheboygen County, Wisconsin

NASA Technical Reports Server (NTRS)

Scarpace, F. L.; Quirk, B. K.; Kiefer, R. W.; Wynn, S. L.

1981-01-01

Computer assisted interpretation of small scale aerial imagery was found to be a cost effective and accurate method of mapping complex vegetation patterns if high resolution information is desired. This type of technique is suited for problems such as monitoring changes in species composition due to environmental factors and is a feasible method of monitoring and mapping large areas of wetlands. The technique has the added advantage of being in a computer compatible form which can be transformed into any georeference system of interest.

Chapter 8 - Mapping existing vegetation composition and structure for the LANDFIRE Prototype Project

Treesearch

Zhiliang Zhu; James Vogelmann; Donald Ohlen; Jay Kost; Xuexia Chen; Brian Tolk

2006-01-01

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype Project, required the mapping of existing vegetation composition (cover type) and structural stages at a 30-m spatial resolution to provide baseline vegetation data for the development of wildland fuel maps and for comparison to simulated historical vegetation reference...

Integrating Vegetation Classification, Mapping, and Strategic Inventory for Forest Management

Treesearch

C. K. Brewer; R. Bush; D. Berglund; J. A. Barber; S. R. Brown

2006-01-01

Many of the analyses needed to address multiple resource issues are focused on vegetation pattern and process relationships and most rely on the data models produced from vegetation classification, mapping, and/or inventory. The Northern Region Vegetation Mapping Project (R1-VMP) data models are based on these three integrally related, yet separate processes. This...

Procedures for woody vegetation surveys in the Kazgail rural council area, Kordofan, Sudan

USGS Publications Warehouse

Falconer, Allan; Cross, Matthew D.; Orr, Donald G.

1990-01-01

Efforts to reforest parts of the Kordofan Province of Sudan are receiving support from international development agencies. These efforts include planning and implementing reforestation activities that require the collection of natural resources and socioeconomic data, and the preparation of base maps. A combination of remote sensing, geographic information system and global positioning systems procedures are used in this study to meet these requirements.Remote sensing techniques were used to provide base maps and to guide the compilation of vegetation resources maps. These techniques provided a rapid and efficient method for documenting available resources. Pocket‐sized global positioning system units were used to establish the location of field data collected for mapping and resource analysis. A microcomputer data management system tabulated and displayed the field data. The resulting system for data analysis, management, and planning has been adopted for the mapping and inventory of the Gum Belt of Sudan.

Using Vegetation Maps to Provide Information on Soil Distribution

NASA Astrophysics Data System (ADS)

José Ibáñez, Juan; Pérez-Gómez, Rufino; Brevik, Eric C.; Cerdà, Artemi

2016-04-01

Many different types of maps (geology, hydrology, soil, vegetation, etc.) are created to inventory natural resources. Each of these resources is mapped using a unique set of criteria, including scales and taxonomies. Past research has indicated that comparing the results of different but related maps (e.g., soil and geology maps) may aid in identifying deficiencies in those maps. Therefore, this study was undertaken in the Almería Province (Andalusia, Spain) to (i) compare the underlying map structures of soil and vegetation maps and (ii) to investigate if a vegetation map can provide useful soil information that was not shown on a soil map. To accomplish this soil and vegetation maps were imported into ArcGIS 10.1 for spatial analysis. Results of the spatial analysis were exported to Microsoft Excel worksheets for statistical analyses to evaluate fits to linear and power law regression models. Vegetative units were grouped according to the driving forces that determined their presence or absence (P/A): (i) climatophilous (climate is the only determinant of P/A) (ii); lithologic-climate (climate and parent material determine PNV P/A); and (iii) edaphophylous (soil features determine PNV P/A). The rank abundance plots for both the soil and vegetation maps conformed to Willis or Hollow Curves, meaning the underlying structures of both maps were the same. Edaphophylous map units, which represent 58.5% of the vegetation units in the study area, did not show a good correlation with the soil map. Further investigation revealed that 87% of the edaphohygrophylous units (which demand more soil water than is supplied by other soil types in the surrounding landscape) were found in ramblas, ephemeral riverbeds that are not typically classified and mapped as soils in modern systems, even though they meet the definition of soil given by the most commonly used and most modern soil taxonomic systems. Furthermore, these edaphophylous map units tend to be islands of biodiversity that are threatened by anthropogenic activity in the region. Therefore, this study revealed areas in Almería Province that need to be revisited and studied pedologically. The vegetation mapped in these areas and the soils that support it are key components of the earth's critical zone that must be studied, understood, and preserved.

Depth Estimation of Submerged Aquatic Vegetation in Clear Water Streams Using Low-Altitude Optical Remote Sensing

PubMed Central

Visser, Fleur; Buis, Kerst; Verschoren, Veerle; Meire, Patrick

2015-01-01

UAVs and other low-altitude remote sensing platforms are proving very useful tools for remote sensing of river systems. Currently consumer grade cameras are still the most commonly used sensors for this purpose. In particular, progress is being made to obtain river bathymetry from the optical image data collected with such cameras, using the strong attenuation of light in water. No studies have yet applied this method to map submergence depth of aquatic vegetation, which has rather different reflectance characteristics from river bed substrate. This study therefore looked at the possibilities to use the optical image data to map submerged aquatic vegetation (SAV) depth in shallow clear water streams. We first applied the Optimal Band Ratio Analysis method (OBRA) of Legleiter et al. (2009) to a dataset of spectral signatures from three macrophyte species in a clear water stream. The results showed that for each species the ratio of certain wavelengths were strongly associated with depth. A combined assessment of all species resulted in equally strong associations, indicating that the effect of spectral variation in vegetation is subsidiary to spectral variation due to depth changes. Strongest associations (R2-values ranging from 0.67 to 0.90 for different species) were found for combinations including one band in the near infrared (NIR) region between 825 and 925 nm and one band in the visible light region. Currently data of both high spatial and spectral resolution is not commonly available to apply the OBRA results directly to image data for SAV depth mapping. Instead a novel, low-cost data acquisition method was used to obtain six-band high spatial resolution image composites using a NIR sensitive DSLR camera. A field dataset of SAV submergence depths was used to develop regression models for the mapping of submergence depth from image pixel values. Band (combinations) providing the best performing models (R2-values up to 0.77) corresponded with the OBRA findings. A 10% error was achieved under sub-optimal data collection conditions, which indicates that the method could be suitable for many SAV mapping applications. PMID:26437410

Depth Estimation of Submerged Aquatic Vegetation in Clear Water Streams Using Low-Altitude Optical Remote Sensing.

PubMed

Visser, Fleur; Buis, Kerst; Verschoren, Veerle; Meire, Patrick

2015-09-30

UAVs and other low-altitude remote sensing platforms are proving very useful tools for remote sensing of river systems. Currently consumer grade cameras are still the most commonly used sensors for this purpose. In particular, progress is being made to obtain river bathymetry from the optical image data collected with such cameras, using the strong attenuation of light in water. No studies have yet applied this method to map submergence depth of aquatic vegetation, which has rather different reflectance characteristics from river bed substrate. This study therefore looked at the possibilities to use the optical image data to map submerged aquatic vegetation (SAV) depth in shallow clear water streams. We first applied the Optimal Band Ratio Analysis method (OBRA) of Legleiter et al. (2009) to a dataset of spectral signatures from three macrophyte species in a clear water stream. The results showed that for each species the ratio of certain wavelengths were strongly associated with depth. A combined assessment of all species resulted in equally strong associations, indicating that the effect of spectral variation in vegetation is subsidiary to spectral variation due to depth changes. Strongest associations (R²-values ranging from 0.67 to 0.90 for different species) were found for combinations including one band in the near infrared (NIR) region between 825 and 925 nm and one band in the visible light region. Currently data of both high spatial and spectral resolution is not commonly available to apply the OBRA results directly to image data for SAV depth mapping. Instead a novel, low-cost data acquisition method was used to obtain six-band high spatial resolution image composites using a NIR sensitive DSLR camera. A field dataset of SAV submergence depths was used to develop regression models for the mapping of submergence depth from image pixel values. Band (combinations) providing the best performing models (R²-values up to 0.77) corresponded with the OBRA findings. A 10% error was achieved under sub-optimal data collection conditions, which indicates that the method could be suitable for many SAV mapping applications.

Spectroscopic Methods of Remote Sensing for Vegetation Characterization

NASA Astrophysics Data System (ADS)

Kokaly, R. F.

2013-12-01

Imaging spectroscopy (IS), often referred to as hyperspectral remote sensing, is one of the latest innovations in a very long history of spectroscopy. Spectroscopic methods have been used for understanding the composition of the world around us, as well as, the solar system and distant parts of the universe. Continuous sampling of the electromagnetic spectrum in narrow bands is what separates IS from previous forms of remote sensing. Terrestrial imaging spectrometers often have hundreds of channels that cover the wavelength range of reflected solar radiation, including the visible, near-infrared (NIR), and shortwave infrared (SWIR) regions. In part due to the large number of channels, a wide variety of methods have been applied to extract information from IS data sets. These can be grouped into several broad classes, including: multi-channel indices, statistical procedures, full spectrum mixing models, and spectroscopic methods. Spectroscopic methods carry on the more than 150 year history of laboratory-based spectroscopy applied to material identification and characterization. Spectroscopic methods of IS relate the positions and shapes of spectral features resolved by airborne and spaceborne sensors to the biochemical and physical composition of vegetation in a pixel. The chlorophyll 680nm, water 980nm, water 1200nm, SWIR 1700nm, SWIR 2100nm, and SWIR 2300nm features have been the subject of study. Spectral feature analysis (SFA) involves isolating such an absorption feature using continuum removal (CR) and calculating descriptors of the feature, such as center position, depth, width, area, and asymmetry. SFA has been applied to quantify pigment and non-pigment biochemical concentrations in leaves, plants, and canopies. Spectral feature comparison (SFC) utilizes CR of features in each pixel's spectrum and linear regression with continuum-removed features in reference spectra in a library of known vegetation types to map vegetation species and communities. SFC has been applied to map the distributions of minerals in soils and rocks; however, its application to characterize vegetation cover has been less widespread than SFA. Using IS data and the USGS Processing Routines in IDL for Spectroscopic Measurements (PRISM; http://pubs.usgs.gov/of/2011/1155/), this talk will examine requirements for and limitations in applying SFA and SFC to characterize vegetation. A time series of Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) data collected in the marshes of Louisiana following the Deepwater Horizon oil spill will be used to examine the impact of varying leaf water content on the shapes of the SWIR 1700, 2100, and 2300 nm features and the implications of these changes on vegetation identification and biochemical estimation. The USGS collection of HyMap data over Afghanistan, the largest terrestrial coverage of IS data to date, will be used to demonstrate the characterization of vegetation in arid and semi-arid regions, in which chlorophyll absorption is often weak and soil and rock mineral absorption features overlap vegetation features. Hyperion data, overlapping the HyMap data, will be presented to illustrate the complications that arise when signal-to-noise is low. The benefits of and challenges to applying a spectroscopic remote sensing approach to imaging spectrometer data will be discussed.

Ambient Ozone Exposure in Czech Forests: A GIS-Based Approach to Spatial Distribution Assessment

PubMed Central

Hůnová, I.; Horálek, J.; Schreiberová, M.; Zapletal, M.

2012-01-01

Ambient ozone (O3) is an important phytotoxic pollutant, and detailed knowledge of its spatial distribution is becoming increasingly important. The aim of the paper is to compare different spatial interpolation techniques and to recommend the best approach for producing a reliable map for O3 with respect to its phytotoxic potential. For evaluation we used real-time ambient O3 concentrations measured by UV absorbance from 24 Czech rural sites in the 2007 and 2008 vegetation seasons. We considered eleven approaches for spatial interpolation used for the development of maps for mean vegetation season O3 concentrations and the AOT40F exposure index for forests. The uncertainty of maps was assessed by cross-validation analysis. The root mean square error (RMSE) of the map was used as a criterion. Our results indicate that the optimal interpolation approach is linear regression of O3 data and altitude with subsequent interpolation of its residuals by ordinary kriging. The relative uncertainty of the map of O3 mean for the vegetation season is less than 10%, using the optimal method as for both explored years, and this is a very acceptable value. In the case of AOT40F, however, the relative uncertainty of the map is notably worse, reaching nearly 20% in both examined years. PMID:22566757

Vegetational analysis with Skylab-3 imagery. [Perquimans County, North Carolina

NASA Technical Reports Server (NTRS)

Welby, C. W. (Principal Investigator); Holman, R. E.

1975-01-01

The author has identified the following significant results. Color infrared photography from Skylab 3 appeared to be superior to ERTS imagery in a vegetational study of northeastern North Carolina. An accuracy of 87% was achieved in delimiting species composition and zonation patterns of three coastal, vegetation classes. A vegetation map of Perquimans County, North Carolina, seemed to have a high degree of correlation with information provided by high altitude U-2 photography. Random verification sites revealed an overall interpretation accuracy above 84%. Comparison of maps drawn utilizing Skylab photography with North Carolina Dept. of Agriculture estimates of crop acreage revealed some marked discrepancies. The chief difference lies in the nonagricultural category in which there is a 30% discrepancy. This fact raised some questions as to the definition of nonagricultural land uses and methods used by the State Dept. of Agriculture to determine actual percentages of crops grown.

3D Vegetation Mapping Using UAVSAR, LVIS, and LIDAR Data Acquisition Methods

NASA Technical Reports Server (NTRS)

Calderon, Denice

2011-01-01

The overarching objective of this ongoing project is to assess the role of vegetation within climate change. Forests capture carbon, a green house gas, from the atmosphere. Thus, any change, whether, natural (e.g. growth, fire, death) or due to anthropogenic activity (e.g. logging, burning, urbanization) may have a significant impact on the Earth's carbon cycle. Through the use of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and NASA's Laser Vegetation Imaging Sensor (LVIS), which are airborne Light Detection and Ranging (LIDAR) remote sensing technologies, we gather data to estimate the amount of carbon contained in forests and how the content changes over time. UAVSAR and LVIS sensors were sent all over the world with the objective of mapping out terrain to gather tree canopy height and biomass data; This data is in turn used to correlate vegetation with the global carbon cycle around the world.

Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region

PubMed Central

Callister, Kate E.; Griffioen, Peter A.; Avitabile, Sarah C.; Haslem, Angie; Kelly, Luke T.; Kenny, Sally A.; Nimmo, Dale G.; Farnsworth, Lisa M.; Taylor, Rick S.; Watson, Simon J.; Bennett, Andrew F.; Clarke, Michael F.

2016-01-01

Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km2 study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (rs = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery. PMID:27029046

Vegetation analysis in the Laramie Basin, Wyoming from ERTS-1 imagery

NASA Technical Reports Server (NTRS)

Evans, M. A.; Redfern, F. R.

1973-01-01

The author has identified the following significant results. The application of ERTS-1 imagery to vegetation mapping and identification was tested and confirmed by field checking. ERTS-1 imagery interpretation and density contour mapping allows definition of minute vegetation features and estimation of vegetative biomass and species composition. Large- and small-scale vegetation maps were constructed for test areas in the Laramie Basin and Laramie mountains of Wyoming. Vegetative features reflecting grazing intensity, moisture availability, changes within the growing season, cutting of hay crops, and plant community constituents in forest and grassland are discussed and illustrated. Theoretical considerations of scattering, sun angle, slope, and instrument aperture upon image and map resolution were investigated. Future suggestions for applications of ERTS-1 data to vegetative analysis are included.

USING IMAGE PROCESSING METHODS WITH RASTER EDITING TOOLS FOR MAPPING EELGRASS DISTRIBUTIONS IN PACIFIC NORHWEST ESTUARIES

EPA Science Inventory

False-color near-infrared (CIR) aerial photography of seven Oregon estuaries was acquired at extreme low tides and digitally orthorectified with a ground pixel resolution of 25 cm to provide data for intertidal vegetation mapping. Exposed, semi-exposed and some submerged eelgras...

A HYBRID HIGH RESOLUTION IMAGE CLASSIFICATION METHOD FOR MAPPING EELGRASS DISTRIBUTIONS IN YAQUINA BAY ESTUARY, OREGON

EPA Science Inventory

False-color infrared aerial photography of the Yaquina Bay Estuary, Oregon was acquired at extreme low tides and digitally orthorectified with a ground pixel resolution of 20 cm to provide data for intertidal vegetation mapping. Submerged, semi-exposed and exposed eelgrass mead...

Mapping rice extent map with crop intensity in south China through integration of optical and microwave images based on google earth engine

NASA Astrophysics Data System (ADS)

Zhang, X.; Wu, B.; Zhang, M.; Zeng, H.

2017-12-01

Rice is one of the main staple foods in East Asia and Southeast Asia, which has occupied more than half of the world's population with 11% of cultivated land. Study on rice can provide direct or indirect information on food security and water source management. Remote sensing has proven to be the most effective method to monitoring the cropland in large scale by using temporary and spectral information. There are two main kinds of satellite have been used to mapping rice including microwave and optical. Rice, as the main crop of paddy fields, the main feature different from other crops is flooding phenomenon at planning stage (Figure 1). Microwave satellites can penetrate through clouds and efficiency on monitoring flooding phenomenon. Meanwhile, the vegetation index based on optical satellite can well distinguish rice from other vegetation. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for processing very large geospatial datasets. Google has collected large number of remote sensing satellite data around the world, which providing researchers with the possibility of doing application by using multi-source remote sensing data in a large area. In this work, we map rice planting area in south China through integration of Landsat-8 OLI, Sentienl-2, and Sentinel-1 Synthetic Aperture Radar (SAR) images. The flowchart is shown in figure 2. First, a threshold method the VH polarized backscatter from SAR sensor and vegetation index including normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) from optical sensor were used the classify the rice extent map. The forest and water surface extent map provided by earth engine were used to mask forest and water. To overcome the problem of the "salt and pepper effect" by Pixel-based classification when the spatial resolution increased, we segment the optical image and use the pixel- based classification results to merge the object-oriented segmentation data, and finally get the rice extent map. At last, by using the time series analysis, the peak count was obtained for each rice area to ensure the crop intensity. In this work, the rice ground point from a GVG crowdsourcing smartphone and rice area statistical results from National Bureau of Statistics were used to validate and evaluate our result.

Object-based vegetation classification with high resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Yu, Qian

Vegetation species are valuable indicators to understand the earth system. Information from mapping of vegetation species and community distribution at large scales provides important insight for studying the phenological (growth) cycles of vegetation and plant physiology. Such information plays an important role in land process modeling including climate, ecosystem and hydrological models. The rapidly growing remote sensing technology has increased its potential in vegetation species mapping. However, extracting information at a species level is still a challenging research topic. I proposed an effective method for extracting vegetation species distribution from remotely sensed data and investigated some ways for accuracy improvement. The study consists of three phases. Firstly, a statistical analysis was conducted to explore the spatial variation and class separability of vegetation as a function of image scale. This analysis aimed to confirm that high resolution imagery contains the information on spatial vegetation variation and these species classes can be potentially separable. The second phase was a major effort in advancing classification by proposing a method for extracting vegetation species from high spatial resolution remote sensing data. The proposed classification employs an object-based approach that integrates GIS and remote sensing data and explores the usefulness of ancillary information. The whole process includes image segmentation, feature generation and selection, and nearest neighbor classification. The third phase introduces a spatial regression model for evaluating the mapping quality from the above vegetation classification results. The effects of six categories of sample characteristics on the classification uncertainty are examined: topography, sample membership, sample density, spatial composition characteristics, training reliability and sample object features. This evaluation analysis answered several interesting scientific questions such as (1) whether the sample characteristics affect the classification accuracy and how significant if it does; (2) how much variance of classification uncertainty can be explained by above factors. This research is carried out on a hilly peninsular area in Mediterranean climate, Point Reyes National Seashore (PRNS) in Northern California. The area mainly consists of a heterogeneous, semi-natural broadleaf and conifer woodland, shrub land, and annual grassland. A detailed list of vegetation alliances is used in this study. Research results from the first phase indicates that vegetation spatial variation as reflected by the average local variance (ALV) keeps a high level of magnitude between 1 m and 4 m resolution. (Abstract shortened by UMI.)

A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data

Treesearch

B. Tyler Wilson; Andrew J. Lister; Rachel I. Riemann

2012-01-01

The paper describes an efficient approach for mapping multiple individual tree species over large spatial domains. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-...

Regolith-geology mapping with support vector machine: A case study over weathered Ni-bearing peridotites, New Caledonia

NASA Astrophysics Data System (ADS)

De Boissieu, Florian; Sevin, Brice; Cudahy, Thomas; Mangeas, Morgan; Chevrel, Stéphane; Ong, Cindy; Rodger, Andrew; Maurizot, Pierre; Laukamp, Carsten; Lau, Ian; Touraivane, Touraivane; Cluzel, Dominique; Despinoy, Marc

2018-02-01

Accurate maps of Earth's geology, especially its regolith, are required for managing the sustainable exploration and development of mineral resources. This paper shows how airborne imaging hyperspectral data collected over weathered peridotite rocks in vegetated, mountainous terrane in New Caledonia were processed using a combination of methods to generate a regolith-geology map that could be used for more efficiently targeting Ni exploration. The image processing combined two usual methods, which are spectral feature extraction and support vector machine (SVM). This rationale being the spectral features extraction can rapidly reduce data complexity by both targeting only the diagnostic mineral absorptions and masking those pixels complicated by vegetation, cloud and deep shade. SVM is a supervised classification method able to generate an optimal non-linear classifier with these features that generalises well even with limited training data. Key minerals targeted are serpentine, which is considered as an indicator for hydrolysed peridotitic rock, and iron oxy-hydroxides (hematite and goethite), which are considered as diagnostic of laterite development. The final classified regolith map was assessed against interpreted regolith field sites, which yielded approximately 70% similarity for all unit types, as well as against a regolith-geology map interpreted using traditional datasets (not hyperspectral imagery). Importantly, the hyperspectral derived mineral map provided much greater detail enabling a more precise understanding of the regolith-geological architecture where there are exposed soils and rocks.

Three approaches to the classification of inland wetlands. [Dismal Swamp, Tennessee, and Florida

NASA Technical Reports Server (NTRS)

Gammon, P. T.; Malone, D.; Brooks, P. D.; Carter, V.

1977-01-01

In the Dismal Swamp project, seasonal, color-infrared aerial photographs and LANDSAT digital data were interpreted for a detailed analysis of the vegetative communities in a large, highly altered wetland. In Western Tennessee, seasonal high altitude color-infrared aerial photographs provided the hydrologic and vegetative information needed to map inland wetlands, using a classification system developed for the Tennessee Valley Region. In Florida, color-infrared aerial photographs were analyzed to produce wetland maps using three existing classification systems to evaluate the information content and mappability of each system. The methods used in each of the three projects can be extended or modified for use in the mapping of inland wetlands in other parts of the United States.

Spatial and spectral resolution necessary for remotely sensed vegetation studies

NASA Technical Reports Server (NTRS)

Rock, B. N.

1982-01-01

An outline is presented of the required spatial and spectral resolution needed for accurate vegetation discrimination and mapping studies as well as for determination of state of health (i.e., detection of stress symptoms) of actively growing vegetation. Good success was achieved in vegetation discrimination and mapping of a heterogeneous forest cover in the ridge and valley portion of the Appalachians using multispectral data acquired with a spatial resolution of 15 m (IFOV). A sensor system delivering 10 to 15 m spatial resolution is needed for both vegetation mapping and detection of stress symptoms. Based on the vegetation discrimination and mapping exercises conducted at the Lost River site, accurate products (vegetation maps) are produced using broad-band spectral data ranging from the .500 to 2.500 micron portion of the spectrum. In order of decreasing utility for vegetation discrimination, the four most valuable TM simulator VNIR bands are: 6 (1.55 to 1.75 microns), 3 (0.63 to 0.69 microns), 5 (1.00 to 1.30 microns) and 4 (0.76 to 0.90 microns).

Mapping coastal vegetation, land use and environmental impact from ERTS-1. [Delaware Bay area

NASA Technical Reports Server (NTRS)

Klemas, V. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Vegetation map overlays at a scale of 1:24,000 compiled by multispectral analysis from NASA aircraft imagery for all of Delaware's wetlands are being used as ground truth for ERTS-1 mapping and by state agencies for wetlands management. Six major vegetation species were discriminated and mapped, including percentages of minor species. Analogue enhancements of wetlands vegetation and dredge-fill operations have been produced using General Electric's GEMS data processing and ERTS-1 false color composites. Digital, thematic land use, and vegetation mapping of entire Delaware Bay area is in progress using Bendix Corporation's Earth Resources Data System and ERTS-1 digital tapes. Statistical evaluation of target-group selection reliability has been completed. Three papers have been published on ERTS-1 coastal vegetation and land use. Local and state officials are participating in the ERTS-1 program as co-investigators.

Airborne Laser Scanning - based vegetation classification in grasslands: a feasibility study

NASA Astrophysics Data System (ADS)

Zlinszky, András; Vári, Ágnes; Deák, Balázs; Mücke, Werner; Székely, Balázs

2013-04-01

Airborne Laser Scanning is traditionally used for topography mapping, exploiting its ability to map terrain elevation under vegetation cover. Parallel to this, the application of ALS for vegetation classification and mapping of ecological variables is rapidly emerging. Point clouds surveyed by ALS provide accurate representations of vegetation structure and are therefore considered suitable for mapping vegetation classes as long as their vertical structure is characteristic. For this reason, most ALS-based vegetation mapping studies have been carried out in forests, with some rare applications for shrublands or tall grass vegetation such as reeds. The use of remote-sensing derived vegetation maps is widespread in ecological research and is also gaining importance in practical conservation. There is an increasing demand for reliable, high-resolution datasets covering large protected areas. ALS can provide both the coverage and the high resolution, and can prove to be an economical solution due to the potential for automatic processing and the wide range of uses that allows spreading costs. Grasslands have a high importance in nature conservation as due to the drastical land use changes (arable lands, afforestation, fragmentation by linear structures) in the last centuries the extent of these habitats have been considerably reduced. Among the habitat types protected by the Habitat Directive of the Natura 2000 system, several grassland habitat types (e.g. hay meadows, dry grasslands harbouring rare Orchid species) have special priority for conservation. For preserving these habitat types application of a proper management - including mowing or grazing - has a crucial role. Therefore not only the mapping of the locations of habitats but the way of management is needed for representing the natural processes. The objective of this study was to test the applicability of airborne laser scanning for ecological vegetation mapping in and around grasslands. The study site is situated in the Sopron mountains (Western Hungary), in the Soproni-hegység Natura 2000 site which has an area of 52 km2 protected under Natura 2000. While the Natura 2000 site is dominated by forests, it also holds several grassland habitats: lowland and mountain hay meadows, dry grasslands, fringe communities and disturbed secondary grasslands in the forest clearings. In the framework of the Changehabitats2 project, ALS surveys of the area were carried out, under leaf-on conditions in July 2012 and March 2011, with a full-waveform sensor (Riegl LMS-Q680i) operating at 1550 nm. The resulting point density was 12.8 echoes/m2. 10 grasslands were selected from the study site varying in size between 0.1 and 3 km2. The ALS datasets of these sites were processed in OPALS software to a set of rasters representing different variables of the leaf-on and leaf-off point cloud, each with a raster size of 0.5 m * 0.5 m. Echo amplitude for single echoes was calibrated to reflectance using estimated reflectivity of an asphalt surface. A Digital Terrain Model was created using hierarchical robust filtering in SCOP++ software, and normalized digital surface models were calculated by subtracting this from the local surface models. Echo width and surface roughness were also calculated in OPALS. Surface openness was measured in order to distinguish circular and linear features, such as sedge clumps and ploughing marks. Finally, all these rasters were stacked in ENVI software, resulting in a pseudo-image, where each pseudo-channel corresponded to a different ALS-derived variable. Reference datasets were collected in the field using differential GNSS. Habitat type, dominant vegetation species and other features of interest were noted in the point attributes, and a set of 90 circular plots was recorded. These were overlain on the pseudo-image to create regions of interest (ROI), resulting in 100-4000 ROI pixels for each vegetation category. Multivariate statistics were then used in order to analyse the trends in the data: interdependence of the ALS-derived variables was tested by calculating covariance matrices and the separability of the vegetation categories was tested by the Jeffries-Matusita index. The results of the multivariate analysis were used for merging and excluding classes from the initial 24 to find those where the accuracy was sufficient and the results were relevant for conservation. Results show that a large number of variables can be derived from leaf-on and leaf-off ALS surveys that are statistically independent from each other and thus provide a good basis for classification. With appropriate calculation methods, a number of pseudo-bands comparable to multispectral imagery can be reached. These categories are of course not equally relevant for vegetation mapping. Not surprisingly, the categories with the strongest separability are those closely linked to vegetation height or texture. Mown grasslands can be well separated from abandoned grasslands; tussock-forming grasses from more uniform textured tall herbs. While quality control is still in progress and the trade-off between the number of categories and the classification accuracy is evident, it is expected that for a limited range of vegetation classes, the reliability of the method will be comparable to passive optical image processing. Using a classification method slightly different from the conventional ALS-based vegetation mapping approach, encouraging results have been obtained for an area where the vertical structure of the vegetation is limited. The present state of the art of ALS sensors including full waveform processing and calibration of surface reflectance allows vegetation mapping even in grasslands. It is expected that the further development of waveform digitization and the ever-increasing scanning frequencies will further support such studies in the future.

Method and apparatus for remote measurement of terrestrial biomass

NASA Technical Reports Server (NTRS)

Johnson, Patrick W. (Inventor)

1999-01-01

Method and apparatus for remote measurement of terrestrial biomass contained in vegetative elements, such as large tree boles or trunks present in an area of interest. The method includes providing an airborne radar system, overflying the area of interest while directing radar energy having a frequency of under 400 MHz, and preferably between 80 and 120 MHz, toward the area of interest, using the radar system to collect backscatter data from the radar energy as a function of incidence angle and frequency, and using an inversion algorithm to determine a magnitude of the biomass from the backscatter data for each radar resolution cell. A biomass map is generated showing the magnitude of the biomass of the vegetative elements as a function of location on the map by using each resolution cell as a unique location thereon.

Mapping current and potential distribution of non-native Prosopis juliflora in the Afar region of Ethiopia

USGS Publications Warehouse

Wakie, Tewodros; Evangelista, Paul H.; Jarnevich, Catherine S.; Laituri, Melinda

2014-01-01

We used correlative models with species occurrence points, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, and topo-climatic predictors to map the current distribution and potential habitat of invasive Prosopis juliflora in Afar, Ethiopia. Time-series of MODIS Enhanced Vegetation Indices (EVI) and Normalized Difference Vegetation Indices (NDVI) with 250 m2 spatial resolution were selected as remote sensing predictors for mapping distributions, while WorldClim bioclimatic products and generated topographic variables from the Shuttle Radar Topography Mission product (SRTM) were used to predict potential infestations. We ran Maxent models using non-correlated variables and the 143 species-occurrence points. Maxent generated probability surfaces were converted into binary maps using the 10-percentile logistic threshold values. Performances of models were evaluated using area under the receiver-operating characteristic (ROC) curve (AUC). Our results indicate that the extent of P. juliflora invasion is approximately 3,605 km2 in the Afar region (AUC = 0.94), while the potential habitat for future infestations is 5,024 km2 (AUC = 0.95). Our analyses demonstrate that time-series of MODIS vegetation indices and species occurrence points can be used with Maxent modeling software to map the current distribution of P. juliflora, while topo-climatic variables are good predictors of potential habitat in Ethiopia. Our results can quantify current and future infestations, and inform management and policy decisions for containing P. juliflora. Our methods can also be replicated for managing invasive species in other East African countries.

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.

Multiscale sampling of plant diversity: Effects of minimum mapping unit size

USGS Publications Warehouse

Stohlgren, T.J.; Chong, G.W.; Kalkhan, M.A.; Schell, L.D.

1997-01-01

Only a small portion of any landscape can be sampled for vascular plant diversity because of constraints of cost (salaries, travel time between sites, etc.). Often, the investigator decides to reduce the cost of creating a vegetation map by increasing the minimum mapping unit (MMU), and/or by reducing the number of vegetation classes to be considered. Questions arise about what information is sacrificed when map resolution is decreased. We compared plant diversity patterns from vegetation maps made with 100-ha, 50-ha, 2-ha, and 0.02-ha MMUs in a 754-ha study area in Rocky Mountain National Park, Colorado, United States, using four 0.025-ha and 21 0.1-ha multiscale vegetation plots. We developed and tested species-log(area) curves, correcting the curves for within-vegetation type heterogeneity with Jaccard's coefficients. Total species richness in the study area was estimated from vegetation maps at each resolution (MMU), based on the corrected species-area curves, total area of the vegetation type, and species overlap among vegetation types. With the 0.02-ha MMU, six vegetation types were recovered, resulting in an estimated 552 species (95% CI = 520-583 species) in the 754-ha study area (330 plant species were observed in the 25 plots). With the 2-ha MMU, five vegetation types were recognized, resulting in an estimated 473 species for the study area. With the 50-ha MMU, 439 plant species were estimated for the four vegetation types recognized in the study area. With the 100-ha MMU, only three vegetation types were recognized, resulting in an estimated 341 plant species for the study area. Locally rare species and keystone ecosystems (areas of high or unique plant diversity) were missed at the 2-ha, 50-ha, and 100-ha scales. To evaluate the effects of minimum mapping unit size requires: (1) an initial stratification of homogeneous, heterogeneous, and rare habitat types; and (2) an evaluation of within-type and between-type heterogeneity generated by environmental gradients and other factors. We suggest that at least some portions of vegetation maps created at a coarser level of resolution be validated at a higher level of resolution.

Semisupervised GDTW kernel-based fuzzy c-means algorithm for mapping vegetation dynamics in mining region using normalized difference vegetation index time series

NASA Astrophysics Data System (ADS)

Jia, Duo; Wang, Cangjiao; Lei, Shaogang

2018-01-01

Mapping vegetation dynamic types in mining areas is significant for revealing the mechanisms of environmental damage and for guiding ecological construction. Dynamic types of vegetation can be identified by applying interannual normalized difference vegetation index (NDVI) time series. However, phase differences and time shifts in interannual time series decrease mapping accuracy in mining regions. To overcome these problems and to increase the accuracy of mapping vegetation dynamics, an interannual Landsat time series for optimum vegetation growing status was constructed first by using the enhanced spatial and temporal adaptive reflectance fusion model algorithm. We then proposed a Markov random field optimized semisupervised Gaussian dynamic time warping kernel-based fuzzy c-means (FCM) cluster algorithm for interannual NDVI time series to map dynamic vegetation types in mining regions. The proposed algorithm has been tested in the Shengli mining region and Shendong mining region, which are typical representatives of China's open-pit and underground mining regions, respectively. Experiments show that the proposed algorithm can solve the problems of phase differences and time shifts to achieve better performance when mapping vegetation dynamic types. The overall accuracies for the Shengli and Shendong mining regions were 93.32% and 89.60%, respectively, with improvements of 7.32% and 25.84% when compared with the original semisupervised FCM algorithm.

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.

Vegetation, plant biomass, and net primary productivity patterns in the Canadian Arctic

NASA Astrophysics Data System (ADS)

Gould, W. A.; Raynolds, M.; Walker, D. A.

2003-01-01

We have developed maps of dominant vegetation types, plant functional types, percent vegetation cover, aboveground plant biomass, and above and belowground annual net primary productivity for Canada north of the northern limit of trees. The area mapped covers 2.5 million km2 including glaciers. Ice-free land covers 2.3 million km2 and represents 42% of all ice-free land in the Circumpolar Arctic. The maps combine information on climate, soils, geology, hydrology, remotely sensed vegetation classifications, previous vegetation studies, and regional expertise to define polygons drawn using photo-interpretation of a 1:4,000,000 scale advanced very high resolution radiometer (AVHRR) color infrared image basemap. Polygons are linked to vegetation description, associated properties, and descriptive literature through a series of lookup tables in a graphic information systems (GIS) database developed as a component of the Circumpolar Arctic Vegetation Map (CAVM) project. Polygons are classified into 20 landcover types including 17 vegetation types. Half of the region is sparsely vegetated (<50% vegetation cover), primarily in the High Arctic (bioclimatic subzones A-C). Whereas most (86%) of the estimated aboveground plant biomass (1.5 × 1015 g) and 87% of the estimated above and belowground annual net primary productivity (2.28 × 1014 g yr-1) are concentrated in the Low Arctic (subzones D and E). The maps present more explicit spatial patterns of vegetation and ecosystem attributes than have been previously available, the GIS database is useful in summarizing ecosystem properties and can be easily updated and integrated into circumpolar mapping efforts, and the derived estimates fall within the range of current published estimates.

Multiscale remote sensing analysis to monitor riparian and upland semiarid vegetation

NASA Astrophysics Data System (ADS)

Nguyen, Uyen

The health of natural vegetation communities is of concern due to observed changes in the climatic-hydrological regime and land cover changes particularly in arid and semiarid regions. Monitoring vegetation at multi temporal and spatial scales can be the most informative approach for detecting change and inferring causal agents of change and remediation strategies. Riparian communities are tightly linked to annual stream hydrology, ground water elevations and sediment transport. These processes are subject to varying magnitudes of disturbance overtime and are candidates for multi-scale monitoring. My first research objective focused on the response of vegetation in the Upper San Pedro River, Arizona, to reduced base flows and climate change. I addressed the correlation between riparian vegetation and hydro-climate variables during the last three decades in one of the remaining undammed rivers in the southwestern U.S. Its riparian forest is threatened by the diminishing base flows, attributed by different studies either to increases in evapotranspiration (ET) due to conversion of grasslands to mesquite shrublands in the adjacent uplands, or to increased regional groundwater pumping to serve growing populations in surrounding urban areas and or to some interactions of those causes. Landsat 5 imagery was acquired for pre- monsoon period, when riparian trees had leafed out but before the arrival of summer monsoon rains in July. The result has showed Normalized Difference Vegetation Index (NDVI) values from both Landsat and Moderate Resolution Imaging Spectrometer (MODIS) had significant decreases which positively correlated to river flows, which decreased over the study period, and negatively correlated with air temperatures, which have increased by about 1.4°C from 1904 to the present. The predictions from other studies that decreased river flows could negatively impact the riparian forest were supported by this study. The pre-monsoon Normalized Different Vegetation Index (NDVI) average values in the adjacent uplands also decreased over thirty years and were correlated with the previous year's annual precipitation. Hence an increase in ET in the uplands did not appear to be responsible for the decrease in river flows in this study, leaving increased regional groundwater pumping as a feasible alternative explanation for decreased flows and deterioration of the riparian forest. The second research objective was to develop a new method of classification using very high-resolution aerial photo to map riparian vegetation at the species level in the Colorado River Ecosystem, Grand Canyon area, Arizona. Ground surveys have showed an obvious trend in which non-native saltcedar (Tamarix spp.) has replaced native vegetation over time. Our goal was to develop a quantitative mapping procedure to detect changes in vegetation as the ecosystem continues to respond to hydrological and climate changes. Vegetation mapping for the Colorado River Ecosystem needed an updated database map of the area covered by riparian vegetation and an indicator of species composition in the river corridor. The objective of this research was to generate a new riparian vegetation map at species level using a supervised image classification technique for the purpose of patch and landscape change detection. A new classification approach using multispectral images allowed us to successfully identify and map riparian species coverage the over whole Colorado River Ecosystem, Grand Canyon area. The new map was an improvement over the initial 2002 map since it reduced fragmentation from mixed riparian vegetation areas. The most dominant tree species in the study areas is saltcedar (Tamarix spp.). The overall accuracy is 93.48% and the kappa coefficient is 0.88. The reference initial inventory map was created using 2002 images to compare and detect changes through 2009. The third objective of my research focused on using multiplatform of remote sensing and ground calibration to estimate the effects of vegetation, land use patterns and water cycles. Climate change, hydrological and human uses are also leading to riparian, upland, grassland and crop vegetation changes at a variety of temporal and spatial scales, particularly in the arid and semi arid ecosystems, which are more sensitive to changes in water availability than humid ecosystems. The objectives of these studies from the last three articles were to evaluate the effect of water balance on vegetation indices in different plant communities based on relevant spatial and temporal scales. The new methodology of estimating water requirements using remote sensing data and ground calibration with flux tower data has been successfully tested at a variety sites, a sparse desert shrub environment as well as mixed riparian and cropland systems and upland vegetation in the arid and semi-arid regions. The main finding form these studies is that vegetation-index methods have to be calibrated with ground data for each new ecosystem but once calibrated they can accurately scale ET over wide areas and long time spans.

Assessment of seasonal features based on Landsat time series for tree crown cover mapping in Burkina Faso

NASA Astrophysics Data System (ADS)

Liu, Jinxiu; Heiskanen, Janne; Aynekuly, Ermias; Pellikka, Petri

2016-04-01

Tree crown cover (CC) is an important vegetation attribute for land cover characterization, and for mapping and monitoring forest cover. Free data from Landsat and Sentinel-2 allow construction of fine resolution satellite image time series and extraction of seasonal features for predicting vegetation attributes. In the savannas, surface reflectance vary distinctively according to the rainy and dry seasons, and seasonal features are useful information for CC mapping. However, it is unclear if it is better to use spectral bands or vegetation indices (VI) for computation of seasonal features, and how feasible different VI are for CC prediction in the savanna woodlands and agroforestry parklands of West Africa. In this study, the objective was to compare seasonal features based on spectral bands and VI for CC mapping in southern Burkina Faso. A total of 35 Landsat images from November 2013 to October 2014 were processed. Seasonal features were computed using a harmonic model with three parameters (mean, amplitude and phase), and spectral bands, normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), normalized difference water index (NDWI), tasseled cap (TC) indices (brightness, greenness, wetness) as input data. The seasonal features were employed to predict field estimated CC (n = 160) using Random Forest algorithm. The most accurate results were achieved when using seasonal features based on TC indices (R2: 0.65; RMSE: 10.7%) and spectral bands (R2: 0.64; RMSE: 10.8%). GNDVI performed better than NDVI or NDWI, and NDWI resulted in the poorest results (R2: 0.56; RMSE: 11.9%). The results indicate that spectral features should be carefully selected for CC prediction as shown by relatively poor performance of commonly used NDVI. The seasonal features based on three TC indices and all the spectral bands provided superior accuracy in comparison to single VI. The method presented in this study provides a feasible method to map CC based on seasonal features with possibility to integrate medium resolution satellite observation from several sensors (e.g. Landsat and Sentinel-2) in the future.

Mapping forest height in Alaska using GLAS, Landsat composites, and airborne LiDAR

USGS Publications Warehouse

Peterson, Birgit; Nelson, Kurtis

2014-01-01

Vegetation structure, including forest canopy height, is an important input variable to fire behavior modeling systems for simulating wildfire behavior. As such, forest canopy height is one of a nationwide suite of products generated by the LANDFIRE program. In the past, LANDFIRE has relied on a combination of field observations and Landsat imagery to develop existing vegetation structure products. The paucity of field data in the remote Alaskan forests has led to a very simple forest canopy height classification for the original LANDFIRE forest height map. To better meet the needs of data users and refine the map legend, LANDFIRE incorporated ICESat Geoscience Laser Altimeter System (GLAS) data into the updating process when developing the LANDFIRE 2010 product. The high latitude of this region enabled dense coverage of discrete GLAS samples, from which forest height was calculated. Different methods for deriving height from the GLAS waveform data were applied, including an attempt to correct for slope. These methods were then evaluated and integrated into the final map according to predefined criteria. The resulting map of forest canopy height includes more height classes than the original map, thereby better depicting the heterogeneity of the landscape, and provides seamless data for fire behavior analysts and other users of LANDFIRE data.

Classifying plant series-level forest potential types: methods for subbasins sampled in the midscale assessment of the interior Columbia basin.

Treesearch

Paul F. Hessburg; Bradley G. Smith; Scott D. Kreiter; Craig A. Miller; Cecilia H. McNicoll; Michele. Wasienko-Holland

2000-01-01

In the interior Columbia River basin midscale ecological assessment, we mapped and characterized historical and current vegetation composition and structure of 337 randomly sampled subwatersheds (9500 ha average size) in 43 subbasins (404 000 ha average size). We compared landscape patterns, vegetation structure and composition, and landscape vulnerability to wildfires...

Assessment of sand encroachment in Kuwait using GIS

NASA Astrophysics Data System (ADS)

Al-Helal, Anwar B.; Al-Awadhi, Jasem M.

2006-04-01

Assessment of sand encroachment in Kuwait using Geographical Information System (GIS) technology has been formulated as a Multi-Criteria Decision Making problem. The Delphi method and Analytical Hierarchy Process were adopted as evaluating techniques, in which experts’ judgments were analyzed for objectively estimating and weighting control factors. Seven triggering factors, depicted in the form of maps, were identified and ordered according to their priority. These factors are (1) wind energy; (2) surface sediment; (3) vegetation density; (4) land use; (5) drainage density; (6) topographic change and (7) vegetation type. The factor maps were digitized, converted to raster data and overlaid to determine their possible spatial relationships. Applying a susceptibility model, a map of sand encroachment susceptibility in Kuwait was developed. The map showed that the areas of very high and high sand encroachment susceptibility are located within the main corridor of sand pathway that coincides with the northwesterly dominant wind direction.

Observing Crop-Height Dynamics Using a UAV

NASA Astrophysics Data System (ADS)

Ziliani, M. G.; Parkes, S. D.; McCabe, M.

2017-12-01

Retrieval of vegetation height during a growing season is a key indicator for monitoring crop status, offering insight to the forecast yield relative to previous planting cycles. Improvement in Unmanned Aerial Vehicle (UAV) technologies, supported by advances in computer vision and photogrammetry software, has enabled retrieval of crop heights with much higher spatial resolution and coverage. These methodologies retrieve a Digital Surface Map (DSM), which combine terrain and crop elements to obtain a Crop Surface Map (CSM). Here we describe an automated method for deriving high resolution CSMs from a DSM, using RGB imagery from a UAV platform. Importantly, the approach does not require the need for a digital terrain map (DTM). The method involves distinguishing between vegetation and bare-ground cover pixels, using vegetation index maps from the RGB orthomosaic derived from the same flight as the DSM. We show that the absolute crop height can be extracted to within several centimeters, exploiting the data captured from a single UAV flight. In addition, the method is applied across five surveys during a maize growing cycle and compared against a terrain map constructed from a baseline UAV survey undertaken prior to crop growth. Results show that the approach is able to reproduce the observed spatial variability of the crop height within the maize field throughout the duration of the growing season. This is particularly valuable since it may be employed to detect intra-field problems (i.e. fertilizer variability, inefficiency in the irrigation system, salinity etc.) at different stages of the season, from which remedial action can be initiated to mitigate against yield loss. The method also demonstrates that UAV imagery combined with commercial photogrammetry software can determine a CSM from a single flight without the requirement of a prior DTM. This, together with the dynamic crop height estimation, provide useful information with which to inform precision agricultural management at the local scale.

Vegetation map of the watersheds between Kawela and Kamalō Gulches, Island of Molokaʻi, Hawaiʻi

USGS Publications Warehouse

Jacobi, James D.; Ambagis, Stephen

2013-01-01

In this document we describe the methods and results of a project to produce a large-scale map of the dominant plant communities for an area of 5,118.5 hectares encompassing the Kawela and Kamalō watersheds on the island of Molokaʻi, Hawaiʻi, using digital image analysis of multi-spectral satellite imagery. Besides providing a base map of the area for land managers to use, this vegetation map serves as spatial background for the U.S. Geological Survey’s (USGS) Molokaʻi Ridge-to-Reef project, which is an interdisciplinary study of erosion and sediment transport within these watersheds. A total of 14 mapping units were identified for the Kawela-Kamalō project area. The most widespread units were the ʻŌhiʻa montane wet or mesic forest and No vegetation or very sparse grasses/shrubs communities, each present in more than 800 hectares, or 16 percent of the mapping area. Next largest were the Kiawe woodland with alien grass understory and ʻAʻaliʻi dry shrubland units, each of which covered more than 500 hectares, or more than 12 percent of the area; followed by the Mixed native mesic shrubland, ʻIlima and mixed grass dry shrubland, Mixed alien grass with ʻilima shrubs, and the Mixed alien forest with alien shrub/grass understory communities, which ranged in size from approximately 391 to 491 hectares, or 7.6 to 9.6 percent of the project site. The other six mapped units covered less than 170 hectares of the landscape. Six of the map units were dominated by native vegetation, covering a total of 2,535.2 hectares combined, or approximately 50 percent of the project area. The remaining map units were dominated by nonnative species and represent vegetation types that have resulted from invasion and establishment of plant species that had been either purposely or accidently introduced into Hawaiʻi since humans arrived in these islands more than 1,500 years ago. The preponderance of mapping units that are dominated by alien species of plants is a strong indication of how much anthropogenic disturbance has occurred in this area. The native-dominated ʻŌhiʻa forest and uluhe fern communities are probably most similar to the vegetation that was originally found in the upper part of the project area this area. Portions of the mixed mesic native shrub community still persist in the lowland mesic zone, but below that area, the vegetation is either dominated by alien species, or artificially opened by animal grazing and erosion, even in the few units that are still dominated by native species. The map produced for the Kawela to Kamalō watersheds can be used as a baseline for assessing the distribution and abundance of the various plant communities found across this landscape at the time of the imagery (2004). It can also be used to help understand the dynamics of the vegetation and other attributes of this watershed—such as erosion and surface transport of sediment, relative to current and future habitat conditions.

Chapter 6 - Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Treesearch

Jennifer L. Long; Melanie Miller; James P. Menakis; Robert E. Keane

2006-01-01

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype Project, required a system for classifying vegetation composition, biophysical settings, and vegetation structure to facilitate the mapping of vegetation and wildland fuel characteristics and the simulation of vegetation dynamics using landscape modeling. We developed...

Vegetation recovery patterns assessment at landslides caused by catastrophic earthquake: a case study in central Taiwan.

PubMed

Chou, Wen-Chieh; Lin, Wen-Tzu; Lin, Chao-Yuan

2009-05-01

The catastrophic earthquake, 7.3 on the Richter scale, occurred on September 21, 1999 in Central Taiwan. Much of standing vegetation on slopes was eliminated and massive, scattered landslides were induced at the Jou-Jou Mountain area of the Wu-Chi basin in Nantou County. We evaluated three methods for assessing landslide hazard and vegetation recovery conditions. (1) Self-organizing map (SOM) neural network coupled with fuzzy technique was used to quickly extract the landslide. (2) The NDVI-based vegetation recovery index derived from multi-temporal SPOT satellite images was used to evaluate vegetation recovery rate in the denudation sites. (3) The spatial distribution index (SDI) based on land-cover topographic location was employed to analyze vegetation recovery patterns, including the invading, surviving and mixed patterns at the Jou-Jou Mountain area. On September 27, 1999, there were 849.20 ha of landslide area extracted using the self-organizing map and fuzzy technique combined model. After six years of natural vegetation succession, the landslide has gradually restored, and vegetation recovery rate reached up to 86%. On-site observation shows that many native pioneer plants have invaded onto the denudation sites even if disturbed by several typhoons. Two native surviving plants, Arundo formosana Hack and Pinus taiwanensis Hayata, play a vital role in natural vegetation succession in this area, especially for the sites on ridgeline and steep slopes.

Assessment of vegetation change in a fire-altered forest landscape

NASA Technical Reports Server (NTRS)

Jakubauskas, Mark E.; Lulla, Kamlesh P.; Mausel, Paul W.

1990-01-01

This research focused on determining the degree to which differences in burn severity relate to postfire vegetative cover within a Michigan pine forest. Landsat MSS data from June 1973 and TM data from October 1982 were classified using an unsupervised approach to create prefire and postfire cover maps of the study area. Using a raster-based geographic information system (GIS), the maps were compared, and a map of vegetation change was created. An IR/red band ratio from a June 1980 Landsat scene was classified to create a map of three degres of burn severity, which was then compared with the vegetation change map using a GIS. Classification comparisons of pine and deciduous forest classes (1973 to 1982) revealed that the most change in vegetation occurred in areas subjected to the most intense burn. Two classes of regenerating forest comprised the majority of the change, while the remaining change was associated with shrub vegetation or another forest class.

Vegetative and geologic mapping of the western Seward Peninsula, Alaska, based on ERTS-1 imagery

NASA Technical Reports Server (NTRS)

Anderson, J. H.; Shapiro, L. H.; Belon, A. E.

1973-01-01

ERTS-1 scene 1009-22095 (Western Seward Peninsula, Alaska) has been studied, partly as a training exercise, to evaluate whether direct visual examination of individual and custom color-composite prints can provide new information on the vegetation and geology of this relatively well known area of Alaska. The vegetation analysis reveals seven major vegetation types, only four of which are described on existing vegetation maps. In addition, the ERTS analysis provides greater detail than the existing maps on the areal distribution of vegetation types. The geologic analysis demonstrates that most of the major rock units and geomorphic boundaries shown on the available geologic maps could also be identified on the ERTS data. Several major high-angle faults were observed, but the zones of thrust faults which are much less obvious.

Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images

NASA Technical Reports Server (NTRS)

Menenti, M.; Azzali, S.; Verhoef, W.; Van Swol, R.

1993-01-01

Examples are presented of applications of a fast Fourier transform algorithm to analyze time series of images of Normalized Difference Vegetation Index values. The results obtained for a case study on Zambia indicated that differences in vegetation development among map units of an existing agroclimatic map were not significant, while reliable differences were observed among the map units obtained using the Fourier analysis.

System, method, and apparatus for remote measurement of terrestrial biomass

DOEpatents

Johnson, Patrick W [Jefferson, MD

2011-04-12

A system, method, and/or apparatus for remote measurement of terrestrial biomass contained in vegetative elements, such as large tree boles or trunks present in an area of interest, are provided. The method includes providing an airborne VHF radar system in combination with a LiDAR system, overflying the area of interest while directing energy toward the area of interest, using the VHF radar system to collect backscatter data from the trees as a function of incidence angle and frequency, and determining a magnitude of the biomass from the backscatter data and data from the laser radar system for each radar resolution cell. A biomass map is generated showing the magnitude of the biomass of the vegetative elements as a function of location on the map by using each resolution cell as a unique location thereon. In certain preferred embodiments, a single frequency is used with a linear array antenna.

[Extracting black soil border in Heilongjiang province based on spectral angle match method].

PubMed

Zhang, Xin-Le; Zhang, Shu-Wen; Li, Ying; Liu, Huan-Jun

2009-04-01

As soils are generally covered by vegetation most time of a year, the spectral reflectance collected by remote sensing technique is from the mixture of soil and vegetation, so the classification precision based on remote sensing (RS) technique is unsatisfied. Under RS and geographic information systems (GIS) environment and with the help of buffer and overlay analysis methods, land use and soil maps were used to derive regions of interest (ROI) for RS supervised classification, which plus MODIS reflectance products were chosen to extract black soil border, with methods including spectral single match. The results showed that the black soil border in Heilongjiang province can be extracted with soil remote sensing method based on MODIS reflectance products, especially in the north part of black soil zone; the classification precision of spectral angel mapping method is the highest, but the classifying accuracy of other soils can not meet the need, because of vegetation covering and similar spectral characteristics; even for the same soil, black soil, the classifying accuracy has obvious spatial heterogeneity, in the north part of black soil zone in Heilongjiang province it is higher than in the south, which is because of spectral differences; as soil uncovering period in Northeastern China is relatively longer, high temporal resolution make MODIS images get the advantage over soil remote sensing classification; with the help of GIS, extracting ROIs by making the best of auxiliary data can improve the precision of soil classification; with the help of auxiliary information, such as topography and climate, the classification accuracy was enhanced significantly. As there are five main factors determining soil classes, much data of different types, such as DEM, terrain factors, climate (temperature, precipitation, etc.), parent material, vegetation map, and remote sensing images, were introduced to classify soils, so how to choose some of the data and quantify the weights of different data layers needs further study.

Extracting Forest Canopy Characteristics from Remote Sensing Imagery: Implications for Sentinel-2 Mission

NASA Astrophysics Data System (ADS)

Gholizadeh, Asa; Kopaekova, Veronika; Rogass, Christian; Mielke, Christian; Misurec, Jan

2016-08-01

Systematic quantification and monitoring of forest biophysical and biochemical variables is required to assess the response of ecosystems to climate change. Remote sensing has been introduced as a time and cost- efficient way to carry out large scale monitoring of vegetation parameters. Red-Edge Position (REP) is a hyperspectrally detectable parameter which is sensitive to vegetation Chl. In the current study, REP was modelled for the Norway spruce forest canopy resampled to HyMap and Sentinel-2 spectral resolution as well as calculated from the real HyMap and Sentinel-2 simulated data. Different REP extraction methods (4PLI, PF, LE, 4PLIH and 4PLIS) were assessed. The study showed the way for effective utilization of the forthcoming hyper and superspectral remote sensing sensors from orbit to monitor vegetation attributes.

Regulation leads to increases in riparian vegetation, but not direct allochthonous inputs, along the Colorado River in Grand Canyon, Arizona

USGS Publications Warehouse

Kennedy, T.A.; Ralston, B.E.

2012-01-01

Dams and associated river regulation have led to the expansion of riparian vegetation, especially nonnative species, along downstream ecosystems. Nonnative saltcedar is one of the dominant riparian plants along virtually every major river system in the arid western United States, but allochthonous inputs have never been quantified along a segment of a large river that is dominated by saltcedar. We developed a novel method for estimating direct allochthonous inputs along the 387km-long reach of the Colorado River downstream of Glen Canyon Dam that utilized a GIS vegetation map developed from aerial photographs, empirical and literature-derived litter production data for the dominant vegetation types, and virtual shorelines of annual peak discharge (566m 3s -1 stage elevation). Using this method, we estimate that direct allochthonous inputs from riparian vegetation for the entire reach studied total 186metric tonsyear -1, which represents mean inputs of 470gAFDMm -1year -1 of shoreline or 5.17gAFDMm -2year -1 of river surface. These values are comparable to allochthonous inputs for other large rivers and systems that also have sparse riparian vegetation. Nonnative saltcedar represents a significant component of annual allochthonous inputs (36% of total direct inputs) in the Colorado River. We also estimated direct allochthonous inputs for 46.8km of the Colorado River prior to closure of Glen Canyon Dam using a vegetation map that was developed from historical photographs. Regulation has led to significant increases in riparian vegetation (270-319% increase in cover, depending on stage elevation), but annual allochthonous inputs appear unaffected by regulation because of the lower flood peaks on the post-dam river. Published in 2010 by John Wiley & Sons, Ltd.

A Subpixel Classification of Multispectral Satellite Imagery for Interpetation of Tundra-Taiga Ecotone Vegetation (Case Study on Tuliok River Valley, Khibiny, Russia)

NASA Astrophysics Data System (ADS)

Mikheeva, A. I.; Tutubalina, O. V.; Zimin, M. V.; Golubeva, E. I.

2017-12-01

The tundra-taiga ecotone plays significant role in northern ecosystems. Due to global climatic changes, the vegetation of the ecotone is the key object of many remote-sensing studies. The interpretation of vegetation and nonvegetation objects of the tundra-taiga ecotone on satellite imageries of a moderate resolution is complicated by the difficulty of extracting these objects from the spectral and spatial mixtures within a pixel. This article describes a method for the subpixel classification of Terra ASTER satellite image for vegetation mapping of the tundra-taiga ecotone in the Tuliok River, Khibiny Mountains, Russia. It was demonstrated that this method allows to determine the position of the boundaries of ecotone objects and their abundance on the basis of quantitative criteria, which provides a more accurate characteristic of ecotone vegetation when compared to the per-pixel approach of automatic imagery interpretation.

Vegetation database for land-cover mapping, Clark and Lincoln Counties, Nevada

USGS Publications Warehouse

Charlet, David A.; Damar, Nancy A.; Leary, Patrick J.

2014-01-01

Floristic and other vegetation data were collected at 3,175 sample sites to support land-cover mapping projects in Clark and Lincoln Counties, Nevada, from 2007 to 2013. Data were collected at sample sites that were selected to fulfill mapping priorities by one of two different plot sampling approaches. Samples were described at the stand level and classified into the National Vegetation Classification hierarchy at the alliance level and above. The vegetation database is presented in geospatial and tabular formats.

Use of slope, aspect, and elevation maps derived from digital elevation model data in making soil surveys

USGS Publications Warehouse

Klingebiel, A.A.; Horvath, E.H.; Moore, D.G.; Reybold, W.U.

1987-01-01

Maps showing different classes of slope, aspect, and elevation were developed from U.S. Geological Survey digital elevation model data. The classes were displayed on clear Mylar at 1:24 000-scale and registered with topographic maps and orthophotos. The maps were used with aerial photographs, topographic maps, and other resource data to determine their value in making order-three soil surveys. They were tested on over 600 000 ha in Wyoming, Idaho, and Nevada under various climatic and topographic conditions. Field evaluations showed that the maps developed from digital elevation model data were accurate, except for slope class maps where slopes were <4%. The maps were useful to soil scientists, especially where (i) class boundaries coincided with soil changes, landform delineations, land use and management separations, and vegetation changes, and (ii) rough terrain and dense vegetation made it difficult to traverse the area. In hot, arid areas of sparse vegetation, the relationship of slope classes to kinds of soil and vegetation was less significant.

A new map of standardized terrestrial ecosystems of Africa

USGS Publications Warehouse

Sayre, Roger G.; Comer, Patrick; Hak, Jon; Josse, Carmen; Bow, Jacquie; Warner, Harumi; Larwanou, Mahamane; Kelbessa, Ensermu; Bekele, Tamrat; Kehl, Harald; Amena, Ruba; Andriamasimanana, Rado; Ba, Taibou; Benson, Laurence; Boucher, Timothy; Brown, Matthew; Cress, Jill J.; Dassering, Oueddo; Friesen, Beverly A.; Gachathi, Francis; Houcine, Sebei; Keita, Mahamadou; Khamala, Erick; Marangu, Dan; Mokua, Fredrick; Morou, Boube; Mucina, Ladislav; Mugisha, Samuel; Mwavu, Edward; Rutherford, Michael; Sanou, Patrice; Syampungani, Stephen; Tomor, Bojoi; Vall, Abdallahi Ould Mohamed; Vande Weghe, Jean Pierre; Wangui, Eunice; Waruingi, Lucy

2013-01-01

Terrestrial ecosystems and vegetation of Africa were classified and mapped as part of a larger effort and global protocol (GEOSS – the Global Earth Observation System of Systems), which includes an activity to map terrestrial ecosystems of the earth in a standardized, robust, and practical manner, and at the finest possible spatial resolution. To model the potential distribution of ecosystems, new continental datasets for several key physical environment datalayers (including coastline, landforms, surficial lithology, and bioclimates) were developed at spatial and classification resolutions finer than existing similar datalayers. A hierarchical vegetation classification was developed by African ecosystem scientists and vegetation geographers, who also provided sample locations of the newly classified vegetation units. The vegetation types and ecosystems were then mapped across the continent using a classification and regression tree (CART) inductive model, which predicted the potential distribution of vegetation types from a suite of biophysical environmental attributes including bioclimate region, biogeographic region, surficial lithology, landform, elevation and land cover. Multi-scale ecosystems were classified and mapped in an increasingly detailed hierarchical framework using vegetation-based concepts of class, subclass, formation, division, and macrogroup levels. The finest vegetation units (macrogroups) classified and mapped in this effort are defined using diagnostic plant species and diagnostic growth forms that reflect biogeographic differences in composition and sub-continental to regional differences in mesoclimate, geology, substrates, hydrology, and disturbance regimes (FGDC, 2008). The macrogroups are regarded as meso-scale (100s to 10,000s of hectares) ecosystems. A total of 126 macrogroup types were mapped, each with multiple, repeating occurrences on the landscape. The modeling effort was implemented at a base spatial resolution of 90 m. In addition to creating several rich, new continent-wide biophysical datalayers describing African vegetation and ecosystems, our intention was to explore feasible approaches to rapidly moving this type of standardized, continent-wide, ecosystem classification and mapping effort forward.

Defining functional biomes and monitoring their change globally.

PubMed

Higgins, Steven I; Buitenwerf, Robert; Moncrieff, Glenn R

2016-11-01

Biomes are important constructs for organizing understanding of how the worlds' major terrestrial ecosystems differ from one another and for monitoring change in these ecosystems. Yet existing biome classification schemes have been criticized for being overly subjective and for explicitly or implicitly invoking climate. We propose a new biome map and classification scheme that uses information on (i) an index of vegetation productivity, (ii) whether the minimum of vegetation activity is in the driest or coldest part of the year, and (iii) vegetation height. Although biomes produced on the basis of this classification show a strong spatial coherence, they show little congruence with existing biome classification schemes. Our biome map provides an alternative classification scheme for comparing the biogeochemical rates of terrestrial ecosystems. We use this new biome classification scheme to analyse the patterns of biome change observed over recent decades. Overall, 13% to 14% of analysed pixels shifted in biome state over the 30-year study period. A wide range of biome transitions were observed. For example, biomes with tall vegetation and minimum vegetation activity in the cold season shifted to higher productivity biome states. Biomes with short vegetation and low seasonality shifted to seasonally moisture-limited biome states. Our findings and method provide a new source of data for rigorously monitoring global vegetation change, analysing drivers of vegetation change and for benchmarking models of terrestrial ecosystem function. © 2016 John Wiley & Sons Ltd.

Deep convolutional neural network processing of aerial stereo imagery to monitor vulnerable zones near power lines

NASA Astrophysics Data System (ADS)

Qayyum, Abdul; Saad, Naufal M.; Kamel, Nidal; Malik, Aamir Saeed

2018-01-01

The monitoring of vegetation near high-voltage transmission power lines and poles is tedious. Blackouts present a huge challenge to power distribution companies and often occur due to tree growth in hilly and rural areas. There are numerous methods of monitoring hazardous overgrowth that are expensive and time-consuming. Accurate estimation of tree and vegetation heights near power poles can prevent the disruption of power transmission in vulnerable zones. This paper presents a cost-effective approach based on a convolutional neural network (CNN) algorithm to compute the height (depth maps) of objects proximal to power poles and transmission lines. The proposed CNN extracts and classifies features by employing convolutional pooling inputs to fully connected data layers that capture prominent features from stereo image patches. Unmanned aerial vehicle or satellite stereo image datasets can thus provide a feasible and cost-effective approach that identifies threat levels based on height and distance estimations of hazardous vegetation and other objects. Results were compared with extant disparity map estimation techniques, such as graph cut, dynamic programming, belief propagation, and area-based methods. The proposed method achieved an accuracy rate of 90%.

Winter wheat mapping combining variations before and after estimated heading dates

NASA Astrophysics Data System (ADS)

Qiu, Bingwen; Luo, Yuhan; Tang, Zhenghong; Chen, Chongcheng; Lu, Difei; Huang, Hongyu; Chen, Yunzhi; Chen, Nan; Xu, Weiming

2017-01-01

Accurate and updated information on winter wheat distribution is vital for food security. The intra-class variability of the temporal profiles of vegetation indices presents substantial challenges to current time series-based approaches. This study developed a new method to identify winter wheat over large regions through a transformation and metric-based approach. First, the trend surfaces were established to identify key phenological parameters of winter wheat based on altitude and latitude with references to crop calendar data from the agro-meteorological stations. Second, two phenology-based indicators were developed based on the EVI2 differences between estimated heading and seedling/harvesting dates and the change amplitudes. These two phenology-based indicators revealed variations during the estimated early and late growth stages. Finally, winter wheat data were extracted based on these two metrics. The winter wheat mapping method was applied to China based on the 250 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) 2-band Enhanced Vegetation Index (EVI2) time series datasets. Accuracy was validated with field survey data, agricultural census data, and Landsat-interpreted results in test regions. When evaluated with 653 field survey sites and Landsat image interpreted data, the overall accuracy of MODIS-derived images in 2012-2013 was 92.19% and 88.86%, respectively. The MODIS-derived winter wheat areas accounted for over 82% of the variability at the municipal level when compared with agricultural census data. The winter wheat mapping method developed in this study demonstrates great adaptability to intra-class variability of the vegetation temporal profiles and has great potential for further applications to broader regions and other types of agricultural crop mapping.

Improving salt marsh digital elevation model accuracy with full-waveform lidar and nonparametric predictive modeling

NASA Astrophysics Data System (ADS)

Rogers, Jeffrey N.; Parrish, Christopher E.; Ward, Larry G.; Burdick, David M.

2018-03-01

Salt marsh vegetation tends to increase vertical uncertainty in light detection and ranging (lidar) derived elevation data, often causing the data to become ineffective for analysis of topographic features governing tidal inundation or vegetation zonation. Previous attempts at improving lidar data collected in salt marsh environments range from simply computing and subtracting the global elevation bias to more complex methods such as computing vegetation-specific, constant correction factors. The vegetation specific corrections can be used along with an existing habitat map to apply separate corrections to different areas within a study site. It is hypothesized here that correcting salt marsh lidar data by applying location-specific, point-by-point corrections, which are computed from lidar waveform-derived features, tidal-datum based elevation, distance from shoreline and other lidar digital elevation model based variables, using nonparametric regression will produce better results. The methods were developed and tested using full-waveform lidar and ground truth for three marshes in Cape Cod, Massachusetts, U.S.A. Five different model algorithms for nonparametric regression were evaluated, with TreeNet's stochastic gradient boosting algorithm consistently producing better regression and classification results. Additionally, models were constructed to predict the vegetative zone (high marsh and low marsh). The predictive modeling methods used in this study estimated ground elevation with a mean bias of 0.00 m and a standard deviation of 0.07 m (0.07 m root mean square error). These methods appear very promising for correction of salt marsh lidar data and, importantly, do not require an existing habitat map, biomass measurements, or image based remote sensing data such as multi/hyperspectral imagery.

Mapping dominant annual land cover from 2009 to 2013 across Victoria, Australia using satellite imagery

PubMed Central

Sheffield, Kathryn; Morse-McNabb, Elizabeth; Clark, Rob; Robson, Susan; Lewis, Hayden

2015-01-01

There is a demand for regularly updated, broad-scale, accurate land cover information in Victoria from multiple stakeholders. This paper documents the methods used to generate an annual dominant land cover (DLC) map for Victoria, Australia from 2009 to 2013. Vegetation phenology parameters derived from an annual time series of the Moderate Resolution Imaging Spectroradiometer Vegetation Indices 16-day 250 m (MOD13Q1) product were used to generate annual DLC maps, using a three-tiered hierarchical classification scheme. Classification accuracy at the broadest (primary) class level was over 91% for all years, while it ranged from 72 to 81% at the secondary class level. The most detailed class level (tertiary) had accuracy levels ranging from 61 to 68%. The approach used was able to accommodate variable climatic conditions, which had substantial impacts on vegetation growth patterns and agricultural production across the state between both regions and years. The production of an annual dataset with complete spatial coverage for Victoria provides a reliable base data set with an accuracy that is fit-for-purpose for many applications. PMID:26602009

Spatial fuel data products of the LANDFIRE Project

USGS Publications Warehouse

Reeves, M.C.; Ryan, K.C.; Rollins, M.G.; Thompson, T.G.

2009-01-01

The Landscape Fire and Resource Management Planning Tools (LANDFIRE) Project is mapping wildland fuels, vegetation, and fire regime characteristics across the United States. The LANDFIRE project is unique because of its national scope, creating an integrated product suite at 30-m spatial resolution and complete spatial coverage of all lands within the 50 states. Here we describe development of the LANDFIRE wildland fuels data layers for the conterminous 48 states: surface fire behavior fuel models, canopy bulk density, canopy base height, canopy cover, and canopy height. Surface fire behavior fuel models are mapped by developing crosswalks to vegetation structure and composition created by LANDFIRE. Canopy fuels are mapped using regression trees relating field-referenced estimates of canopy base height and canopy bulk density to satellite imagery, biophysical gradients and vegetation structure and composition data. Here we focus on the methods and data used to create the fuel data products, discuss problems encountered with the data, provide an accuracy assessment, demonstrate recent use of the data during the 2007 fire season, and discuss ideas for updating, maintaining and improving LANDFIRE fuel data products.

Accuracy assessment of vegetation community maps generated by aerial photography interpretation: perspective from the tropical savanna, Australia

NASA Astrophysics Data System (ADS)

Lewis, Donna L.; Phinn, Stuart

2011-01-01

Aerial photography interpretation is the most common mapping technique in the world. However, unlike an algorithm-based classification of satellite imagery, accuracy of aerial photography interpretation generated maps is rarely assessed. Vegetation communities covering an area of 530 km2 on Bullo River Station, Northern Territory, Australia, were mapped using an interpretation of 1:50,000 color aerial photography. Manual stereoscopic line-work was delineated at 1:10,000 and thematic maps generated at 1:25,000 and 1:100,000. Multivariate and intuitive analysis techniques were employed to identify 22 vegetation communities within the study area. The accuracy assessment was based on 50% of a field dataset collected over a 4 year period (2006 to 2009) and the remaining 50% of sites were used for map attribution. The overall accuracy and Kappa coefficient for both thematic maps was 66.67% and 0.63, respectively, calculated from standard error matrices. Our findings highlight the need for appropriate scales of mapping and accuracy assessment of aerial photography interpretation generated vegetation community maps.

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...

Mapping Collective Identity: Territories and Boundaries of Human Terrain

DTIC Science & Technology

2011-06-10

Line MAP-HT Mapping the Human Terrain NDVI Normalized Difference Vegetation Index NGA National Geospatial-Intelligence Agency xi OBIA Object-Based...The Normalized Difference Vegetation Index ( NDVI ) uses the red band to represent the low reflectance from vegetation and the expanded near infrared...spectrum to provide greater delineation of agricultural areas. This layer highlights different fields, crops, and their boundaries. NDVI layers are

Generalized vegetation map of north Merrit Island based on a simplified multispectral analysis

NASA Technical Reports Server (NTRS)

Poonai, P.; Floyd, W. J.; Rahmani, M. A.

1977-01-01

A simplified system for classification of multispectral data was used for making a generalized map of ground features of North Merritt Island. Subclassification of vegetation within broad categories yielded promising results which led to a completely automatic method and to the production of satisfactory detailed maps. Changes in an area north of Happy Hammocks are evidently related to water relations of the soil and are not associated with the last winter freeze-damage which affected mainly the mangrove species, likely to reestablish themselves by natural processes. A supplementary investigation involving reflectance studies in the laboratory has shown that the reflectance by detached citrus leaves, of wavelengths lying between 400 microns and 700 microns, showed some variation over a period of seven days during which the leaves were kept in a laboratory atmosphere.

Vegetation types on acid soils of Micronesia

Treesearch

Marjorie C. Falanruw; Thomas G.. Cole; Craig D. Whitesell

1987-01-01

The soils and vegetation of the Caroline high islands, Federated States of Micronesia, are being mapped by the U.S. Department of Agriculture's Forest Service and Soil Conservation Service. By the end of 1987, vegetation maps and reports on Kosrae, Pohnpei, Yap, four Truk Islands, and Palau are expected to be available. To compare soil types with vegetation types...

Quantifying biological integrity of California sage scrub communities using plant life-form cover.

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

Hamada, Y.; Stow, D. A.; Franklin, J.

2010-01-01

The California sage scrub (CSS) community type in California's Mediterranean-type ecosystems supports a large number of rare, threatened, and endangered species, and is critically degraded and endangered. Monitoring ecological variables that provide information about community integrity is vital to conserving these biologically diverse communities. Fractional cover of true shrub, subshrub, herbaceous vegetation, and bare ground should fill information gaps between generalized vegetation type maps and detailed field-based plot measurements of species composition and provide an effective means for quantifying CSS community integrity. Remote sensing is the only tool available for estimating spatially comprehensive fractional cover over large extent, and fractionalmore » cover of plant life-form types is one of the measures of vegetation state that is most amenable to remote sensing. The use of remote sensing does not eliminate the need for either field surveying or vegetation type mapping; rather it will likely require a combination of approaches to reliably estimate life-form cover and to provide comprehensive information for communities. According to our review and synthesis, life-form fractional cover has strong potential for providing ecologically meaningful intermediate-scale information, which is unattainable from vegetation type maps and species-level field measurements. Thus, we strongly recommend incorporating fractional cover of true shrub, subshrub, herb, and bare ground in CSS community monitoring methods. Estimating life-form cover at a 25 m x 25 m spatial scale using remote sensing would be an appropriate approach for initial implementation. Investigation of remote sensing techniques and an appropriate spatial scale; collaboration of resource managers, biologists, and remote sensing specialists, and refinement of protocols are essential for integrating life-form fractional cover mapping into strategies for sustainable long-term CSS community management.« less

A GIS-BASED METHOD FOR MULTI-OBJECTIVE EVALUATION OF PARK VEGETATION. (R824766)

EPA Science Inventory

Abstract

In this paper we describe a method for evaluating the concordance between a set of mapped landscape attributes and a set of quantitatively expressed management priorities. The method has proved to be useful in planning urban green areas, allowing objectively d...

Modeling change in potential landscape vulnerability to forest insect and pathogen disturbances: methods for forested subwatersheds sampled in the midscale interior Columbia River basin assessment.

Treesearch

Paul F. Hessburg; Bradley G. Smith; Craig A. Miller; Scott D. Kreiter; R. Brion Salter

1999-01-01

In the interior Columbia River basin midscale ecological assessment, including portions of the Klamath and Great Basins, we mapped and characterized historical and current vegetation composition and structure of 337 randomly sampled subwatersheds (9500 ha average size) in 43 subbasins (404 000 ha average size). We compared landscape patterns, vegetation structure and...

Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images

NASA Astrophysics Data System (ADS)

Rougier, Simon; Puissant, Anne; Stumpf, André; Lachiche, Nicolas

2016-09-01

Vegetation monitoring is becoming a major issue in the urban environment due to the services they procure and necessitates an accurate and up to date mapping. Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical learning techniques have provided good results for the detection of urban vegetation but necessitate a large amount of training data. In this context, this study proposes to investigate the performances of different sampling strategies in order to reduce the number of examples needed. Two windows based active learning algorithms from state-of-art are compared to a classical stratified random sampling and a third combining active learning and stratified strategies is proposed. The efficiency of these strategies is evaluated on two medium size French cities, Strasbourg and Rennes, associated to different datasets. Results demonstrate that classical stratified random sampling can in some cases be just as effective as active learning methods and that it should be used more frequently to evaluate new active learning methods. Moreover, the active learning strategies proposed in this work enables to reduce the computational runtime by selecting multiple windows at each iteration without increasing the number of windows needed.

Comparison of ASTER- and AVIRIS-Derived Mineraland Vegetation Maps of the White Horse Replacement Alunite Deposit and Surrounding Area, Marysvale Volcanic Field, Utah

USGS Publications Warehouse

Rockwell, Barnaby W.

2009-01-01

This report presents and compares mineral and vegetation maps of parts of the Marysvale volcanic field in west-central Utah that were published in a recent paper describing the White Horse replacement alunite deposit. Detailed, field-verified maps of the deposit were produced from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired from a low-altitude Twin Otter turboprop airborne platform. Reconnaissance-level maps of surrounding areas including the central and northern Tushar Mountains, Pahvant Range, and portions of the Sevier Plateau to the east were produced from visible, near-infrared, and shortwave-infrared data acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor carried aboard the Terra satellite platform. These maps are also compared to a previously published mineral map of the same area generated from AVIRIS data acquired from the high-altitude NASA ER-2 jet platform. All of the maps were generated by similar analysis methods, enabling the direct comparison of the spatial scale and mineral composition of surface geologic features that can be identified using the three types of remote sensing data. The high spatial (2-17 meter) and spectral (224 bands) resolution AVIRIS data can be used to generate detailed mineral and vegetation maps suitable for geologic and geoenvironmental studies of individual deposits, mines, and smelters. The lower spatial (15-30 meter) and spectral (9 bands) resolution ASTER data are better suited to less detailed mineralogical studies of lithology and alteration across entire hydrothermal systems and mining districts, including regional mineral resource and geoenvironmental assessments. The results presented here demonstrate that minerals and mineral mixtures can be directly identified using AVIRIS and ASTER data to elucidate spatial patterns of mineralogic zonation; AVIRIS data can enable the generation of maps with significantly greater detail and accuracy. The vegetation mapping results suggest that ASTER data may provide an efficient alternative to spectroscopic data for studies of burn severity after wildland fires. A new, semiautomated methodology for the analysis of ASTER data is presented that is currently being applied to ASTER data coverage of large areas for regional assessments of mineral-resource potential and mineral-environmental effects. All maps are presented in a variety of digital formats, including jpeg, pdf, and ERDAS Imagine (.img). The Imagine format files are georeferenced and suitable for viewing with other geospatial data in Imagine, ArcGIS, and ENVI. The mineral and vegetation maps are attributed so that the material identified for a pixel can be determined easily in ArcMap by using the Identify tool and in Imagine by using the Inquire Cursor tool.

On the potential of long wavelength imaging radars for mapping vegetation types and woody biomass in tropical rain forests

NASA Technical Reports Server (NTRS)

Rignot, Eric J.; Zimmermann, Reiner; Oren, Ram

1995-01-01

In the tropical rain forests of Manu, in Peru, where forest biomass ranges from 4 kg/sq m in young forest succession up to 100 kg/sq m in old, undisturbed floodplain stands, the P-band polarimetric radar data gathered in June of 1993 by the AIRSAR (Airborne Synthetic Aperture Radar) instrument separate most major vegetation formations and also perform better than expected in estimating woody biomass. The worldwide need for large scale, updated biomass estimates, achieved with a uniformly applied method, as well as reliable maps of land cover, justifies a more in-depth exploration of long wavelength imaging radar applications for tropical forests inventories.

Vegetation Water Content Mapping for Agricultural Regions in SMAPVEX16

NASA Astrophysics Data System (ADS)

White, W. A.; Cosh, M. H.; McKee, L.; Berg, A. A.; McNairn, H.; Hornbuckle, B. K.; Colliander, A.; Jackson, T. J.

2017-12-01

Vegetation water content impacts the ability of L-band radiometers to measure surface soil moisture. Therefore it is necessary to quantify the amount of water held in surface vegetation for an accurate soil moisture remote sensing retrieval. A methodology is presented for generating agricultural vegetation water content maps using Landsat 8 scenes for agricultural fields of Iowa and Manitoba for the Soil Moisture Active Passive Validation Experiments in 2016 (SMAPVEX16). Manitoba has a variety of row crops across the region, and the study period encompasses the time frame from emergence to reproduction, as well as a forested region. The Iowa study site is dominated by corn and soybeans, presenting an easier challenge. Ground collection of vegetation biomass and water content were also collected to provide a ground truth data source. Errors for the resulting vegetation water content maps ranged depending upon crop type, but generally were less than 15% of the total plant water content per crop type. Interpolation is done between Landsat overpasses to produce daily vegetation water content maps for the summer of 2016 at a 30 meter resolution.

New forest vegetation maps facilitate assessment of biodiversity indicators over large, multi-ownership regions.

Treesearch

Janet L. Ohmann

2003-01-01

Natural resource policy analysis and conservation planning are best served by broad-scale information about vegetation that is detailed, spatially complete, and consistent across land ownerships and allocations. In this paper I describe how a new generation of forest vegetation maps can be used to assess the distribution of vegetation biodiversity among land ownerships...

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.

Feasibility study for airborne fluorescence/reflectivity lidar bathymetry

NASA Astrophysics Data System (ADS)

Steinvall, Ove; Kautsky, Hans; Tulldahl, Michael; Wollner, Erika

2012-06-01

There is a demand from the authorities to have good maps of the coastal environment for their exploitation and preservation of the coastal areas. The goal for environmental mapping and monitoring is to differentiate between vegetation and non-vegetated bottoms and, if possible, to differentiate between species. Airborne lidar bathymetry is an interesting method for mapping shallow underwater habitats. In general, the maximum depth range for airborne laser exceeds the possible depth range for passive sensors. Today, operational lidar systems are able to capture the bottom (or vegetation) topography as well as estimations of the bottom reflectivity using e.g. reflected bottom pulse power. In this paper we study the possibilities and advantages for environmental mapping, if laser sensing would be further developed from single wavelength depth sounding systems to include multiple emission wavelengths and fluorescence receiver channels. Our results show that an airborne fluorescence lidar has several interesting features which might be useful in mapping underwater habitats. An example is the laser induced fluorescence giving rise to the emission spectrum which could be used for classification together with the elastic lidar signal. In the first part of our study, vegetation and substrate samples were collected and their spectral reflectance and fluorescence were subsequently measured in laboratory. A laser wavelength of 532 nm was used for excitation of the samples. The choice of 532 nm as excitation wavelength is motivated by the fact that this wavelength is commonly used in bathymetric laser scanners and that the excitation wavelengths are limited to the visual region as e.g. ultraviolet radiation is highly attenuated in water. The second part of our work consisted of theoretical performance calculations for a potential real system, and comparison of separability between species and substrate signatures using selected wavelength regions for fluorescence sensing.

Vegetation mapping from ERTS imagery of the Okavango Delta. [Botswana

NASA Technical Reports Server (NTRS)

Willamson, D. T.

1974-01-01

The Okavango is Botswana's major water resource. The present study has been specifically directed at mapping vegetation types within the delta and generally concerned with finding what information of value to plant and animal ecologists could be extracted from the imagery. To date it has been found that. (1) It is possible to map broad vegetation types from the imagery. (2) Imagery of the delta records the state of the system in a manner which will facilitate long-term studies of plant succession. (3) Phenological events can be detected. (4) The imagery can be used to detect and map wild fires. This will be useful in determining the role of fire in the ecology of the region. Using the imagery it is thus possible to map existing vegetation and monitor both short and long-term changes.

Association between mapped vegetation and Quaternary geology on Santa Rosa Island, California

NASA Astrophysics Data System (ADS)

Cronkite-Ratcliff, C.; Corbett, S.; Schmidt, K. M.

2017-12-01

Vegetation and surficial geology are closely connected through the interface generally referred to as the critical zone. Not only do they influence each other, but they also provide clues into the effects of climate, topography, and hydrology on the earth's surface. This presentation describes quantitative analyses of the association between the recently compiled, independently generated vegetation and geologic map units on Santa Rosa Island, part of the Channel Islands National Park in Southern California. Santa Rosa Island was heavily grazed by sheep and cattle ranching for over one hundred years prior to its acquisition by the National Park Service. During this period, the island experienced significant erosion and spatial reduction and diversity of native plant species. Understanding the relationship between geology and vegetation is necessary for monitoring the recovery of native plant species, enhancing the viability of restoration sites, and understanding hydrologic conditions favorable for plant growth. Differences in grain size distribution and soil depth between geologic units support different plant communities through their influence on soil moisture, while differences in unit age reflect different degrees of pedogenic maturity. We find that unsupervised machine learning methods provide more informative insight into vegetation-geology associations than traditional measures such as Cramer's V and Goodman and Kruskal's lambda. Correspondence analysis shows that unique vegetation-geology patterns associated with beach/dune, grassland, hillslope/colluvial, and fluvial/wetland environments can be discerned from the data. By combining geology and vegetation with topographic variables, mixture models can be used to partition the landscape into multiple representative types, which then be compared with conceptual models of plant growth and succession over different landforms. Using this collection of methods, we show various ways that that Quaternary geology provides valuable information on the distribution of vegetation species in recovering ecosystems. Going forward, these analyses provide insights on favorable areas for natural and managed recovery of native vegetation species as well as criteria for future field sampling and monitoring.

Comparing Pixel and Object-Based Approaches to Map an Understorey Invasive Shrub in Tropical Mixed Forests

PubMed Central

Niphadkar, Madhura; Nagendra, Harini; Tarantino, Cristina; Adamo, Maria; Blonda, Palma

2017-01-01

The establishment of invasive alien species in varied habitats across the world is now recognized as a genuine threat to the preservation of biodiversity. Specifically, plant invasions in understory tropical forests are detrimental to the persistence of healthy ecosystems. Monitoring such invasions using Very High Resolution (VHR) satellite remote sensing has been shown to be valuable in designing management interventions for conservation of native habitats. Object-based classification methods are very helpful in identifying invasive plants in various habitats, by their inherent nature of imitating the ability of the human brain in pattern recognition. However, these methods have not been tested adequately in dense tropical mixed forests where invasion occurs in the understorey. This study compares a pixel-based and object-based classification method for mapping the understorey invasive shrub Lantana camara (Lantana) in a tropical mixed forest habitat in the Western Ghats biodiversity hotspot in India. Overall, a hierarchical approach of mapping top canopy at first, and then further processing for the understorey shrub, using measures such as texture and vegetation indices proved effective in separating out Lantana from other cover types. In the first method, we implement a simple parametric supervised classification for mapping cover types, and then process within these types for Lantana delineation. In the second method, we use an object-based segmentation algorithm to map cover types, and then perform further processing for separating Lantana. The improved ability of the object-based approach to delineate structurally distinct objects with characteristic spectral and spatial characteristics of their own, as well as with reference to their surroundings, allows for much flexibility in identifying invasive understorey shrubs among the complex vegetation of the tropical forest than that provided by the parametric classifier. Conservation practices in tropical mixed forests can benefit greatly by adopting methods which use high resolution remotely sensed data and advanced techniques to monitor the patterns and effective functioning of native ecosystems by periodically mapping disturbances such as invasion. PMID:28620400

Collapse susceptibility mapping in karstified gypsum terrain (Sivas basin - Turkey) by conditional probability, logistic regression, artificial neural network models

NASA Astrophysics Data System (ADS)

Yilmaz, Isik; Keskin, Inan; Marschalko, Marian; Bednarik, Martin

2010-05-01

This study compares the GIS based collapse susceptibility mapping methods such as; conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) applied in gypsum rock masses in Sivas basin (Turkey). Digital Elevation Model (DEM) was first constructed using GIS software. Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index- TWI, stream power index- SPI, Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from CP, LR and ANN models, and they were then compared by means of their validations. Area Under Curve (AUC) values obtained from all three methodologies showed that the map obtained from ANN model looks like more accurate than the other models, and the results also showed that the artificial neural networks is a usefull tool in preparation of collapse susceptibility map and highly compatible with GIS operating features. Key words: Collapse; doline; susceptibility map; gypsum; GIS; conditional probability; logistic regression; artificial neural networks.

Extending a field-based Sonoran desert vegetation classification to a regional scale using optical and microwave satellite imagery

NASA Astrophysics Data System (ADS)

Shupe, Scott Marshall

2000-10-01

Vegetation mapping in and regions facilitates ecological studies, land management, and provides a record to which future land changes can be compared. Accurate and representative mapping of desert vegetation requires a sound field sampling program and a methodology to transform the data collected into a representative classification system. Time and cost constraints require that a remote sensing approach be used if such a classification system is to be applied on a regional scale. However, desert vegetation may be sparse and thus difficult to sense at typical satellite resolutions, especially given the problem of soil reflectance. This study was designed to address these concerns by conducting vegetation mapping research using field and satellite data from the US Army Yuma Proving Ground (USYPG) in Southwest Arizona. Line and belt transect data from the Army's Land Condition Trend Analysis (LCTA) Program were transformed into relative cover and relative density classification schemes using cluster analysis. Ordination analysis of the same data produced two and three-dimensional graphs on which the homogeneity of each vegetation class could be examined. It was found that the use of correspondence analysis (CA), detrended correspondence analysis (DCA), and non-metric multidimensional scaling (NMS) ordination methods was superior to the use of any single ordination method for helping to clarify between-class and within-class relationships in vegetation composition. Analysis of these between-class and within-class relationships were of key importance in examining how well relative cover and relative density schemes characterize the USYPG vegetation. Using these two classification schemes as reference data, maximum likelihood and artificial neural net classifications were then performed on a coregistered dataset consisting of a summer Landsat Thematic Mapper (TM) image, one spring and one summer ERS-1 microwave image, and elevation, slope, and aspect layers. Classifications using a combination of ERS-1 imagery and elevation, slope, and aspect data were superior to classifications carried out using Landsat TM data alone. In all classification iterations it was consistently found that the highest classification accuracy was obtained by using a combination of Landsat TM, ERS-1, and elevation, slope, and aspect data. Maximum likelihood classification accuracy was found to be higher than artificial neural net classification in all cases.

Use of LANDSAT for land use and habitat inventories for the New Jersey Pinelands

NASA Technical Reports Server (NTRS)

Tracy, C.

1981-01-01

The New Jersey Heritage program surveyed available mapping information on landcover and vegetative communities and found that most commonly used sources, such as local land use maps and aerial photographs, were useful for individual sites, but either varied in their classifications or could not be used over extensive areas. A demonstration project using LANDSAT satellite information for the Great Egg Harbor/Tuckahoe watersheds was initiated with the goals of application (providing an inventory of vegetative communities and land use) flexibility (providing a method of collecting data which can be updated or modified in the future); and efficiency (allowing for an acceptable cost level by having staff undertake the project and avoid the more costly methods from air photo interpretation or on-site surveying. The classification procedure used is described and the spatial distributions of the 10 landcover classes determined are listed.

Chapter 7 - Mapping potential vegetation type for the LANDFIRE Prototype Project

Treesearch

Tracey S. Frescino; Matthew G. Rollins

2006-01-01

Mapped potential vegetation functioned as a key component in the Landscape Fire and Resource Management Planning Tools Prototype Project (LANDFIRE Prototype Project). Disturbance regimes, vegetation response and succession, and wildland fuel dynamics across landscapes are controlled by patterns of the environmental factors (biophysical settings) that entrain the...

Using Remote Sensing to Estimate Crop Water Use to Improve Irrigation Water Management

NASA Astrophysics Data System (ADS)

Reyes-Gonzalez, Arturo

Irrigation water is scarce. Hence, accurate estimation of crop water use is necessary for proper irrigation managements and water conservation. Satellite-based remote sensing is a tool that can estimate crop water use efficiently. Several models have been developed to estimate crop water requirement or actual evapotranspiration (ETa) using remote sensing. One of them is the Mapping EvapoTranspiration at High Resolution using Internalized Calibration (METRIC) model. This model has been compared with other methods for ET estimations including weighing lysimeters, pan evaporation, Bowen Ratio Energy Balance System (BREBS), Eddy Covariance (EC), and sap flow. However, comparison of METRIC model outputs to an atmometer for ETa estimation has not yet been attempted in eastern South Dakota. The results showed a good relationship between ETa estimated by the METRIC model and estimated with atmometer (r2 = 0.87 and RMSE = 0.65 mm day-1). However, ETa values from atmometer were consistently lower than ET a values from METRIC. The verification of remotely sensed estimates of surface variables is essential for any remote-sensing study. The relationships between LAI, Ts, and ETa estimated using the remote sensing-based METRIC model and in-situ measurements were established. The results showed good agreement between the variables measured in situ and estimated by the METRIC model. LAI showed r2 = 0.76, and RMSE = 0.59 m2 m -2, Ts had r2 = 0.87 and RMSE 1.24 °C and ETa presented r2= 0.89 and RMSE = 0.71 mm day -1. Estimation of ETa using energy balance method can be challenging and time consuming. Thus, there is a need to develop a simple and fast method to estimate ETa using minimum input parameters. Two methods were used, namely 1) an energy balance method (EB method) that used input parameters of the Landsat image, weather data, a digital elevation map, and a land cover map and 2) a Kc-NDVI method that use two input parameters: the Landsat image and weather data. A strong relationship was found between the two methods with r2 of 0.97 and RMSE of 0.37 mm day -1. Hence, the Kc-NDVI method performed well for ET a estimations, indicating that Kc-NDVI method can be a robust and reliable method to estimate ETa in a short period of time. Estimation of crop evapotranspiration (ETc) using satellite remote sensing-based vegetation index such as the Normalized Difference Vegetation Index (NDVI). The NDVI was calculated using near-infrared and red wavebands. The relationship between NDVI and tabulated Kc's was used to generate Kc maps. ETc maps were developed as an output of Kc maps multiplied by reference evapotranspiration (ETr). Daily ETc maps helped to explain the variability of crop water use during the growing season. Based on the results we can conclude that ETc maps developed from remotely sensed multispectral vegetation indices are a useful tool for quantifying crop water use at regional and field scales.

Development and characterization of a 3D high-resolution terrain database

NASA Astrophysics Data System (ADS)

Wilkosz, Aaron; Williams, Bryan L.; Motz, Steve

2000-07-01

A top-level description of methods used to generate elements of a high resolution 3D characterization database is presented. The database elements are defined as ground plane elevation map, vegetation height elevation map, material classification map, discrete man-made object map, and temperature radiance map. The paper will cover data collection by means of aerial photography, techniques of soft photogrammetry used to derive the elevation data, and the methodology followed to generate the material classification map. The discussion will feature the development of the database elements covering Fort Greely, Alaska. The developed databases are used by the US Army Aviation and Missile Command to evaluate the performance of various missile systems.

Mapping and Monitoring Stream Aquatic Habitat With a Narrow-Beam Green Lidar

NASA Astrophysics Data System (ADS)

McKean, J.; Wright, W.; Kinzel, P.; Isaak, D.

2006-12-01

Stream environments are structured by complex biophysical processes that operate across multiple spatial and temporal scales. Disentangling these multiscalar and multicausal relationships is difficult, but fundamental to understanding, managing, and monitoring channel aquatic ecosystems. Standard field wading surveys of stream physical habitat are limited by cost and logistics to relatively small, isolated samples. Traditional remotely sensed surveys, including methods such as photogrammetry and near-infrared lidar, suffer from attenuation by water and do not directly map submerged channel topography. The Experimental Advanced Airborne Research Lidar (EAARL) is a full-waveform lidar with a unique ability to simultaneously map, with relatively high resolution, subaqueous and subaerial topography and the vegetation canopy. We have used the EAARL instrument to investigate two dissimilar stream ecosystems. We mapped 40km of low gradient, meandering, gravel-bed streams in central Idaho that are spawning habitat for threatened Chinook salmon. We are using the continuous three-dimensional channel maps to quantitatively explore how channel features affect the distribution of salmon spawning at multiple spatial scales and how modern stream and floodplain topography is related to post-glacial valley evolution. In contrast, the Platte River in central Nebraska is a wide and shallow, sand-bedded river that provides habitat for migratory water birds, including endangered species such as the whooping crane and least tern. Multi-temporal EAARL data are being used to map and monitor the physical response of the Platte River to habitat improvement projects that include in-channel and riparian vegetation removal and river flow augmentation to limit vegetation encroachment.

Identification, definition and mapping of terrestrial ecosystems in interior Alaska

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The primary objective is to identify and analyze vegetation types in as great of detail as possible on ERTS-1 imagery and to classify and delineate them through mapping. This is basic to the identification, definition, and mapping of ecosystems. Major conclusions are: (1) the ERTS-1 system is useful for regional scale studies of broadly defined Alaskan vegetation types; (2) the resolution and spectral capabilities of ERTS-1 MSS imagery in photographic formats is adequate for certain phytecenologic purposes; and (3) preparation of an improved State vegetation map will be feasible.

Rethinking Chlorophyll Responses To Stress: Fluorescence and Flectance Remote Sensing in a Coastal Environment

DTIC Science & Technology

2010-11-15

fluorescence emission of vegetation for mapping vegetation stress as chlorophyll content and/or carotenoid content changes. 1. REPORT DATE (DD-MM-YYYY...that estimate fluorescence emission of vegetation for mapping vegetation stress as chlorophyll content and/or carotenoid content changes...not related to changes in chlorophyll content or the carotenoids /chlorophyll ratio. PRI is an indicator of chronic salinity stress and may be used as

UAS Photogrammetry for Rapid Response Characterization of Subaerial Coastal Change

NASA Astrophysics Data System (ADS)

Do, C.; Anarde, K.; Figlus, J.; Prouse, W.; Bedient, P. B.

2016-12-01

Unmanned aerial systems (UASs) provide an exciting new platform for rapid response measurement of subaerial coastal change. Here we validate the use of a coupled hobbyist UAS and optical photogrammetry framework for high-resolution mapping of portions of a low-lying barrier island along the Texas Gulf Coast. A DJI Phantom 3 Professional was used to capture 2D nadir images of the foreshore and back-beach environments containing both vegetated and non-vegetated features. The images were georeferenced using ground-truth markers surveyed via real-time kinematic (RTK) GPS and were then imported into Agisoft Photoscan, a photo-processing software, to generate 3D point clouds and digital elevation maps (DEMs). The georeferenced elevation models were then compared to RTK measurements to evaluate accuracy and precision. Thus far, DEMs derived from UAS photogrammetry show centimeter resolution for renderings of non-vegetated landforms. High-resolution renderings of vegetated and back-barrier regions have proven more difficult due to interstitial wetlands (surface reflectance) and uneven terrain for GPS backpack surveys. In addition to producing high-quality models, UAS photogrammetry has demonstrated to be more time-efficient than traditional mapping methods, making it advantageous for rapid response deployments. This study is part of a larger effort to relate field measurements of storm hydrodynamics to subaerial evidence of geomorphic change to better understand barrier island response to extreme storms.

Forgotten forests--issues and prospects in biome mapping using Seasonally Dry Tropical Forests as a case study.

PubMed

Särkinen, Tiina; Iganci, João R V; Linares-Palomino, Reynaldo; Simon, Marcelo F; Prado, Darién E

2011-11-24

South America is one of the most species diverse continents in the world. Within South America diversity is not distributed evenly at both local and continental scales and this has led to the recognition of various areas with unique species assemblages. Several schemes currently exist which divide the continental-level diversity into large species assemblages referred to as biomes. Here we review five currently available biome maps for South America, including the WWF Ecoregions, the Americas basemap, the Land Cover Map of South America, Morrone's Biogeographic regions of Latin America, and the Ecological Systems Map. The comparison is performed through a case study on the Seasonally Dry Tropical Forest (SDTF) biome using herbarium data of habitat specialist species. Current biome maps of South America perform poorly in depicting SDTF distribution. The poor performance of the maps can be attributed to two main factors: (1) poor spatial resolution, and (2) poor biome delimitation. Poor spatial resolution strongly limits the use of some of the maps in GIS applications, especially for areas with heterogeneous landscape such as the Andes. Whilst the Land Cover Map did not suffer from poor spatial resolution, it showed poor delimitation of biomes. The results highlight that delimiting structurally heterogeneous vegetation is difficult based on remote sensed data alone. A new refined working map of South American SDTF biome is proposed, derived using the Biome Distribution Modelling (BDM) approach where georeferenced herbarium data is used in conjunction with bioclimatic data. Georeferenced specimen data play potentially an important role in biome mapping. Our study shows that herbarium data could be used as a way of ground-truthing biome maps in silico. The results also illustrate that herbarium data can be used to model vegetation maps through predictive modelling. The BDM approach is a promising new method in biome mapping, and could be particularly useful for mapping poorly known, fragmented, or degraded vegetation. We wish to highlight that biome delimitation is not an exact science, and that transparency is needed on how biomes are used as study units in macroevolutionary and ecological research.

Forgotten forests - issues and prospects in biome mapping using Seasonally Dry Tropical Forests as a case study

PubMed Central

2011-01-01

Background South America is one of the most species diverse continents in the world. Within South America diversity is not distributed evenly at both local and continental scales and this has led to the recognition of various areas with unique species assemblages. Several schemes currently exist which divide the continental-level diversity into large species assemblages referred to as biomes. Here we review five currently available biome maps for South America, including the WWF Ecoregions, the Americas basemap, the Land Cover Map of South America, Morrone's Biogeographic regions of Latin America, and the Ecological Systems Map. The comparison is performed through a case study on the Seasonally Dry Tropical Forest (SDTF) biome using herbarium data of habitat specialist species. Results Current biome maps of South America perform poorly in depicting SDTF distribution. The poor performance of the maps can be attributed to two main factors: (1) poor spatial resolution, and (2) poor biome delimitation. Poor spatial resolution strongly limits the use of some of the maps in GIS applications, especially for areas with heterogeneous landscape such as the Andes. Whilst the Land Cover Map did not suffer from poor spatial resolution, it showed poor delimitation of biomes. The results highlight that delimiting structurally heterogeneous vegetation is difficult based on remote sensed data alone. A new refined working map of South American SDTF biome is proposed, derived using the Biome Distribution Modelling (BDM) approach where georeferenced herbarium data is used in conjunction with bioclimatic data. Conclusions Georeferenced specimen data play potentially an important role in biome mapping. Our study shows that herbarium data could be used as a way of ground-truthing biome maps in silico. The results also illustrate that herbarium data can be used to model vegetation maps through predictive modelling. The BDM approach is a promising new method in biome mapping, and could be particularly useful for mapping poorly known, fragmented, or degraded vegetation. We wish to highlight that biome delimitation is not an exact science, and that transparency is needed on how biomes are used as study units in macroevolutionary and ecological research. PMID:22115315

Algorithms and methodology used in constructing high-resolution terrain databases

NASA Astrophysics Data System (ADS)

Williams, Bryan L.; Wilkosz, Aaron

1998-07-01

This paper presents a top-level description of methods used to generate high-resolution 3D IR digital terrain databases using soft photogrammetry. The 3D IR database is derived from aerial photography and is made up of digital ground plane elevation map, vegetation height elevation map, material classification map, object data (tanks, buildings, etc.), and temperature radiance map. Steps required to generate some of these elements are outlined. The use of metric photogrammetry is discussed in the context of elevation map development; and methods employed to generate the material classification maps are given. The developed databases are used by the US Army Aviation and Missile Command to evaluate the performance of various missile systems. A discussion is also presented on database certification which consists of validation, verification, and accreditation procedures followed to certify that the developed databases give a true representation of the area of interest, and are fully compatible with the targeted digital simulators.

Cloud-based computation for accelerating vegetation mapping and change detection at regional to national scales

Treesearch

Matthew J. Gregory; Zhiqiang Yang; David M. Bell; Warren B. Cohen; Sean Healey; Janet L. Ohmann; Heather M. Roberts

2015-01-01

Mapping vegetation and landscape change at fine spatial scales is needed to inform natural resource and conservation planning, but such maps are expensive and time-consuming to produce. For Landsat-based methodologies, mapping efforts are hampered by the daunting task of manipulating multivariate data for millions to billions of pixels. The advent of cloud-based...

Identification, definition and mapping of terrestrial ecosystems in interior Alaska

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1973-01-01

The author has identified the following significant results. A reconstituted, simulated color-infrared print, enlarged to a scale of 1:250,000, was used to make a vegetation map of a 3,110 sq km area just west of Fairbanks, Alaska. Information was traced from the print which comprised the southeastern part of ERTS-1 scene 1033-21011. A 1:1,000,000 scale color-infrared transparency of this scene, obtained from NASA, was used along side the print as an aid in recognizing colors, color intensities and blends, and mosaics of different colors. Color units on the transparency and print were identified according to vegetation types using NASA air photos, U.S. Forest Service air photos, and experience of the investigator. Five more or less pure colors were identified and associated with vegetation types. These colors were designated according to their appearances on the print: (1) orange for forest vegetation dominated by broad-leaved trees: (2) gray for forest vegetation dominated by needle-leaved trees; (3) violet for scrub vegetation; (4) light violet denoting herbaceous tundra vegetation; and (5) dull violet for muskeg vegetation. This study has shown, through close examinations of the NASA transparency, that much more detailed vegetation landscape, or ecosystem maps could be produced, if only spectral signatures could be consistently and reliably recognized and transferred to a map of suitable scale.

Unveiling topographical changes using LiDAR mapping capability: case study of Belaga in Sarawak, East-Malaysia

NASA Astrophysics Data System (ADS)

Ganendra, T. R.; Khan, N. M.; Razak, W. J.; Kouame, Y.; Mobarakeh, E. T.

2016-06-01

The use of Light Detection and Ranging (LiDAR) remote sensing technology to scan and map landscapes has proven to be one of the most popular techniques to accurately map topography. Thus, LiDAR technology is the ultimate method of unveiling the surface feature under dense vegetation, and, this paper intends to emphasize the diverse techniques that can be utilized to elucidate topographical changes over the study area, using multi-temporal airborne full waveform LiDAR datasets collected in 2012 and 2014. Full waveform LiDAR data offers access to an almost unlimited number of returns per shot, which enables the user to explore in detail topographical changes, such as vegetation growth measurement. The study also found out topography changes at the study area due to earthwork activities contributing to soil consolidation, soil erosion and runoff, requiring cautious monitoring. The implications of this study not only concurs with numerous investigations undertaken by prominent researchers to improve decision making, but also corroborates once again that investigations employing multi-temporal LiDAR data to unveil topography changes in vegetated terrains, produce more detailed and accurate results than most other remote sensing data.

Profiles of California vegetation

Treesearch

William B. Critchfield

1971-01-01

This publication brings together 57 elevational profiles illustrating the dominant vegetation of much of the Sierra Nevada, southern Coast Ranges, and montane southern California as it existed in the 1930's. The profiles were drawn by Michael N. Dobrotin for the U.S. Forest Service's Vegetation Type Map survey, which mapped nearly half of the State's...

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...

Simulation of boreal Summer Monsoon Rainfall using CFSV2_SSiB model: sensitivity to Land Use Land Cover (LULC)

NASA Astrophysics Data System (ADS)

Chilukoti, N.; Xue, Y.

2016-12-01

The land surface play a vital role in determining the surface energy budget, accurate representation of land use and land cover (LULC) is necessary to improve forecast. In this study, we have investigated the influence of surface vegetation maps with different LULC on simulating the boreal summer monsoon rainfall. Using a National Centres for Environmental Prediction (NCEP) Coupled Forecast System version 2(CFSv2) model coupled with Simplified Simple Biosphere (SSiB) model, two experiments were conducted: one with old vegetation map and one with new vegetation map. The significant differences between new and old vegetation map were in semi-arid and arid areas. For example, in old map Tibetan plateau classified as desert, which is not appropriate, while in new map it was classified as grasslands or shrubs with bare soil. Old map classified the Sahara desert as a bare soil and shrubs with bare soil, whereas in new map it was classified as bare ground. In addition to central Asia and the Sahara desert, in new vegetation map, Europe had more cropped area and India's vegetation cover was changed from crops and forests to wooded grassland and small areas of grassland and shrubs. The simulated surface air temperature with new map shows a significant improvement over Asia, South Africa, and northern America by some 1 to 2ºC and 2 to 3ºC over north east China and these are consistent with the reduced rainfall biases over Africa, near Somali coast, north east India, Bangladesh, east China sea, eastern Pacific and northern USA. Over Indian continent and bay of Bengal dry rainfall anomalies that is the only area showing large dry rainfall bias, however, they were unchanged with new map simulation. Overall the CFSv2(coupled with SSiB) model with new vegetation map show a promising result in improving the monsoon forecast by improving the Land -Atmosphere interactions. To compare with the LULC forcing, experiment was conducted using the Global Forecast System (GFS) simulations forced with different observed Sea Surface Temperatures (SST) for the same period: one is from NCEP reanalysis and one from Hadley Center. They have substantial difference in Indian Ocean. Preliminary analysis shows that, the impact of these two SST data sets on Indian summer monsoon rainfall has no significant impact.

Management of natural resources through automatic cartographic inventory

NASA Technical Reports Server (NTRS)

Rey, P. A.; Gourinard, Y.; Cambou, F. (Principal Investigator)

1974-01-01

The author has identified the following significant results. Significant correspondence codes relating ERTS imagery to ground truth from vegetation and geology maps have been established. The use of color equidensity and color composite methods for selecting zones of equal densitometric value on ERTS imagery was perfected. Primary interest of temporal color composite is stressed. A chain of transfer operations from ERTS imagery to the automatic mapping of natural resources was developed.

Integrated terrain mapping with digital Landsat images in Queensland, Australia

USGS Publications Warehouse

Robinove, Charles Joseph

1979-01-01

Mapping with Landsat images usually is done by selecting single types of features, such as soils, vegetation, or rocks, and creating visually interpreted or digitally classified maps of each feature. Individual maps can then be overlaid on or combined with other maps to characterize the terrain. Integrated terrain mapping combines several terrain features into each map unit which, in many cases, is more directly related to uses of the land and to methods of land management than the single features alone. Terrain brightness, as measured by the multispectral scanners in Landsat 1 and 2, represents an integration of reflectance from the terrain features within the scanner's instantaneous field of view and is therefore more correlatable with integrated terrain units than with differentiated ones, such as rocks, soils, and vegetation. A test of the feasibilty of the technique of mapping integrated terrain units was conducted in a part of southwestern Queensland, Australia, in cooperation with scientists of the Queensland Department of Primary Industries. The primary purpose was to test the use of digital classification techniques to create a 'land systems map' usable for grazing land management. A recently published map of 'land systems' in the area (made by aerial photograph interpretation and ground surveys), which are integrated terrain units composed of vegetation, soil, topography, and geomorphic features, was used as a basis for comparison with digitally classified Landsat multispectral images. The land systems, in turn, each have a specific grazing capacity for cattle (expressed in beasts per km 2 ) which is estimated following analysis of both research results and property carrying capacities. Landsat images, in computer-compatible tape form, were first contrast-stretched to increase their visual interpretability, and digitally classified by the parallelepiped method into distinct spectral classes to determine their correspondence to the land systems classes and to areally smaller, but readily recognizable, 'land units.' Many land systems appeared as distinct spectral classes or as acceptably homogeneous combinations of several spectral classes. The digitally classified map corresponded to the general geographic patterns of many of the land systems. Statistical correlation of the digitally classified map and the published map was not possible because the published map showed only land systems whereas the digitally classified map showed some land units as well as systems. The general correspondence of spectral classes to the integrated terrain units means that the digital mapping of the units may precede fieldwork and act as a guide to field sampling and detailed terrain unit description as well as measuring of the location, area, and extent of each unit. Extension of the Landsat mapping and classification technique to other arid and semi-arid regions of the world may be feasible.

An integrated pan-tropical biomass map using multiple reference datasets.

PubMed

Avitabile, Valerio; Herold, Martin; Heuvelink, Gerard B M; Lewis, Simon L; Phillips, Oliver L; Asner, Gregory P; Armston, John; Ashton, Peter S; Banin, Lindsay; Bayol, Nicolas; Berry, Nicholas J; Boeckx, Pascal; de Jong, Bernardus H J; DeVries, Ben; Girardin, Cecile A J; Kearsley, Elizabeth; Lindsell, Jeremy A; Lopez-Gonzalez, Gabriela; Lucas, Richard; Malhi, Yadvinder; Morel, Alexandra; Mitchard, Edward T A; Nagy, Laszlo; Qie, Lan; Quinones, Marcela J; Ryan, Casey M; Ferry, Slik J W; Sunderland, Terry; Laurin, Gaia Vaglio; Gatti, Roberto Cazzolla; Valentini, Riccardo; Verbeeck, Hans; Wijaya, Arief; Willcock, Simon

2016-04-01

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha(-1) vs. 21 and 28 Mg ha(-1) for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets. © 2015 John Wiley & Sons Ltd.

Monitoring grasshopper and locust habitats in Sahelian Africa using GIS and remote sensing technology

USGS Publications Warehouse

Tappan, G. Gray; Moore, Donald G.; Knauseberger, Walter I.

1991-01-01

Development programmes in Sahelian Africa are beginning to use geographic information system (GIS) technology. One of the GIS and remote sensing programmes introduced to the region in the late 1980s was the use of seasonal vegetation maps made from satellite data to support grasshopper and locust control. Following serious outbreaks of these pests in 1987, the programme addressed a critical need, by national and international crop protection organizations, to monitor site-specific dynamic vegetation conditions associated with grasshopper and locust breeding. The primary products used in assessing vegetation conditions were vegetation index (greenness) image maps derived from National Oceanic and Atmospheric Administration satellite imagery. Vegetation index data were integrated in a GIS with digital cartographic data of individual Sahelian countries. These near-real-time image maps were used regularly in 10 countries for locating potential grasshopper and locust habitats. The programme to monitor vegetation conditions is currently being institutionalized in the Sahel.

Rule-based mapping of fire-adapted vegetation and fire regimes for the Monongahela National Forest

Treesearch

Melissa A. Thomas-Van Gundy; Gregory J. Nowacki; Thomas M. Schuler

2007-01-01

A rule-based approach was employed in GIS to map fire-adapted vegetation and fire regimes within the proclamation boundary of the Monongahela National Forest. Spatial analyses and maps were generated using ArcMap 9.1. The resulting fireadaptation scores were then categorized into standard fire regime groups. Fire regime group V (200+ yrs) was the most common, assigned...

Identification of marsh vegetation and coastal land use in ERTS-1 imagery

NASA Technical Reports Server (NTRS)

Klemas, V.; Daiber, F. C.; Bartlett, D. S.

1973-01-01

Coastal vegetation species appearing in the ERTS-1 images taken of Delaware Bay on August 16, and October 10, 1972 have been correlated with ground truth vegetation maps and imagery obtained from high altitude RB-57 and U-2 overflights. The vegetation maps of the entire Delaware Coast were prepared during the summer of 1972 and checked out with ground truth data collected on foot, in small boats, and from low-altitude aircraft. Multispectral analysis of high altitude RB-57 and U-2 photographs indicated that five vegetation communities could be clearly discriminated from 60,000 feet altitude including: (1) salt marsh cord grass, (2) salt marsh hay and spike grass, (3) reed grass, (4) high tide bush and sea myrtle, and (5) a group of fresh water species found in impoundments built to attract water fowl. All of these species are shown in fifteen overlay maps, covering all of Delaware's wetlands prepared to match the USGS topographic map size of 1:24,000.

Land cover mapping of the National Park Service northwest Alaska management area using Landsat multispectral and thematic mapper satellite data

USGS Publications Warehouse

Markon, C.J.; Wesser, Sara

1998-01-01

A land cover map of the National Park Service northwest Alaska management area was produced using digitally processed Landsat data. These and other environmental data were incorporated into a geographic information system to provide baseline information about the nature and extent of resources present in this northwest Alaskan environment.This report details the methodology, depicts vegetation profiles of the surrounding landscape, and describes the different vegetation types mapped. Portions of nine Landsat satellite (multispectral scanner and thematic mapper) scenes were used to produce a land cover map of the Cape Krusenstern National Monument and Noatak National Preserve and to update an existing land cover map of Kobuk Valley National Park Valley National Park. A Bayesian multivariate classifier was applied to the multispectral data sets, followed by the application of ancillary data (elevation, slope, aspect, soils, watersheds, and geology) to enhance the spectral separation of classes into more meaningful vegetation types. The resulting land cover map contains six major land cover categories (forest, shrub, herbaceous, sparse/barren, water, other) and 19 subclasses encompassing 7 million hectares. General narratives of the distribution of the subclasses throughout the project area are given along with vegetation profiles showing common relationships between topographic gradients and vegetation communities.

Use of remote sensing techniques for geological hazard surveys in vegetated urban regions. [multispectral imagery for lithological mapping

NASA Technical Reports Server (NTRS)

Stow, S. H.; Price, R. C.; Hoehner, F.; Wielchowsky, C.

1976-01-01

The feasibility of using aerial photography for lithologic differentiation in a heavily vegetated region is investigated using multispectral imagery obtained from LANDSAT satellite and aircraft-borne photography. Delineating and mapping of localized vegetal zones can be accomplished by the use of remote sensing because a difference in morphology and physiology results in different natural reflectances or signatures. An investigation was made to show that these local plant zones are affected by altitude, topography, weathering, and gullying; but are controlled by lithology. Therefore, maps outlining local plant zones were used as a basis for lithologic map construction.

Circumpolar Arctic vegetation mapping workshop

USGS Publications Warehouse

Walker, D. A.; Markon, C.J.

1996-01-01

The first Circumpolar Arctic Vegetation Mapping Workshop was held in the historic village of Lakta on the outskirts of St. Petersburg, Russia, March 21-25, 1994. The primary goals of the workshop were to: (1) review the status of arctic vegetation mapping in the circumpolar countries and (2) develop a strategy for synthesizing and updating the existing information into a new series of maps that portray the current state of knowledge. Such products are important for a number of purposes, such as the international effort to understand the consequences of global change in Arctic regions, to predict the direction of future changes, and for informed planning of resource development in the Arctic.

Seeing the trees for the forest: mapping vegetation biodiversity in coastal Oregon forests.

Treesearch

Sally. Duncan

2003-01-01

In order to address policy issues relating to biodiversity, productivity, and sustainability, we need detailed understanding of forest vegetation at broad geographic and time scales. Most existing maps developed from satellite imagery describe only general characteristics of the upper canopy. Detailed vegetation data are available from regional grids of field plots,...

Distribution of submerged aquatic vegetation in the St. Louis River estuary: Maps and models

EPA Science Inventory

In late summer of 2011 and 2012 we used echo-sounding gear to map the distribution of submerged aquatic vegetation (SAV) in the St. Louis River Estuary (SLRE). From these data we produced maps of SAV distribution and we created logistic models to predict the probability of occurr...

A Biome map for Modelling Global Mid-Pliocene Climate Change

NASA Astrophysics Data System (ADS)

Salzmann, U.; Haywood, A. M.

2006-12-01

The importance of vegetation-climate feedbacks was highlighted by several paleo-climate modelling exercises but their role as a boundary condition in Tertiary modelling has not been fully recognised or explored. Several paleo-vegetation datasets and maps have been produced for specific time slabs or regions for the Tertiary, but the vegetation classifications that have been used differ, thus making meaningful comparisons difficult. In order to facilitate further investigations into Tertiary climate and environmental change we are presently implementing the comprehensive GIS database TEVIS (Tertiary Environment and Vegetation Information System). TEVIS integrates marine and terrestrial vegetation data, taken from fossil pollen, leaf or wood, into an internally consistent classification scheme to produce for different time slabs global Tertiary Biome and Mega- Biome maps (Harrison & Prentice, 2003). In the frame of our ongoing 5-year programme we present a first global vegetation map for the mid-Pliocene time slab, a period of sustained global warmth. Data were synthesised from the PRISM data set (Thompson and Fleming 1996) after translating them to the Biome classification scheme and from new literature. The outcomes of the Biome map are compared with modelling results using an advanced numerical general circulation model (HadAM3) and the BIOME 4 vegetation model. Our combined proxy data and modelling approach will provide new palaeoclimate datasets to test models that are used to predict future climate change, and provide a more rigorous picture of climate and environmental changes during the Neogene.

Estimation of regional differences in wind erosion sensitivity in Hungary

NASA Astrophysics Data System (ADS)

Mezősi, G.; Blanka, V.; Bata, T.; Kovács, F.; Meyer, B.

2015-01-01

In Hungary, wind erosion is one of the most serious natural hazards. Spatial and temporal variation in the factors that determine the location and intensity of wind erosion damage are not well known, nor are the regional and local sensitivities to erosion. Because of methodological challenges, no multi-factor, regional wind erosion sensitivity map is available for Hungary. The aim of this study was to develop a method to estimate the regional differences in wind erosion sensitivity and exposure in Hungary. Wind erosion sensitivity was modelled using the key factors of soil sensitivity, vegetation cover and wind erodibility as proxies. These factors were first estimated separately by factor sensitivity maps and later combined by fuzzy logic into a regional-scale wind erosion sensitivity map. Large areas were evaluated by using publicly available data sets of remotely sensed vegetation information, soil maps and meteorological data on wind speed. The resulting estimates were verified by field studies and examining the economic losses from wind erosion as compensated by the state insurance company. The spatial resolution of the resulting sensitivity map is suitable for regional applications, as identifying sensitive areas is the foundation for diverse land development control measures and implementing management activities.

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

NASA Astrophysics Data System (ADS)

Verrelst, Jochem; Malenovský, Zbyněk; Van der Tol, Christiaan; Camps-Valls, Gustau; Gastellu-Etchegorry, Jean-Philippe; Lewis, Philip; North, Peter; Moreno, Jose

2018-06-01

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given.

The mapping of marsh vegetation using aircraft multispectral scanner data. [in Louisiana

NASA Technical Reports Server (NTRS)

Butera, M. K.

1975-01-01

A test was conducted to determine if salinity regimes in coastal marshland could be mapped and monitored by the identification and classification of marsh vegetative species from aircraft multispectral scanner data. The data was acquired at 6.1 km (20,000 ft.) on October 2, 1974, over a test area in the coastal marshland of southern Louisiana including fresh, intermediate, brackish, and saline zones. The data was classified by vegetational species using a supervised, spectral pattern recognition procedure. Accuracies of training sites ranged from 67% to 96%. Marsh zones based on free soil water salinity were determined from the species classification to demonstrate a practical use for mapping marsh vegetation.

Basis and methods of NASA airborne topographic mapper lidar surveys for coastal studies

USGS Publications Warehouse

Brock, John C.; Wright, C. Wayne; Sallenger, Asbury H.; Krabill, William B.; Swift, Robert N.

2002-01-01

This paper provides an overview of the basic principles of airborne laser altimetry for surveys of coastal topography, and describes the methods used in the acquisition and processing of NASA Airborne Topographic Mapper (ATM) surveys that cover much of the conterminous US coastline. This form of remote sensing, also known as "topographic lidar", has undergone extremely rapid development during the last two decades, and has the potential to contribute within a wide range of coastal scientific investigations. Various airborne laser surveying (ALS) applications that are relevant to coastal studies are being pursued by researchers in a range of Earth science disciplines. Examples include the mapping of "bald earth" land surfaces below even moderately dense vegetation in studies of geologic framework and hydrology, and determination of the vegetation canopy structure, a key variable in mapping wildlife habitats. ALS has also proven to be an excellent method for the regional mapping of geomorphic change along barrier island beaches and other sandy coasts due to storms or long-term sedimentary processes. Coastal scientists are adopting ALS as a basic method in the study of an array of additional coastal topics. ALS can provide useful information in the analysis of shoreline change, the prediction and assessment of landslides along seacliffs and headlands, examination of subsidence causing coastal land loss, and in predicting storm surge and tsunami inundation.

A comparison of change detection methods using multispectral scanner data

USGS Publications Warehouse

Seevers, Paul M.; Jones, Brenda K.; Qiu, Zhicheng; Liu, Yutong

1994-01-01

Change detection methods were investigated as a cooperative activity between the U.S. Geological Survey and the National Bureau of Surveying and Mapping, People's Republic of China. Subtraction of band 2, band 3, normalized difference vegetation index, and tasseled cap bands 1 and 2 data from two multispectral scanner images were tested using two sites in the United States and one in the People's Republic of China. A new statistical method also was tested. Band 2 subtraction gives the best results for detecting change from vegetative cover to urban development. The statistical method identifies areas that have changed and uses a fast classification algorithm to classify the original data of the changed areas by land cover type present for each image date.

The Circumpolar Arctic Vegetation Map: A tool for analysis of change in permafrost regions

NASA Astrophysics Data System (ADS)

Walker, D. A.; Raynolds, M. K.; Maier, H. A.

2003-12-01

Arctic vegetation occurs beyond the northern limit of trees, in areas that have an Arctic climate and Arctic flora. Here we present an overview of the recently published Circumpolar Arctic Vegetation Map (CAVM), an area analysis of the vegetation map, and a discussion of its potential for analysis of change in the Arctic. Six countries have Arctic tundra vegetation, Canada, Greenland, Iceland, Russia, Norway (Svalbard), and the US (Total Arctic area = 7.1 million km2). Some treeless areas, such as most of Iceland and the Aluetian Islands are excluded from the map because they lack an Arctic climate. The CAVM divides the Arctic into five bioclimate subzones, A thru E (Subzone A is the coldest and Subzone E is the warmest), based on a combination of summer temperature and vegetation. Fifteen vegetation types are mapped based on the dominant plant growth forms. More detailed, plant-community-level, information is contained in the database used to construct the map. The reverse side of the vegetation map has a false-color infrared image constructed from Advanced Very-High Resolution (AVHRR) satellite-derived raster data, and maps of bioclimate subzones, elevation, landscape types, lake cover, substrate chemistry, floristic provinces, the maximum normalized difference vegetation index (NDVI), and aboveground phytomass. The vegetation map was analyzed by vegetation type and biomass for each county, bioclimate subzone, and floristic province. Biomass distribution was analyzed by means of a correlation between aboveground phytomass and the normalized difference vegetation index (NDVI), a remote-sensing index of surface greenness. Biomass on zonal surfaces roughly doubles within each successively warmer subzone, from about 50 g m-2 in Subzone A to 800 g m-2- in Subzone E. But the pattern of vegetation increase is highly variable, and depends on a number of other factors. The most important appears to be the glacial history of the landscape. Areas that were glaciated during the late-Pleistocene, such as Canada, Svalbard, and Greenland, do not show such strong increases in NDVI with temperature as do areas that were not glaciated. Abundant lakes and rocky surfaces limit the greenness of these recently glaciated surfaces. The highest NDVI and phytomass are found in non-glaciated regions of Alaska and Russia. Soil acidity also affects NDVI patterns. In Subzone D, where the NDVI/ soil acidity relationship has been studied most closely, NDVI is lower on nonacidic surfaces. This has been attributed to fewer shrubs and higher proportion of graminoids (more standing dead sedge leaves) in nonacidic areas. This trend is probably caused by generally drier soils, with less production, on limestone-derived soils. The trend is less clear in Subzone E because of fewer nonacidic surfaces, and the abundance of glacial lakes with low NDVI on the acidic shield areas of Canada. Time series analysis of trends in NDVI in Subzones C, D, and E in Alaska have shown a 17% increase in the NDVI over the 21-year record. The increases have been greatest in moist nonacidic tundra. Future analyses of the circumpolar database will be directed at examining which geographic regions and vegetation types have shown the strongest increases, and how these are correlated with temperature changes.

Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method

NASA Astrophysics Data System (ADS)

Song, Wanjuan; Mu, Xihan; Ruan, Gaiyan; Gao, Zhan; Li, Linyuan; Yan, Guangjian

2017-06-01

Normalized difference vegetation index (NDVI) of highly dense vegetation (NDVIv) and bare soil (NDVIs), identified as the key parameters for Fractional Vegetation Cover (FVC) estimation, are usually obtained with empirical statistical methods However, it is often difficult to obtain reasonable values of NDVIv and NDVIs at a coarse resolution (e.g., 1 km), or in arid, semiarid, and evergreen areas. The uncertainty of estimated NDVIs and NDVIv can cause substantial errors in FVC estimations when a simple linear mixture model is used. To address this problem, this paper proposes a physically based method. The leaf area index (LAI) and directional NDVI are introduced in a gap fraction model and a linear mixture model for FVC estimation to calculate NDVIv and NDVIs. The model incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters product (MCD43B1) and LAI product, which are convenient to acquire. Two types of evaluation experiments are designed 1) with data simulated by a canopy radiative transfer model and 2) with satellite observations. The root-mean-square deviation (RMSD) for simulated data is less than 0.117, depending on the type of noise added on the data. In the real data experiment, the RMSD for cropland is 0.127, for grassland is 0.075, and for forest is 0.107. The experimental areas respectively lack fully vegetated and non-vegetated pixels at 1 km resolution. Consequently, a relatively large uncertainty is found while using the statistical methods and the RMSD ranges from 0.110 to 0.363 based on the real data. The proposed method is convenient to produce NDVIv and NDVIs maps for FVC estimation on regional and global scales.

DOES ANTHROPOGENIC ACTIVITIES OR NATURE DOMINATE THE SHAPING OF THE LANDSCAPE IN THE OREGON PILOT STUDY AREA FOR 1990-1997?

EPA Science Inventory

We developed a simple method to locate changes in vegetation greenness, which can be used to identify areas under stress. The method only requires inexpensive NDVI data, which can be derived from many sources, and basic statistical and mapping software. AVHRR data are useful fo...

A Comparison of Electromagnetic Induction Mapping to Measurements of Maximum Effluent Flow Depth for Assessing Flow Paths in Vegetative Treatment Areas

USDA-ARS?s Scientific Manuscript database

Vegetative treatment systems (VTSs) are one type of control structure that has shown potential to control runoff from open feedlots. To achieve maximum performance, sheet-flow over the width of the vegetative treatment area (VTA) is required. Tools, such as maps of flow paths through the VTA, are ne...

The Effectiveness of Hydrothermal Alteration Mapping based on Hyperspectral Data in Tropical Region

NASA Astrophysics Data System (ADS)

Muhammad, R. R. D.; Saepuloh, A.

2016-09-01

Hyperspectral remote sensing could be used to characterize targets at earth's surface based on their spectra. This capability is useful for mapping and characterizing the distribution of host rocks, alteration assemblages, and minerals. Contrary to the multispectral sensors, the hyperspectral identifies targets with high spectral resolution. The Wayang Windu Geothermal field in West Java, Indonesia was selected as the study area due to the existence of surface manifestation and dense vegetation environment. Therefore, the effectiveness of hyperspectral remote sensing in tropical region was targeted as the study objective. The Spectral Angle Mapper (SAM) method was used to detect the occurrence of clay minerals spatially from Hyperion data. The SAM references of reflectance spectra were obtained from field observation at altered materials. To calculate the effectiveness of hyperspectral data, we used multispectral data from Landsat-8. The comparison method was conducted by comparing the SAM's rule images from Hyperion and Landsat-8, resulting that hyperspectral was more accurate than multispectral data. Hyperion SAM's rule images showed lower value compared to Landsat-8, the significant number derived from using Hyperion was about 24% better. This inferred that the hyperspectral remote sensing is preferable for mineral mapping even though vegetation covered study area.

Automated mapping of impervious surfaces in urban and suburban areas: Linear spectral unmixing of high spatial resolution imagery

NASA Astrophysics Data System (ADS)

Yang, Jian; He, Yuhong

2017-02-01

Quantifying impervious surfaces in urban and suburban areas is a key step toward a sustainable urban planning and management strategy. With the availability of fine-scale remote sensing imagery, automated mapping of impervious surfaces has attracted growing attention. However, the vast majority of existing studies have selected pixel-based and object-based methods for impervious surface mapping, with few adopting sub-pixel analysis of high spatial resolution imagery. This research makes use of a vegetation-bright impervious-dark impervious linear spectral mixture model to characterize urban and suburban surface components. A WorldView-3 image acquired on May 9th, 2015 is analyzed for its potential in automated unmixing of meaningful surface materials for two urban subsets and one suburban subset in Toronto, ON, Canada. Given the wide distribution of shadows in urban areas, the linear spectral unmixing is implemented in non-shadowed and shadowed areas separately for the two urban subsets. The results indicate that the accuracy of impervious surface mapping in suburban areas reaches up to 86.99%, much higher than the accuracies in urban areas (80.03% and 79.67%). Despite its merits in mapping accuracy and automation, the application of our proposed vegetation-bright impervious-dark impervious model to map impervious surfaces is limited due to the absence of soil component. To further extend the operational transferability of our proposed method, especially for the areas where plenty of bare soils exist during urbanization or reclamation, it is still of great necessity to mask out bare soils by automated classification prior to the implementation of linear spectral unmixing.

Northern Everglades, Florida, satellite image map

USGS Publications Warehouse

Thomas, Jean-Claude; Jones, John W.

2002-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program with support from the Everglades National Park. The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

A temperature and vegetation adjusted NTL urban index for urban area mapping and analysis

NASA Astrophysics Data System (ADS)

Zhang, Xiya; Li, Peijun

2018-01-01

Accurate and timely information regarding the extent and spatial distribution of urban areas on regional and global scales is crucially important for both scientific and policy-making communities. Stable nighttime light (NTL) data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) provides a unique proxy of human settlement and activity, which has been used in the mapping and analysis of urban areas and urbanization dynamics. However, blooming and saturation effects of DMSP/OLS NTL data are two unresolved problems in regional urban area mapping and analysis. This study proposed a new urban index termed the Temperature and Vegetation Adjusted NTL Urban Index (TVANUI). It is intended to reduce blooming and saturation effects and to enhance urban features by combining DMSP/OLS NTL data with Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer onboard the Terra satellite. The proposed index was evaluated in two study areas by comparison with established urban indices. The results demonstrated the proposed TVANUI was effective in enhancing the variation of DMSP/OLS light in urban areas and in reducing blooming and saturation effects, showing better performance than three established urban indices. The TVANUI also significantly outperformed the established urban indices in urban area mapping using both the global-fixed threshold and the local-optimal threshold methods. Thus, the proposed TVANUI provides a useful variable for urban area mapping and analysis on regional scale, as well as for urbanization dynamics using time-series DMSP/OLS and related satellite data.

Global hierarchical classification of deepwater and wetland environments from remote sensing products

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.; Aires, F.; Prigent, C.; McIntyre, P. B.

2017-12-01

Global surface water maps have improved in spatial and temporal resolutions through various remote sensing methods: open water extents with compiled Landsat archives and inundation with topographically downscaled multi-sensor retrievals. These time-series capture variations through time of open water and inundation without discriminating between hydrographic features (e.g. lakes, reservoirs, river channels and wetland types) as other databases have done as static representation. Available data sources present the opportunity to generate a comprehensive map and typology of aquatic environments (deepwater and wetlands) that improves on earlier digitized inventories and maps. The challenge of classifying surface waters globally is to distinguishing wetland types with meaningful characteristics or proxies (hydrology, water chemistry, soils, vegetation) while accommodating limitations of remote sensing data. We present a new wetland classification scheme designed for global application and produce a map of aquatic ecosystem types globally using state-of-the-art remote sensing products. Our classification scheme combines open water extent and expands it with downscaled multi-sensor inundation data to capture the maximal vegetated wetland extent. The hierarchical structure of the classification is modified from the Cowardin Systems (1979) developed for the USA. The first level classification is based on a combination of landscape positions and water source (e.g. lacustrine, riverine, palustrine, coastal and artificial) while the second level represents the hydrologic regime (e.g. perennial, seasonal, intermittent and waterlogged). Class-specific descriptors can further detail the wetland types with soils and vegetation cover. Our globally consistent nomenclature and top-down mapping allows for direct comparison across biogeographic regions, to upscale biogeochemical fluxes as well as other landscape level functions.

The influence of badland surfaces and erosion processes on vegetation cover

NASA Astrophysics Data System (ADS)

Hardenbicker, Ulrike; Matheis, Sarah

2014-05-01

To assess the links between badland geomorphology and vegetation cover, we used detailed mapping in the Avonlea badlands, 60 km southwest of Regina, Saskatchewan Canada. Three badlands surfaces are typical in the study area: a basal pediment surface, a mid-slope of bentonitic mudstone with typical popcorn surface, and an upper slope with mud-cemented sandstone. Badland development was triggered by rapid post Pleistocene incision of a meltwater channel in Upper Cretaceous marine and lagoonal sediments. After surveying and mapping of a test area, sediment samples were taken to analyze geophysical parameters. A detailed geomorphic map and vegetation map (1:1000) were compared and analyzed in order to determine the geomorphic environment for plant colonization. The shrink-swell capacity of the bentonitic bedrock, slaking potential and dispersivity are controlled by soil texture, clay mineralogy and chemistry, strongly influencing the timing and location of runoff and the relative significance of surface and subsurface erosional processes. The absence of shrink-swell cracking of the alluvial surfaces of the pediments indicates a low infiltration capacity and sheetflow. The compact lithology of the sandstone is responsible for its low permeability and high runoff coefficient. Slope drainage of steep sandstone slopes is routed through a deep corrasional pipe network. Silver sagebrush (Artemisia cana) is the only species growing on the popcorn surface of the mudrock, which is in large parts vegetation free. The basal pediment shows a distinct 2 m band surrounding the mudrock outcrop without vegetation as a result of high sedimentation rate due to slope wash. Otherwise the typical pioneer vegetation of this basal pediment are grasses. In the transition zone below the steep sandstone cliffs and above the gentle bentonitic mudrock surfaces patches of short-grass vegetation are found, marking slumped blocks with intact vegetation and soil cover. These patches are surrounded by less dense pioneer vegetation consisting of grasses and sage bushes indicating minimal surface erosion or sedimentation. Geomorphic mapping documented a high density of active pipes in this area, transporting silt and fine sand from the sandstone cliffs to lower and basal pediments. Vegetation cover alone is a poor indicator of badland surfaces and erosion processes because of the three-dimensional nature of badland erosion processes, and the shrink-swell capacity of the bentonitic bedrock. A combination of geomorphic and vegetation mapping is needed to identify badland surfaces and processes in the study area.

Spectroscopic remote sensing for material identification, vegetation characterization, and mapping

USGS Publications Warehouse

Kokaly, Raymond F.; Lewis, Paul E.; Shen, Sylvia S.

2012-01-01

Identifying materials by measuring and analyzing their reflectance spectra has been an important procedure in analytical chemistry for decades. Airborne and space-based imaging spectrometers allow materials to be mapped across the landscape. With many existing airborne sensors and new satellite-borne sensors planned for the future, robust methods are needed to fully exploit the information content of hyperspectral remote sensing data. A method of identifying and mapping materials using spectral feature analyses of reflectance data in an expert-system framework called MICA (Material Identification and Characterization Algorithm) is described. MICA is a module of the PRISM (Processing Routines in IDL for Spectroscopic Measurements) software, available to the public from the U.S. Geological Survey (USGS) at http://pubs.usgs.gov/of/2011/1155/. The core concepts of MICA include continuum removal and linear regression to compare key diagnostic absorption features in reference laboratory/field spectra and the spectra being analyzed. The reference spectra, diagnostic features, and threshold constraints are defined within a user-developed MICA command file (MCF). Building on several decades of experience in mineral mapping, a broadly-applicable MCF was developed to detect a set of minerals frequently occurring on the Earth's surface and applied to map minerals in the country-wide coverage of the 2007 Afghanistan HyMap data set. MICA has also been applied to detect sub-pixel oil contamination in marshes impacted by the Deepwater Horizon incident by discriminating the C-H absorption features in oil residues from background vegetation. These two recent examples demonstrate the utility of a spectroscopic approach to remote sensing for identifying and mapping the distributions of materials in imaging spectrometer data.

Identification, definition and mapping of terrestrial ecosystems in interior Alaska. [vegetation, land use, glaciology

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The vegetation map in preparation at the time of the last report was refined and labeled. This map is presented as an indication of the spatial and classificatory detail possible from interpretations of enlarged ERTS-1 color photographs. Using this map, areas covered by the several vegetation types characterized by white spruce were determined by planimetry. A 1:63,360 scale land use map of the Juneau area was drawn. This map incorporates the land use classification system now under development by the U.S. Geological Survey. The ERTS-1 images used in making the Juneau map were used to determine changes in surface area of the terminal zones of advancing and receding glaciers, the Taku, Norris, and Mendenhall. A new 1:63,360 scale land use map of the Bonanza Creek Experimental Forest and vicinity was drawn. Several excellent new sciences of test areas were received from NASA in color-infrared transparency format. These are being used for making photographic prints for analysis and mapping according to procedures outlined in this report.

[Application of biotope mapping model integrated with vegetation cover continuity attributes in urban biodiversity conservation].

PubMed

Gao, Tian; Qiu, Ling; Chen, Cun-gen

2010-09-01

Based on the biotope classification system with vegetation structure as the framework, a modified biotope mapping model integrated with vegetation cover continuity attributes was developed, and applied to the study of the greenbelts in Helsingborg in southern Sweden. An evaluation of the vegetation cover continuity in the greenbelts was carried out by the comparisons of the vascular plant species richness in long- and short-continuity forests, based on the identification of woodland continuity by using ancient woodland indicator species (AWIS). In the test greenbelts, long-continuity woodlands had more AWIS. Among the forests where the dominant trees were more than 30-year-old, the long-continuity ones had a higher biodiversity of vascular plants, compared with the short-continuity ones with the similar vegetation structure. The modified biotope mapping model integrated with the continuity features of vegetation cover could be an important tool in investigating urban biodiversity, and provide corresponding strategies for future urban biodiversity conservation.

An analysis and comparison of LANDSAT-1, Skylab (S-192) and aircraft data for delineation of land-water cover types of the Green Swamp, Florida

NASA Technical Reports Server (NTRS)

Higer, A. L. (Principal Investigator); Coker, A. E.; Schmidt, N. F.; Reed, I. E.

1975-01-01

The author has identified the following significant results. LANDSAT 1 and Skylab (S192) data from the Green Swamp area of central Florida were categorized into five classes: water, cypress, other wetlands, pine, and pasture. These categories were compared with similar categories on a detailed vegetative map made using low altitude aerial photography. Agreement of LANDSAT and Skylab categorized data with the vegetation map was 87 percent and 83 percent respectively. The Green Swamp vegetative categories may be widespread but often consist of numerous small isolated areas, because LANDSAT has a greater resolution than Skylab, it is more favorable for mapping the small vegetative categories.

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.

A new approach to monitoring spatial distribution and dynamics of wetlands and associated flows of Australian Great Artesian Basin springs using QuickBird satellite imagery

NASA Astrophysics Data System (ADS)

White, Davina C.; Lewis, Megan M.

2011-09-01

SummaryThis study develops an expedient digital mapping technique using Very High Resolution satellite imagery to monitor the temporal response of permanent wetland vegetation to changes in spring flow rates from the Australian Great Artesian Basin at Dalhousie Springs Complex, South Australia. Three epochs of QuickBird satellite multispectral imagery acquired between 2006 and 2010 were analysed using the Normalised Difference Vegetation Index (NDVI). A regression of 2009 NDVI values against vegetation cover from field botanical survey plots provided a relationship of increasing NDVI with increased vegetation cover ( R2 = 0.86; p < 0.001). On the basis of this relationship a vegetation threshold was determined (NDVI ⩾ 0.35), which discriminated perennial and ephemeral wetland vegetation from surrounding dryland vegetation in the imagery. The extent of wetlands for the entire Dalhousie Springs Complex mapped from the imagery increased from 607 ha in December 2006 to 913 ha in May 2009 and 1285 ha in May 2010. Comparison of the three NDVI images showed considerable localised change in wetland vegetation greenness, distribution and extent in response to fires, alien vegetation removal, rainfall and fluctuations in spring flow. A strong direct relationship ( R2 = 0.99; p < 0.001) was exhibited between spring flow rate and the area of associated wetland vegetation for eight individual springs. This relationship strongly infers that wetland area is an indicator of spring flow and can be used for monitoring purposes. This method has the potential to determine the sensitivity of spring wetland vegetation extent and distribution to associated changes in spring flow rates due to land management and aquifer extractions. Furthermore, this approach is timely and provides reliable and repeatable monitoring, particularly needed given the projected increased demand for groundwater extractions from the GAB for mining operations.

Hyperspectral remote sensing of vegetation: knowledge gain and knowledge gap after 50 years of research (Conference Presentation)

NASA Astrophysics Data System (ADS)

Thenkabail, Prasad S.

2017-04-01

This presentation summarizes the advances made over 40+ years in understanding, modeling, and mapping terrestrial vegetation as reported in the new book on "Hyperspectral Remote Sensing of Vegetation" (Publisher:Taylor and Francis inc.). The advent of spaceborne hyperspectral sensors or imaging spectroscopy (e.g., NASA's Hyperion, ESA's PROBA, and upcoming Italy's ASI's Prisma, Germany's DLR's EnMAP, Japanese HIUSI, NASA's HyspIRI) as well as the advances made in processing when handling large volumes of hyperspectral data have generated tremendous interest in advancing the hyperspectral applications' knowledge base to large areas. Advances made in using hyperspectral data, relative to broadband data, include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) ability to discriminate plant species and vegetation types with high degree of accuracy, (c) reducing uncertainties in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (e) ability to assess stress resulting from causes such as management practices, pests and disease, water deficit or water excess, and (f) establishing more sensitive wavebands and indices to study vegetation characteristics. The presentation will discuss topics such as: (1) hyperspectral sensors and their characteristics, (2) methods of overcoming the Hughes phenomenon, (3) characterizing biophysical and biochemical properties, (4) advances made in using hyperspectral data in modeling evapotranspiration or actual water use by plants, (5) study of phenology, light use efficiency, and gross primary productivity, (5) improved accuracies in species identification and land cover classifications, and (6) applications in precision farming.

Mapping SOC (Soil Organic Carbon) using LiDAR-derived vegetation indices in a random forest regression model

NASA Astrophysics Data System (ADS)

Will, R. M.; Glenn, N. F.; Benner, S. G.; Pierce, J. L.; Spaete, L.; Li, A.

2015-12-01

Quantifying SOC (Soil Organic Carbon) storage in complex terrain is challenging due to high spatial variability. Generally, the challenge is met by transforming point data to the entire landscape using surrogate, spatially-distributed, variables like elevation or precipitation. In many ecosystems, remotely sensed information on above-ground vegetation (e.g. NDVI) is a good predictor of below-ground carbon stocks. In this project, we are attempting to improve this predictive method by incorporating LiDAR-derived vegetation indices. LiDAR provides a mechanism for improved characterization of aboveground vegetation by providing structural parameters such as vegetation height and biomass. In this study, a random forest model is used to predict SOC using a suite of LiDAR-derived vegetation indices as predictor variables. The Reynolds Creek Experimental Watershed (RCEW) is an ideal location for a study of this type since it encompasses a strong elevation/precipitation gradient that supports lower biomass sagebrush ecosystems at low elevations and forests with more biomass at higher elevations. Sagebrush ecosystems composed of Wyoming, Low and Mountain Sagebrush have SOC values ranging from .4 to 1% (top 30 cm), while higher biomass ecosystems composed of aspen, juniper and fir have SOC values approaching 4% (top 30 cm). Large differences in SOC have been observed between canopy and interspace locations and high resolution vegetation information is likely to explain plot scale variability in SOC. Mapping of the SOC reservoir will help identify underlying controls on SOC distribution and provide insight into which processes are most important in determining SOC in semi-arid mountainous regions. In addition, airborne LiDAR has the potential to characterize vegetation communities at a high resolution and could be a tool for improving estimates of SOC at larger scales.

Vegetation Water Content Mapping in a Diverse Agricultural Landscape: National Airborne Field Experiment 2006

NASA Technical Reports Server (NTRS)

Cosh, Michael H.; Jing Tao; Jackson, Thomas J.; McKee, Lynn; O'Neill, Peggy

2011-01-01

Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE 06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE 06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/sq m. The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy. Keywords: Vegetation, field experimentation, thematic mapper, NDWI, agriculture.

South Florida Everglades: satellite image map

USGS Publications Warehouse

Jones, John W.; Thomas, Jean-Claude; Desmond, G.B.

2001-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program (http://access.usgs.gov/) with support from the Everglades National Park (http://www.nps.gov/ever/). The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

Response of Alpine Grassland Vegetation Phenology to Snow Accumulation and Melt in Namco Basin

NASA Astrophysics Data System (ADS)

Chen, S.; Cui, X.; Liang, T.

2018-04-01

Snow/ice accumulation and melt, as a vital part of hydrological processes, is close related with vegetation activities. Taking Namco basin for example, based on multisource remote sensing data and the ground observation data of temperature and precipitation, phenological information was extracted by S-G filtering and dynamic threshold method. Daily snow cover fraction was calculated with daily cloud-free snow cover maps. Evolution characteristics of grassland vegetation greening, growth length and daily snow cover fraction and their relationship were analyzed from 2001 to 2013. The results showed that most of grassland vegetation had advanced greening and prolong growth length trend in Namco basin. There were negative correlations between snow cover fraction and vegetation greening or growth length. The response of vegetation phenology to snow cover fraction is more sensitive than that to temperature in spring. Meanwhile, vegetation growth condition turned worse with advanced greening and prolong growth length. To a certain extent, our research reveals the relationship between grassland vegetation growth cycle and snow in alpine ecosystem. It has provided reference to research the response mechanism of alpine grassland ecosystem to climate changes.

Postfire soil burn severity mapping with hyperspectral image unmixing

USGS Publications Warehouse

Robichaud, P.R.; Lewis, S.A.; Laes, D.Y.M.; Hudak, A.T.; Kokaly, R.F.; Zamudio, J.A.

2007-01-01

Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after the 2002 Hayman Fire in Colorado to assess the application of high resolution imagery for burn severity mapping and to compare it to standard burn severity mapping methods. Mixture Tuned Matched Filtering (MTMF), a partial spectral unmixing algorithm, was used to identify the spectral abundance of ash, soil, and scorched and green vegetation in the burned area. The overall performance of the MTMF for predicting the ground cover components was satisfactory (r2 = 0.21 to 0.48) based on a comparison to fractional ash, soil, and vegetation cover measured on ground validation plots. The relationship between Landsat-derived differenced Normalized Burn Ratio (dNBR) values and the ground data was also evaluated (r2 = 0.20 to 0.58) and found to be comparable to the MTMF. However, the quantitative information provided by the fine-scale hyperspectral imagery makes it possible to more accurately assess the effects of the fire on the soil surface by identifying discrete ground cover characteristics. These surface effects, especially soil and ash cover and the lack of any remaining vegetative cover, directly relate to potential postfire watershed response processes. ?? 2006 Elsevier Inc. All rights reserved.

Response of spatial vegetation distribution in China to climate changes since the Last Glacial Maximum (LGM)

PubMed Central

Wang, Siyang; Xu, Xiaoting; Shrestha, Nawal; Zimmermann, Niklaus E.; Tang, Zhiyao; Wang, Zhiheng

2017-01-01

Analyzing how climate change affects vegetation distribution is one of the central issues of global change ecology as this has important implications for the carbon budget of terrestrial vegetation. Mapping vegetation distribution under historical climate scenarios is essential for understanding the response of vegetation distribution to future climatic changes. The reconstructions of palaeovegetation based on pollen data provide a useful method to understand the relationship between climate and vegetation distribution. However, this method is limited in time and space. Here, using species distribution model (SDM) approaches, we explored the climatic determinants of contemporary vegetation distribution and reconstructed the distribution of Chinese vegetation during the Last Glacial Maximum (LGM, 18,000 14C yr BP) and Middle-Holocene (MH, 6000 14C yr BP). The dynamics of vegetation distribution since the LGM reconstructed by SDMs were largely consistent with those based on pollen data, suggesting that the SDM approach is a useful tool for studying historical vegetation dynamics and its response to climate change across time and space. Comparison between the modeled contemporary potential natural vegetation distribution and the observed contemporary distribution suggests that temperate deciduous forests, subtropical evergreen broadleaf forests, temperate deciduous shrublands and temperate steppe have low range fillings and are strongly influenced by human activities. In general, the Tibetan Plateau, North and Northeast China, and the areas near the 30°N in Central and Southeast China appeared to have experienced the highest turnover in vegetation due to climate change from the LGM to the present. PMID:28426780

Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data

USGS Publications Warehouse

Markon, Carl J.; Fleming, Michael D.; Binnian, Emily F.

1995-01-01

Advanced Very High Resolution Radiometer (AVHRR) satellite data were acquired and composited into twice-a-month periods from 1 May 1991 to 15 October 1991 in order to map vegetation characteristics of the Alaskan landscape. Unique spatial and temporal qualities of the AVHRR data provide information that leads to a better understanding of regional biophysical characteristics of vegetation communities and patterns. These data provided synoptic views of the landscape and depicted phenological diversity, temporal vegetation phenology (green-up, peak of green, and senescence), photosynthetic activity, and regional landscape patterns. Products generated from the data included a phenological class map, phenological composite maps (onset, peak, and duration), and photosynthetic activity maps (mean and maximum greenness). The time-series data provide opportunities to study phenological processes at small landscape scales over time periods of weeks, months, and years. Regional patterns identified on some of the maps are unique to specific areas; others correspond to biophysical or ecoregional boundaries. The data provide new insights to landscape processes, ecology, and landscape physiognomy that allow scientists to look at landscapes in ways that were previously difficult to achieve.

Use of satellite imagery for wildland resource evaluation in the Great Basin

NASA Technical Reports Server (NTRS)

Tueller, P. T. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Most major vegetation types of Nevada have been mapped with success. The completed set of mosaic overlays will be more accurate and detailed than previous maps compiled by various State and Federal agencies due to the excellent vantage point that ERTS-1 data affords. This new vegetation type map will greatly aid resource agencies in their daily work. Such information as suitable grazing areas, wildlife habitat, forage production, and approximate wildland production potentials can be inferred from such a map. There has been some success in detecting vegetational changes with the use of ERTS-1 MSS imagery, but exposure differences have somewhat confounded the results. Future plans include work to solve this problem.

Locating Changes in Land Use from Long Term Remote Sensing Data in Morocco

EPA Science Inventory

We present a method that allows mapping changes in vegetation cover over large areas quickly and inexpensively, thus providing policy makers with the capability to locate and assess areas undergoing environmental change, and improving their ability to positively respond or adapt ...

Vegetation Mapping in a Dryland Ecosystem Using Multi-temporal Sentinel-2 Imagery and Ensemble Learning

NASA Astrophysics Data System (ADS)

Enterkine, J.; Spaete, L.; Glenn, N. F.; Gallagher, M.

2017-12-01

Remote sensing and mapping of dryland ecosystem vegetation is notably problematic due to the low canopy cover and fugacious growing seasons. Recent improvements in available satellite imagery and machine learning techniques have enabled enhanced approaches to mapping and monitoring vegetation across dryland ecosystems. The Sentinel-2 satellites (launched June 2015 and March 2017) of ESA's Copernicus Programme offer promising developments from existing multispectral satellite systems such as Landsat. Freely-available, Sentinel-2 imagery offers a five-day revisit frequency, thirteen spectral bands (in the visible, near infrared, and shortwave infrared), and high spatial resolution (from 10m to 60m). Three narrow spectral bands located between the visible and the near infrared are designed to observe changes in photosynthesis. The high temporal, spatial, and spectral resolution of this imagery makes it ideal for monitoring vegetation in dryland ecosystems. In this study, we calculated a large number of vegetation and spectral indices from Sentinel-2 imagery spanning a growing season. This data was leveraged with robust field data of canopy cover at precise geolocations. We then used a Random Forests ensemble learning model to identify the most predictive variables for each landcover class, which were then used to impute landcover over the study area. The resulting vegetation map product will be used by land managers, and the mapping approaches will serve as a basis for future remote sensing projects using Sentinel-2 imagery and machine learning.

Classification of wetlands vegetation using small scale color infrared imagery

NASA Technical Reports Server (NTRS)

Williamson, F. S. L.

1975-01-01

A classification system for Chesapeake Bay wetlands was derived from the correlation of film density classes and actual vegetation classes. The data processing programs used were developed by the Laboratory for the Applications of Remote Sensing. These programs were tested for their value in classifying natural vegetation, using digitized data from small scale aerial photography. Existing imagery and the vegetation map of Farm Creek Marsh were used to determine the optimal number of classes, and to aid in determining if the computer maps were a believable product.

A study of the usefulness of Skylab EREP data for earth resources studies in Australia

NASA Technical Reports Server (NTRS)

Lambert, B. P.; Benson, M. L.; Borough, C. J.; Myers, B. J.; Maffi, C. E.; Simpson, C. J.; Perry, W. J.; Burns, K. L.; Shepherd, J.; Beattie, R. (Principal Investigator)

1975-01-01

The author has identified the following significant results. In subhumid, vegetated areas, S190B photography: (1) has a potentially operational role in detecting lineaments in 1:100,000 scale geological mapping and in major civil engineering surveys; (2) is of limited value for regional lithological mapping at 1:500,000 scale; and (3) provided much useful synoptic information and some detailed information of direct value to the mapping of nonmineral natural resources such as vegetation, land soil, and water. In arid, well exposed areas, S190B photography could be used: (1) with a limited amount of field traverses, to produce reliable 1:500,000 scale geological maps of sedimentary sequences; (2) to update superficial geology on 1:250,000 scale maps; and (3) together with the necessary field studies, to prepare landform, soil, and vegetation maps at 1:1,000,000 scale. Skylab photography was found to be more useful than LANDSAT images for small scale mapping of geology and land types, and for the revision of topographic maps at 1:100,000 scale, because of superior spatial resolution and stereoscopic coverage.

PRELIMINARY INVESTIGATION OF SUBMERGED AQUATIC VEGETATION MAPPING USING HYPERSPECTRAL REMOTE SENSING

EPA Science Inventory

The use of airborne hyperspectral remote sensing imagery for automated mapping of submersed aquatic vegetation in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery, together with in-situ spectral refl...

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...

Monitoring forest dynamics with multi-scale and time series imagery.

PubMed

Huang, Chunbo; Zhou, Zhixiang; Wang, Di; Dian, Yuanyong

2016-05-01

To learn the forest dynamics and evaluate the ecosystem services of forest effectively, a timely acquisition of spatial and quantitative information of forestland is very necessary. Here, a new method was proposed for mapping forest cover changes by combining multi-scale satellite remote-sensing imagery with time series data. Using time series Normalized Difference Vegetation Index products derived from the Moderate Resolution Imaging Spectroradiometer images (MODIS-NDVI) and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as data source, a hierarchy stepwise analysis from coarse scale to fine scale was developed for detecting the forest change area. At the coarse scale, MODIS-NDVI data with 1-km resolution were used to detect the changes in land cover types and a land cover change map was constructed using NDVI values at vegetation growing seasons. At the fine scale, based on the results at the coarse scale, Landsat TM/ETM+ data with 30-m resolution were used to precisely detect the forest change location and forest change trend by analyzing time series forest vegetation indices (IFZ). The method was tested using the data for Hubei Province, China. The MODIS-NDVI data from 2001 to 2012 were used to detect the land cover changes, and the overall accuracy was 94.02 % at the coarse scale. At the fine scale, the available TM/ETM+ images at vegetation growing seasons between 2001 and 2012 were used to locate and verify forest changes in the Three Gorges Reservoir Area, and the overall accuracy was 94.53 %. The accuracy of the two layer hierarchical monitoring results indicated that the multi-scale monitoring method is feasible and reliable.

The identification of selected vegetation types in Arizona through the photointerpretation of intermediate scale aerial photography. M.S. Thesis

NASA Technical Reports Server (NTRS)

Ross, G. F. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Nine photography interpretation tests were performed with a total of 19 different interpreters. Three tests were conducted with black and white intermediate scale photography and six tests with color infrared intermediate scale photography. The black and white test results show that the interpretation of vegetation mapped at the association level of classification is reliable for all the classes used at 61%. The color infrared tests indicate that the association level of mapping is unsatisfactory for vegetation interpretation of classes 1 and 6. Students' t-test indicated that intermediate scale black and white photography is significantly better than this particular color infrared photography for the interpretation of southeastern Arizona vegetation mapped at the association level.

Identification, definition and mapping of terrestrial ecosystems in interior Alaska

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Two new, as yet unfinished vegetation maps are presented. These tend further to substantiate the belief that ERTS-1 imagery is a valuable mapping tool. Newly selected scenes show that vegetation interpretations can be refined through use of non-growing season imagery, particularly through the different spectral characteristics of vegetation lacking foliage and through the effect of vegetation structure on apparent snow cover. Scenes now are available for all test area north of the Alaska Range except Mt. McKinley National Park. No support was obtained for the hypothesis that similar interband ratios, from two areas apparently different spectrally because of different sun angles, would indicate similar surface features. However, attempts to test this hypothesis have so far been casual.

Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data

USGS Publications Warehouse

Kokaly, Raymond F.; Despain, Don G.; Clark, Roger N.; Livo, K. Eric

2003-01-01

Knowledge of the distribution of vegetation on the landscape can be used to investigate ecosystem functioning. The sizes and movements of animal populations can be linked to resources provided by different plant species. This paper demonstrates the application of imaging spectroscopy to the study of vegetation in Yellowstone National Park (Yellowstone) using spectral feature analysis of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data, acquired on August 7, 1996, were calibrated to surface reflectance using a radiative transfer model and field reflectance measurements of a ground calibration site. A spectral library of canopy reflectance signatures was created by averaging pixels of the calibrated AVIRIS data over areas of known forest and nonforest vegetation cover types in Yellowstone. Using continuum removal and least squares fitting algorithms in the US Geological Survey's Tetracorder expert system, the distributions of these vegetation types were determined by comparing the absorption features of vegetation in the spectral library with the spectra from the AVIRIS data. The 0.68 μm chlorophyll absorption feature and leaf water absorption features, centered near 0.98 and 1.20 μm, were analyzed. Nonforest cover types of sagebrush, grasslands, willows, sedges, and other wetland vegetation were mapped in the Lamar Valley of Yellowstone. Conifer cover types of lodgepole pine, whitebark pine, Douglas fir, and mixed Engelmann spruce/subalpine fir forests were spectrally discriminated and their distributions mapped in the AVIRIS images. In the Mount Washburn area of Yellowstone, a comparison of the AVIRIS map of forest cover types to a map derived from air photos resulted in an overall agreement of 74.1% (kappa statistic=0.62).

Efficiency of the spectral-spatial classification of hyperspectral imaging data

NASA Astrophysics Data System (ADS)

Borzov, S. M.; Potaturkin, O. I.

2017-01-01

The efficiency of methods of the spectral-spatial classification of similarly looking types of vegetation on the basis of hyperspectral data of remote sensing of the Earth, which take into account local neighborhoods of analyzed image pixels, is experimentally studied. Algorithms that involve spatial pre-processing of the raw data and post-processing of pixel-based spectral classification maps are considered. Results obtained both for a large-size hyperspectral image and for its test fragment with different methods of training set construction are reported. The classification accuracy in all cases is estimated through comparisons of ground-truth data and classification maps formed by using the compared methods. The reasons for the differences in these estimates are discussed.

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.

Application of thematic mapper-type data over a porphyry-molybdenum deposit in Colorado

NASA Technical Reports Server (NTRS)

Rickman, D. L.; Sadowski, R. M.

1983-01-01

The objective of the study was to evaluate the utility of thematic mapper data as a source of geologically useful information for mountainous areas of varying vegetation density. Much of the processing was done in an a priori manner without prior ground-based information. This approach resulted in a successfull mapping of the alteration associated with the Mt. Emmons molybdenum ore body as well as several other hydrothermal systems. Supervised classification produced a vegetation map at least as accurate as the mapping done for the environmental impact statement. Principal components were used to map zones of general, subtle alteration and to separate hematitically stained rock from staining associated with hydrothermal activity. Decorrelation color composites were found to be useful field mapping aids, easily delineating many lithologies and vegetation classes of interest. The factors restricting the interpretability and computer manipulation of the data are examined.

Mapping the Potential for Eolian Surface Activity in Grasslands of the High Plains using Landsat Images

NASA Technical Reports Server (NTRS)

Gutmann, Ethan Dain

2002-01-01

There are over 100,000 square kilometers of eolian sand dunes and sand sheets in the High Plains of the central United States. These land-forms may be unstable and may reactivate again as a result of land-use, climate change, or natural climatic variability. The main goal of this thesis was to develop a model that could be used to map an estimate of future dune activity. Multi-temporal calibrated Landsats 5 Thematic Mapper (TM) and 7 Enhanced Thematic Map per Plus (ETM+) NDVI imagery were used in conjunction with the CENTURY vegetation model to correlate vegetation cover to climatic variability. This allows the creation of a predicted vegetation map which, combined with current wind and soil data, was used to create a potential sand transport map for range land in the High Plains under drought conditions.

Investigation of environmental change pattern in Japan

NASA Technical Reports Server (NTRS)

Maruyasu, T. (Principal Investigator)

1977-01-01

The author has identified the following significant results. In the Plains of Tokachi, where the scale of agricultural field was comparatively large in Japan, LANDSAT data with its accuracy have proved to be useful enough to observe the actual condition of agricultural land use and changes more accurately than present methods. Species and ages of grasses in pasture were identified and soils were classified into several types. The actual land cover and ecological environment were remarkably changeable at the rapidly industrialized area by the urbanization in the flat plane and also by the forest works and road construction in the mountainous area. The practical use of the recognition results was proved as the base map of the field survey or the retouching work of the vegetation and land use. There was a 10% cut in cost, labor, and time. Vegetation cover in Tokyo districts was estimated by both the multiregression model and the parametric model. Multicorrelation coefficient between observed value and estimated value was 0.87 and standard deviation was + or - 15%. Vegetation cover in Tokyo was mapped into five levels with equal intervals of 20%.

Hydrological model uncertainty due to spatial evapotranspiration estimation methods

NASA Astrophysics Data System (ADS)

Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub

2016-05-01

Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.

A DECADE OF MAPPING SUBMERGED AQUATIC VEGETATION USING COLOR INFRARED AERIAL PHOTOGRAPHY: METHODS USED AND LESSONS LEARNED

EPA Science Inventory

Annual color infrared aerial photographs acquired annually between 1997 and 2007 were used to classify distributions of intertidal and shallow subtidal native eelgrass Zostera marina and non-indigenous dwarf eelgrass Z. japonica in lower Yaquina estuary, Oregon. The use of digit...

A GUIDE TO MAPPING INTERTIDAL EELGRASS AND NONVEGETATED HABITATS IN ESTUARIES OF THE PACIFIC NORTHWEST USA

EPA Science Inventory

This document provides technical guidance for planning and implementing the production of aerial photomaps of intertidal vegetative habitats in coastal estuaries of the Pacific Northwest USA (PNW). The focus is on methods of documenting the intertidal distribution of the seagras...

REGIONAL ASSESSMENT OF LANDSCAPE AND DEMOGRAPHIC CHANGE EFFECTS IN THE MEDITERRANEAN REGION, THE MOROCCO CASE STUDY (1981-2003)

EPA Science Inventory

The method presented here allows mapping changes in vegetation cover trends over large areas quickly and inexpensively, thus providing policy-makers with a technical capacity to locate and assess areas of environmental instability and improve their ability to positively respond o...

The comparative evaluation of ERTS-1 imagery for resource inventory in land use planning. [Oregon

NASA Technical Reports Server (NTRS)

Simonson, G. H. (Principal Investigator); Paine, D. P.; Lawrence, R. D.; Pyott, W. T.; Herzog, J. H.; Murray, R. J.; Norgren, J. A.; Cornwell, J. A.; Rogers, R. A.

1973-01-01

The author has identified the following significant results. Multidiscipline team interpretation and mapping of resources for Crook County is nearly complete on 1:250,000 scale enlargements of ERTS-1 imagery. Maps of geology, landforms, soils and vegetation-land use are being interpreted to show limitations, suitabilities and geologic hazards for land use planning. Mapping of lineaments and structures from ERTS-1 imagery has shown a number of features not previously mapped in Oregon. A timber inventory of Ochoco National Forest has been made. Inventory of forest clear-cutting practices has been successfully demonstrated with ERTS-1 color composites. Soil tonal differences in fallow fields shown on ERTS-1 correspond with major soil boundaries in loess-mantled terrain. A digital classification system used for discriminating natural vegetation and geologic materials classes has been successful in separation of most major classes around Newberry Cauldera, Mt. Washington and Big Summit Prairie. Computer routines are available for correction of scanner data variations; and for matching scales and coordinates between digital and photographic imagery. Methods of Diazo film color printing of computer classifications and elevation-slope perspective plots with computer are being developed.

Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Laporte, Nadine; Netanyahuy, Nathan S.; Zukor, Dorothy (Technical Monitor)

2001-01-01

The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.

The spatial pattern and dominant drivers of woody cover change in Latin America and Caribbean from 2001 to 2010

NASA Astrophysics Data System (ADS)

Clark, M.; Aide, T.; Riner, G.; Redo, D.; Grau, H.; Bonilla-Moheno, M.; Lopez-Carr, D.; Levy, M.

2011-12-01

Change in woody vegetation (i.e., forests, shrublands) is a major component of global environmental change: it directly affects biodiversity, the global carbon budget, and ecosystem function. For several decades, remote sensing technology has been used to document deforestation in Latin America and the Caribbean (LAC), although mostly at local to regional scales (e.g., moist forests of the Amazon basin). Most studies have focused on forest loss, some local-scale studies have mapped forest recovery, with contrasting forest dynamics attributed to shifting demographic and socio-economic factors. For example, local population change (rural-urban migration) can stimulate forest recovery on abandoned land, while increasing global food demand may drive regional expansion of mechanized agriculture. However, there are no studies in LAC that simultaneously map both loss and gain in woody vegetation at continental, national, and municipality scales with consistent data sources, methods and accuracy; and thus, we lack a comprehensive assessment of the spatial distribution of woody vegetation change and the relative importance of the multi-scale drivers of this change. We overcame this limitation by producing annual land-cover maps between 2001 and 2010 for each of the >16,000 municipalities in LAC. We focused on mapping municipality-scale trends in three broad classes: woody vegetation, mixed woody/plantations, and agriculture/herbaceous vegetation. Our area estimates show that woody vegetation change during the past decade was dominated by deforestation, or loss (-541,830 km2), particularly in the Amazon basin moist forest and the tropical-subtropical Cerrado and Chaco ecoregions, where large swaths of forest have been transformed to pastures and agricultural lands. Extensive areas (362,431 km2) in LAC also gained woody vegetation, particularly in regions too dry or too steep for modern agriculture, including the desert/xeric shrub biome in NE Brazil and northern Mexico, the conifer forest and tropical dry forest biomes in Central America, and Andean montane areas. We used Random Forests regression, a non-linear and non-parametric analytical technique, as a means to assess the relative importance of demographic and environmental variables in explaining trends in woody vegetation at the municipality scale. We found no association between population change and woody vegetation change at this scale, suggesting that global demand for food (e.g., soybean production for export to China) is a more important driver of deforestation than local population change. Our results emphasize that both loss and gain (i.e., deforestation and reforestation) need to be addressed in a research framework that links multiple spatial scales of land change with global drivers of change.

Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

USGS Publications Warehouse

West, Amanda M.; Evangelista, Paul H.; Jarnevich, Catherine S.; Young, Nicholas E.; Stohlgren, Thomas J.; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-01-01

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM).

PubMed

West, Amanda M; Evangelista, Paul H; Jarnevich, Catherine S; Young, Nicholas E; Stohlgren, Thomas J; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-10-11

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Mapping the spectral variability in photosynthetic and non-photosynthetic vegetation, soils, and shade using AVIRIS

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Smith, Milton O.; Sabol, Donald E.; Adams, John B.; Ustin, Susan L.

1992-01-01

The primary objective of this research was to map as many spectrally distinct types of green vegetation (GV), non-photosynthetic vegetation (NPV), shade, and soil (endmembers) in an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scene as is warranted by the spectral variability of the data. Once determined, a secondary objective was to interpret these endmembers and their abundances spatially and spectrally in an ecological context.

Agriculture/forestry hydrology

NASA Technical Reports Server (NTRS)

Vanderoord, W. J. (Principal Investigator)

1977-01-01

The author has identified the following significant results. The main vegetation units of the lower Mekong basin and the land development conditions were mapped by interpretation of LANDSAT 1 data. By interpretation of various shades of gray on satellite images, it was possible to map the density of the vegetation cover. Study of seasonal variations makes it possible to distinguish between mainly deciduous forests. In the Mekong basin area, these are generally related to the vegetation cover density.

Mapping the response of riparian vegetation to possible flow reductions in the Snake River, Idaho

USGS Publications Warehouse

Johnson, W. Carter; Dixon, Mark D.; Simons, Robert W.; Jenson, Susan; Larson, Kevin

1995-01-01

This study was initiated to determine the general effects of potential flow reductions in the middle Snake River (Swan Falls Dam downstream to the Idaho-Oregon border) on its riparian vegetation. Considerable water from the river is currently used to irrigate the adjacent Snake River Plain, and increased demand for water in the future is likely. The problem was subdivided into several research components including: field investigation of the existing riparian vegetation and river environment, hydrological modeling to calculate the effects of one flow scenario on hydrological regime, and integration of vegetation and hydrological modeling results with a Geographic Information System (GIs) to map the riverbed, island, and bank conditions under the scenario flow. Field work was conducted in summer 1990. Riparian vegetation along 40 U.S. Geological Survey cross-sections was sampled at approximately 1.25 mile intervals within the 50 mile long study area. Cross-section and flow data were provided by the U.S. Geological. Survey. GIs mapping of land/water cover using ARC/INFO was based on 1987 aerial photographs. Riverbed contour maps were produced by linking cross-section data, topographic contouring software (anudem), and GIs. The maps were used to spatially display shallow areas in the channel likely to become vegetated under reduced flow conditions. The scenario would reduce flow by approximately 20% (160 MAF) and lower the river an average of 0.5 ft. The scenario flow could cause a drop in the elevation of the riparian zone comparable to the drop in mean river level and expansion of the lower riparian zone into shallow areas of the channel. The GIs maps showed that the shallow areas of the channel more likely to become vegetated under the scenario flow are located in wide reaches near islands. Some possible ecological consequences of the scenario flow include a greater area of riparian habitat, reduced flow velocity and sedimentation in shallow channels leading to channel deactivation, increased island visitation and nest predation by predatory mammals due to loss of a water barrier between some islands and banks, and larger populations of alien plant species in the new riparian vegetation.

Polinsar Experiments of Multi-Mode X-Band Data Over South Area of China

NASA Astrophysics Data System (ADS)

Lu, L.; Yan, Q.; Duan, M.; Zhang, Y.

2012-08-01

This paper makes the polarimetric and polarimetric interferometric synthetic aperture radar (PolInSAR) experiments with the high-resolution X-band data acquired by Multi-mode airborne SAR system over an area around Linshui, south of China containing tropic vegetation and urban areas. Polarimetric analysis for typical tropic vegetations and man-made objects are presented, some polarimetric descriptors sensitive to vegetations and man-made objects are selected. Then, the PolInSAR information contained in the data is investigated, considering characteristics of the Multi-mode-XSAR dataset, a dual-baseline polarimetric interferometry method is proposed in this paper. The method both guarantees the high coherence on fully polarimetric data and combines the benefits of short and long baseline that helpful to the phase unwrapping and height sensitivity promotion. PolInSAR experiment results displayed demonstrates Multi-mode-XSAR datasets have intuitive capabilities for amount of application of land classification, objects detection and DSM mapping.

FORUM: A Suggestion for an Improved Vegetation Scheme for Local and Global Mapping and Monitoring.

PubMed

ADAMS

1999-01-01

/ Understanding of global ecological problems is at least partly dependent on clear assessments of vegetation change, and such assessment is always dependent on the use of a vegetation classification scheme. Use of satellite remotely sensed data is the only practical means of carrying out any global-scale vegetation mapping exercise, but if the resulting maps are to be useful to most ecologists and conservationists, they must be closely tied to clearly defined features of vegetation on the ground. Furthermore, much of the mapping that does take place involves more local-scale description of field sites; for purposes of cost and practicality, such studies usually do not involve remote sensing using satellites. There is a need for a single scheme that integrates the smallest to the largest scale in a way that is meaningful to most environmental scientists. Existing schemes are unsatisfactory for this task; they are ambiguous, unnecessarily complex, and their categories do not correspond to common-sense definitions. In response to these problems, a simple structural-physiognomically based scheme with 23 fundamental categories is proposed here for mapping and monitoring on any scale, from local to global. The fundamental categories each subdivide into more specific structural categories for more detailed mapping, but all the categories can be used throughout the world and at any scale, allowing intercomparison between regions. The next stage in the process will be to obtain the views of as many people working in as many different fields as possible, to see whether the proposed scheme suits their needs and how it should be modified. With a few modifications, such a scheme could easily be appended to an existing land cover classification scheme, such as the FAO system, greatly increasing the usefulness and accessability of the results of the landcover classification. KEY WORDS: Vegetation scheme; Mapping; Monitoring; Land cover

Development of a high-resolution binational vegetation map of the Santa Cruz River riparian corridor and surrounding watershed, southern Arizona and northern Sonora, Mexico

USGS Publications Warehouse

Wallace, Cynthia S.A.; Villarreal, Miguel L.; Norman, Laura M.

2011-01-01

This report summarizes the development of a binational vegetation map developed for the Santa Cruz Watershed, which straddles the southern border of Arizona and the northern border of Sonora, Mexico. The map was created as an environmental input to the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM) that is being created by the U.S. Geological Survey for the watershed. The SCWEPM is a map-based multicriteria evaluation tool that allows stakeholders to explore tradeoffs between valued ecosystem services at multiple scales within a participatory decision-making process. Maps related to vegetation type and are needed for use in modeling wildlife habitat and other ecosystem services. Although detailed vegetation maps existed for the U.S. side of the border, there was a lack of consistent data for the Santa Cruz Watershed in Mexico. We produced a binational vegetation classification of the Santa Cruz River riparian habitat and watershed vegetation based on NatureServe Terrestrial Ecological Systems (TES) units using Classification And Regression Tree (CART) modeling. Environmental layers used as predictor data were derived from a seasonal set of Landsat Thematic Mapper (TM) images (spring, summer, and fall) and from a 30-meter digital-elevation-model (DEM) grid. Because both sources of environmental data are seamless across the international border, they are particularly suited to this binational modeling effort. Training data were compiled from existing field data for the riparian corridor and data collected by the NM-GAP (New Mexico Gap Analysis Project) team for the original Southwest Regional Gap Analysis Project (SWReGAP) modeling effort. Additional training data were collected from core areas of the SWReGAP classification itself, allowing the extrapolation of the SWReGAP mapping into the Mexican portion of the watershed without collecting additional training data.

Vegetation mapping of Nowitna National Wildlife Reguge, Alaska using Landsat MSS digital data

USGS Publications Warehouse

Talbot, S. S.; Markon, Carl J.

1986-01-01

A Landsat-derived vegetation map was prepared for Nowitna National Wildlife Refuge. The refuge lies within the middle boreal subzone of north central Alaska. Seven major vegetation classes and sixteen subclasses were recognized: forest (closed needleleaf, open needleleaf, needleleaf woodland, mixed, and broadleaf); broadleaf scrub (lowland, alluvial, subalpine); dwarf scrub (prostrate dwarf shrub tundra, dwarf shrub-graminoid tussock peatland); herbaceous (graminoid bog, marsh and meadow); scarcely vegetated areas (scarcely vegetated scree and floodplain); water (clear, turbid); and other areas (mountain shadow). The methodology employed a cluster-block technique. Sample areas were described based on a combination of helicopter-ground survey, aerial photointerpretation, and digital Landsat data. Major steps in the Landsat analysis involved preprocessing (geometric correction), derivation of statistical parameters for spectral classes, spectral class labeling of sample areas, preliminary classification of the entire study area using a maximum-likelihood algorithm, and final classification utilizing ancillary information such as digital elevation data. The final product is a 1:250,000-scale vegetation map representative of distinctive regional patterns and suitable for use in comprehensive conservation planning.

Satellite detection of vegetative damage and alteration caused by pollutants emitted by a zinc smelter

NASA Technical Reports Server (NTRS)

Mcmurtry, G. J.; Petersen, G. W. (Principal Investigator); Fritz, E. L.; Pennypacker, S. P.

1974-01-01

The author has identified the following significant results. Field observations and data collected by low flying aircraft were used to verify the accuracy of maps produced from the satellite data. Although areas of vegetation as small as six acres can accurately be detected, a white pine stand that was severely damaged by sulfur dioxide could not be differentiated from a healthy white pine stand because spectral differences were not large enough. When winter data were used to eliminate interference from herbaceous and deciduous vegetation, the damage was still undetectable. The analysis was able to produce a character map that accurately delineated areas of vegetative alteration due to high zinc levels accumulating in the soil. The map depicted a distinct gradient of less damage and alteration as the distance from the smelter increased. Although the satellite data will probably not be useful for detecting small acreages of damaged vegetation, it is concluded that the data may be very useful as an inventory tool to detect and delineate large vegetative areas possessing differing spectral signatures.

A technique for the determination of Louisiana marsh salinity zone from vegetation mapped by multispectral scanner data: A comparison of satellite and aircraft data

NASA Technical Reports Server (NTRS)

Butera, M. K.

1977-01-01

Vegetation in selected study areas on the Louisiana coast was mapped using low altitude aircraft and satellite (LANDSAT) multispectral scanner data. Fresh, brackish, and saline marshes were then determined from the remotely sensed presence of dominant indicator plant associations. Such vegetational classifications were achieved from data processed through a standard pattern recognition computer program. The marsh salinity zone maps from the aircraft and satellite data compared favorably within the broad salinity regimes. The salinity zone boundaries determined by remote sensing compared favorably with those interpolated from line-transect field observations from an earlier year.

Testing Methods for Challenging the National Wetland Plant List: Using Tsuga canadensis (L.) Carr. (Eastern Hemlock) as a Case Study

DTIC Science & Technology

2017-07-01

ESRI (Nature Conservancy and Environmental Systems Research Institute). 1994. Field Methods . In Field Methods for Vegetation Mapping: United States...ER D C/ CR RE L TR -1 7- 9 Wetlands Regulatory Assistance Program (WRAP) Testing Methods for Challenging the National Wetland Plant List...Robert W. Lichvar and Jennifer J. Goulet July 2017 Approved for public release; distribution is unlimited. The U.S. Army Engineer Research

Assessment of Atmospheric Algorithms to Retrieve Vegetation in Natural Protected Areas Using Multispectral High Resolution Imagery

PubMed Central

Marcello, Javier; Eugenio, Francisco; Perdomo, Ulises; Medina, Anabella

2016-01-01

The precise mapping of vegetation covers in semi-arid areas is a complex task as this type of environment consists of sparse vegetation mainly composed of small shrubs. The launch of high resolution satellites, with additional spectral bands and the ability to alter the viewing angle, offers a useful technology to focus on this objective. In this context, atmospheric correction is a fundamental step in the pre-processing of such remote sensing imagery and, consequently, different algorithms have been developed for this purpose over the years. They are commonly categorized as imaged-based methods as well as in more advanced physical models based on the radiative transfer theory. Despite the relevance of this topic, a few comparative studies covering several methods have been carried out using high resolution data or which are specifically applied to vegetation covers. In this work, the performance of five representative atmospheric correction algorithms (DOS, QUAC, FLAASH, ATCOR and 6S) has been assessed, using high resolution Worldview-2 imagery and field spectroradiometer data collected simultaneously, with the goal of identifying the most appropriate techniques. The study also included a detailed analysis of the parameterization influence on the final results of the correction, the aerosol model and its optical thickness being important parameters to be properly adjusted. The effects of corrections were studied in vegetation and soil sites belonging to different protected semi-arid ecosystems (high mountain and coastal areas). In summary, the superior performance of model-based algorithms, 6S in particular, has been demonstrated, achieving reflectance estimations very close to the in-situ measurements (RMSE of between 2% and 3%). Finally, an example of the importance of the atmospheric correction in the vegetation estimation in these natural areas is presented, allowing the robust mapping of species and the analysis of multitemporal variations related to the human activity and climate change. PMID:27706064

Assessment of Atmospheric Algorithms to Retrieve Vegetation in Natural Protected Areas Using Multispectral High Resolution Imagery.

PubMed

Marcello, Javier; Eugenio, Francisco; Perdomo, Ulises; Medina, Anabella

2016-09-30

The precise mapping of vegetation covers in semi-arid areas is a complex task as this type of environment consists of sparse vegetation mainly composed of small shrubs. The launch of high resolution satellites, with additional spectral bands and the ability to alter the viewing angle, offers a useful technology to focus on this objective. In this context, atmospheric correction is a fundamental step in the pre-processing of such remote sensing imagery and, consequently, different algorithms have been developed for this purpose over the years. They are commonly categorized as imaged-based methods as well as in more advanced physical models based on the radiative transfer theory. Despite the relevance of this topic, a few comparative studies covering several methods have been carried out using high resolution data or which are specifically applied to vegetation covers. In this work, the performance of five representative atmospheric correction algorithms (DOS, QUAC, FLAASH, ATCOR and 6S) has been assessed, using high resolution Worldview-2 imagery and field spectroradiometer data collected simultaneously, with the goal of identifying the most appropriate techniques. The study also included a detailed analysis of the parameterization influence on the final results of the correction, the aerosol model and its optical thickness being important parameters to be properly adjusted. The effects of corrections were studied in vegetation and soil sites belonging to different protected semi-arid ecosystems (high mountain and coastal areas). In summary, the superior performance of model-based algorithms, 6S in particular, has been demonstrated, achieving reflectance estimations very close to the in-situ measurements (RMSE of between 2% and 3%). Finally, an example of the importance of the atmospheric correction in the vegetation estimation in these natural areas is presented, allowing the robust mapping of species and the analysis of multitemporal variations related to the human activity and climate change.

Environmental Assessments in the Riparian Corridor of the Colorado River Delta

NASA Technical Reports Server (NTRS)

2001-01-01

We will develop remote sensing methods to conduct environmental assessments in the riparian corridor of the Colorado River delta, shared by the United States and Mexico. This important regional ecosystem is dependent upon US water flows, yet the most important wildlife habitats are in Mexico. The delta region is poorly known and difficult to monitor on the ground. We will use ground-validated, aerial and satellite methods to develop accurate vegetation and habitat maps and predictive hydrological and vegetation models of this ecosystem in response to US flood releases. The work products will advance our understanding of water resource issues in dryland climates and provide a specific application tool for a critical binational natural resource area.

A methodology to estimate the future extent of dryland salinity in the southwest of Western Australia.

PubMed

Caccetta, Peter; Dunne, Robert; George, Richard; McFarlane, Don

2010-01-01

In the southwestern agricultural region of Western Australia, the clearing of the original perennial vegetation for annual vegetation-based dryland agriculture has lead to rising saline groundwater levels. This has had effects such as reduced productivity of agricultural land, death of native vegetation, reduced stream water quality and infrastructure damage. These effects have been observed at many locations within the 18 million ha of cleared land. This has lead to efforts to quantify, in a spatially explicit way, the historical and likely future extent of the area affected, with the view to informing management decisions. This study was conducted to determine whether the likely future extent of the area affected by dryland salinity could be estimated by means of developing spatially explicit maps for use in management and planning. We derived catchment-related variables from digital elevation models and perennial vegetation presence/absence maps. We then used these variables to predict the salinity hazard extent by applying a combination of decision tree classification and morphological image processing algorithms. Sufficient objective data such as groundwater depth, its rate of rise, and its concentration of dissolved salts were generally not available, so we used regional expert opinion (derived from the limited existing studies on salinity hazard extent) as training and validation data. We obtained an 87% agreement in the salinity hazard extent estimated by this method compared with the validation data, and conclude that the maps are sufficient for planning. We estimate that the salinity hazard extent is 29.7% of the agricultural land.

Revisions to the 1995 map of ecological subregions that affect users of the southern variant of the Forest Vegetation Simulator

Treesearch

W. Henry McNab; Chad E. Keyser

2011-01-01

The Southern Variant of the Forest Vegetation Simulator utilizes ecological units mapped in 1995 by the Forest Service, U.S. Department of Agriculture, to refine tree growth models for the Southern United States. The 2007 revision of the 1995 map resulted in changes of identification and boundary delineation for some ecoregion units. In this report, we summarize the...

Model of Peatland Vegetation Species using HyMap Image and Machine Learning

NASA Astrophysics Data System (ADS)

Dayuf Jusuf, Muhammad; Danoedoro, Projo; Muljo Sukojo, Bangun; Hartono

2017-12-01

Species Tumih / Parepat (Combretocarpus-rotundatus Mig. Dancer) family Anisophylleaceae and Meranti (Shorea Belangerang, Shorea Teysmanniana Dyer ex Brandis) family Dipterocarpaceae is a group of vegetation species distribution model. Species pioneer is predicted as an indicator of the succession of ecosystem restoration of tropical peatland characteristics and extremely fragile (unique) in the endemic hot spot of Sundaland. Climate change projections and conservation planning are hot topics of current discussion, analysis of alternative approaches and the development of combinations of species projection modelling algorithms through geospatial information systems technology. Approach model to find out the research problem of vegetation level based on the machine learning hybrid method, wavelet and artificial neural networks. Field data are used as a reference collection of natural resource field sample objects and biodiversity assessment. The testing and training ANN data set iterations times 28, achieve a performance value of 0.0867 MSE value is smaller than the ANN training data, above 50%, and spectral accuracy 82.1 %. Identify the location of the sample point position of the Tumih / Parepat vegetation species using HyMap Image is good enough, at least the modelling, design of the species distribution can reach the target in this study. The computation validation rate above 90% proves the calculation can be considered.

Mapping impervious surfaces using object-oriented classification in a semiarid urban region

USDA-ARS?s Scientific Manuscript database

Mapping the expansion of impervious surfaces in urbanizing areas is important for monitoring and understanding the hydrologic impacts of land development. The most common approach using spectral vegetation indices, however, is difficult in arid and semiarid environments where vegetation is sparse an...

ISSUES IN DIGITAL IMAGE PROCESSING OF AERIAL PHOTOGRAPHY FOR MAPPING SUBMERSED AQUATIC VEGETATION

EPA Science Inventory

The paper discusses the numerous issues that needed to be addressed when developing a methodology for mapping Submersed Aquatic Vegetation (SAV) from digital aerial photography. Specifically, we discuss 1) choice of film; 2) consideration of tide and weather constraints; 3) in-s...

Southern Arizona riparian habitat: Spatial distribution and analysis

NASA Technical Reports Server (NTRS)

Lacey, J. R.; Ogden, P. R.; Foster, K. E.

1975-01-01

The objectives of this study were centered around the demonstration of remote sensing as an inventory tool and researching the multiple uses of riparian vegetation. Specific study objectives were to: (1) map riparian vegetation along the Gila River, San Simon Creek, San Pedro River, Pantano Wash, (2) determine the feasibility of automated mapping using LANDSAT-1 computer compatible tapes, (3) locate and summarize existing mpas delineating riparian vegetation, (4) summarize data relevant to Southern Arizona's riparian products and uses, (5) document recent riparian vegetation changes along a selected portion of the San Pedro River, (6) summarize historical changes in composition and distribution of riparian vegetation, and (7) summarize sources of available photography pertinent to Southern Arizona.

Object-Based Classification and Change Detection of Hokkaido, Japan

NASA Astrophysics Data System (ADS)

Park, J. G.; Harada, I.; Kwak, Y.

2016-06-01

Topography and geology are factors to characterize the distribution of natural vegetation. Topographic contour is particularly influential on the living conditions of plants such as soil moisture, sunlight, and windiness. Vegetation associations having similar characteristics are present in locations having similar topographic conditions unless natural disturbances such as landslides and forest fires or artificial disturbances such as deforestation and man-made plantation bring about changes in such conditions. We developed a vegetation map of Japan using an object-based segmentation approach with topographic information (elevation, slope, slope direction) that is closely related to the distribution of vegetation. The results found that the object-based classification is more effective to produce a vegetation map than the pixel-based classification.

Quantifying Fractional Ground Cover on the Climate Sensitive High Plains Using AVIRIS and Landsat TM Data

NASA Technical Reports Server (NTRS)

Warner, Amanda Susan

2002-01-01

The High Plains is an economically important and climatologically sensitive region of the United States and Canada. The High Plains contain 100,000 sq km of Holocene sand dunes and sand sheets that are currently stabilized by natural vegetation. Droughts and the larger threat of global warming are climate phenomena that could cause depletion of natural vegetation and make this region susceptible to sand dune reactivation. This thesis is part of a larger study that is assessing the effect of climate variability on the natural vegetation that covers the High Plains using Landsat 5 and Landsat 7 data. The question this thesis addresses is how can fractional vegetation cover be mapped with the Landsat instruments using linear spectral mixture analysis and to what accuracy. The method discussed in this thesis made use of a high spatial and spectral resolution sensor called AVIRIS (Airborne Visible and Infrared Imaging Spectrometer) and field measurements to test vegetation mapping in three Landsat 7 sub-scenes. Near-simultaneous AVIRIS images near Ft. Morgan, Colorado and near Logan, New Mexico were acquired on July 10, 1999 and September 30, 1999, respectively. The AVIRIS flights preceded Landsat 7 overpasses by approximately one hour. These data provided the opportunity to test spectral mixture algorithms with AVIRIS and to use these data to constrain the multispectral mixed pixels of Landsat 7. The comparisons of mixture analysis between the two instruments showed that AVIRIS endmembers can be used to unmix Landsat 7 data with good estimates of soil cover, and reasonable estimates of non-photosynthetic vegetation and green vegetation. Landsat 7 derived image endmembers correlate with AVIRIS fractions, but the error is relatively large and does not give a precise estimate of cover.

Vegetation map of the greater Denver area, Front Range urban corridor, Colorado

USGS Publications Warehouse

Marr, J.W.; Boyd, W.S.

1979-01-01

Vegetation is one of our most valuable renewable resources; it is the primary producer of organic matter on which all nongreen organisms are dependent for energy, construction materials, aesthetic enjoyment, and other necessities of life. In order to secure the greatest possible returns from the utilization of the different types of vegetation, people need to know what species are present, the ecological processes in which they are involved, and the ways in which they are arranged in the landscape. This vegetation map is designed to help persons in a wide variety of activities to secure that information.

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.

Mapping of the Land Cover Spatiotemporal Characteristics in Northern Russia Caused by Climate Change

NASA Astrophysics Data System (ADS)

Panidi, E.; Tsepelev, V.; Torlopova, N.; Bobkov, A.

2016-06-01

The study is devoted to the investigation of regional climate change in Northern Russia. Due to sparseness of the meteorological observation network in northern regions, we investigate the application capabilities of remotely sensed vegetation cover as indicator of climate change at the regional scale. In previous studies, we identified statistically significant relationship between the increase of surface air temperature and increase of the shrub vegetation productivity. We verified this relationship using ground observation data collected at the meteorological stations and Normalised Difference Vegetation Index (NDVI) data produced from Terra/MODIS satellite imagery. Additionally, we designed the technique of growing seasons separation for detailed investigation of the land cover (shrub cover) dynamics. Growing seasons are the periods when the temperature exceeds +5°C and +10°C. These periods determine the vegetation productivity conditions (i.e., conditions that allow growth of the phytomass). We have discovered that the trend signs for the surface air temperature and NDVI coincide on planes and river floodplains. On the current stage of the study, we are working on the automated mapping technique, which allows to estimate the direction and magnitude of the climate change in Northern Russia. This technique will make it possible to extrapolate identified relationship between land cover and climate onto territories with sparse network of meteorological stations. We have produced the gridded maps of NDVI and NDWI for the test area in European part of Northern Russia covered with the shrub vegetation. Basing on these maps, we may determine the frames of growing seasons for each grid cell. It will help us to obtain gridded maps of the NDVI linear trend for growing seasons on cell-by-cell basis. The trend maps can be used as indicative maps for estimation of the climate change on the studied areas.

Use of radar remote sensing (RADARSAT) to map winter wetland habitat for shorebirds in an agricultural landscape.

PubMed

Taft, Oriane W; Haig, Susan M; Kiilsgaard, Chris

2004-05-01

Many of today's agricultural landscapes once held vast amounts of wetland habitat for waterbirds and other wildlife. Successful restoration of these landscapes relies on access to accurate maps of the wetlands that remain. We used C-band (5.6-cm-wavelength), HH-polarized radar remote sensing (RADARSAT) at a 38 degrees incidence angle (8-m resolution) to map the distribution of winter shorebird (Charadriiformes) habitat on agricultural lands in the Willamette Valley of western Oregon. We acquired imagery on three dates (10 December 1999, 27 January 2000, and 15 March 2000) and simultaneously collected ground reference data to classify radar signatures and evaluate map accuracy of four habitat classes: (1) wet with < or = 50% vegetation (considered optimal shorebird habitat), (2) wet with > 50% vegetation, (3) dry with < or = 50% vegetation, and (4) dry with > 50% vegetation. Overall accuracy varied from 45 to 60% among the three images, but the accuracy of focal class 1 was greater, ranging from 72 to 80%. Class 4 coverage was stable and dominated maps (40% of mapped study area) for all three dates, while coverage of class 3 decreased slightly throughout the study period. Among wet classes, class 1 was most abundant (about 30% coverage) in December and January, decreasing in March to approximately 15%. Conversely, class 2 increased dramatically from January to March, likely due to transition from class 1 as vegetation grew. This approach was successful in detecting optimal habitat for shorebirds on agricultural lands. For modest classification schemes, radar remote sensing is a valuable option for wetland mapping in areas where cloud cover is persistent.

Estimation of Monthly Near Surface Air Temperature Using Geographically Weighted Regression in China

NASA Astrophysics Data System (ADS)

Wang, M. M.; He, G. J.; Zhang, Z. M.; Zhang, Z. J.; Liu, X. G.

2018-04-01

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environment conditions. The availability of NSAT with high spatial resolution is deemed necessary for several applications such as hydrology, meteorology and ecology. In this study, a regression-based NSAT mapping method is proposed. This method is combined remote sensing variables with geographical variables, and uses geographically weighted regression to estimate NSAT. The altitude was selected as geographical variable; and the remote sensing variables include land surface temperature (LST) and Normalized Difference vegetation index (NDVI). The performance of the proposed method was assessed by predict monthly minimum, mean, and maximum NSAT from point station measurements in China, a domain with a large area, complex topography, and highly variable station density, and the NSAT maps were validated against the meteorology observations. Validation results with meteorological data show the proposed method achieved an accuracy of 1.58 °C. It is concluded that the proposed method for mapping NSAT is very operational and has good precision.

Trend analysis of vegetation in Louisiana's Atchafalaya river basin

USGS Publications Warehouse

O'Neil, Calvin P.; deSteiguer, J. Edward; North, Gary W.

1978-01-01

The purpose of the study was to determine vegetation succession trends; produce a current vegetation map of the basin; and to develop a mathematical model capable of predicting vegetation changes based on hydrologic factors. A statistical relationship of forests and hydrological variables with forest succession constraints predicted forest acreage totals for 16 forest categories within 70% or better of actual values in two-thirds of the cases. Using time-lapsed photography covering 42 years, 23 categories were described. The succession trend of vegetation since 1930, by sedimentation, had been toward mixed hardwoods, except for isolated areas. Satellite MSS Band 7 imagery was used to map the current vegetation into three main categories and for assessment of acreage. Additionally, a geological anomaly was recognized on satellite imagery indication an effect on drainage and sedimentation.

Alaska Interim Land Cover Mapping Program; final report

USGS Publications Warehouse

Fitzpatrick-Lins, Katherine; Doughty, E.F.; Shasby, Mark; Benjamin, Susan

1989-01-01

In 1985, the U.S. Geological Survey initiated a research project to develop an interim land cover data base for Alaska as an alternative to the nationwide Land Use and Land Cover Mapping Program. The Alaska Interim Land Cover Mapping Program was subsequently created to develop methods for producing a series of land cover maps that utilized the existing Landsat digital land cover classifications produced by and for the major land management agencies for mapping the vegetation of Alaska. The program was successful in producing digital land cover classifications and statistical summaries using a common statewide classification and in reformatting these data to produce l:250,000-scale quadrangle-based maps directly from the Scitex laser plotter. A Federal and State agency review of these products found considerable user support for the maps. Presently the Geological Survey is committed to digital processing of six to eight quadrangles each year.

Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland mapping and assessment

USDA-ARS?s Scientific Manuscript database

Ultra high resolution digital aerial photography has great potential to complement or replace ground measurements of vegetation cover for rangeland monitoring and assessment. We investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern U.S. arid rangelands...

Regional landscape ecosystems of Michigan, Minnesota and Wisconsin: a working map and classification.

Treesearch

Dennis A. Albert

1995-01-01

Describes the landscape ecosystems (ecoregions) of Michigan, Minnesota, and Wisconsin and includes maps of all three states. Regional descriptions include climate, bedrock geology, landforms, lakes and streams, soils, presettlement vegetation, natural disturbance, present vegetation and land use, rare biota, natural areas, public land managers, and conservation...

Using high-resolution digital aerial imagery to map land cover

USGS Publications Warehouse

Dieck, J.J.; Robinson, Larry

2014-01-01

The Upper Midwest Environmental Sciences Center (UMESC) has used aerial photography to map land cover/land use on federally owned and managed lands for over 20 years. Until recently, that process used 23- by 23-centimeter (9- by 9-inch) analog aerial photos to classify vegetation along the Upper Mississippi River System, on National Wildlife Refuges, and in National Parks. With digital aerial cameras becoming more common and offering distinct advantages over analog film, UMESC transitioned to an entirely digital mapping process in 2009. Though not without challenges, this method has proven to be much more accurate and efficient when compared to the analog process.

An expanded map of vegetation communities at Big Muddy National Fish and Wildlife Refuge

USGS Publications Warehouse

Struckhoff, Matthew A.

2013-01-01

In 2012, a map of vegetation communities on Big Muddy National Fish and Wildlife Refuge was expanded based on interpretation of aerial photographs and field data. National Agricultural Imagery Program aerial photographs were used to identify distinct communities on previously unmapped refuge units and newly acquired parcels. Newly mapped polygons were then visited to adjust map boundaries, classify communities according to the National Vegetation Classification System, and quantify the abundance of dominant species and non-native, invasive species of concern to the refuge and other resource management agencies along the Missouri River. The expanded map now covers 6,136 hectares representing 33 community types, including 6 previously unmapped types. The full map includes 1,113 polygons, of which 627 are new, 21 are updated from the 2009 mapping effort, and 465 are unchanged from 2009. Mortality of primarily cottonwood stems, because of growing-season floods between 2008 and 2011, has reduced foliar cover of woody stems and created more open wooded communities. In herbaceous communities, dominance by herbaceous old fields has increased due to the inclusion of refuge units dominated by lands in recent agricultural production in the expanded map. Wetland community abundance has increased slightly due to recent flooding.

Evolution of regional to global paddy rice mapping methods

NASA Astrophysics Data System (ADS)

Dong, J.; Xiao, X.

2016-12-01

Paddy rice agriculture plays an important role in various environmental issues including food security, water use, climate change, and disease transmission. However, regional and global paddy rice maps are surprisingly scarce and sporadic despite numerous efforts in paddy rice mapping algorithms and applications. In this presentation we would like to review the existing paddy rice mapping methods from the literatures ranging from the 1980s to 2015. In particular, we illustrated the evolution of these paddy rice mapping efforts, looking specifically at the future trajectory of paddy rice mapping methodologies. The biophysical features and growth phases of paddy rice were analyzed first, and feature selections for paddy rice mapping were analyzed from spectral, polarimetric, temporal, spatial, and textural aspects. We sorted out paddy rice mapping algorithms into four categories: 1) Reflectance data and image statistic-based approaches, 2) vegetation index (VI) data and enhanced image statistic-based approaches, 3) VI or RADAR backscatter-based temporal analysis approaches, and 4) phenology-based approaches through remote sensing recognition of key growth phases. The phenology-based approaches using unique features of paddy rice (e.g., transplanting) for mapping have been increasingly used in paddy rice mapping. Based on the literature review, we discussed a series of issues for large scale operational paddy rice mapping.

A Method for Application of Classification Tree Models to Map Aquatic Vegetation Using Remotely Sensed Images from Different Sensors and Dates

PubMed Central

Jiang, Hao; Zhao, Dehua; Cai, Ying; An, Shuqing

2012-01-01

In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT), the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model development came, greatly limiting the application of CT. We have developed an effective normalization method to improve the robustness of CT models when applied to images originating from different sensors and dates. A total of 965 ground-truth samples of aquatic vegetation types were obtained in 2009 and 2010 in Taihu Lake, China. Using relevant spectral indices (SI) as classifiers, we manually developed a stable CT model structure and then applied a standard CT algorithm to obtain quantitative (optimal) thresholds from 2009 ground-truth data and images from Landsat7-ETM+, HJ-1B-CCD, Landsat5-TM and ALOS-AVNIR-2 sensors. Optimal CT thresholds produced average classification accuracies of 78.1%, 84.7% and 74.0% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. However, the optimal CT thresholds for different sensor images differed from each other, with an average relative variation (RV) of 6.40%. We developed and evaluated three new approaches to normalizing the images. The best-performing method (Method of 0.1% index scaling) normalized the SI images using tailored percentages of extreme pixel values. Using the images normalized by Method of 0.1% index scaling, CT models for a particular sensor in which thresholds were replaced by those from the models developed for images originating from other sensors provided average classification accuracies of 76.0%, 82.8% and 68.9% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. Applying the CT models developed for normalized 2009 images to 2010 images resulted in high classification (78.0%–93.3%) and overall (92.0%–93.1%) accuracies. Our results suggest that Method of 0.1% index scaling provides a feasible way to apply CT models directly to images from sensors or time periods that differ from those of the images used to develop the original models.

Mapping invasive species and spectral mixture relationships with neotropical woody formations in southeastern Brazil

NASA Astrophysics Data System (ADS)

Amaral, Cibele H.; Roberts, Dar A.; Almeida, Teodoro I. R.; Souza Filho, Carlos R.

2015-10-01

Biological invasion substantially contributes to the increasing extinction rates of native vegetative species. The remote detection and mapping of invasive species is critical for environmental monitoring. This study aims to assess the performance of a Multiple Endmember Spectral Mixture Analysis (MESMA) applied to imaging spectroscopy data for mapping Dendrocalamus sp. (bamboo) and Pinus elliottii L. (slash pine), which are invasive plant species, in a Brazilian neotropical landscape within the tropical Brazilian savanna biome. The work also investigates the spectral mixture between these exotic species and the native woody formations, including woodland savanna, submontane and alluvial seasonal semideciduous forests (SSF). Visible to Shortwave Infrared (VSWIR) imaging spectroscopy data at one-meter spatial resolution were atmospherically corrected and subset into the different spectral ranges (VIS-NIR1: 530-919 nm; and NIR2-SWIR: 1141-2352 nm). The data were further normalized via continuum removal (CR). Multiple endmember selection methods, including Interactive Endmember Selection (IES), Endmember average root mean square error (EAR), Minimum average spectral angle (MASA) and Count-based (CoB) (collectively called EMC), were employed to create endmember libraries for the targeted vegetation classes. The performance of the MESMA was assessed at the pixel and crown scales. Statistically significant differences (α = 0.05) were observed between overall accuracies that were obtained at various spectral ranges. The infrared region (IR) was critical for detecting the vegetation classes using spectral data. The invasive species endmembers exhibited spectral patterns in the IR that were not observed in the native formations. Bamboo was characterized as having a high green vegetation (GV) fraction, lower non-photosynthetic vegetation (NPV) and a low shade fraction, while pine exhibited higher NPV and shade fractions. The invasive species showed a statistically significant larger number of spectra erroneously assigned to the woodland savanna class versus the alluvial and submontane SSF classes. Consequently, the invasive species tended to be overestimated, especially in the woodland savanna. Bamboo was best classified using the VSWIR(CR) data with the EMC endmember selection method (User's accuracy and Producer's accuracy = 98.11% and 72.22%, respectively). Pine was best classified using NIR2-SWIR(CR) data with the IES selected endmembers (97.06% and 62.26%, respectively). The results obtained during the two-endmember modeling were fully translated into the three-endmember unmixed images. The sub-pixel invasive species abundance analysis showed that MESMA performs well when unmixing at the pixel scale and for mapping invasive species fractions in a complex neotropical environment, at pixel and crown scales with 1-m spatial resolution data.

Correcting the influence of vegetation on surface soil moisture indices by using hyperspectral artificial 3D-canopy models

NASA Astrophysics Data System (ADS)

Spengler, D.; Kuester, T.; Frick, A.; Scheffler, D.; Kaufmann, H.

2013-10-01

Surface soil moisture content is one of the key variables used for many applications especially in hydrology, meteorology and agriculture. Hyperspectral remote sensing provides effective methodologies for mapping soil moisture content over a broad area by different indices such as NSMI [1,2] and SMGM [3]. Both indices can achieve a high accuracy for non-vegetation influenced soil samples, but their accuracy is limited in case of the presence of vegetation. Since, the increase of the vegetation cover leads to non-linear variations of the indices. In this study a new methodology for moisture indices correcting the influence of vegetation is presented consisting of several processing steps. First, hyperspectral reflectance data are classified in terms of crop type and growth stage. Second, based on these parameters 3D plant models from a database used to simulate typical canopy reflectance considering variations in the canopy structure (e.g. plant density and distribution) and the soil moisture content for actual solar illumination and sensor viewing angles. Third, a vegetation correction function is developed, based on the calculated soil moisture indices and vegetation indices of the simulated canopy reflectance data. Finally this function is applied on hyperspectral image data. The method is tested on two hyperspectral image data sets of the AISA DUAL at the test site Fichtwald in Germany. The results show a significant improvements compared to solely use of NSMI index. Up to a vegetation cover of 75 % the correction function minimise the influences of vegetation cover significantly. If the vegetation is denser the method leads to inadequate quality to predict the soil moisture content. In summary it can be said that applying the method on weakly to moderately overgrown with vegetation locations enables a significant improvement in the quantification of soil moisture and thus greatly expands the scope of NSMI.

Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA

USGS Publications Warehouse

Miller, J.D.; Knapp, E.E.; Key, C.H.; Skinner, C.N.; Isbell, C.J.; Creasy, R.M.; Sherlock, J.W.

2009-01-01

Multispectral satellite data have become a common tool used in the mapping of wildland fire effects. Fire severity, defined as the degree to which a site has been altered, is often the variable mapped. The Normalized Burn Ratio (NBR) used in an absolute difference change detection protocol (dNBR), has become the remote sensing method of choice for US Federal land management agencies to map fire severity due to wildland fire. However, absolute differenced vegetation indices are correlated to the pre-fire chlorophyll content of the vegetation occurring within the fire perimeter. Normalizing dNBR to produce a relativized dNBR (RdNBR) removes the biasing effect of the pre-fire condition. Employing RdNBR hypothetically allows creating categorical classifications using the same thresholds for fires occurring in similar vegetation types without acquiring additional calibration field data on each fire. In this paper we tested this hypothesis by developing thresholds on random training datasets, and then comparing accuracies for (1) fires that occurred within the same geographic region as the training dataset and in similar vegetation, and (2) fires from a different geographic region that is climatically and floristically similar to the training dataset region but supports more complex vegetation structure. We additionally compared map accuracies for three measures of fire severity: the composite burn index (CBI), percent change in tree canopy cover, and percent change in tree basal area. User's and producer's accuracies were highest for the most severe categories, ranging from 70.7% to 89.1%. Accuracies of the moderate fire severity category for measures describing effects only to trees (percent change in canopy cover and basal area) indicated that the classifications were generally not much better than random. Accuracies of the moderate category for the CBI classifications were somewhat better, averaging in the 50%-60% range. These results underscore the difficulty in isolating fire effects to individual vegetation strata when fire effects are mixed. We conclude that the models presented here and in Miller and Thode ([Miller, J.D. & Thode, A.E., (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 109, 66-80.]) can produce fire severity classifications (using either CBI, or percent change in canopy cover or basal area) that are of similar accuracy in fires not used in the original calibration process, at least in conifer dominated vegetation types in Mediterranean-climate California.

Uav-Based Detection of Unknown Radioactive Biomass Deposits in Chernobyl's Exclusion Zone

NASA Astrophysics Data System (ADS)

Briechle, S.; Sizov, A.; Tretyak, O.; Antropov, V.; Molitor, N.; Krzystek, P.

2018-05-01

Shortly after the explosion of the Chernobyl nuclear power plant (ChNPP) in 1986, radioactive fall-out and contaminated trees (socalled Red Forest) were buried in the Chernobyl Exclusion Zone (ChEZ). These days, exact locations of the buried contaminated material are needed. Moreover, 3D vegetation maps are necessary to simulate the impact of tornados and forest fire. After 30 years, some of the so-called trenches and clamps are visible. However, some of them are overgrown and have slightly settled in the centimeter and decimeter range. This paper presents a pipeline that comprises 3D vegetation mapping and machine learning methods to precisely map trenches and clamps from remote sensing data. The dataset for our experiments consists of UAV-based LiDAR data, multi-spectral data, and aerial gamma-spectrometry data. Depending on the study areas overall accuracies ranging from 95.6 % to 99.0 % were reached for the classification of radioactive deposits. Our first results demonstrate an accurate and reliable UAV-based detection of unknown radioactive biomass deposits in the ChEZ.

Atmospheric Correction of High-Spatial-Resolution Commercial Satellite Imagery Products Using MODIS Atmospheric Products

NASA Technical Reports Server (NTRS)

Pagnutti, Mary; Holekamp, Kara; Ryan, Robert E.; Vaughan, Ronand; Russell, Jeff; Prados, Don; Stanley, Thomas

2005-01-01

Remotely sensed ground reflectance is the foundation of any interoperability or change detection technique. Satellite intercomparisons and accurate vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), require the generation of accurate reflectance maps (NDVI is used to describe or infer a wide variety of biophysical parameters and is defined in terms of near-infrared (NIR) and red band reflectances). Accurate reflectance-map generation from satellite imagery relies on the removal of solar and satellite geometry and of atmospheric effects and is generally referred to as atmospheric correction. Atmospheric correction of remotely sensed imagery to ground reflectance has been widely applied to a few systems only. The ability to obtain atmospherically corrected imagery and products from various satellites is essential to enable widescale use of remotely sensed, multitemporal imagery for a variety of applications. An atmospheric correction approach derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) that can be applied to high-spatial-resolution satellite imagery under many conditions was evaluated to demonstrate a reliable, effective reflectance map generation method. Additional information is included in the original extended abstract.

Wetland Classification for Black Duck Habitat Management Using Combined Polarimetric RADARSAT 2 and SPOT Imagery

NASA Astrophysics Data System (ADS)

Zhang, W.; Hu, B.; Brown, G.

2018-04-01

The black duck population has decreased significantly due to loss of its breeding habitat. Wetlands are an important feature that relates to habitat management and requires monitoring. Synthetic Aperture Radar (SAR) systems are helpful to map the wetland as the microwave signals are sensitive to water content and can be used to map surface water extent, saturated soils, and flooded vegetation. In this study, RadarSat 2 Polarimetric data is employed to map surface water and track changes in extent over the years through image thresholding and reviewed different approaches of Polarimetric decompositions for detecting flooded vegetation. Also, object-based analysis associated with beaver activity is conducted with combined multispectral SPOT satellite imagery. Results show SAR data has proven ability to improve mapping open water areas and locate flooded vegetation areas.

Hyperspectral remote sensing of vegetation

USGS Publications Warehouse

Thenkabail, Prasad S.; Lyon, John G.; Huete, Alfredo

2011-01-01

Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research.

Estimating Achievable Accuracy for Global Imaging Spectroscopy Measurement of Non-Photosynthetic Vegetation Cover

NASA Astrophysics Data System (ADS)

Dennison, P. E.; Kokaly, R. F.; Daughtry, C. S. T.; Roberts, D. A.; Thompson, D. R.; Chambers, J. Q.; Nagler, P. L.; Okin, G. S.; Scarth, P.

2016-12-01

Terrestrial vegetation is dynamic, expressing seasonal, annual, and long-term changes in response to climate and disturbance. Phenology and disturbance (e.g. drought, insect attack, and wildfire) can result in a transition from photosynthesizing "green" vegetation to non-photosynthetic vegetation (NPV). NPV cover can include dead and senescent vegetation, plant litter, agricultural residues, and non-photosynthesizing stem tissue. NPV cover is poorly captured by conventional remote sensing vegetation indices, but it is readily separable from substrate cover based on spectral absorption features in the shortwave infrared. We will present past research motivating the need for global NPV measurements, establishing that mapping seasonal NPV cover is critical for improving our understanding of ecosystem function and carbon dynamics. We will also present new research that helps determine a best achievable accuracy for NPV cover estimation. To test the sensitivity of different NPV cover estimation methods, we simulated satellite imaging spectrometer data using field spectra collected over mixtures of NPV, green vegetation, and soil substrate. We incorporated atmospheric transmittance and modeled sensor noise to create simulated spectra with spectral resolutions ranging from 10 to 30 nm. We applied multiple methods of NPV estimation to the simulated spectra, including spectral indices, spectral feature analysis, multiple endmember spectral mixture analysis, and partial least squares regression, and compared the accuracy and bias of each method. These results prescribe sensor characteristics for an imaging spectrometer mission with NPV measurement capabilities, as well as a "Quantified Earth Science Objective" for global measurement of NPV cover. Copyright 2016, all rights reserved.

Inventory and analysis of natural vegetation and related resources from space and high altitude photography

NASA Technical Reports Server (NTRS)

Poulton, C. E.; Faulkner, D. P.; Johnson, J. R.; Mouat, D. A.; Schrumpf, B. J.

1971-01-01

A high altitude photomosaic resource map of Site 29 was produced which provided an opportunity to test photo interpretation accuracy of natural vegetation resource features when mapped at a small (1:133,400) scale. Helicopter reconnaissance over 144 previously selected test points revealed a highly adequate level of photo interpretation accuracy. In general, the reasons for errors could be accounted for. The same photomosaic resource map enabled construction of interpretive land use overlays. Based on features of the landscape, including natural vegetation types, judgements for land use suitability were made and have been presented for two types of potential land use. These two, agriculture and urbanization, represent potential land use conflicts.

Global Urban Mapping and Modeling for Sustainable Urban Development

NASA Astrophysics Data System (ADS)

Zhou, Y.; Li, X.; Asrar, G.; Yu, S.; Smith, S.; Eom, J.; Imhoff, M. L.

2016-12-01

In the past several decades, the world has experienced fast urbanization, and this trend is expected to continue for decades to come. Urbanization, one of the major land cover and land use changes (LCLUC), is becoming increasingly important in global environmental changes, such as urban heat island (UHI) growth and vegetation phenology change. Better scientific insights and effective decision-making unarguably require reliable science-based information on spatiotemporal changes in urban extent and their environmental impacts. In this study, we developed a globally consistent 20-year urban map series to evaluate the time-reactive nature of global urbanization from the nighttime lights remote sensing data, and projected future urban expansion in the 21st century by employing an integrated modeling framework (Zhou et al. 2014, Zhou et al. 2015). We then evaluated the impacts of urbanization on building energy use and vegetation phenology that affect both ecosystem services and human health. We extended the modeling capability of building energy use in the Global Change Assessment Model (GCAM) with consideration of UHI effects by coupling the remote sensing based urbanization modeling and explored the impact of UHI on building energy use. We also investigated the impact of urbanization on vegetation phenology by using an improved phenology detection algorithm. The derived spatiotemporal information on historical and potential future urbanization and its implications in building energy use and vegetation phenology will be of great value in sustainable urban design and development for building energy use and human health (e.g., pollen allergy), especially when considered together with other factors such as climate variability and change. Zhou, Y., S. J. Smith, C. D. Elvidge, K. Zhao, A. Thomson & M. Imhoff (2014) A cluster-based method to map urban area from DMSP/OLS nightlights. Remote Sensing of Environment, 147, 173-185. Zhou, Y., S. J. Smith, K. Zhao, M. Imhoff, A. Thomson, B. Bond-Lamberty, G. R. Asrar, X. Zhang, C. He & C. D. Elvidge (2015) A global map of urban extent from nightlights. Environmental Research Letters, 10, 054011.

REGIONAL ASSESSMENT OF LANDSCAPE AND DEMOGRAPHIC CHANGE EFFECTS IN THE MEDITERRANEAN REGION, THE MOROCCO CASE STUDY (1981 - 2003)

EPA Science Inventory

The effect of changes in landscape factors on socioeconomics was analyzed

locally and regionally. The method presented here allows mapping changes in vegetation cover

trends over large areas quickly and inexpensively, thus providing policy-makers with a technical

A DECADE OF MAPPING SUBMERGED AQUATIC VEGETATION USING COLOR INFRARED AERIAL PHOTOGRAPHY: METHODS USED AND LESSONS LEARNED - 5-14-2014

EPA Science Inventory

Annual color infrared (CIR) aerial photographs acquired annually between 1997 and 2007 were used to classify distributions of intertidal and shallow subtidal native eelgrass Zostera marina and non-indigenous dwarf eelgrass Z. japonica in lower Yaquina estuary, Oregon. The use of...

Pilot Tests of Satellite Snowcover/Runoff Forecasting Systems. [Arizona, Sierra Nevada Mountains (Ca), Colorado, Rocky Mountains (North America)

NASA Technical Reports Server (NTRS)

Rango, A.

1978-01-01

Major snow zones of the western U.S. were selected to test the capability of satellite systems for mapping snowcover in various snow, cloud, climatic, and vegetation regimes. Different satellite snowcover analysis methods used in each area are described along with results.

Categorical likelihood method for combining NDVI and elevation information for cotton precision agricultural applications

USDA-ARS?s Scientific Manuscript database

This presentation investigates an algorithm to fuse the Normalized Difference Vegetation Index (NDVI) with LiDAR elevation data to produce a map useful for the site-specific scouting and pest management (Willers et al. 1999; 2005; 2009) of the cotton insect pests, the tarnished plant bug (Lygus lin...

The response of vegetation dynamics of the different alpine grassland types to temperature and precipitation on the Tibetan Plateau.

PubMed

Sun, Jian; Qin, Xiaojing; Yang, Jun

2016-01-01

The spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) of three vegetation types (alpine steppe, alpine meadow, and alpine desert steppe) across the Tibetan Plateau was analyzed from 1982 to 2013. In addition, the annual mean temperature (MAT) and annual mean precipitation (MAP) trends were quantified to define the spatiotemporal climate patterns. Meanwhile, the relationships between climate factors and NDVI were analyzed in order to understand the impact of climate change on vegetation dynamics. The results indicate that the maximum of NDVI increased by 0.3 and 0.2 % per 10 years in the entire regions of alpine steppe and alpine meadow, respectively. However, no significant change in the NDVI of the alpine desert steppe has been observed since 1982. A negative relationship between NDVI and MAT was found in all these alpine grassland types, while MAP positively impacted the vegetation dynamics of all grasslands. Also, the effects of temperature and precipitation on different vegetation types differed, and the correlation coefficient for MAP and NDVI in alpine meadow is larger than that for other vegetation types. We also explored the percentages of precipitation and temperature influence on NDVI variation, using redundancy analysis at the observation point scale. The results show that precipitation is a primary limiting factor for alpine vegetation dynamic, rather than temperature. Most importantly, the results can serve as a tool for grassland ecosystem management.

Map the Permafrost and its Affected Soils and Vegetation on the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Zhao, L.; Sheng, Y.; Pang, Q.; Zou, D.; Wang, Z.; Li, W.; Wu, X.; Yue, G.; Fang, H.; Zhao, Y.

2015-12-01

Great amount of literatures had been published to deal with the actual distribution and changes of permafrost on the Tibetan Plateau (TP) on the basis of observed ground temperature dataset along Qinghai-Xizang Highway and/or Railway (QXH/R) during the last several decades. But there is very limited data available in the eastern part of the QXH/R and almost no observation in the western part of QXH/R not only for the observed permafrost data, but also for the dataset on ground surface conditions, such as soil and vegetation, which are used as model parameters, initial variables, or benchmark data sets for calibration, validation, and comparison in various Earth System Models (ESMs). To evaluate the status of permafrost and its environmental conditions, such as the distribution and thermal state of permafrost, soil and vegetation on the TP, detailed investigation on permafrost were conducted in 5 regions with different climatic and geologic conditions over the whole plateau from 2009 to 2013, and more than 100 ground temperatures (GTs) monitoring boreholes were drilled and equipped with thermistors, of which 10 sites were equipped with automatic meteorological stations. Geophysical prospecting methods, such as ground penetrating radar (GPR) and electromagnetic prospecting, were used in the same time to detect the permafrost distribution and thicknesses. The monitoring data revealed that the thermal state of permafrost was well correlated with elevation, and regulated by annual precipitation, local geological, geomorphological and hydrological conditions through heat exchanges between ground and atmosphere. Different models, including GTs statistical model, Common Land Surface Model (CoLM), Noah land surface model and TTOP models, were used to map the permafrost in 5 selected regions and the whole TP, while the investigated and monitored data were used as calibration and validation for all models. Finally, we compiled the permafrost map of the TP, soil and vegetation map within the permafrost regions on the TP. We also compiled the soil organic carbon density map of permafrost affected soils on the TP. An overview on permafrost thickness, GTs, ice content was statistically summarized based on investigation data.

Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index Composites

USGS Publications Warehouse

,

2005-01-01

The Advanced Very High Resolution Radiometer (AVHRR) is a broad-band scanner with four to six bands, depending on the model. The AVHRR senses in the visible, near-, middle-, and thermal- infrared portions of the electromagnetic spectrum. This sensor is carried on a series of National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites (POES), beginning with the Television InfraRed Observation Satellite (TIROS-N) in 1978. Since 1989, the United States Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) has been mapping the vegetation condition of the United States and Alaska using satellite information from the AVHRR sensor. The vegetation condition composites, more commonly called greenness maps, are produced every week using the latest information on the growth and condition of the vegetation. One of the most important aspects of USGS greenness mapping is the historical archive of information dating back to 1989. This historical stretch of information has allowed the USGS to determine a 'normal' vegetation condition. As a result, it is possible to compare the current week's vegetation condition with normal vegetation conditions. An above normal condition could indicate wetter or warmer than normal conditions, while a below normal condition could indicate colder or dryer than normal conditions. The interpretation of departure from normal will depend on the season and geography of a region.

Using airborne LiDAR in geoarchaeological contexts: Assessment of an automatic tool for the detection and the morphometric analysis of grazing archaeological structures (French Massif Central).

NASA Astrophysics Data System (ADS)

Roussel, Erwan; Toumazet, Jean-Pierre; Florez, Marta; Vautier, Franck; Dousteyssier, Bertrand

2014-05-01

Airborne laser scanning (ALS) of archaeological regions of interest is nowadays a widely used and established method for accurate topographic and microtopographic survey. The penetration of the vegetation cover by the laser beam allows the reconstruction of reliable digital terrain models (DTM) of forested areas where traditional prospection methods are inefficient, time-consuming and non-exhaustive. The ALS technology provides the opportunity to discover new archaeological features hidden by vegetation and provides a comprehensive survey of cultural heritage sites within their environmental context. However, the post-processing of LiDAR points clouds produces a huge quantity of data in which relevant archaeological features are not easily detectable with common visualizing and analysing tools. Undoubtedly, there is an urgent need for automation of structures detection and morphometric extraction techniques, especially for the "archaeological desert" in densely forested areas. This presentation deals with the development of automatic detection procedures applied to archaeological structures located in the French Massif Central, in the western forested part of the Puy-de-Dôme volcano between 950 and 1100 m a.s.l.. These unknown archaeological sites were discovered by the March 2011 ALS mission and display a high density of subcircular depressions with a corridor access. The spatial organization of these depressions vary from isolated to aggregated or aligned features. Functionally, they appear to be former grazing constructions built from the medieval to the modern period. Similar grazing structures are known in other locations of the French Massif Central (Sancy, Artense, Cézallier) where the ground is vegetation-free. In order to develop a reliable process of automatic detection and mapping of these archaeological structures, a learning zone has been delineated within the ALS surveyed area. The grazing features were mapped and typical morphometric attributes were calculated based on 2 methods: (i) The mapping of the archaeological structures by a human operator using common visualisation tools (DTM, multi-direction hillshading & local relief models) within a GIS environment; (ii) The automatic detection and mapping performed by a recognition algorithm based on a user defined geometric pattern of the grazing structures. The efficiency of the automatic tool has been assessed by comparing the number of structures detected and the morphometric attributes calculated by the two methods. Our results indicate that the algorithm is efficient for the detection and the location of grazing structures. Concerning the morphometric results, there is still a discrepancy between automatic and expert calculations, due to both the expert mapping choices and the algorithm calibration.

Development of a Dynamic Web Mapping Service for Vegetation Productivity Using Earth Observation and in situ Sensors in a Sensor Web Based Approach

PubMed Central

Kooistra, Lammert; Bergsma, Aldo; Chuma, Beatus; de Bruin, Sytze

2009-01-01

This paper describes the development of a sensor web based approach which combines earth observation and in situ sensor data to derive typical information offered by a dynamic web mapping service (WMS). A prototype has been developed which provides daily maps of vegetation productivity for the Netherlands with a spatial resolution of 250 m. Daily available MODIS surface reflectance products and meteorological parameters obtained through a Sensor Observation Service (SOS) were used as input for a vegetation productivity model. This paper presents the vegetation productivity model, the sensor data sources and the implementation of the automated processing facility. Finally, an evaluation is made of the opportunities and limitations of sensor web based approaches for the development of web services which combine both satellite and in situ sensor sources. PMID:22574019

Utilization of LANDSAT imagery for mapping vegetation on the millionth scale

NASA Technical Reports Server (NTRS)

Williams, D. L.; Coiner, J. C.

1975-01-01

A series of test sites were examined to determine if the information content of the LANDSAT imagery that may be obtained of these sites is sufficient to permit their mapping according to the vegetation classification system recently published by Unesco. These sites include examples from the humid tropics, arid and semi-arid subtropics and temperature zones: Western Highlands of Papua New Guinea, Mindoro Island in the Philippines, Great Smoky Mountains of the southeastern United States, East Tennessee Valley, interior of Western Australia, northeastern Uganda, and south-central Kansas. The results of the experiment were presented in the form of vegetation maps and annotated images which serve to illustrate the detectability of various formations. It was concluded that, for the test sites examined, the formations of the Unesco vegetation classification can be satisfactorily distinguished on LANDSAT MSS images, especially when used as color composites and judiciously chosen as to season.

Object-based landslide mapping on satellite images from different sensors

NASA Astrophysics Data System (ADS)

Hölbling, Daniel; Friedl, Barbara; Eisank, Clemens; Blaschke, Thomas

2015-04-01

Several studies have proven that object-based image analysis (OBIA) is a suitable approach for landslide mapping using remote sensing data. Mostly, optical satellite images are utilized in combination with digital elevation models (DEMs) for semi-automated mapping. The ability of considering spectral, spatial, morphometric and contextual features in OBIA constitutes a significant advantage over pixel-based methods, especially when analysing non-uniform natural phenomena such as landslides. However, many of the existing knowledge-based OBIA approaches for landslide mapping are rather complex and are tailored to specific data sets. These restraints lead to a lack of transferability of OBIA mapping routines. The objective of this study is to develop an object-based approach for landslide mapping that is robust against changing input data with different resolutions, i.e. optical satellite imagery from various sensors. Two study sites in Taiwan were selected for developing and testing the landslide mapping approach. One site is located around the Baolai village in the Huaguoshan catchment in the southern-central part of the island, the other one is a sub-area of the Taimali watershed in Taitung County near the south-eastern Pacific coast. Both areas are regularly affected by severe landslides and debris flows. A range of very high resolution (VHR) optical satellite images was used for the object-based mapping of landslides and for testing the transferability across different sensors and resolutions: (I) SPOT-5, (II) Formosat-2, (III) QuickBird, and (IV) WorldView-2. Additionally, a digital elevation model (DEM) with 5 m spatial resolution and its derived products (e.g. slope, plan curvature) were used for supporting the semi-automated mapping, particularly for differentiating source areas and accumulation areas according to their morphometric characteristics. A focus was put on the identification of comparatively stable parameters (e.g. relative indices), which could be transferred to the different satellite images. The presence of bare ground was assumed to be an evidence for the occurrence of landslides. For separating vegetated from non-vegetated areas the Normalized Difference Vegetation Index (NDVI) was primarily used. Each image was divided into two respective parts based on an automatically calculated NDVI threshold value in eCognition (Trimble) software by combining the homogeneity criterion of multiresolution segmentation and histogram-based methods, so that heterogeneity is increased to a maximum. Expert knowledge models, which depict features and thresholds that are usually used by experts for digital landslide mapping, were considered for refining the classification. The results were compared to the respective results from visual image interpretation (i.e. manually digitized reference polygons for each image), which were produced by an independent local expert. By that, the spatial overlaps as well as under- and over-estimated areas were identified and the performance of the approach in relation to each sensor was evaluated. The presented method can complement traditional manual mapping efforts. Moreover, it contributes to current developments for increasing the transferability of semi-automated OBIA approaches and for improving the efficiency of change detection approaches across multi-sensor imagery.

Advances in Remote Sensing for Vegetation Dynamics and Agricultural Management

NASA Technical Reports Server (NTRS)

Tucker, Compton; Puma, Michael

2015-01-01

Spaceborne remote sensing has led to great advances in the global monitoring of vegetation. For example, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group has developed widely used datasets from the Advanced Very High Resolution Radiometer (AVHRR) sensors as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) map imagery and normalized difference vegetation index datasets. These data are valuable for analyzing vegetation trends and variability at the regional and global levels. Numerous studies have investigated such trends and variability for both natural vegetation (e.g., re-greening of the Sahel, shifts in the Eurasian boreal forest, Amazonian drought sensitivity) and crops (e.g., impacts of extremes on agricultural production). Here, a critical overview is presented on recent developments and opportunities in the use of remote sensing for monitoring vegetation and crop dynamics.

Use of SIR-A and Landsat MSS data in mapping shrub and intershrub vegetation at Koonamore, South Australia

NASA Technical Reports Server (NTRS)

Green, G. M.

1986-01-01

Shrublands cover much of the interior of the Australian continent and support a large grazing industry. Distinguishing the woody perennial vegetation from the smaller herbaceous vegetation and soil-encrusting lichen found between the shrubs is critical for range management but is difficult to do using Landsat data alone. In this study Shuttle Imaging Radar-A (SIR-A) and Landsat data acquired over Koonamore Station are examined together. Given the low topography and fine textured soils at Koonamore, radar return should be primarily determined by the percent area occupied by shrubs. During periods when most of the vegetation was non-vigorous and spectrally homogeneous, SIR-A data, as a surrogate measure of shrub cover, allowed the reflectance due to shrubs in Landsat data to be separated from the reflectance due to the intervening ground. This method allows estimation of the intershrub reflectance properties that are related to herbaceous vegetation, lichen, and bare soil exposures.

Estimating Vegetation Structure in African Savannas using High Spatial Resolution Imagery

NASA Astrophysics Data System (ADS)

Axelsson, C.; Hanan, N. P.

2016-12-01

High spatial resolution satellite imagery allows for detailed mapping of trees in savanna landscapes, including estimates of woody cover, tree densities, crown sizes, and the spatial pattern of trees. By linking these vegetation parameters to rainfall and soil properties we gain knowledge of how the local environment influences vegetation. A thorough understanding of the underlying ecosystem processes is key to assessing the future productivity and stability of these ecosystems. In this study, we have processed and analyzed hundreds of sites sampled from African savannas across a wide range of rainfall and soil conditions. The vegetation at each site is classified using unsupervised classification with manual assignment into woody, herbaceous and bare cover classes. A crown delineation method further divides the woody areas into individual tree crowns. The results show that rainfall, soil, and topography interactively influence vegetation structure. We see that both total rainfall and rainfall seasonality play important roles and that soil type influences woody cover and the sizes of tree crowns.

Analysis of Decadal Vegetation Dynamics Using Multi-Scale Satellite Images

NASA Astrophysics Data System (ADS)

Chiang, Y.; Chen, K.

2013-12-01

This study aims at quantifying vegetation fractional cover (VFC) by incorporating multi-resolution satellite images, including Formosat-2(RSI), SPOT(HRV/HRG), Landsat (MSS/TM) and Terra/Aqua(MODIS), to investigate long-term and seasonal vegetation dynamics in Taiwan. We used 40-year NDVI records for derivation of VFC, with field campaigns routinely conducted to calibrate the critical NDVI threshold. Given different sensor capabilities in terms of their spatial and spectral properties, translation and infusion of NDVIs was used to assure NDVI coherence and to determine the fraction of vegetation cover at different spatio-temporal scales. Based on the proposed method, a bimodal sequence of intra-annual VFC which corresponds to the dual-cropping agriculture pattern was observed. Compared to seasonal VFC variation (78~90%), decadal VFC reveals moderate oscillations (81~86%), which were strongly linked with landuse changes and several major disturbances. This time-series mapping of VFC can be used to examine vegetation dynamics and its response associated with short-term and long-term anthropogenic/natural events.

Use of FIA plot data in the LANDFIRE project

Treesearch

Chris Toney; Matthew Rollins; Karen Short; Tracey Frescino; Ronald Tymcio; Birgit Peterson

2007-01-01

LANDFIRE is an interagency project that will generate consistent maps and data describing vegetation, fire, and fuel characteristics across the United States within a 5-year timeframe. Modeling and mapping in LANDFIRE depend extensively on a large database of georeferenced field measurements describing vegetation, site characteristics, and fuel. The LANDFIRE Reference...

Mapping of submerged vegetation using remote sensing technology

NASA Technical Reports Server (NTRS)

Savastano, K. J.; Faller, K. H.; Mcfadin, L. W.; Holley, H.

1981-01-01

Techniques for mapping submerged sea grasses using aircraft supported remote sensors are described. The 21 channel solid state array spectroradiometer was successfully used as a remote sensor in the experiment in that the system operated without problem and obtained data. The environmental conditions of clear water, bright sandy bottom and monospecific vegetation (Thalassia) were ideal.

Description and validation of an automated methodology for mapping mineralogy, vegetation, and hydrothermal alteration type from ASTER satellite imagery with examples from the San Juan Mountains, Colorado

USGS Publications Warehouse

Rockwell, Barnaby W.

2012-01-01

The efficacy of airborne spectroscopic, or "hyperspectral," remote sensing for geoenvironmental watershed evaluations and deposit-scale mapping of exposed mineral deposits has been demonstrated. However, the acquisition, processing, and analysis of such airborne data at regional and national scales can be time and cost prohibitive. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor carried by the NASA Earth Observing System Terra satellite was designed for mineral mapping and the acquired data can be efficiently used to generate uniform mineral maps over very large areas. Multispectral remote sensing data acquired by the ASTER sensor were analyzed to identify and map minerals, mineral groups, hydrothermal alteration types, and vegetation groups in the western San Juan Mountains, Colorado, including the Silverton and Lake City calderas. This mapping was performed in support of multidisciplinary studies involving the predictive modeling of surface water geochemistry at watershed and regional scales. Detailed maps of minerals, vegetation groups, and water were produced from an ASTER scene using spectroscopic, expert system-based analysis techniques which have been previously described. New methodologies are presented for the modeling of hydrothermal alteration type based on the Boolean combination of the detailed mineral maps, and for the entirely automated mapping of alteration types, mineral groups, and green vegetation. Results of these methodologies are compared with the more detailed maps and with previously published mineral mapping results derived from analysis of high-resolution spectroscopic data acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. Such comparisons are also presented for other mineralized and (or) altered areas including the Goldfield and Cuprite mining districts, Nevada and the central Marysvale volcanic field, Wah Wah Mountains, and San Francisco Mountains, Utah. The automated mineral group mapping products described in this study are ideal for application to mineral resource and mineral-environmental assessments at regional and national scales.

Intermediate-scale vegetation mapping of Innoko National Wildlife Refuge, Alaska using Landsat MSS digital data

USGS Publications Warehouse

Talbot, Stephen S.; Markon, Carl J.

1988-01-01

A Landsat-derived vegetation map was prepared for lnnoko National Wildlife Refuge. The refuge lies within the northern boreal subzone of northwestern central Alaska. Six major vegetation classes and 21 subclasses were recognized: forest (closed needleleaf, open needleleaf, needleleaf woodland, mixed, and broadleaf); broadleaf scrub (lowland, upland burn regeneration, subalpine); dwarf scrub (prostrate dwarf shrub tundra, erect dwarf shrub heath, dwarf shrub-graminoid peatland, dwarf shrub-graminoid tussock peatland, dwarf shrub raised bog with scattered trees, dwarf shrub-graminoid marsh); herbaceous (graminoid bog, graminoid marsh, graminoid tussock-dwarf shrub peatland); scarcely vegetated areas (scarcely vegetated scree and floodplain); and water (clear, sedimented). The methodology employed a cluster-block technique. Sample areas were described based on a combination of helicopter-ground survey, aerial photo-interpretation, and digital Landsat data. Major steps in the Landsat analysis involved preprocessing (geometric correction), derivation of statistical parameters for spectral classes, spectral class labeling of sample areas, preliminary classification of the entire study area using a maximum-likelihood algorithm, and final classification utilizing ancillary information such as digital elevation data. The final product is 1:250,000-scale vegetation map representative of distinctive regional patterns and suitable for use in comprehensive conservation planning.

Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2015-01-01

Accurately estimating aboveground vegetation biomass productivity is essential for local ecosystem assessment and best land management practice. Satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. A 250-m grassland biomass productivity map for the Greater Platte River Basin had been developed based on the relationship between Moderate Resolution Imaging Spectroradiometer (MODIS) GSN and Soil Survey Geographic (SSURGO) annual grassland productivity. However, the 250-m MODIS grassland biomass productivity map does not capture detailed ecological features (or patterns) and may result in only generalized estimation of the regional total productivity. Developing a high or moderate spatial resolution (e.g., 30-m) productivity map to better understand the regional detailed vegetation condition and ecosystem services is preferred. The 30-m Landsat data provide spatial detail for characterizing human-scale processes and have been successfully used for land cover and land change studies. The main goal of this study is to develop a 30-m grassland biomass productivity estimation map for central Nebraska, leveraging 250-m MODIS GSN and 30-m Landsat data. A rule-based piecewise regression GSN model based on MODIS and Landsat (r = 0.91) was developed, and a 30-m MODIS equivalent GSN map was generated. Finally, a 30-m grassland biomass productivity estimation map, which provides spatially detailed ecological features and conditions for central Nebraska, was produced. The resulting 30-m grassland productivity map was generally supported by the SSURGO biomass production map and will be useful for regional ecosystem study and local land management practices.

Age discrimination among eruptives of Menengai Caldera, Kenya, using vegetation parameters from satellite imagery

NASA Technical Reports Server (NTRS)

Blodget, Herbert W.; Heirtzler, James R.

1993-01-01

Results are presented of an investigation to determine the degree to which digitally processed Landsat TM imagery can be used to discriminate among vegetated lava flows of different ages in the Menengai Caldera, Kenya. A selective series of five images, consisting of a color-coded Landsat 5 classification and four color composites, are compared with geologic maps. The most recent of more than 70 postcaldera flows within the caldera are trachytes, which are variably covered by shrubs and subsidiary grasses. Soil development evolves as a function of time, and as such supports a changing plant community. Progressively older flows exhibit the increasing dominance of grasses over bushes. The Landsat images correlated well with geologic maps, but the two mapped age classes could be further subdivided on the basis of different vegetation communities. It is concluded that field maps can be modified, and in some cases corrected by use of such imagery, and that digitally enhanced Landsat imagery can be a useful aid to field mapping in similar terrains.

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.

Detection of anomalous crop condition and soil variability mapping using a 26 year Landsat record and the Palmer crop moisture index

NASA Astrophysics Data System (ADS)

Venteris, E. R.; Tagestad, J. D.; Downs, J. L.; Murray, C. J.

2015-07-01

Cost-effective and reliable vegetation monitoring methods are needed for applications ranging from traditional agronomic mapping, to verifying the safety of geologic injection activities. A particular challenge is defining baseline crop conditions and subsequent anomalies from long term imagery records (Landsat) in the face of large spatiotemporal variability. We develop a new method for defining baseline crop response (near peak growth) using the normalized difference vegetation index (NDVI) from 26 years (1986-2011) of Landsat data for 400 km2 surrounding a planned geologic carbon sequestration site near Jacksonville, Illinois. The normal score transform (yNDVI) was applied on a field by field basis to accentuate spatial patterns and level differences due to planting times. We tested crop type and soil moisture (Palmer crop moisture index (CMI)) as predictors of expected crop condition. Spatial patterns in yNDVI were similar between corn and soybeans - the two major crops. Linear regressions between yNDVI and the cumulative CMI (CCMI) exposed complex interactions between crop condition, field location (topography and soils), and annual moisture. Wet toposequence positions (depressions) were negatively correlated to CCMI and dry positions (crests) positively correlated. However, only 21% of the landscape showed a statistically significant (p < 0.05) linear relationship. To map anomalous crop conditions, we defined a tolerance interval based on yNDVI statistics. Tested on an independent image (2013), 63 of 1483 possible fields showed unusual crop condition. While the method is not directly suitable for crop health assessment, the spatial patterns in correlation between yNDVI and CCMI have potential applications for pest damage detection and edaphological soil mapping, especially in the developing world.

Using Bi-Seasonal WorldView-2 Multi-Spectral Data and Supervised Random Forest Classification to Map Coastal Plant Communities in Everglades National Park.

PubMed

Wendelberger, Kristie S; Gann, Daniel; Richards, Jennifer H

2018-03-09

Coastal plant communities are being transformed or lost because of sea level rise (SLR) and land-use change. In conjunction with SLR, the Florida Everglades ecosystem has undergone large-scale drainage and restoration, altering coastal vegetation throughout south Florida. To understand how coastal plant communities are changing over time, accurate mapping techniques are needed that can define plant communities at a fine-enough resolution to detect fine-scale changes. We explored using bi-seasonal versus single-season WorldView-2 satellite data to map three mangrove and four adjacent plant communities, including the buttonwood/glycophyte community that harbors the federally-endangered plant Chromolaena frustrata . Bi-seasonal data were more effective than single-season to differentiate all communities of interest. Bi-seasonal data combined with Light Detection and Ranging (LiDAR) elevation data were used to map coastal plant communities of a coastal stretch within Everglades National Park (ENP). Overall map accuracy was 86%. Black and red mangroves were the dominant communities and covered 50% of the study site. All the remaining communities had ≤10% cover, including the buttonwood/glycophyte community. ENP harbors 21 rare coastal species threatened by SLR. The spatially explicit, quantitative data provided by our map provides a fine-scale baseline for monitoring future change in these species' habitats. Our results also offer a method to monitor vegetation change in other threatened habitats.

Using Bi-Seasonal WorldView-2 Multi-Spectral Data and Supervised Random Forest Classification to Map Coastal Plant Communities in Everglades National Park

PubMed Central

Richards, Jennifer H.

2018-01-01

Coastal plant communities are being transformed or lost because of sea level rise (SLR) and land-use change. In conjunction with SLR, the Florida Everglades ecosystem has undergone large-scale drainage and restoration, altering coastal vegetation throughout south Florida. To understand how coastal plant communities are changing over time, accurate mapping techniques are needed that can define plant communities at a fine-enough resolution to detect fine-scale changes. We explored using bi-seasonal versus single-season WorldView-2 satellite data to map three mangrove and four adjacent plant communities, including the buttonwood/glycophyte community that harbors the federally-endangered plant Chromolaena frustrata. Bi-seasonal data were more effective than single-season to differentiate all communities of interest. Bi-seasonal data combined with Light Detection and Ranging (LiDAR) elevation data were used to map coastal plant communities of a coastal stretch within Everglades National Park (ENP). Overall map accuracy was 86%. Black and red mangroves were the dominant communities and covered 50% of the study site. All the remaining communities had ≤10% cover, including the buttonwood/glycophyte community. ENP harbors 21 rare coastal species threatened by SLR. The spatially explicit, quantitative data provided by our map provides a fine-scale baseline for monitoring future change in these species’ habitats. Our results also offer a method to monitor vegetation change in other threatened habitats. PMID:29522476

Characterizing Exterior and Interior Tropical Forest Structure Variability with Full-Waveform Airborne LIDAR Data in Lopé, Gabon

NASA Astrophysics Data System (ADS)

Marselis, S.; Tang, H.; Blair, J. B.; Hofton, M. A.; Armston, J.; Dubayah, R.

2017-12-01

Terrestrial ecotones, transition zones between ecological systems, have been identified as important regions to monitor the effects of environmental and human pressures on ecosystems. To observe such changes, the variability in vegetation horizontal and vertical structure must be characterized. The objective of this study is to quantify changes in vegetation structure in a tropical forest-savanna mosaic using airborne waveform lidar data. The study area is located in the northern part of the Lopé National Park in Gabon and is comprised of the vegetation types: savanna, colonizing forest, monodominant Okoumé forest, young Marantaceae forest and mixed Marantaceae forest. The lidar data were collected by the Land Vegetation and Ice Sensor (LVIS) in early March 2016, during the AfriSAR campaign. Metrics derived from the LVIS waveforms were then used to classify the five main vegetation types and characterize observed structural variability within types and across ecotones. Several supervised and unsupervised classification alogrithms, in combination with statistical analysis, were applied. The investigated methods are promising in their use to directly describe the structural variability within and between different vegetation types, map these vegetation types and the extent and location of their transition zones, and to characterize, among other attributes, the sharpness and width of such ecotones. These results provide important information in ecosystem studies as these methods can be used to study changes in vegetation structure, species-specific habitat, or the effects of deforestation and other human and natural pressures on the exterior and interior forest structure. These methods thus provide ample opportunity to assess the vegetation structure in degraded and second growth tropical forests to explore effects of e.g. grazing, logging or fragmentation. From this study we can conclude that lidar waveform remote sensing is highly useful in distinguishing vegetation types and their transition zones which will be increasingly important when assessing the impact of natural and human pressures on the world's tropical forests.

High-Resolution Urban Greenery Mapping for Micro-Climate Modelling Based on 3d City Models

NASA Astrophysics Data System (ADS)

Hofierka, J.; Gallay, M.; Kaňuk, J.; Šupinský, J.; Šašak, J.

2017-10-01

Urban greenery has various positive micro-climate effects including mitigation of heat islands. The primary root of heat islands in cities is in absorption of solar radiation by the mass of building structures, roads and other solid materials. The absorbed heat is subsequently re-radiated into the surroundings and increases ambient temperatures. The vegetation can stop and absorb most of incoming solar radiation mostly via the photosynthesis and evapotranspiration process. However, vegetation in mild climate of Europe manifests considerable annual seasonality which can also contribute to the seasonal change in the cooling effect of the vegetation on the urban climate. Modern methods of high-resolution mapping and new generations of sensors have brought opportunity to record the dynamics of urban greenery in a high resolution in spatial, spectral, and temporal domains. In this paper, we use the case study of the city of Košice in Eastern Slovakia to demonstrate the methodology of 3D mapping and modelling the urban greenery during one vegetation season in 2016. The purpose of this monitoring is to capture 3D effects of urban greenery on spatial distribution of solar radiation in urban environment. Terrestrial laser scanning was conducted on four selected sites within Košice in ultra-high spatial resolution. The entire study area, which included these four smaller sites, comprised 4 km2 of the central part of the city was flown within a single airborne lidar and photogrammetric mission to capture the upper parts of buildings and vegetation. The acquired airborne data were used to generate a 3D city model and the time series of terrestrial lidar data were integrated with the 3D city model. The results show that the terrestrial and airborne laser scanning techniques can be effectively used to monitor seasonal changes in foliage of trees in order to assess the transmissivity of the canopy for microclimate modelling.

The Importance of Temporal and Spatial Vegetation Structure Information in Biotope Mapping Schemes: A Case Study in Helsingborg, Sweden

NASA Astrophysics Data System (ADS)

Gao, Tian; Qiu, Ling; Hammer, Mårten; Gunnarsson, Allan

2012-02-01

Temporal and spatial vegetation structure has impact on biodiversity qualities. Yet, current schemes of biotope mapping do only to a limited extend incorporate these factors in the mapping. The purpose of this study is to evaluate the application of a modified biotope mapping scheme that includes temporal and spatial vegetation structure. A refined scheme was developed based on a biotope classification, and applied to a green structure system in Helsingborg city in southern Sweden. It includes four parameters of vegetation structure: continuity of forest cover, age of dominant trees, horizontal structure, and vertical structure. The major green structure sites were determined by interpretation of panchromatic aerial photographs assisted with a field survey. A set of biotope maps was constructed on the basis of each level of modified classification. An evaluation of the scheme included two aspects in particular: comparison of species richness between long-continuity and short-continuity forests based on identification of woodland continuity using ancient woodland indicators (AWI) species and related historical documents, and spatial distribution of animals in the green space in relation to vegetation structure. The results indicate that (1) the relationship between forest continuity: according to verification of historical documents, the richness of AWI species was higher in long-continuity forests; Simpson's diversity was significantly different between long- and short-continuity forests; the total species richness and Shannon's diversity were much higher in long-continuity forests shown a very significant difference. (2) The spatial vegetation structure and age of stands influence the richness and abundance of the avian fauna and rabbits, and distance to the nearest tree and shrub was a strong determinant of presence for these animal groups. It is concluded that continuity of forest cover, age of dominant trees, horizontal and vertical structures of vegetation should now be included in urban biotope classifications.

High spatial resolution mapping of the Cerrado's land cover and land use types in the priority area for conservation Chapada da Contagem, Brazil.

NASA Astrophysics Data System (ADS)

Ribeiro, F.; Roberts, D. A.; Davis, F. W.; Antunes Daldegan, G.; Nackoney, J.; Hess, L. L.

2016-12-01

The Brazilian savanna, Cerrado, is the second largest biome over South America and the most floristically diverse savanna in the world. This biome is considered a conservation hotspot in respect to its biodiversity importance and rapid transformation of its landscape. The Cerrado's natural vegetation has been severely transformed by agriculture and pasture activities. Currently it is the main agricultural frontier in Brazil and one of the most threatened Brazilian biomes. This scenario results in environmental impacts such as ecosystems fragmentation as well as losses in connectivity, biodiversity and gene flow, changes in the microclimate and energy, carbon and nutrients cycles, among others. The Priority Areas for Conservation is a governmental program from Brazil that identifies areas with high conservation priority. One of this program's recommendation is a natural vegetation map including their major ecosystem classes. This study aims to generate more precise information for the Cerrado's vegetation. The main objective of this study is to identify which ecosystems are being prioritized and/or threatened by land use, refining information for further protection. In order to test methods, the priority area for conservation Chapada da Contagem was selected as the study site. This area is ranked as "extremely high priority" by the government and is located in the Federal District and Goias State, Brazil. Satellites with finer spatial resolution may improve the classification of the Cerrado's vegetation. Remote sensing methods and two criteria were tested using RapidEye 3A imagery (5m spatial resolution) collected in 2014 in order to classify the Cerrado's major land cover types of this area, as well as its land use. One criterion considers the Cerrado's major terrestrial ecosystems, which are divided into forest, savanna and grassland. The other involves scaling it down to the major physiognomic groups of each ecosystem. Other sources of environmental dataset such as soil type and slope were incorporated into this test as they are correlated with the ecosystems and physiognomies presence. A Decision Tree was used to map the land cover and land use types present in the region and demonstrated to have an effective result due to the map's high accuracy and incorporation of environmental dataset.

A study to explore the use of orbital remote sensing to determine native arid plant distribution. [Arizona Regional Ecological Test Site

NASA Technical Reports Server (NTRS)

Mcginnies, W. G. (Principal Investigator); Lepley, L. K.; Haase, E. F.; Conn, J. S.; Musick, H. B.; Foster, K. E.

1974-01-01

The author has identified the following significant results. It is possible to determine, from ERTS imagery, native arid plant distribution. Using techniques of multispectral masking and extensive fieldwork, three native vegetation communities were defined and mapped in the Avra Valley study area. A map was made of the Yuma area with the aid of ground truth correlations between areas of desert pavement visible on ERTS images and unique vegetation types. With the exception of the Yuma soil-vegetation correlation phenomena, only very gross differentiations of desert vegetation communities can be made from ERTS data. Vegetation communities with obvious vegetation density differences such as saguaro-paloverde, creosote bush, and riparian vegetation can be separated on the Avra Valley imagery while more similar communities such as creosote bush and saltbush could not be differentiated. It is suggested that large differences in vegetation density are needed before the signatures of two different vegetation types can be differentiated on ERTS imagery. This is due to the relatively insignificant contribution of vegetation to the total radiometric signature of a given desert scene. Where more detailed information concerning the vegetation of arid regions is required, large scale imagery is appropriate.

Quantification of soil mapping by digital analysis of LANDSAT data. [Clinton County, Indiana

NASA Technical Reports Server (NTRS)

Kirschner, F. R.; Kaminsky, S. A.; Hinzel, E. J.; Sinclair, H. R.; Weismiller, R. A.

1977-01-01

Soil survey mapping units are designed such that the dominant soil represents the major proportion of the unit. At times, soil mapping delineations do not adequately represent conditions as stated in the mapping unit descriptions. Digital analysis of LANDSAT multispectral scanner (MSS) data provides a means of accurately describing and quantifying soil mapping unit composition. Digital analysis of LANDSAT MSS data collected on 9 June 1973 was used to prepare a spectral soil map for a 430-hectare area in Clinton County, Indiana. Fifteen spectral classes were defined, representing 12 soil and 3 vegetation classes. The 12 soil classes were grouped into 4 moisture regimes based upon their spectral responses; the 3 vegetation classes were grouped into one all-inclusive class.

The landslide susceptibility mapping and assessment with ZY satellite data

NASA Astrophysics Data System (ADS)

Zhang, R.; Zhang, Z.; Zhao, Y.

2012-12-01

Natural hazards can result in enormous property damage and casualties in mountainous regions. In China, the direct loss of hazards is about 400 million yuan in 2011. Especially the landslide, the most common natural hazards, got the wide attention of each country. Landslide susceptibility mapping is of great importance for landslide hazard mitigation efforts throughout the world. In Southwest Hubei, there are much mineral mining activities, which may trigger the landslide. In addition the Three Gorges reservoir is located in this area, and the storage changed the geological and hydrological environment, which may increase the frequency of the ancient landslide reactivation, and the new landslide occurrence. There are more than 200 landslide hazards happened since 2003. So producing a regional-scaled landslide susceptibility map is necessary. For the above purpose, the landslide susceptibility mapping was produced by using the ZY-3 and ZY-1-02C satellite data, the DEMs and the conventional topographic data.(1) The DEM derivatives slope gradient, the slope aspect and the topographic wetness index (TWI) ; (2) in order to acquire the spatially continuous vegetation information, Normalized Difference Vegetation Index (NDVI) was computed using ZY-1-02C and ZY-3; (3) the regional lithologic information (i.e. mineral distribution) and the tectonic information obtained from remote sensing data in combination with regional geological survey; (4) the regional hydrogeological information was produced by using the remote sensing data in combination with the DEMs; (5) the existed landslides information obtained from remote sensing. To model the landslide hazard assessment using variety of statistic methods and evaluation methods, the cross application model yields reasonable results which can be applied for preliminary landslide hazard mapping and the hazard grade division.

[Estimating heavy metal concentrations in topsoil from vegetation reflectance spectra of Hyperion images: A case study of Yushu County, Qinghai, China.

PubMed

Yang, Ling Yu; Gao, Xiao Hong; Zhang, Wei; Shi, Fei Fei; He, Lin Hua; Jia, Wei

2016-06-01

In this study, we explored the feasibility of estimating the soil heavy metal concentrations using the hyperspectral satellite image. The concentration of As, Pb, Zn and Cd elements in 48 topsoil samples collected from the field in Yushu County of the Sanjiangyuan regions was measured in the laboratory. We then extracted 176 vegetation spectral reflectance bands of 48 soil samples as well as five vegetation indices from two Hyperion images. Following that, the partial least squares regression (PLSR) method was employed to estimate the soil heavy metal concentrations using the above two independent sets of Hyperion-derived variables, separately constructed the estimation model between the 176 vegetation spectral reflectance bands and the soil heavy metal concentrations (called the vegetation spectral reflectance-based estimation model), and between the five vegetation indices being used as the independent variable and the soil heavy metal concentrations (called synthetic vegetation index-based estimation model). Using RPD (the ratio of standard deviation from the 4 heavy metals measured values of the validation samples to RMSE) as the validation criteria, the RPDs of As and Pb concentrations from the two models were both less than 1.4, which suggested that both models were incapable of roughly estimating As and Pb concentrations; whereas the RPDs of Zn and Cd were 1.53, 1.46 and 1.46, 1.42, respectively, which implied that both models had the ability for rough estimation of Zn and Cd concentrations. Based on those results, the vegetation spectral-based estimation model was selected to obtain the spatial distribution map of Zn concentration in combination with the Hyperion image. The estimated Zn map showed that the zones with high Zn concentrations were distributed near the provincial road 308, national road 214 and towns, which could be influenced by human activities. Our study proved that the spectral reflectance of Hyperion image was useful in estimating the soil concentrations of Zn and Cd.

Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series

PubMed Central

Bajocco, Sofia; Dragoz, Eleni; Gitas, Ioannis; Smiraglia, Daniela; Salvati, Luca; Ricotta, Carlo

2015-01-01

Traditionally fuel maps are built in terms of ‘fuel types’, thus considering the structural characteristics of vegetation only. The aim of this work is to derive a phenological fuel map based on the functional attributes of coarse-scale vegetation phenology, such as seasonality and productivity. MODIS NDVI 250m images of Sardinia (Italy), a large Mediterranean island with high frequency of fire incidence, were acquired for the period 2000–2012 to construct a mean annual NDVI profile of the vegetation at the pixel-level. Next, the following procedure was used to develop the phenological fuel map: (i) image segmentation on the Fourier components of the NDVI profiles to identify phenologically homogeneous landscape units, (ii) cluster analysis of the phenological units and post-hoc analysis of the fire-proneness of the phenological fuel classes (PFCs) obtained, (iii) environmental characterization (in terms of land cover and climate) of the PFCs. Our results showed the ability of coarse-resolution satellite time-series to characterize the fire-proneness of Sardinia with an adequate level of accuracy. The remotely sensed phenological framework presented may represent a suitable basis for the development of fire distribution prediction models, coarse-scale fuel maps and for various biogeographic studies. PMID:25822505

The vegetation outlook (VegOut): a new method for predicting vegetation seasonal greenness

USGS Publications Warehouse

Tadesse, T.; Wardlow, B.; Hayes, M.; Svoboda, M.; Brown, J.

2010-01-01

The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of "historical patterns" (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate-vegetation interactions and ocean-climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation's response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regionallevel vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing-based approaches that monitor "current" vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions.

Detection and characterizacion of Colombian wetlands using Alos Palsar and MODIS imagery

NASA Astrophysics Data System (ADS)

Estupinan-Suarez, L. M.; Florez-Ayala, C.; Quinones, M. J.; Pacheco, A. M.; Santos, A. C.

2015-04-01

Wetlands regulate the flow of water and play a key role in risk management of extreme flooding and drought. In Colombia, wetland conservation has been a priority for the government. However, there is an information gap neither an inventory nor a national baseline map exists. In this paper, we present a method that combines a wetlands thematic map with remote sensing derived data, and hydrometeorological stations data in order to characterize the Colombian wetlands. Following the adopted definition of wetlands, available spatial data on land forms, soils and vegetation was integrated in order to characterize spatially the occurrence of wetlands. This data was then complemented with remote sensing derived data from active and passive sensors. A flood frequency map derived from dense time series analysis of the ALOS PALSAR FBD /FBS data (2007-2010) at 50m resolution was used to analyse the recurrence of flooding. In this map, flooding under the canopy and open water classes could be mapped due to the capabilities of the L-band radar. In addition, MODIS NDVI profiles (2007-2012) were used to characterize temporally water mirrors and vegetation, founding different patterns at basin levels. Moreover, the Colombian main basins were analysed and typified based on hydroperiods, highlighting different hydrological regimes within each basin. The combination of thematic maps, SAR data, optical imagery and hydrological data provided information on the spatial and temporal dynamics of wetlands at regional scales. Our results provide the first validated baseline wetland map for Colombia, this way providing valuable information for ecosystem management.

Identification, definition and mapping of terrestrial ecosystems in interior Alaska

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1972-01-01

The author has identified the following significant results. A reconstituted color infrared image covering the western Seward Peninsula was used for identifying vegetation types by simple visual examination. The image was taken by ERTS-1 approximately 1120 hours on August 1, 1972. Seven major colors were identified. Four of these were matched with four units on existing vegetation maps: bright red - shrub thicket; light gray-red - upland tundra; medium gray-red - coastal wet tundra; gray - alpine barrens. In the bright red color, two phases, violet and orange, were recognized and tentatively ascribed to differences in species composition in the shrub thicket type. The three colors which had no map unit equivalents were interpreted as follows: pink - grassland tundra; dark gray-red - burn scars; light orange-red - senescent vegetation. It was concluded that the image provides a considerable amount of information regarding the distribution of vegetation types, even at so simple a leval of analysis. It was also concluded that sequential imagery of this type could provide useful information on vegetation fires and phenologic events.

Estimating vegetation vulnerability to detect areas prone to land degradation in the Mediterranean basin

NASA Astrophysics Data System (ADS)

Imbrenda, Vito; Coluzzi, Rosa; D'Emilio, Mariagrazia; Lanfredi, Maria; Simoniello, Tiziana

2013-04-01

Vegetation is one of the key components to study land degradation vulnerability because of the complex interactions and feedbacks that link it to soil. In the Mediterranean region, degradation phenomena are due to a mix of predisposing factors (thin soil horizons, low soil organic matter, increasing aridity, etc.) and bad management practices (overgrazing, deforestation, intensification of agriculture, tourism development). In particular, in areas threatened by degradation processes but still covered by vegetation, large scale soil condition evaluation is a hard task and the detection of stressed vegetation can be useful to identify on-going soil degradation phenomena and to reduce their impacts through interventions for recovery/rehabilitation. In this context the use of satellite time series can increase the efficacy and completeness of the land degradation assessment, providing precious information to understand vegetation dynamics. In order to estimate vulnerability levels in Basilicata (a Mediterranean region of Southern Italy) in the framework of PRO-LAND project (PO-FESR Basilicata 2007-2013), we crossed information on potential vegetation vulnerability with information on photosynthetic activity dynamics. Potential vegetation vulnerability represents the vulnerability related to the type of present cover in terms of fire risk, erosion protection, drought resistance and plant cover distribution. It was derived from an updated land cover map by separately analyzing each factor, and then by combining them to obtain concise information on the possible degradation exposure. The analysis of photosynthetic activity dynamics provides information on the status of vegetation, that is fundamental to discriminate the different vulnerability levels within the same land cover, i.e. the same potential vulnerability. For such a purpose, we analyzed a time series (2000-2010) of a satellite vegetation index (MODIS NDVI) with 250m resolution, available as 16-day composite from the NASA LP DAAC dataset. Vegetation activity trends were estimated and then normalized to the starting conditions to obtain the percentage variation (NDVI-PV) for the considered period. Information on the potential vulnerability and vegetation activity dynamics were classified into indexes and combined to obtain the final map of the actual vegetation vulnerability and to identify on-going degradation phenomena and priority sites within areas already compromised. As for the investigated area, this map shows a composite picture in which only a few values of high vulnerability are scattered along areas where medium-high vulnerability values generally prevail. Here, we singled out two kind of areas: one largely devoted to intensive agriculture, and other one mostly characterized by bare soils and sparse vegetation. On the contrary, a large part of natural and seminatural vegetation located along the Apennine chain does not show critical vulnerability values. By comparing the vegetation vulnerability map with the vulnerability map due to anthropic factors (pressure induced by agricultural and grazing activities, estimated by indicators derived from census data), we found correlation, confirming the anthropogenic cause of vulnerability and therefore the major role held by soil management in areas mainly devoted to intensive farming.

Mapping minerals, amorphous materials, environmental materials, vegetation, water, ice and snow, and other materials: The USGS tricorder algorithm

NASA Technical Reports Server (NTRS)

Clark, Roger N.; Swayze, Gregg A.

1995-01-01

One of the challenges of Imaging Spectroscopy is the identification, mapping and abundance determination of materials, whether mineral, vegetable, or liquid, given enough spectral range, spectral resolution, signal to noise, and spatial resolution. Many materials show diagnostic absorption features in the visual and near infrared region (0.4 to 2.5 micrometers) of the spectrum. This region is covered by the modern imaging spectrometers such as AVIRIS. The challenge is to identify the materials from absorption bands in their spectra, and determine what specific analyses must be done to derive particular parameters of interest, ranging from simply identifying its presence to deriving its abundance, or determining specific chemistry of the material. Recently, a new analysis algorithm was developed that uses a digital spectral library of known materials and a fast, modified-least-squares method of determining if a single spectral feature for a given material is present. Clark et al. made another advance in the mapping algorithm: simultaneously mapping multiple minerals using multiple spectral features. This was done by a modified-least-squares fit of spectral features, from data in a digital spectral library, to corresponding spectral features in the image data. This version has now been superseded by a more comprehensive spectral analysis system called Tricorder.

An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

NASA Astrophysics Data System (ADS)

Yilmaz, Işik; Marschalko, Marian; Bednarik, Martin

2013-04-01

The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.

Bank Erosion Vulnerability Zonation (BEVZ) -A Proposed Method of Preparing Bank Erosion Zonation and Its Application on the River Haora, Tripura, India

NASA Astrophysics Data System (ADS)

Bandyopadhyay, Shreya; de, Sunil Kumar

2014-05-01

In the present paper an attempt has been made to propose RS-GIS based method for erosion vulnerability zonation for the entire river based on simple techniques that requires very less field investigation. This method consist of 8 parameters, such as, rainfall erosivity, lithological factor, bank slope, meander index, river gradient, soil erosivity, vegetation cover and anthropogenic impact. Meteorological data, GSI maps, LISS III (30m resolution), SRTM DEM (56m resolution) and Google Images have been used to determine rainfall erosivity, lithological factor, bank slope, meander index, river gradient, vegetation cover and anthropogenic impact; Soil map of the NBSSLP, India has been used for assessing Soil Erosivity index. By integrating the individual values of those six parameters (the 1st two parameters are remained constant for this particular study area) a bank erosion vulnerability zonation map of the River Haora, Tripura, India (23°37' - 23°53'N and 91°15'-91°37'E) has been prepared. The values have been compared with the existing BEHI-NBS method of 60 spots and also with field data of 30 cross sections (covering the 60 spots) taken along 51 km stretch of the river in Indian Territory and found that the estimated values are matching with the existing method as well as with field data. The whole stretch has been divided into 5 hazard zones, i.e. Very High, High, Moderate, Low and Very Low Hazard Zones and they are covering 5.66 km, 16.81 km, 40.82km, 29.67 km and 9.04 km respectively. KEY WORDS: Bank erosion, Bank Erosion Hazard Index (BEHI), Near Bank Stress (NBS), Erosivity, Bank Erosion Vulnerability Zonation.

Woodland Mapping at Single-Tree Levels Using Object-Oriented Classification of Unmanned Aerial Vehicle (uav) Images

NASA Astrophysics Data System (ADS)

Chenari, A.; Erfanifard, Y.; Dehghani, M.; Pourghasemi, H. R.

2017-09-01

Remotely sensed datasets offer a reliable means to precisely estimate biophysical characteristics of individual species sparsely distributed in open woodlands. Moreover, object-oriented classification has exhibited significant advantages over different classification methods for delineation of tree crowns and recognition of species in various types of ecosystems. However, it still is unclear if this widely-used classification method can have its advantages on unmanned aerial vehicle (UAV) digital images for mapping vegetation cover at single-tree levels. In this study, UAV orthoimagery was classified using object-oriented classification method for mapping a part of wild pistachio nature reserve in Zagros open woodlands, Fars Province, Iran. This research focused on recognizing two main species of the study area (i.e., wild pistachio and wild almond) and estimating their mean crown area. The orthoimage of study area was consisted of 1,076 images with spatial resolution of 3.47 cm which was georeferenced using 12 ground control points (RMSE=8 cm) gathered by real-time kinematic (RTK) method. The results showed that the UAV orthoimagery classified by object-oriented method efficiently estimated mean crown area of wild pistachios (52.09±24.67 m2) and wild almonds (3.97±1.69 m2) with no significant difference with their observed values (α=0.05). In addition, the results showed that wild pistachios (accuracy of 0.90 and precision of 0.92) and wild almonds (accuracy of 0.90 and precision of 0.89) were well recognized by image segmentation. In general, we concluded that UAV orthoimagery can efficiently produce precise biophysical data of vegetation stands at single-tree levels, which therefore is suitable for assessment and monitoring open woodlands.

An empirical study on the utility of BRDF model parameters and topographic parameters for mapping vegetation in a semi-arid region with MISR imagery

USDA-ARS?s Scientific Manuscript database

Multi-angle remote sensing has been proved useful for mapping vegetation community types in desert regions. Based on Multi-angle Imaging Spectro-Radiometer (MISR) multi-angular images, this study compares roles played by Bidirectional Reflectance Distribution Function (BRDF) model parameters with th...

The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps

USGS Publications Warehouse

Ackers, Steven H.; Davis, Raymond J.; Olsen, K.; Dugger, Catherine

2015-01-01

Wildlife habitat mapping has evolved at a rapid pace over the last few decades. Beginning with simple, often subjective, hand-drawn maps, habitat mapping now involves complex species distribution models (SDMs) using mapped predictor variables derived from remotely sensed data. For species that inhabit large geographic areas, remote sensing technology is often essential for producing range wide maps. Habitat monitoring for northern spotted owls (Strix occidentalis caurina), whose geographic covers about 23 million ha, is based on SDMs that use Landsat Thematic Mapper imagery to create forest vegetation data layers using gradient nearest neighbor (GNN) methods. Vegetation data layers derived from GNN are modeled relationships between forest inventory plot data, climate and topographic data, and the spectral signatures acquired by the satellite. When used as predictor variables for SDMs, there is some transference of the GNN modeling error to the final habitat map.Recent increases in the use of light detection and ranging (lidar) data, coupled with the need to produce spatially accurate and detailed forest vegetation maps have spurred interest in its use for SDMs and habitat mapping. Instead of modeling predictor variables from remotely sensed spectral data, lidar provides direct measurements of vegetation height for use in SDMs. We expect a SDM habitat map produced from directly measured predictor variables to be more accurate than one produced from modeled predictors.We used maximum entropy (Maxent) SDM modeling software to compare predictive performance and estimates of habitat area between Landsat-based and lidar-based northern spotted owl SDMs and habitat maps. We explored the differences and similarities between these maps, and to a pre-existing aerial photo-interpreted habitat map produced by local wildlife biologists. The lidar-based map had the highest predictive performance based on 10 bootstrapped replicate models (AUC = 0.809 ± 0.011), but the performance of the Landsat-based map was within acceptable limits (AUC = 0.717 ± 0.021). As is common with photo-interpreted maps, there was no accuracy assessment available for comparison. The photo-interpreted map produced the highest and lowest estimates of habitat area, depending on which habitat classes were included (nesting, roosting, and foraging habitat = 9962 ha, nesting habitat only = 6036 ha). The Landsat-based map produced an estimate of habitat area that was within this range (95% CI: 6679–9592 ha), while the lidar-based map produced an area estimate similar to what was interpreted by local wildlife biologists as nesting (i.e., high quality) habitat using aerial imagery (95% CI: 5453–7216). Confidence intervals of habitat area estimates from the SDMs based on Landsat and lidar overlapped.We concluded that both Landsat- and lidar-based SDMs produced reasonable maps and area estimates for northern spotted owl habitat within the study area. The lidar-based map was more precise and spatially similar to what local wildlife biologists considered spotted owl nesting habitat. The Landsat-based map provided a less precise spatial representation of habitat within the relatively small geographic confines of the study area, but habitat area estimates were similar to both the photo-interpreted and lidar-based maps.Photo-interpreted maps are time consuming to produce, subjective in nature, and difficult to replicate. SDMs provide a framework for efficiently producing habitat maps that can be replicated as habitat conditions change over time, provided that comparable remotely sensed data are available. When the SDM uses predictor variables extracted from lidar data, it can produce a habitat map that is both accurate and useful at large and small spatial scales. In comparison, SDMs using Landsat-based data are more appropriate for large scale analyses of amounts and general spatial patterns of habitat at regional scales.

Computer analysis and mapping of gypsy moth levels in Pennsylvania using LANDSAT-1 digital data

NASA Technical Reports Server (NTRS)

Williams, D. L.

1975-01-01

The effectiveness of using LANDSAT-1 multispectral digital data and imagery, supplemented by ground truth and aerial photography, was investigated as a new method of surveying gypsy moth (Porthetria dispar (L.)) (Lepidoptera; Lymantriidae) defoliation, which has greatly increased in Pennsylvania in recent years. Since the acreage and severity of gypsy moth defoliation reaches a peak from mid-June through the first few days of July, the July 8, 1973, LANDSAT-1 scene was chosen for analysis. Results indicate that LANDSAT-1 data can be used to discriminate between defoliated and healthy vegetation in Pennsylvania and that digital processing methods can be used to map the extent and degree of defoliation.

Modeling of vegetation canopy reflectance: Status, issues and recommended future strategy

NASA Technical Reports Server (NTRS)

Goel, N. S. (Editor)

1982-01-01

Various technical issues related to mapping of vegetative type, condition and stage of maturity, utilizing remotely sensed spectral data are reviewed. The existing knowledge base of models, especially of radiative properties of the vegetation canopy and atmosphere, is reviewed to establish the state of the art for addressing the problem of vegetation mapping. Activities to advance the state of the art are recommended. They include working on canopy reflectance and atmospheric scattering models, and field measurements of canopy reflectance as well as of canopy components. Leaf area index (LAI) and solar radiation interception (SRI) are identified as the two most important vegetation variables requiring further investigation. It is recommended that activities related to sensing them or understanding their relationships with measurable variables, should be encouraged and supported.

Synergy between Sentinel-1 radar time series and Sentinel-2 optical for the mapping of restored areas in Danube delta

NASA Astrophysics Data System (ADS)

Niculescu, Simona; Lardeux, Cédric; Hanganu, Jenica

2018-05-01

Wetlands are important and valuable ecosystems, yet, since 1900, more than 50 % of wetlands have been lost worldwide. An example of altered and partially restored coastal wetlands is the Danube Delta in Romania. Over time, human intervention has manifested itself in more than a quarter of the entire Danube surface. This intervention was brutal and has rendered ecosystem restoration very difficult. Studies for the rehabilitation / re-vegetation were started immediately after the Danube Delta was declared as a Biosphere Reservation in 1990. Remote sensing offers accurate methods for detecting and mapping change in restored wetlands. Vegetation change detection is a powerful indicator of restoration success. The restoration projects use vegetative cover as an important indicator of restoration success. To follow the evolution of the vegetation cover of the restored areas, satellite images radar and optical of last generation have been used, such as Sentinel-1 and Sentinel-2. Indeed the sensor sensitivity to the landscape depends on the wavelength what- ever radar or optical data and their polarization for radar data. Combining this kind of data is particularly relevant for the classification of wetland vegetation, which are associated with the density and size of the vegetation. In addition, the high temporal acquisition frequency of Sentinel-1 which are not sensitive to cloud cover al- low to use temporal signature of the different land cover. Thus we analyse the polarimetric and temporal signature of Sentinel-1 data in order to better understand the signature of the different study classes. In a second phase, we performed classifications based on the Random Forest supervised classification algorithm involving the entire Sentinel-1 time series, then starting from a Sentinel-2 collection and finally involving combinations of Sentinel-1 and -2 data.

Coupling Analysis of Heat Island Effects, Vegetation Coverage and Urban Flood in Wuhan

NASA Astrophysics Data System (ADS)

Liu, Y.; Liu, Q.; Fan, W.; Wang, G.

2018-04-01

In this paper, satellite image, remote sensing technique and geographic information system technique are main technical bases. Spectral and other factors comprehensive analysis and visual interpretation are main methods. We use GF-1 and Landsat8 remote sensing satellite image of Wuhan as data source, and from which we extract vegetation distribution, urban heat island relative intensity distribution map and urban flood submergence range. Based on the extracted information, through spatial analysis and regression analysis, we find correlations among heat island effect, vegetation coverage and urban flood. The results show that there is a high degree of overlap between of urban heat island and urban flood. The area of urban heat island has buildings with little vegetation cover, which may be one of the reasons for the local heavy rainstorms. Furthermore, the urban heat island has a negative correlation with vegetation coverage, and the heat island effect can be alleviated by the vegetation to a certain extent. So it is easy to understand that the new industrial zones and commercial areas which under constructions distribute in the city, these land surfaces becoming bare or have low vegetation coverage, can form new heat islands easily.

Comparison modeling for alpine vegetation distribution in an arid area.

PubMed

Zhou, Jihua; Lai, Liming; Guan, Tianyu; Cai, Wetao; Gao, Nannan; Zhang, Xiaolong; Yang, Dawen; Cong, Zhentao; Zheng, Yuanrun

2016-07-01

Mapping and modeling vegetation distribution are fundamental topics in vegetation ecology. With the rise of powerful new statistical techniques and GIS tools, the development of predictive vegetation distribution models has increased rapidly. However, modeling alpine vegetation with high accuracy in arid areas is still a challenge because of the complexity and heterogeneity of the environment. Here, we used a set of 70 variables from ASTER GDEM, WorldClim, and Landsat-8 OLI (land surface albedo and spectral vegetation indices) data with decision tree (DT), maximum likelihood classification (MLC), and random forest (RF) models to discriminate the eight vegetation groups and 19 vegetation formations in the upper reaches of the Heihe River Basin in the Qilian Mountains, northwest China. The combination of variables clearly discriminated vegetation groups but failed to discriminate vegetation formations. Different variable combinations performed differently in each type of model, but the most consistently important parameter in alpine vegetation modeling was elevation. The best RF model was more accurate for vegetation modeling compared with the DT and MLC models for this alpine region, with an overall accuracy of 75 % and a kappa coefficient of 0.64 verified against field point data and an overall accuracy of 65 % and a kappa of 0.52 verified against vegetation map data. The accuracy of regional vegetation modeling differed depending on the variable combinations and models, resulting in different classifications for specific vegetation groups.

A categorical, improper probability method for combining NDVI and LiDAR elevation information for potential cotton precision agricultural applications

USDA-ARS?s Scientific Manuscript database

An algorithm is presented to fuse the Normalized Difference Vegetation Index (NDVI) with Light Detection and Ranging (LiDAR) elevation data to produce a map potentially useful for the site-specific scouting and pest management of several insect pests. In cotton, these pests include the Tarnished Pl...

Vegetation survey in Amazonia using LANDSAT data. [Brazil

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Shimabukuro, Y. E.; Dossantos, J. R.; Deaquino, L. C. S.

1982-01-01

Automatic Image-100 analysis of LANDSAT data was performed using the MAXVER classification algorithm. In the pilot area, four vegetation units were mapped automatically in addition to the areas occupied for agricultural activities. The Image-100 classified results together with a soil map and information from RADAR images, permitted the establishment of the final legend with six classes: semi-deciduous tropical forest; low land evergreen tropical forest; secondary vegetation; tropical forest of humid areas, predominant pastureland and flood plains. Two water types were identified based on their sediments indicating different geological and geomorphological aspects.

Vegetation map for the Hakalau Forest Unit of the Big Island National Wildlife Refuge Complex on the island of Hawai‘i

USGS Publications Warehouse

Jacobi, James D.

2017-01-01

This vegetation map was produced to serve as an updated habitat base for management of natural resources of the Hakalau Forest Unit (HFU) of the Big Island National Wildlife Refuge Complex (Refuge) on the island of Hawai‘i. The map is based on a vegetation map originally produced as part of the U.S. Fish and Wildlife Service’s Hawai‘i Forest Bird Survey to depict the distribution, structure, and composition of plant communities on the island of Hawai‘i as they existed in 1977. The current map has been updated to represent current conditions of plant communities in the HFU, based on WorldView 2 imagery taken in 2012 and very-high-resolution imagery collected by Pictometry International from 2010 to 2014. Thirty-one detailed plant communities are identified on this map, and fourteen of these units are found within the boundaries of HFU. Additionally, the mapped units can be displayed as five tree canopy cover units, three moisture zones units, eight dominant tree species units, and four habitat status units by choosing the various fields to group the units from the map attribute table. This updated map will provide a foundation for the refinement and tracking of management actions on the Refuge for the near future, particularly as the habitats in this area are subject to projected climate change.

The Laser Vegetation Imaging Sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography

NASA Astrophysics Data System (ADS)

Blair, J. Bryan; Rabine, David L.; Hofton, Michelle A.

The Laser Vegetation Imaging Sensor (LVIS) is an airborne, scanning laser altimeter, designed and developed at NASA's Goddard Space Flight Center (GSFC). LVIS operates at altitudes up to 10 km above ground, and is capable of producing a data swath up to 1000 m wide nominally with 25-m wide footprints. The entire time history of the outgoing and return pulses is digitised, allowing unambiguous determination of range and return pulse structure. Combined with aircraft position and attitude knowledge, this instrument produces topographic maps with dm accuracy and vertical height and structure measurements of vegetation. The laser transmitter is a diode-pumped Nd:YAG oscillator producing 1064 nm, 10 ns, 5 mJ pulses at repetition rates up to 500 Hz. LVIS has recently demonstrated its ability to determine topography (including sub-canopy) and vegetation height and structure on flight missions to various forested regions in the US and Central America. The LVIS system is the airborne simulator for the Vegetation Canopy Lidar (VCL) mission (a NASA Earth remote sensing satellite due for launch in year 2000), providing simulated data sets and a platform for instrument proof-of-concept studies. The topography maps and return waveforms produced by LVIS provide Earth scientists with a unique data set allowing studies of topography, hydrology, and vegetation with unmatched accuracy and coverage.

Reconnaissance geologic mapping in the Dry Valleys of Antarctica using the Earth Resources Technology Satellite

NASA Technical Reports Server (NTRS)

Houston, R. S. (Principal Investigator); Zochol, F. W.; Smithson, S. B.

1973-01-01

The author has identified the following significant results. Reconnaissance geologic mapping can be done with 60-70% accuracy in the Dry Valleys of Antarctica using ERTS-1 imagery. Bedrock geology can be mapped much better than unconsolidated deposits of Quaternary age. Mapping of bedrock geology is facilitated by lack of vegetation, whereas mapping of Quaternary deposits is hindered by lack of vegetation. Antarctic images show remarkable clarity and under certain conditions (moderate relief, selection of the optimum band for specific rock types, stereo-viewing) irregular contacts can be mapped in local areas that are amazing like those mapped at a scale of 1:25,000, but, of course, lack details due to resolution limitations. ERTS-1 images should be a valuable aid to Antarctic geologists who have some limited ground truth and wish to extend boundaries of geologic mapping from known areas.

[Retrieval of Copper Pollution Information from Hyperspectral Satellite Data in a Vegetation Cover Mining Area].

PubMed

Qu, Yong-hua; Jiao, Si-hong; Liu, Su-hong; Zhu, Ye-qing

2015-11-01

Heavy metal mining activities have caused the complex influence on the ecological environment of the mining regions. For example, a large amount of acidic waste water containing heavy metal ions have be produced in the process of copper mining which can bring serious pollution to the ecological environment of the region. In the previous research work, bare soil is mainly taken as the research target when monitoring environmental pollution, and thus the effects of land surface vegetation have been ignored. It is well known that vegetation condition is one of the most important indictors to reflect the ecological change in a certain region and there is a significant linkage between the vegetation spectral characteristics and the heavy metal when the vegetation is effected by the heavy metal pollution. It means the vegetation is sensitive to heavy metal pollution by their physiological behaviors in response to the physiological ecology change of their growing environment. The conventional methods, which often rely on large amounts of field survey data and laboratorial chemical analysis, are time consuming and costing a lot of material resources. The spectrum analysis method using remote sensing technology can acquire the information of the heavy mental content in the vegetation without touching it. However, the retrieval of that information from the hyperspectral data is not an easy job due to the difficulty in figuring out the specific band, which is sensitive to the specific heavy metal, from a huge number of hyperspectral bands. Thus the selection of the sensitive band is the key of the spectrum analysis method. This paper proposed a statistical analysis method to find the feature band sensitive to heavy metal ion from the hyperspectral data and to then retrieve the metal content using the field survey data and the hyperspectral images from China Environment Satellite HJ-1. This method selected copper ion content in the leaves as the indicator of copper pollution level, using stepwise multiple linear regression and cross validation on the dataset which is consisting of 44 groups of copper ion content information in the polluted vegetation leaves from Dexing Copper Mine in Jiangxi Province to build up a statistical model by also incorporating the HJ-1 satellite images. This model was then used to estimate the copper content distribution over the whole research area at Dexing Copper Mine. The result has shown that there is strong statistical significance of the model which revealed the most sensitive waveband to copper ion is located at 516 nm. The distribution map illustrated that the copper ion content is generally in the range of 0-130 mg · kg⁻¹ in the vegetation covering area at Dexing Copper Mine and the most seriously polluted area is located at the South-east corner of Dexing City as well as the mining spots with a higher value between 80 and 100 mg · kg⁻¹. This result is consistent with the ground observation experiment data. The distribution map can certainly provide some important basic data on the copper pollution monitoring and treatment.

Identification and Mapping of Tree Species in Urban Areas Using WORLDVIEW-2 Imagery

NASA Astrophysics Data System (ADS)

Mustafa, Y. T.; Habeeb, H. N.; Stein, A.; Sulaiman, F. Y.

2015-10-01

Monitoring and mapping of urban trees are essential to provide urban forestry authorities with timely and consistent information. Modern techniques increasingly facilitate these tasks, but require the development of semi-automatic tree detection and classification methods. In this article, we propose an approach to delineate and map the crown of 15 tree species in the city of Duhok, Kurdistan Region of Iraq using WorldView-2 (WV-2) imagery. A tree crown object is identified first and is subsequently delineated as an image object (IO) using vegetation indices and texture measurements. Next, three classification methods: Maximum Likelihood, Neural Network, and Support Vector Machine were used to classify IOs using selected IO features. The best results are obtained with Support Vector Machine classification that gives the best map of urban tree species in Duhok. The overall accuracy was between 60.93% to 88.92% and κ-coefficient was between 0.57 to 0.75. We conclude that fifteen tree species were identified and mapped at a satisfactory accuracy in urban areas of this study.

Satellite-derived land covers for runoff estimation using SCS-CN method in Chen-You-Lan Watershed, Taiwan

NASA Astrophysics Data System (ADS)

Zhang, Wen-Yan; Lin, Chao-Yuan

2017-04-01

The Soil Conservation Service Curve Number (SCS-CN) method, which was originally developed by the USDA Natural Resources Conservation Service, is widely used to estimate direct runoff volume from rainfall. The runoff Curve Number (CN) parameter is based on the hydrologic soil group and land use factors. In Taiwan, the national land use maps were interpreted from aerial photos in 1995 and 2008. Rapid updating of post-disaster land use map is limited due to the high cost of production, so the classification of satellite images is the alternative method to obtain the land use map. In this study, Normalized Difference Vegetation Index (NDVI) in Chen-You-Lan Watershed was derived from dry and wet season of Landsat imageries during 2003 - 2008. Land covers were interpreted from mean value and standard deviation of NDVI and were categorized into 4 groups i.e. forest, grassland, agriculture and bare land. Then, the runoff volume of typhoon events during 2005 - 2009 were estimated using SCS-CN method and verified with the measured runoff data. The result showed that the model efficiency coefficient is 90.77%. Therefore, estimating runoff by using the land cover map classified from satellite images is practicable.

NOAA-AVHRR image mosaics applied to vegetation identification

NASA Astrophysics Data System (ADS)

de Almeida, Maria d. G.; Ruddorff, Bernardo F.; Shimabukuro, Yosio E.

2001-06-01

In this paper, the maximum-value composite of images procedure from Normalized Difference Vegetation Index is used to get a cloud free image mosaic. The image mosaic is used to identify vegetation targets such as tropical forest, savanna and caatinga as well to make the vegetation cover mapping of Minas Gerais state, Brazil.

Vegetation Change in Blue Oak Woodlands in California

Treesearch

Barbara A. Holzman; Barbara H. Allen-Diaz

1991-01-01

A preliminary report of a statewide project investigating vegetation change in blue oak (Quercus douglasii) woodlands in California is presented. Vegetation plots taken in the 1930s, as part of a statewide vegetation mapping project, were relocated and surveyed. Species composition, cover and tree stand structure data from the earlier study were...

Model Development of Cold Chains for Fresh Fruits and Vegetables Distribution: A Case Study in Bali Province

NASA Astrophysics Data System (ADS)

Waisnawa, I. N. G. S.; Santosa, I. D. M. C.; Sunu, I. P. W.; Wirajati, IGAB

2018-01-01

In developing countries such as Indonesia, as much as 40% of total vegetables and fruits production becomes waste because of lack refrigeration. This condition also contributes a food crisis problem besides other factor such as, climate change and number of population. Cold chain system that will be modelled in this study is for vegetables and fruits and refrigeration system as the main devices. In future, this system will play an important role for the food crisis solution where fresh food can be distributed very well with significant low waste. The fresh food also can be kept with good quality and hygienist (bacteria contaminated). Cold Chain model will be designed using refrigeration components including, pre cooling chiller, cold room, and truck refrigeration. This study will be conducted by survey and observation di around Bali Province focus on vegetables and fruits production center. Interviews and questionnaire will be also done to get some information about the conventional distribution obstacles and problem. Distribution mapping will be developed and created. The data base of the storage characteristic of the fruits and vegetable also collected through experiment and secondary data. Depend on the mapping and data base can be developed a cold chain model that has the best performance application. The model will be can directly apply in Bali to get eligible cold chain in Bali. The cold chain model will be compared with the conventional distribution system using ALCC/LCC method and also others factor and will be weighted to get better results.

Using Intervention Mapping for Systematic Development of Two School-Based Interventions Aimed at Increasing Children's Fruit and Vegetable Intake

ERIC Educational Resources Information Center

Reinaerts, E.; De Nooijer, J.; De Vries, N. K.

2008-01-01

Purpose: The purpose of this paper is to show how the intervention mapping (IM) protocol could be applied to the development of two school-based interventions. It provides an extensive description of the development, implementation and evaluation of two interventions which aimed to increase fruit and vegetable (F&V) consumption among primary…

Regional modeling of wind erosion in the North West and South West of Iran

NASA Astrophysics Data System (ADS)

Mirmousavi, S. H.

2016-08-01

About two-thirds of the Iran's area is located in the arid and semiarid region. Lack of soil moisture and vegetation is poor in most areas can lead to soil erosion caused by wind. So that the annual suffered severe damage to large areas of rich soils. Modeling studies of wind erosion in Iran is very low and incomplete. Therefore, this study aimed to wind erosion modeling, taking into three factors: wind speed, vegetation and soil types have been done. Wind erosion sensitivity was modeled using the key factors of soil sensitivity, vegetation cover and wind erodibility as proxies. These factors were first estimated separately by factor sensitivity maps and later combined by fuzzy logic into a regional-scale wind erosion sensitivity map. Large areas were evaluated by using publicly available datasets of remotely sensed vegetation information, soil maps and meteorological data on wind speed. The resulting estimates were verified by field studies and examining the economic losses from wind erosion as compensated by the state insurance company. The spatial resolution of the resulting sensitivity map is suitable for regional applications, as identifying sensitive areas is the foundation for diverse land development control measures and implementing management activities.

Landsat-faciliated vegetation classification of the Kenai National Wildlife Refuge and adjacent areas, Alaska

USGS Publications Warehouse

Talbot, S. S.; Shasby, M.B.; Bailey, T.N.

1985-01-01

A Landsat-based vegetation map was prepared for Kenai National Wildlife Refuge and adjacent lands, 2 million and 2.5 million acres respectively. The refuge lies within the middle boreal sub zone of south central Alaska. Seven major classes and sixteen subclasses were recognized: forest (closed needleleaf, needleleaf woodland, mixed); deciduous scrub (lowland and montane, subalpine); dwarf scrub (dwarf shrub tundra, lichen tundra, dwarf shrub and lichen tundra, dwarf shrub peatland, string bog/wetlands); herbaceous (graminoid meadows and marshes); scarcely vegetated areas ; water (clear, moderately turbid, highly turbid); and glaciers. The methodology employed a cluster-block technique. Sample areas were described based on a combination of helicopter-ground survey, aerial photo interpretation, and digital Landsat data. Major steps in the Landsat analysis involved: preprocessing (geometric connection), spectral class labeling of sample areas, derivation of statistical parameters for spectral classes, preliminary classification of the entree study area using a maximum-likelihood algorithm, and final classification through ancillary information such as digital elevation data. The vegetation map (scale 1:250,000) was a pioneering effort since there were no intermediate-sclae maps of the area. Representative of distinctive regional patterns, the map was suitable for use in comprehensive conservation planning and wildlife management.

Application of ecological, geological and oceanographic ERTS-1 imagery to Delaware's coastal resources planning

NASA Technical Reports Server (NTRS)

Klemas, V. (Principal Investigator); Bartlett, D. S.

1973-01-01

The author has identified the following significant results. Coastal vegetation species appearing in the ERTS-1 images taken of Delaware Bay have been correlated with ground truth vegetation maps and imagery obtained from high altitude overflights. Multispectral analysis of the high altitude photographs indicated that four major vegetation communities could be clearly discriminated from 60,000 feet altitude including: (1) salt marsh cord grass; (2) salt marsh hay and spike grass; (3) reed grass; and (4) high tide bush and sea myrtle. In addition, human impact can be detected in the form of fresh water impoundments built to attract water fowl, dredge-fill operations and other alterations of the coastal environment. Overlay maps matching the USGS topographic map size of 1:24,000 have been prepared showing the four wetland vegetation communities, fresh water impoundments, and alteration of wetlands by mosquito control ditching and dredge-fill operations. Using these maps, ERTS-1 images were examined by human interpreters and automated multispectral analyzers. Major plant communities of (1) Spartina alterniflora, (2) Spartina patens and Distichlis spicata, and (3) Iva frutescens and Baccharis halimifolia can be distinguished from each other and from surrounding uplands in ERTS-1 scanner bands 6 and 7.

GeoInformation studies of soil and vegetation patterns along Climatic Gradients: A Review

NASA Astrophysics Data System (ADS)

Shoshany, M.

2009-04-01

Global evidence regarding magnitudes of desertification processes and recognition in their societal, ecological and climatological consequences had lead the international community to establish the United Nations Convention to Combat Desertification (UNCCD). Within the framework of this convention it is perceived that Desertification is a complex poorly understood phenomena which is " first and foremost, the result of resource management failures". Scientific research within this context have three primary roles: monitoring the situation, developing the understanding of relationships between factors promoting desertification and finally providing the international community with efficient recommendations regarding actions which may slow down these processes. Study of desertification processes in regions of sharp climatic gradients is of special importance within this framework since they represent areas where the processes are most intensive and where most deserts actually expand. The detection of threshold zones coupling sever land degradation with loss of resilience in their eco-geomophic systems is fundamental for the efficient combating of global desertification. Application of geoinformation tools and techniques is instrumental for this purpose: mapping biological, chemical and physical surface properties using remote sensing techniques, mapping historical patch-pattern changes using air-photographs, analysis of spatio-temporal variations in pattern properties and analysis of informational relationships between these surface properties and patterns with climatoloical, topographic, lithological and human factors. Numerous Remote Sensing studies had been undertaken during the last four decades in monitoring desertification through the provision of maps describing spatial distributions of biophysical surface parameters at resolutions between few meters to few kilometers and temporal resolutions between hours and weeks. These studies utilized radar backscattering , spectral reflectance at the visible, NIR and SWIR ranges and emissions in the thermal spectrum. However, despite the magnitude of these projects very few of the methods were proved to be operational yet. The main shortcomings of exiting methods are: - They are highly dependent on accurate calibration which for large region is impractical. - Most of the methods are semi-empirical: case dependent rather than representing robust physical indicators. - There is no one imagery source which is good for all mapping purposes, most of the methods use single imagery source and there is relatively little synergy (fusion) between imagery sources. - Data continuity for long time periods exits mainly for low resolution sources which are limited in supporting modeling of processes. - Difficulties in scaling-up results and methods from the local to the broad-regional scales Within the scope of interest here the most important shortcoming concern the fact that relatively little work treated explicitly regions of high climatic gradient partly due to their high spatio-temporal heterogeneity. Three areas of recent advancements in studying explicitly transition zones between humid and arid regions : - Mapping bio-physical properties of vegetation forms (herbaceous, dwarf-shrubs and shrubs): cover proportions, biomass, primary productivity using synergy between optical (phonologies) and SAR imagery. - Mapping chemical and physical soil properties and estimating their erodibility using hyper and multi spectral methods, and SAR backscattering. - Mapping soil and vegetation patch patterns and their changes within the last decades using historical air-photographs. These advancement s lead to the detection of threshold zones between regions along these gradients according to following indicators: - Life-forms compositions, biomass and primary productivity. Analysis of relationships between biomass and rainfall allow differentiation between cases were vegetation compositions and properties which follow 'expected' successional sequences and those which represent harsh land degradation with productivity significantly less than would be expected according to their average annual precipitation. - Soil chemical compositions referring mainly to organic carbon, inorganic carbon and ferrum. These mapping allowed the detection of 'tipping points' in the high transition zones. Analysis of historical patch-patterns ' evolution modes using air-photographs and GIS techniques allowed insight into soil and vegetation pattern dynamics. Recent results had revealed that in some areas of low biomass there is maintained similar pattern fragmentation as in areas of higher rainfall. This signifies the functioning of self-organization and consequently the potential resilience of some areas of relatively low primary productivity located at desert margins. In conclusion, current geoinformation tools and techniques on one hand had shown their potential contribution to the modeling and understanding of desertification processes in general and the formation of thresholds through the functioning of 'tipping' mechanisms and 'catastrophic shifts'. However, these tools and techniques are not yet operational at the wide regional scale. Better synergy of remote sensing sources and availability of longer time series of surface properties will facilitate the combat of desertification with both better understanding of the processes and predictions of expected spatial change in different warming and human disturbance scenarios.

Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites

PubMed Central

Mitchard, Edward T A; Feldpausch, Ted R; Brienen, Roel J W; Lopez-Gonzalez, Gabriela; Monteagudo, Abel; Baker, Timothy R; Lewis, Simon L; Lloyd, Jon; Quesada, Carlos A; Gloor, Manuel; ter Steege, Hans; Meir, Patrick; Alvarez, Esteban; Araujo-Murakami, Alejandro; Aragão, Luiz E O C; Arroyo, Luzmila; Aymard, Gerardo; Banki, Olaf; Bonal, Damien; Brown, Sandra; Brown, Foster I; Cerón, Carlos E; Chama Moscoso, Victor; Chave, Jerome; Comiskey, James A; Cornejo, Fernando; Corrales Medina, Massiel; Da Costa, Lola; Costa, Flavia R C; Di Fiore, Anthony; Domingues, Tomas F; Erwin, Terry L; Frederickson, Todd; Higuchi, Niro; Honorio Coronado, Euridice N; Killeen, Tim J; Laurance, William F; Levis, Carolina; Magnusson, William E; Marimon, Beatriz S; Marimon Junior, Ben Hur; Mendoza Polo, Irina; Mishra, Piyush; Nascimento, Marcelo T; Neill, David; Núñez Vargas, Mario P; Palacios, Walter A; Parada, Alexander; Pardo Molina, Guido; Peña-Claros, Marielos; Pitman, Nigel; Peres, Carlos A; Poorter, Lourens; Prieto, Adriana; Ramirez-Angulo, Hirma; Restrepo Correa, Zorayda; Roopsind, Anand; Roucoux, Katherine H; Rudas, Agustin; Salomão, Rafael P; Schietti, Juliana; Silveira, Marcos; de Souza, Priscila F; Steininger, Marc K; Stropp, Juliana; Terborgh, John; Thomas, Raquel; Toledo, Marisol; Torres-Lezama, Armando; van Andel, Tinde R; van der Heijden, Geertje M F; Vieira, Ima C G; Vieira, Simone; Vilanova-Torre, Emilio; Vos, Vincent A; Wang, Ophelia; Zartman, Charles E; Malhi, Yadvinder; Phillips, Oliver L

2014-01-01

Aim The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1 Methods Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. Results The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by > 25%, whereas regional uncertainties for the maps were reported to be < 5%. Main conclusions Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space. PMID:26430387

Unified Ecoregions of Alaska: 2001

USGS Publications Warehouse

Nowacki, Gregory J.; Spencer, Page; Fleming, Michael; Brock, Terry; Jorgenson, Torre

2003-01-01

Major ecosystems have been mapped and described for the State of Alaska and nearby areas. Ecoregion units are based on newly available datasets and field experience of ecologists, biologists, geologists and regional experts. Recently derived datasets for Alaska included climate parameters, vegetation, surficial geology and topography. Additional datasets incorporated in the mapping process were lithology, soils, permafrost, hydrography, fire regime and glaciation. Thirty two units are mapped using a combination of the approaches of Bailey (hierarchial), and Omernick (integrated). The ecoregions are grouped into two higher levels using a 'tri-archy' based on climate parameters, vegetation response and disturbance processes. The ecoregions are described with text, photos and tables on the published map.

Mapping potentialy asbestos-bearing rocks using imaging spectroscopy

USGS Publications Warehouse

Swayze, G.A.; Kokaly, R.F.; Higgins, C.T.; Clinkenbeard, J.P.; Clark, R.N.; Lowers, H.A.; Sutley, S.J.

2009-01-01

Rock and soil that may contain naturally occurring asbestos (NOA), a known human carcinogen, were mapped in the Sierra Nevada, California, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to determine if these materials could be uniquely identified with spectroscopy. Such information can be used to prepare or refine maps of areas that may contain minerals that can be asbestiform, such as serpentine and tremolite-actinolite, which were the focus of this study. Although thick vegetation can conceal underlying rock and soil, use of linear-mixture spectra calculated from spectra of dry grass and serpentine allowed detection of serpentine in some parts of the study area with up to ~80% dry-grass cover. Chaparral vegetation, which was dominantly, but not exclusively, found in areas underlain by serpentinized ultramafic rocks, was also mapped. Overall, field checking at 201 sites indicated highly accurate identification by AVIRIS of mineral (94%) and vegetation (89%) categories. Practical applications of AVIRIS to mapping areas that may contain NOA include locating roads that are surfaced with serpentine aggregate, identifying sites that may require enhanced dust control or other safety measures, and filling gaps in geologic mapping where field access is limited.

Arctic and subarctic environmental analyses utilizing ERTS-1 imagery. [permafrost sediment transport, snow cover, ice conditions, and water runoff in Alaska

NASA Technical Reports Server (NTRS)

Anderson, D. M.; Mckim, H. L.; Haugen, R. K.; Gatto, L. W.; Slaughter, C. W.; Marlar, T. L. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Physiognomic landscape features were used as geologic and vegetative indicators in preparation of a surficial geology, vegetation, and permafrost map at a scale of 1:1 million using ERTS-1 band 7 imagery. The detail from this map compared favorably with USGS maps at 1:250,000 scale. Physical boundaries mapped from ERTS-1 imagery in combination with ground truth obtained from existing small maps and other sources resulted in improved and more detailed maps of permafrost terrain and vegetation for the same area. ERTS-1 imagery provides for the first time, a means of monitoring the following regional estuarine processes: daily and periodic surface water circulation patterns; changes in the relative sediment load of rivers discharging into the inlet; and, several local patterns not recognized before, such as a clockwise back eddy offshore from Clam Gulch and a counterclockwise current north of the Forelands. Comparison of ERTS-1 and Mariner imagery has revealed that the thermokarst depressions found on the Alaskan North Slope and polygonal patterns on the Yukon River Delta are possible analogs to some Martian terrain features.

Monitoring land cover changes by remote sensing in north west Egypt

NASA Astrophysics Data System (ADS)

Richards, Timothy Steven

The Mediterratiean coastal strip of Egypt is a semi-arid environment which supports a variety of agricultural practices ranging from irrigated sedentary agriculture to semi-nomadic pastoralism. The sedentarisation of the nomadic Bedouin coupled with an increase in population of both people and livestock and a decrease in the extent of the rangelands, has resulted in severe pressure being exerted upon the environment. Satellite remote sensing of vegetation offers the potential to aid regional management by complementing conventional techniques of vegetation mapping and monitoring. This thesis examines the different techniques available for vegetation mapping using visible and near infrared spectral wave bands. The different techniques available for vegetation mapping using remotely sensed data are reviewed and discussed with reference to semi-arid environments. The underlying similarity of many of the techniques is emphasised and their individual merits discussed. The spectral feature-space of Landsat data of two representative study areas in northern Egypt is explored and examined using graphical techniques and principal components analysis. Hand held radiometric field data are also presented for individual soil types within the region. It is proposed that by using reference data for individual soil types, improved estimates of vegetation cover can be ascertained. A number of radiometric corrections are applied to the digital Landsat data in order to convert the arbitrary digital values of the different spectral bands into physical values of reflectance. The effect of this standardization on the principal components is examined. The stratified approach to vegetation mapping which was explored using the field data is applied in turn to the digital Landsat images. Whilst the stratified approach was not found to offer significant advantages over the non-stratified approach in this case, the analysis does serve to provide an accurate datum against which to measure vegetation. In conclusion a satellite based system for operational vegetation monitoring is proposed.

VEGETATION AND POLLEN RELATIONSHIP IN EASTERN CANADA

EPA Science Inventory

The relationship between the vegetation and modern pollen assemblages in eastern Canada is summarized and analyzed using isopoll maps, ordination, and cluster analysis. he major vegetation zones recognized in the region are the shrub tundra, forest tundra (divided into shrub and ...

Natural resource inventories and management applications in the Great Basin. [Nevada vegetation and wildlands

NASA Technical Reports Server (NTRS)

Tueller, P. T.; Lorain, G.; Halvorson, R. M.

1974-01-01

ERTS-1 resolution capabilities and repetitive coverage have allowed the acquisition of several statewide inventories of natural resource features not previously completed or that could not be completed in any other way. Familiarity with landform, tone, pattern and other converging factors, along with multidate imagery, has been required. Nevada's vegetation has been mapped from ERTS-1. Dynamic characteristics of the landscape have been studied. Sequential ERTS-1 imagery has proved its usefulness for mapping vegetation, following vegetation phenology changes, monitoring changes in lakes and reservoirs (including water quality), determining changes in surface mining use, making fire fuel estimates and determining potential hazard, mapping the distribution of rain and snow events, making range readiness determinations, monitoring marshland management practices and other uses. Feasibility has been determined, but details of incorporating the data in management systems awaits further research and development. The need is to accurately define the steps necessary to extract required or usable information from ERTS imagery and fit it into on-going management programs.

The use of color infrared photography for wetlands mapping with special reference to shoreline and waterfowl habitat assessment

NASA Technical Reports Server (NTRS)

1973-01-01

Evaluation of low altitude oblique photography obtained by hand-held cameras was useful in determining specifications of operational mission requirements for conventional smaller-scaled vertical photography. Remote sensing techniques were used to assess the rapid destruction of marsh areas at Pointe Mouillee. In an estuarian environment where shoreline features change yearly, there is a need for revision in existing area maps. A land cover inventory, mapped from aerial photography, provided essential data necessary for determining adjacent lands suitable for marshland development. To quantitatively assess the wetlands environment, a detailed inventory of vegetative communities (19 categories) was made using color infrared photography and intensive ground truth. A carefully selected and well laid-out transect was found to be a key asset to photointerpretation and to the analysis of vegetative conditions. Transect data provided the interpreter with locally representative areas of various vegetative types. This facilitated development of a photointerpretation key. Additional information on vegetative conditions in the area was also obtained by evaluating the transect data.

The FAO/NASA/NLR Artemis system - An integrated concept for environmental monitoring by satellite in support of food/feed security and desert locust surveillance

NASA Technical Reports Server (NTRS)

Hielkema, J. U.; Howard, J. A.; Tucker, C. J.; Van Ingen Schenau, H. A.

1987-01-01

The African real time environmental monitoring using imaging satellites (Artemis) system, which should monitor precipitation and vegetation conditions on a continental scale, is presented. The hardware and software characteristics of the system are illustrated and the Artemis databases are outlined. Plans for the system include the use of hourly digital Meteosat data and daily NOAA/AVHRR data to study environmental conditions. Planned mapping activities include monthly rainfall anomaly maps, normalized difference vegetation index maps for ten day and monthly periods with a spatial resolution of 7.6 km, ten day crop/rangeland moisture availability maps, and desert locust potential breeding activity factor maps for a plague prevention program.

Influence of different topographic correction strategies on mountain vegetation classification accuracy in the Lancang Watershed, China

NASA Astrophysics Data System (ADS)

Zhang, Zhiming; de Wulf, Robert R.; van Coillie, Frieke M. B.; Verbeke, Lieven P. C.; de Clercq, Eva M.; Ou, Xiaokun

2011-01-01

Mapping of vegetation using remote sensing in mountainous areas is considerably hampered by topographic effects on the spectral response pattern. A variety of topographic normalization techniques have been proposed to correct these illumination effects due to topography. The purpose of this study was to compare six different topographic normalization methods (Cosine correction, Minnaert correction, C-correction, Sun-canopy-sensor correction, two-stage topographic normalization, and slope matching technique) for their effectiveness in enhancing vegetation classification in mountainous environments. Since most of the vegetation classes in the rugged terrain of the Lancang Watershed (China) did not feature a normal distribution, artificial neural networks (ANNs) were employed as a classifier. Comparing the ANN classifications, none of the topographic correction methods could significantly improve ETM+ image classification overall accuracy. Nevertheless, at the class level, the accuracy of pine forest could be increased by using topographically corrected images. On the contrary, oak forest and mixed forest accuracies were significantly decreased by using corrected images. The results also showed that none of the topographic normalization strategies was satisfactorily able to correct for the topographic effects in severely shadowed areas.

Mapping montane vegetation in Southern California from color infrared imagery

NASA Technical Reports Server (NTRS)

Minnich, R. A.; Bowden, L. W.; Pease, R. W.

1969-01-01

Mapping a large area in California like the San Bernardino Mountains, demonstrated that color infrared photography is suitable for detailed mapping and offers potential for quantitative mapping. The level of information presented is comparable or superior to the most detailed mapping by ground survey.

Mapping Evapotranspiration Units in the Basin and Range Carbonate-Rock Aquifer System, White Pine County, Nevada, and Adjacent Areas in Nevada and Utah

USGS Publications Warehouse

Smith, J. LaRue; Laczniak, Randell J.; Moreo, Michael T.; Welborn, Toby L.

2007-01-01

Accurate estimates of ground-water discharge are crucial in the development of a water budget for the Basin and Range carbonate-rock aquifer system study area. One common method used throughout the southwestern United States is to estimate ground-water discharge from evapotranspiration (ET). ET is a process by which water from the Earth's surface is transferred to the atmosphere. The volume of water lost to the atmosphere by ET can be computed as the product of the ET rate and the acreage of vegetation, open water, and moist soil through which ET occurs. The procedure used in the study groups areas of similar vegetation, water, and soil conditions into different ET units, assigns an average annual ET rate to each unit, and computes annual ET from each ET unit within the outer extent of potential areas of ground-water discharge. Data sets and the procedures used to delineate the ET-unit map used to estimate ground-water discharge from the study area and a qualitative assessment of the accuracy of the map are described in this report.

The LANDFIRE Total Fuel Change Tool (ToFuΔ) user’s guide

USGS Publications Warehouse

Smail, Tobin; Martin, Charley; Napoli, Jim

2011-01-01

LANDFIRE fuel data were originally developed from coarse-scale existing vegetation type, existing vegetation cover, existing vegetation height, and biophysical setting layers. Fire and fuel specialists from across the country provided input to the original LANDFIRE National (LF_1.0.0) fuel layers to help calibrate fuel characteristics on a more localized scale. The LANDFIRE Total Fuel Change Tool (ToFu∆) was developed from this calibration process. Vegetation is subject to constant change – and fuels are therefore also dynamic, necessitating a systematic method for reflecting changes spatially so that fire behavior can be accurately accessed. ToFuΔ allows local experts to quickly produce maps that spatially display any proposed fuel characteristics changes. ToFu∆ works through a Microsoft Access database to produce spatial results in ArcMap based on rule sets devised by the user that take into account the existing vegetation type (EVT), existing vegetation cover (EVC), existing vegetation height (EVH), and biophysical setting (BpS) from the LANDFIRE grid data. There are also options within ToFu∆ to add discrete variables in grid format through use of the wildcard option and for subdividing specific areas for different fuel characteristic assignments through the BpS grid. The ToFu∆ user determines the size of the area for assessment by defining a Management Unit, or “MU.” User-defined rule sets made up of EVT, EVC, EVH, and BpS layers, as well as any wildcard selections, are used to change or refine fuel characteristics within the MU. Once these changes have been made to the fuel characteristics, new grids are created for fire behavior analysis or planning. These grids represent the most common ToFu∆ output. ToFuΔ is currently under development and will continue to be updated in the future. The current beta version (0.12), released in March 2011, is compatible with Windows 7 and will be the last release until the fall of 2011.

Assessment of the vegetation cover in a burned area 22-years ago using remote sensing techniques and GIS analysis (Sierra de las Nieves, South of Spain).

NASA Astrophysics Data System (ADS)

Martínez-Murillo, Juan F.; Remond, Ricardo; Ruiz-Sinoga, José D.

2015-04-01

The study aim was to characterize the vegetation cover in a burned area 22-years ago considering the previous situation to wildfire in 1991 and the current one in 2013. The objectives were to: (i) compare the current and previous vegetation cover to widlfire; (ii) evaluate whether the current vegetation has recovered the previous cover to wildfire; and (iii) determine the spatial variability of vegetation recovery after 22-years since the wildfire. The study area is located in Sierra de las Nieves, South of Spain. It corresponds to an area affected by a wildfire in August 8th, 1991. The burned area was equal to 8156 ha. The burn severity was spatially very high. The main geographic features of the burned area are: mountainous topography (altitudes ranging from 250 m to 1500 m; slope gradient >25%; exposure mainly southfacing); igneous (peridotites), metamorphic (gneiss) and calcareous rocks (limestones); and predominant forest land use (Pinus pinaster sp. woodlands, 10%; pinus opened forest + shrubland, 40%; shrubland, 35%; and bare soil + grassland, 15%). Remote sensing techniques and GIS analysis has been applied to achieve the objectives. Landsat 5 and Landsat 8 images were used: July 13th, 1991 and July 1st, 2013, for the previous wildfire situation and 22-years after, respectively. The 1990 CORINE land cover was also considered to map 1991 land uses prior the wildfire. Likewise, the Andalucía Regional Government wildfire historic records were used to select the burned area and its geographical limit. 1991 and 2013 land cover map were obtained by means of object-oriented classifications. Also, NDVI and PVI1 vegetation indexes were calculated and mapped for both years. Finally, some images transformations and kernel density images were applied to determine the most recovered areas and to map the spatial concentration of bare soil and pine cover areas in 1991 and 2013, respectively. According to the results, the combination of remote sensing and GIS analysis let map the most recovered areas affected by the wildfire in 1991. The vegetation indexes indicated that the vegetation cover in 2013 was still lower than that mapped just before the 1991 widlfire in most of the burned area after 22-years. This result was also confirmed by other techniques applied. Finally, the kernel density surface let identify and locate the most recovered areas of pine cover as well as those areas that still remain totally or partially uncovered (bare soil.

Vegetation communities at Big Muddy National Fish and Wildlife Refuge, Missouri

USGS Publications Warehouse

Struckhoff, Matthew A.; Grabner, Keith W.; Stroh, Esther D.

2011-01-01

New and existing data were used to describe and map vegetation communities at Big Muddy National Fish and Wildlife Refuge. Existing data had been gathered during the growing seasons of 2002, 2003, and 2004. New data were collected in 2007 to describe previously unsampled communities and communities within which insufficient data had been collected. Plot data and field observations were used to describe 17 natural and semi-natural communities at the Association level of the National Vegetation Classification System (NVCS). Four ruderal communities not included in the NVCS are also described. Data were used to inform delineation of communities using aerial photos from 2000, 2002, 2003, 2005, 2006, and 2007. During this process, eleven additional land cover classes including cultural features, managed vegetation communities, and water features were identified. These features were mapped, some were described, but no vegetation data were collected. In 2009, nearly all community polygons were field visited and classified to the Association level. When necessary, polygon boundaries were adjusted based on field observations. The final map includes 482 polygons of 27 land cover classes encompassing 3,174 hectares on 5 units of the refuge. Data and information will inform the development of the refuge Comprehensive Conservation Plan.

General classification handbook for floodplain vegetation in large river systems

USGS Publications Warehouse

Dieck, Jennifer J.; Ruhser, Janis; Hoy, Erin E.; Robinson, Larry R.

2015-01-01

This handbook describes the General Wetland Vegetation Classification System developed as part of the U.S. Army Corps of Engineers’ Upper Mississippi River Restoration (UMRR) Program, Long Term Resource Monitoring (LTRM) element. The UMRR is a cooperative effort between the U.S. Army Corps of Engineers, U.S. Geological Survey, U.S. Fish and Wildlife Service, and the states of Illinois, Iowa, Minnesota, Missouri, and Wisconsin. The classification system consists of 31 general map classes and has been used to create systemic vegetation data layers throughout the diverse Upper Mississippi River System (UMRS), which includes the commercially navigable reaches of the Mississippi River from Minneapolis, Minnesota, in the north to Cairo, Illinois, in the south, the Illinois River, and navigable portions of the Kaskaskia, Black, St. Croix, and Minnesota Rivers. In addition, this handbook describes the evolution of the General Wetland Vegetation Classification System, discusses the process of creating a vegetation data layer, and describes each of the 31 map classes in detail. The handbook also acts as a pictorial guide to each of the map classes as they may appear in the field, as well as on color-infrared imagery. This version is an update to the original handbook published in 2004.

Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation: A Database (NDP-017) (2001 version of original 1985 data)

DOE Data Explorer

Olsen, Jerry S. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Watts, Julia A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Allison, Linda J. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2001-01-01

In 1980, this data base and the corresponding map were completed after more than 20 years of field investigations, consultations, and analyses of published literature. They characterize the use and vegetative cover of the Earth's land surface with a 0.5° × 0.5° grid. This world-ecosystem-complex data set and the accompanying map provide a current reference base for interpreting the role of vegetation in the global cycling of CO2 and other gases and a basis for improved estimates of vegetation and soil carbon, of natural exchanges of CO2, and of net historic shifts of carbon between the biosphere and the atmosphere.

Evaluation of a color-coded Landsat 5/6 ratio image for mapping lithologic differences in western South Dakota

USGS Publications Warehouse

Raines, Gary L.; Bretz, R.F.; Shurr, George W.

1979-01-01

From analysis of a color-coded Landsat 5/6 ratio, image, a map of the vegetation density distribution has been produced by Raines of 25,000 sq km of western South Dakota. This 5/6 ratio image is produced digitally calculating the ratios of the bands 5 and 6 of the Landsat data and then color coding these ratios in an image. Bretz and Shurr compared this vegetation density map with published and unpublished data primarily of the U.S. Geological Survey and the South Dakota Geological Survey; good correspondence is seen between this map and existing geologic maps, especially with the soils map. We believe that this Landsat ratio image can be used as a tool to refine existing maps of surficial geology and bedrock, where bedrock is exposed, and to improve mapping accuracy in areas of poor exposure common in South Dakota. In addition, this type of image could be a useful, additional tool in mapping areas that are unmapped.

Long term land cover and seagrass mapping using Landsat and object-based image analysis from 1972 to 2010 in the coastal environment of South East Queensland, Australia

NASA Astrophysics Data System (ADS)

Lyons, Mitchell B.; Phinn, Stuart R.; Roelfsema, Chris M.

2012-07-01

Long term global archives of high-moderate spatial resolution, multi-spectral satellite imagery are now readily accessible, but are not being fully utilised by management agencies due to the lack of appropriate methods to consistently produce accurate and timely management ready information. This work developed an object-based remote sensing approach to map land cover and seagrass distribution in an Australian coastal environment for a 38 year Landsat image time-series archive (1972-2010). Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery were used without in situ field data input (but still using field knowledge) to produce land and seagrass cover maps every year data were available, resulting in over 60 map products over the 38 year archive. Land cover was mapped annually using vegetation, bare ground, urban and agricultural classes. Seagrass distribution was also mapped annually, and in some years monthly, via horizontal projected foliage cover classes, sand and deep water. Land cover products were validated using aerial photography and seagrass maps were validated with field survey data, producing several measures of accuracy. An average overall accuracy of 65% and 80% was reported for seagrass and land cover products respectively, which is consistent with other studies in the area. This study is the first to show moderate spatial resolution, long term annual changes in land cover and seagrass in an Australian environment, created without the use of in situ data; and only one of a few similar studies globally. The land cover products identify several long term trends; such as significant increases in South East Queensland's urban density and extent, vegetation clearing in rural and rural-residential areas, and inter-annual variation in dry vegetation types in western South East Queensland. The seagrass cover products show that there has been a minimal overall change in seagrass extent, but that seagrass cover level distribution is extremely dynamic; evidenced by large scale migrations of higher seagrass cover levels and several sudden and significant changes in cover level. These mapping products will allow management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes.

Using NDVI to assess departure from average greenness and its relation to fire business

Treesearch

Robert E. Burgan; Roberta A. Hartford; Jeffery C. Eidenshink

1996-01-01

A new satellite-derived vegetation greenness map, departure from average, is designed to compare current-year vegetation greenness with average greenness for the same time of year. Live-fuel condition as portrayed on this map, and the calculated 1,000-hour fuel moistures, are compared to fire occurrence and area burned in Montana and Idaho during the 1993 and 1994 fire...

Markers linked to vegetative incompatibility (vic) genes and a region of high heterogeneity and reduced recombination near the mating type locus (MAT) in Cryphonectria parasitica

Treesearch

Thomas L. Kubisiak; Michael g. Milgroom

2006-01-01

To find markers linked to vegetative incompatibility (vic) genes in the chestnut blight fungus, Cryphonectria parasitica, we constructed a preliminary linkage map. In general, this map is characterized by low levels of polymorphism, as evident from the more than 24 linkage groups observed, compared to seven expected from electrophoretic karyotyping....

Covariance of biophysical data with digital topograpic and land use maps over the FIFE site

NASA Technical Reports Server (NTRS)

Davis, F. W.; Schimel, D. S.; Friedl, M. A.; Michaelsen, J. C.; Kittel, T. G. F.; Dubayah, R.; Dozier, J.

1992-01-01

This paper discusses the biophysical stratification of the FIFE site, implementation of the stratification utilizing geographic information system methods, and validation of the stratification with respect to field measurements of biomass, Bowen ratio, soil moisture, and the greenness vegetation index (GVI) derived from TM satellite data. Maps of burning and topographic position were significantly associated with variation in GVI, biomass, and Bowen ratio. The stratified design did not significantly alter the estimated site-wide means for surface climate parameters but accounted for between 25 and 45 percent of the sample variance depending on the variable.

Impact of Land Use Land Cover Change on East Asian monsoon

NASA Astrophysics Data System (ADS)

Chilukoti, N.; Xue, Y.; Liu, Y.; Lee, J.

2017-12-01

Humans modify the Earth's terrestrial surface on a continental scale by removing natural vegetation for crops/grazing. The current rates, extents and intensities of Land Use and Land Cover Change (LULCC) are greater than ever in history. The earlier studies of Land-atmosphere interactions used specified land surface conditions without interannual variations. In this study using NCEP CFSv2 coupled with Simplified Simple Biosphere (SSiB) model, biogeophysical impacts of LULCC on climate variability, anomaly, and changes are investigated by using the LULCC map from the Hurtt et al. (2006, 2011), which covered 66 years from 1950-2015 with annual variability. We combined the changes in crop and pasture fractions and consider as LULCC. A methodology had been developed to convert the Hurtt LULCC change map with 1° resolution to the GCM grid points. Since the GCM has only one dominant type, when the crop and pasture frction value at one point was larger than the critical value, that grid was assigned as degraded. Comprehensive evaluation was conducted to ensure the consistence of the trend of land degradation in the Hurtt's map and in the GCM LULCC map. In the degraded point, trees were changed to low vegetation or grasses, and low vegetation to bare soil. A set of surface parameters such as leaf area index, vegetation height, roughness length, and soil parameters, associated with vegetation are changed to show the degradation effects. We integrated the model with the potential vegetation map and the map with LULCC from 1950 to 2015, and the results indicate the LULCC causes precipitation reduction globally, with the strongest signals over monsoon regions. For instance, the degradation in Mexico, West Africa, south and East Asia and South America produced significant precipitation anomalies, some of which are consistent with observed regional precipitation anomalies. Meanwhile, it has also found that the LULCC enhances the surface warming during the summer in monsoon regions. The LULCC caused reduction in water released into the atmosphere from the surface through a reduction in transpiration and canopy evaporation, and changes in magnitude and pattern of moisture flux convergence, resulting in precipitation changes, and reduced evaporation lead to warm surface temperature during the summer season.

Mapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data.

PubMed

Schulz, Hans Martin; Li, Ching-Feng; Thies, Boris; Chang, Shih-Chieh; Bendix, Jörg

2017-01-01

Up until now montane cloud forest (MCF) in Taiwan has only been mapped for selected areas of vegetation plots. This paper presents the first comprehensive map of MCF distribution for the entire island. For its creation, a Random Forest model was trained with vegetation plots from the National Vegetation Database of Taiwan that were classified as "MCF" or "non-MCF". This model predicted the distribution of MCF from a raster data set of parameters derived from a digital elevation model (DEM), Landsat channels and texture measures derived from them as well as ground fog frequency data derived from the Moderate Resolution Imaging Spectroradiometer. While the DEM parameters and Landsat data predicted much of the cloud forest's location, local deviations in the altitudinal distribution of MCF linked to the monsoonal influence as well as the Massenerhebung effect (causing MCF in atypically low altitudes) were only captured once fog frequency data was included. Therefore, our study suggests that ground fog data are most useful for accurately mapping MCF.

Vegetable parenting practices scale. Item response modeling analyses

PubMed Central

Chen, Tzu-An; O’Connor, Teresia; Hughes, Sheryl; Beltran, Alicia; Baranowski, Janice; Diep, Cassandra; Baranowski, Tom

2015-01-01

Objective To evaluate the psychometric properties of a vegetable parenting practices scale using multidimensional polytomous item response modeling which enables assessing item fit to latent variables and the distributional characteristics of the items in comparison to the respondents. We also tested for differences in the ways item function (called differential item functioning) across child’s gender, ethnicity, age, and household income groups. Method Parents of 3–5 year old children completed a self-reported vegetable parenting practices scale online. Vegetable parenting practices consisted of 14 effective vegetable parenting practices and 12 ineffective vegetable parenting practices items, each with three subscales (responsiveness, structure, and control). Multidimensional polytomous item response modeling was conducted separately on effective vegetable parenting practices and ineffective vegetable parenting practices. Results One effective vegetable parenting practice item did not fit the model well in the full sample or across demographic groups, and another was a misfit in differential item functioning analyses across child’s gender. Significant differential item functioning was detected across children’s age and ethnicity groups, and more among effective vegetable parenting practices than ineffective vegetable parenting practices items. Wright maps showed items only covered parts of the latent trait distribution. The harder- and easier-to-respond ends of the construct were not covered by items for effective vegetable parenting practices and ineffective vegetable parenting practices, respectively. Conclusions Several effective vegetable parenting practices and ineffective vegetable parenting practices scale items functioned differently on the basis of child’s demographic characteristics; therefore, researchers should use these vegetable parenting practices scales with caution. Item response modeling should be incorporated in analyses of parenting practice questionnaires to better assess differences across demographic characteristics. PMID:25895694

Identifying high production, low production and degraded rangelands in Senegal with normalized difference vegetation index data

USGS Publications Warehouse

Tappan, G. Gray; Wood, Lynette; Moore, Donald G.

1993-01-01

Seasonal herbaceous vegetation production on Senegal's native rangelands exhibits high spatial and temporal variability. This variability can be monitored using normalized difference vegetation index (NDVI) data computed from 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) image data. Although annual fluctuations in rainfall account for some of the variability, numerous long-term production patterns are evident in the AVHRR time-series data. Different n productivity reflect variations in the region's climate, topography, soils, and land use. Areas of overgrazing and intensive cultivation have caused long-term soil and vegetation degradation. Rangelands of high and low productivity, and degraded rangelands were identified using NDVI. Time-series image data from 1987 though 1992 were used to map relative rangeland productivity. The results were compared to detailed resource maps on soils, vegetation and land use. Much of the variation in rangeland productivity correlated well to the known distribution of resources. The study developed an approach that identified a number of areas of degraded soils and low vegetation production.

Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation

NASA Technical Reports Server (NTRS)

Rouse, J. W., Jr. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Preliminary evaluation of autumnal phase ground truth data suggests that the sampling procedures at the Great Plains Corridor network test sites are adequate to show relatively small temporal changes in above-ground vegetation biomass and vegetation condition. Vegetation changes measured August through December, reflect grazing intensity and environmental conditions at the test sites. Preliminary analysis of black and white imagery suggests that detail in vegetation patterns is much greater than originally anticipated. A first look analysis of single band imagery and digital data at two locations shows that woodland, grassland, and cropland areas are easily delineated. Computer derived grey-scale maps from MSS digital data were shown to be useful in identifying the location of small fields and features of the natural and cultivated lands. Single band imagery and digital data are believed to have important application for synoptic land use mapping and inventory. Initial ratio analysis, using band 5 and 7 data, suggests the applicability in the greenness of a vegetative scene.

Current state and projection of the probable original vegetation of the São Carlos region of São Paulo State, Brazil.

PubMed

Soares, J J; da Silva, D W; Lima, M I

2003-08-01

A map of the native vegetation remaining in São Carlos County was built based on aerial images, satellite images, and field observations, and a projection of the probable original vegetation was made by checking it against soil and relief surveys. The existing vegetation is very fragmented and improverished, consisting predominantly of cerrados (savanna vegetation of various physiognomies), semideciduous and riparian forest, and regeneration areas. Araucaria angustifolia (Bertol.) Kuntze, found in patches inside the semideciduous forest beginning at a minimum altitude of 850 m, has practically disappeared. By evaluating areas on the map for different forms of vegetation, we obtained the following results for original coverage: 27% cerrado (sparsely arboreal and short-shrub savanna, and wet meadows); 16% cerradão (arboreal savanna); 55% semideciduous and riparian forests; and 2% forest with A. angustifolia. There are now 2% cerrados; 2.5% cerradão; 1% semideciduous forest and riparian forests; 1.5% regeneration areas; and 0% forest with A. angustifolia.

Mapping the Wetland Vegetation Communities of the Australian Great Artesian Basin Springs Using SAM, Mtmf and Spectrally Segmented PCA Hyperspectral Analyses

NASA Astrophysics Data System (ADS)

White, D. C.; Lewis, M. M.

2012-07-01

The Australian Great Artesian Basin (GAB) supports a unique and diverse range of groundwater dependent wetland ecosystems termed GAB springs. In recent decades the ecological sustainability of the springs has become uncertain as demands on this iconic groundwater resource increase. The impacts of existing water extractions for mining and pastoral activities are unknown. This situation is compounded by the likelihood of future increasing demand for extractions. Hyperspectral remote sensing provides the necessary spectral and spatial detail to discriminate wetland vegetation communities. Therefore the objectives of this paper are to discriminate the spatial extent and distribution of key spring wetland vegetation communities associated with the GAB springs evaluating three hyperspectral techniques: Spectral Angle Mapper (SAM), Mixture Tuned Matched Filtering (MTMF) and Spectrally Segmented PCA. In addition, to determine if the hyperspectral techniques developed can be applied at a number of sites representative of the range of spring formations and geomorphic settings and at two temporal intervals. Two epochs of HyMap airborne hyperspectral imagery were captured for this research in March 2009 and April 2011 at a number of sites representative of the floristic and geomorphic diversity of GAB spring groups/complexes within South Australia. Colour digital aerial photography at 30 cm GSD was acquired concurrently with the HyMap imagery. The image acquisition coincided with a field campaign of spectroradiometry measurements and a botanical survey. To identify key wavebands which have the greatest capability to discriminate vegetation communities of the GAB springs and surrounding area three hyperspectral data reduction techniques were employed: (i) Spectrally Segmented PCA (SSPCA); (ii) the Minimum Noise Transform (MNF); and (iii) the Pixel Purity Index (PPI). SSPCA was applied to NDVI-masked vegetation portions of the HyMap imagery with wavelength regions spectrally segmented for the VIS-NIR (450-1,350 nm), SWIR 1 (1,400-1,800 nm) and SWIR 2 (1,950-2,480 nm). The resulting pure endmember image pixels of the vegetation communities identified were used as target spectra for input into the SAM and MTMF algorithms. Spring wetland vegetation communities successfully discriminated include low lying reeds and sedges along spring tails (Baumea spp. and Cyperus spp.), dense homogenous stands of Phragmites australis reeds, and sporadic patches of salt couch grass (Sparabolus spp.). Our results indicate that a combination of hyperspectral remote sensing techniques which reduce superfluous wavebands providing a targeted spectral matching approach are capable of discriminating and mapping key vegetation communities of the GAB springs. This approach provides reliable baseline mapping of the GAB spring wetland vegetation communities, with repeatability over space and time. In addition it has the capability to determine the sensitivity of spring wetland vegetation extent, distribution and diversity, to associated changes in spring flow rates due to water extractions. This approach will ultimately inform water allocation plan management policies.

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 Vegetation Structure in Kakadu National Park: An AIRSAR and GIS Application in Conservation

NASA Technical Reports Server (NTRS)

Imhoff, Marc L.; Sisk, Thomas D.; Hampton, Haydee; Milne, Anthony K.

1999-01-01

Airborne Synthetic Aperture Radar (AIRSAR) data were used to map vegetation structure in Kakadu National Park Australia as part of the PACRIM project. SAR data were co-registered with Landsat TM, aerial photos, and map data in a geographic information system for a small test area consisting of mangrove, floodplain grasslands, lowland tropical evergreen forest and upland mixed deciduous and evergreen tropical forest near the South Alligator River. Landsat (Thematic Mapper) TM very clearly showed the floristic composition and burn scars from the previous years fires and the AIRSAR data provided a profile of vegetation structure. Extensive field data on vegetation species composition and structure were collected across a series of transects in cooperation with a survey of avifauna in an effort to link the habitat edge structure with bird species responses. A test site was found that contained two types of habitat edges: 1) A structure specific edge - characterized by the appearance of a very strong structural change in the forest canopy occurring in the absence of a substantial turnover in floristics. 2) Floristic edge - a sharp transition in vegetation genetic composition with a mixed set of structural changes. Specific polarization combinations were selected that were highly correlated to a set of desired structural parameters found in the field data. Classification routines were employed to group radar pixels into 3 structural classes based on: the Surface Area to Volume ratio (SA/V) of the stems, the SA/V of the branches, and the leaf area index of the canopy. Separate canopy structure maps were then entered into the GIS and bird responses were observed relative to the classes and their boundaries. Follow-on work will consist of extending this approach to neighboring areas, generating structure maps, predicting bird responses across the edges, and make accuracy assessments.

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.

Multilevel Correlates of Satisfaction with Neighborhood Availability of Fresh Fruits and Vegetables

PubMed Central

Zenk, Shannon N.; Schulz, Amy J.; Lachance, Laurie L.; Mentz, Graciela; Kannan, Srimathi; Ridella, William; Galea, Sandro

2009-01-01

Background Little is known about influences on perceptions of neighborhood food environments, despite their relevance for food-shopping behaviors and food choices. Purpose This study examined relationships between multilevel factors (neighborhood structure, independently observed neighborhood food environment, individual socioeconomic position) and satisfaction with neighborhood availability of fruits and vegetables. Methods The multilevel regression analysis drew on data from a community survey of urban adults, in-person audit and mapping of food stores, and the 2000 Census. Results Satisfaction with neighborhood availability of fruits and vegetables was lower in neighborhoods that were further from a supermarket and that had proportionately more African-American residents. Neighborhood poverty and independently observed neighborhood fruit and vegetable characteristics (variety, prices, quality) were not associated with satisfaction. Individual education modified relationships between neighborhood availability of smaller food stores (small grocery stores, convenience stores, liquor stores) and satisfaction. Conclusions Individual-level and neighborhood-level factors affect perceptions of neighborhood food environments. PMID:19809859

Obtaining land cover changes information from multitemporal analysis of Landsat-TM images: results from a case study in West African dryland

NASA Astrophysics Data System (ADS)

Nutini, F.; Boschetti, M.; Brivio, P. A.; Antoninetti, M.

2012-04-01

The Sahelian belt of West Africa is a semiarid region characterized by wide climate variations, which can in turn affect the livelihood of local populations particularly in rangeland areas, as happens during the dramatic food crisis in the 70-80s caused by rainfall scarcity. The monitoring of natural resources and rainfed agricultural activities, with the aim to provide information to support Sahelian food security action, needs the production of detailed thematic maps as emphasized by several scientific papers. In this framework, a study was conducted to develop a method to exploit time series of remote sensed satellite data to 1) provide reliable land cover (LC) map at local scale in a dry region and 2) obtain a LC change (LCC) map that contribute to identify the plausible causes of local environmental instability. Satellite images used for this work consist in a time series of Landsat Thematic Mapper (TM) (path row 195-50) acquired in the 2000 (6 scenes) and 2007 (9 scenes) from February (Dry season) to September (end of wet season). The study investigates the different contribution provided by spectra information of a single Landsat TM image and by time series of derived NDVI. Different tests have been conducted with different combination of data set (spectral and temporal)in order to identify the best approach to obtain a LC map in five classes of interest: Shrubland, Cultivated Land, Water body, Herbaceous vegetation and Bare soil. The best classification approach is exposed and applied on two years in the last decade. The comparison between this two LC results in land cover change map, that displays the changes of vegetation patterns that have been characterized the area. The discussed results show a largely stable dryland region, but locally characterized by hot-spot of decreasing in natural vegetation inside the rangelands and an increasing of cultivations along fossil valleys where human activities are slightly intense. The discussion shows that this hot-spot aren't fully explained by climatic variability as displayed by a comparison with rainfall satellite data, and suggest that there are localized area where vegetation development is driven by other anthropic factors which interfere in the dynamics of plant growing.

Object based technique for delineating and mapping 15 tree species using VHR WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Mustafa, Yaseen T.; Habeeb, Hindav N.

2014-10-01

Monitoring and analyzing forests and trees are required task to manage and establish a good plan for the forest sustainability. To achieve such a task, information and data collection of the trees are requested. The fastest way and relatively low cost technique is by using satellite remote sensing. In this study, we proposed an approach to identify and map 15 tree species in the Mangish sub-district, Kurdistan Region-Iraq. Image-objects (IOs) were used as the tree species mapping unit. This is achieved using the shadow index, normalized difference vegetation index and texture measurements. Four classification methods (Maximum Likelihood, Mahalanobis Distance, Neural Network, and Spectral Angel Mapper) were used to classify IOs using selected IO features derived from WorldView-2 imagery. Results showed that overall accuracy was increased 5-8% using the Neural Network method compared with other methods with a Kappa coefficient of 69%. This technique gives reasonable results of various tree species classifications by means of applying the Neural Network method with IOs techniques on WorldView-2 imagery.

Remote sensing and landslide hazard assessment

NASA Technical Reports Server (NTRS)

Mckean, J.; Buechel, S.; Gaydos, L.

1991-01-01

Remotely acquired multispectral data are used to improve landslide hazard assessments at all scales of investigation. A vegetation map produced from automated interpretation of TM data is used in a GIS context to explore the effect of vegetation type on debris flow occurrence in preparation for inclusion in debris flow hazard modeling. Spectral vegetation indices map spatial patterns of grass senescence which are found to be correlated with soil thickness variations on hillslopes. Grassland senescence is delayed over deeper, wetter soils that are likely debris flow source areas. Prediction of actual soil depths using vegetation indices may be possible up to some limiting depth greater than the grass rooting zone. On forested earthflows, the slow slide movement disrupts the overhead timber canopy, exposes understory vegetation and soils, and alters site spectral characteristics. Both spectral and textural measures from broad band multispectral data are successful at detecting an earthflow within an undisturbed old-growth forest.

Vegetation cover dynamics of the Mongolian semiarid zone according to multi-temporal LANDSAT imagery (the case of Darkhan test range)

NASA Astrophysics Data System (ADS)

Zharnikova, M. A.; Alymbaeva, ZH B.; Ayurzhanaev, A. A.; Garmaev, E. ZH

2016-11-01

At present much attention is given to the spatio-temporal dynamics of plant communities of steppes to assess their response to the current climate changes. In this study, a mapping of a selected modeling polygon was carried out on the basis of data decoding and field surveys of vegetation cover in the semi-arid zone. The resulting large-scale map of actual vegetation reflects the current state of the vegetation cover and its horizontal structure. It is a valuable material for monitoring of changes in the chosen area. With multi-temporal satellite Landsat imagery we consider the vegetation cover dynamics of the test range. To analyze the transformation of the environment by the climatic factors, we compared series of NDVI versus the precipitation and of NDVI versus the temperatures. Then we calculated the degree of correlation between them.

Experimental application of LANDSAT to geobotanical prospecting of serpentine outcrops in the central Appalachian Piedmont of North America

NASA Technical Reports Server (NTRS)

Mielke, H. W. (Principal Investigator)

1980-01-01

The use of LANDSAT as a tool for geobotanical prospecting was studied in a 13,137 sq km area from southeastern Pennsylvania to northern Virginia. Vegetation differences between known serpentine and non-sepentine sites were most easily distinguished on early summer images. A multispectral signature was derived from vegetation of two known serpentine sites and a map was produced of 159 similar signatures of vegetation in the study area. Authenticity of the serpentine nature of the mapped sites was checked via geochemical analysis of collected soils from 14% of the sites. Overall success of geobotanical prospecting was about 35% for the total study area. When vegetation distribution was taken into account, the success rate was 67% for the region north of the Potomac, demonstrates the effectiveness of the multispectral satellite for quickly and accurately locating mineral sensitive vegetation communities over vast tracts of land.

Imaging Laser Altimetry in the Amazon: Mapping Large Areas of Topography, Vegetation Height and Structure, and Biomass

NASA Technical Reports Server (NTRS)

Blair, J. Bryan; Nelson, B.; dosSantos, J.; Valeriano, D.; Houghton, R.; Hofton, M.; Lutchke, S.; Sun, Q.

2002-01-01

A flight mission of NASA GSFC's Laser Vegetation Imaging Sensor (LVIS) is planned for June-August 2003 in the Amazon region of Brazil. The goal of this flight mission is to map the vegetation height and structure and ground topography of a large area of the Amazon. This data will be used to produce maps of true ground topography, vegetation height, and estimated above-ground biomass and for comparison with and potential calibration of Synthetic Aperture Radar (SAR) data. Approximately 15,000 sq. km covering various regions of the Amazon will be mapped. The LVIS sensor has the unique ability to accurately sense the ground topography beneath even the densest of forest canopies. This is achieved by using a high signal-to-noise laser altimeter to detect the very weak reflection from the ground that is available only through small gaps in between leaves and between tree canopies. Often the amount of ground signal is 1% or less of the total returned echo. Once the ground elevation is identified, that is used as the reference surface from which we measure the vertical height and structure of the vegetation. Test data over tropical forests have shown excellent correlation between LVIS measurements and biomass, basal area, stem density, ground topography, and canopy height. Examples of laser altimetry data over forests and the relationships to biophysical parameters will be shown. Also, recent advances in the LVIS instrument will be discussed.

Albedo and land surface temperature shift in hydrocarbon seepage potential area, case study in Miri Sarawak Malaysia

NASA Astrophysics Data System (ADS)

Suherman, A.; Rahman, M. Z. A.; Busu, I.

2014-02-01

The presence of hydrocarbon seepage is generally associated with rock or mineral alteration product exposures, and changes of soil properties which manifest with bare development and stress vegetation. This alters the surface thermodynamic properties, changes the energy balance related to the surface reflection, absorption and emission, and leads to shift in albedo and LST. Those phenomena may provide a guide for seepage detection which can be recognized inexpensively by remote sensing method. District of Miri is used for study area. Available topographic maps of Miri and LANDSAT ETM+ were used for boundary construction and determination albedo and LST. Three land use classification methods, namely fixed, supervised and NDVI base classifications were employed for this study. By the intensive land use classification and corresponding statistical comparison was found a clearly shift on albedo and land surface temperature between internal and external seepage potential area. The shift shows a regular pattern related to vegetation density or NDVI value. In the low vegetation density or low NDVI value, albedo of internal area turned to lower value than external area. Conversely in the high vegetation density or high NDVI value, albedo of internal area turned to higher value than external area. Land surface temperature of internal seepage potential was generally shifted to higher value than external area in all of land use classes. In dense vegetation area tend to shift the temperature more than poor vegetation area.

Spatial patterns of vegetation biomass and soil organic carbon acquired from airborne lidar and hyperspectral imagery at Reynolds Creek Critical Zone Observatory

NASA Astrophysics Data System (ADS)

Will, R. M.; Li, A.; Glenn, N. F.; Benner, S. G.; Spaete, L.; Ilangakoon, N. T.

2015-12-01

Soil organic carbon distribution and the factors influencing this distribution are important for understanding carbon stores, vegetation dynamics, and the overall carbon cycle. Linking soil organic carbon (SOC) with aboveground vegetation biomass may provide a method to better understand SOC distribution in semiarid ecosystems. The Reynolds Creek Critical Zone Observatory (RC CZO) in Idaho, USA, is approximately 240 square kilometers and is situated in the semiarid Great Basin of the sagebrush-steppe ecosystem. Full waveform airborne lidar data and Next-Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-ng) collected in 2014 across the RC CZO are used to map vegetation biomass and SOC and then explore the relationships between them. Vegetation biomass is estimated by identifying vegetation species, and quantifying distribution and structure with lidar and integrating the field-measured biomass. Spectral data from AVIRIS-ng are used to differentiate non-photosynthetic vegetation (NPV) and soil, which are commonly confused in semiarid ecosystems. The information from lidar and AVIRIS-ng are then used to predict SOC by partial least squares regression (PLSR). An uncertainty analysis is provided, demonstrating the applicability of these approaches to improving our understanding of the distribution and patterns of SOC across the landscape.

Global Survey of Anthropogenic Neighborhood Threats to Conservation of Grass-Shrub and Forest Vegetation

EPA Science Inventory

The ecological functions of natural vegetation are threatened when it is subsumed in anthropogenic landscapes. We report the first comparative global survey of anthropogenic landscape threats to forest and grass-shrub vegetation. Using a global land-cover map derived from remote...

Estimating costs and performance of systems for machine processing of remotely sensed data

NASA Technical Reports Server (NTRS)

Ballard, R. J.; Eastwood, L. F., Jr.

1977-01-01

This paper outlines a method for estimating computer processing times and costs incurred in producing information products from digital remotely sensed data. The method accounts for both computation and overhead, and may be applied to any serial computer. The method is applied to estimate the cost and computer time involved in producing Level II Land Use and Vegetative Cover Maps for a five-state midwestern region. The results show that the amount of data to be processed overloads some example computer systems, but that the processing is feasible on others.

Remote sensing-based characterization, 2-m, Plant Functional Type Distributions, Barrow Environmental Observatory, 2010

DOE Data Explorer

Langford, Zachary; Kumar, Jitendra; Hoffman, Forrest

2014-01-01

Arctic ecosystems have been observed to be warming faster than the global average and are predicted to experience accelerated changes in climate due to global warming. Arctic vegetation is particularly sensitive to warming conditions and likely to exhibit shifts in species composition, phenology and productivity under changing climate. Mapping and monitoring of changes in vegetation is essential to understand the effect of climate change on the ecosystem functions. Vegetation exhibits unique spectral characteristics which can be harnessed to discriminate plant types and develop quantitative vegetation indices. We have combined high resolution multi-spectral remote sensing from the WorldView 2 satellite with LIDAR-derived digital elevation models to characterize the tundra landscape on the North Slope of Alaska. Classification of landscape using spectral and topographic characteristics yields spatial regions with expectedly similar vegetation characteristics. A field campaign was conducted during peak growing season to collect vegetation harvests from a number of 1m x 1m plots in the study region, which were then analyzed for distribution of vegetation types in the plots. Statistical relationships were developed between spectral and topographic characteristics and vegetation type distributions at the vegetation plots. These derived relationships were employed to statistically upscale the vegetation distributions for the landscape based on spectral characteristics. Vegetation distributions developed are being used to provide Plant Functional Type (PFT) maps for use in the Community Land Model (CLM).

Calculation of upper confidence bounds on proportion of area containing not-sampled vegetation types: An application to map unit definition for existing vegetation maps

Treesearch

Paul L. Patterson; Mark Finco

2011-01-01

This paper explores the information forest inventory data can produce regarding forest types that were not sampled and develops the equations necessary to define the upper confidence bounds on not-sampled forest types. The problem is reduced to a Bernoulli variable. This simplification allows the upper confidence bounds to be calculated based on Cochran (1977)....

Estimating and mapping forest biomass using regression models and Spot-6 images (case study: Hyrcanian forests of north of Iran).

PubMed

Motlagh, Mohadeseh Ghanbari; Kafaky, Sasan Babaie; Mataji, Asadollah; Akhavan, Reza

2018-05-21

Hyrcanian forests of North of Iran are of great importance in terms of various economic and environmental aspects. In this study, Spot-6 satellite images and regression models were applied to estimate above-ground biomass in these forests. This research was carried out in six compartments in three climatic (semi-arid to humid) types and two altitude classes. In the first step, ground sampling methods at the compartment level were used to estimate aboveground biomass (Mg/ha). Then, by reviewing the results of other studies, the most appropriate vegetation indices were selected. In this study, three indices of NDVI, RVI, and TVI were calculated. We investigated the relationship between the vegetation indices and aboveground biomass measured at sample-plot level. Based on the results, the relationship between aboveground biomass values and vegetation indices was a linear regression with the highest level of significance for NDVI in all compartments. Since at the compartment level the correlation coefficient between NDVI and aboveground biomass was the highest, NDVI was used for mapping aboveground biomass. According to the results of this study, biomass values were highly different in various climatic and altitudinal classes with the highest biomass value observed in humid climate and high-altitude class.

THE HOLDRIDGE LIFE ZONES OF THE CONTERMINOUS UNITED STATES IN RELATION TO ECOSYSTEM MAPPING

EPA Science Inventory

Our main goals were to develop a map of the life zones for the conterminous United States, based on the Holdridge Life Zone system as a tool for ecosystem mapping, and to compare the map of Holdridge life zones with other global vegetation classification and mapping efforts.
...

Enhanced canopy fuel mapping by integrating lidar data

USGS Publications Warehouse

Peterson, Birgit E.; Nelson, Kurtis J.

2016-10-03

BackgroundThe Wildfire Sciences Team at the U.S. Geological Survey’s Earth Resources Observation and Science Center produces vegetation type, vegetation structure, and fuel products for the United States, primarily through the Landscape Fire and Resource Management Planning Tools (LANDFIRE) program. LANDFIRE products are used across disciplines for a variety of applications. The LANDFIRE data retain their currency and relevancy through periodic updating or remapping. These updating and remapping efforts provide opportunities to improve the LANDFIRE product suite by incorporating data from other sources. Light detection and ranging (lidar) is uniquely suitable for gathering information on vegetation structure and spatial arrangement because it can collect data in three dimensions. The Wildfire Sciences Team has several completed and ongoing studies focused on integrating lidar into vegetation and fuels mapping.

New England reservoir management: Land use/vegetation mapping in reservoir management (Merrimack River Basin). [Massachusetts and New Hamshire

NASA Technical Reports Server (NTRS)

Cooper, S. (Principal Investigator); Mckim, H. L.; Gatto, L. W.; Merry, C. J.; Anderson, D. M.; Marlar, T. L.

1974-01-01

The author has identified the following significant results. It is evident from this comparison that for land use/vegetation mapping the S190B Skylab photography compares favorably with the RB-57 photography and is much superior to the ERTS-1 and Skylab 190A imagery. For most purposes the 12.5 meter resolution of the S190B imagery is sufficient to permit extraction of the information required for rapid land use and vegetation surveys necessary in the management of reservoir or watershed. The ERTS-1 and S190A data products are not considered adequate for this purpose, although they are useful for rapid regional surveys at the level 1 category of the land use/vegetation classification system.

Identification of phenological stages and vegetative types for land use classification

NASA Technical Reports Server (NTRS)

Mckendrick, J. D. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Classification of digital data for mapping Alaskan vegetation has been compared to ground truth data and found to have accuracies as high as 90%. These classifications are broad scale types as are currently being used on the Major Ecosystems of Alaska map prepared by the Joint Federal-State Land Use Planning Commission for Alaska. Cost estimates for several options using the ERTS-1 digital data to map the Alaskan land mass at the 1:250,000 scale ranged between $2.17 to $1.49 per square mile.

Bathymetry and vegetation in isolated marsh and cypress wetlands in the northern Tampa Bay Area, 2000-2004

USGS Publications Warehouse

Haag, Kim H.; Lee, Terrie M.; Herndon, Donald C.

2005-01-01

Wetland bathymetry and vegetation mapping are two commonly used lines of evidence for assessing the hydrologic and ecologic status of expansive coastal and riverine wetlands. For small isolated freshwater wetlands, however, bathymetric data coupled with vegetation assessments are generally scarce, despite the prevalence of isolated wetlands in many regions of the United States and the recognized importance of topography as a control on inundation patterns and vegetation distribution. In the northern Tampa Bay area of west-central Florida, bathymetry was mapped and vegetation was assessed in five marsh and five cypress wetlands. These 10 isolated wetlands were grouped into three categories based on the effects of ground-water withdrawals from regional municipal well fields: natural (no effect), impaired (drier than natural), and augmented (wetlands with artificially augmented water levels). Delineation of the wetland perimeter was a critical component for estimating wetland-surface area and stored water volume. The wetland perimeter was delineated by the presence of Serenoa repens (the 'palmetto fringe') at 9 of the 10 sites. At the 10th site, where the palmetto fringe was absent, hydric-soils indicators were used to delineate the perimeter. Bathymetric data were collected using one or more techniques, depending on the physical characteristics of each wetland. Wetland stage was measured hourly using continuous stage recorders. Wetland vegetation was assessed semiannually for 2 1/2 years in fixed plots located at three distinct elevations. Vegetation assessments were used to determine the community composition and the relative abundance of obligate, facultative wet, and facultative species at each elevation. Bathymetry maps were generated, and stage-area and stage-volume relations were developed for all 10 wetlands. Bathymetric data sets containing a high density of data points collected at frequent and regular spatial intervals provided the most useful stage-area and stage-volume relations. Bathymetric maps of several wetlands also were generated using a low density of data points collected along transect lines or contour lines. In a comparative analysis of the three mapping approaches, stage-area and stage-volume relations based on transect data alone underestimated (by 50-100 percent over certain ranges of stage) the wetland area and volume compared to results using a high density of data points. Adding data points collected along one elevation contour below the wetland perimeter to the transect data set greatly improved the agreement of the resulting stage-area and stage-volume relations to the high-density mapping approach. Stage-area relations and routinely monitored stage data were used to compare and contrast the average flooded area in a natural marsh and an impaired marsh over a 2-year period. Vegetation assessments used together with flooded-area information provided the potential for extrapolating vegetation results from points or transects to wetlands as a whole. A comparison of the frequency of flooding of different areas of the wetland and the species composition in vegetation plots at different elevations indicated the dependence of vegetation on inundation frequency. Because of the broad tolerances of many wetlands plants to a range of inundation conditions, however, vegetation assessments alone provided less definitive evidence of the hydrologic differences between the two sites, and hydrologic changes occurring during the 2 years, than the flooded-area frequencies. Combining flooded-area frequencies with vegetation assessments could provide a more versatile and insightful approach for determining the ecological status of wetlands than using vegetation and stage data alone. Flooded-area frequencies may further provide a useful approach for assessing the ecological status of wetlands where historical vegetation surveys and stage data are lacking. Comparing the contemporary flooded-area frequencies a

Mapping Landslides Susceptibility in a Traditional Northern Nigerian City

NASA Astrophysics Data System (ADS)

Oluwafemi, Olawale A.; Yakubu, Tahir A.; Muhammad, Mahmud U.; Shitta, Nyofo; Akinwumiju, Akinola S.

2018-05-01

As a result of dearth of relevant information about Landslides Susceptibility in Nigeria, the monitoring and assessment appears intractable. Hence, the study developed a Remote Sensing approach to mapping landslides susceptibility, landuse and landcover analysis in Jos South LGA, Plateau State, Nigeria. Field Observation, SPOT 5 2009 and 2012, ASTER DEM 2009, Geological Map 2006, Topographical Map 1966 were used to map Landslide Susceptibility and Landuse /Lancover Analysis in the study area. Geospatial Analytical Operations employed using ArcGIS 10.3 and Erdas Imagine 2014 include Spatial Modeling, Vectorization, Pre-lineament Extraction, Image Processing among others. Result showed that 72.38 % of the study area is underlain by granitic rocks. The landuse/cover types delineated for the study area include floodplain (29.27 %), farmland (23.96 %), sparsely vegetated land (15.43 %), built up area (13.65 %), vegetated outcrop (8.48 %), light vegetation (5.37 %), thick vegetation (2.39 %), water body (0.58 %), plantation (0.50 %) and mining pond (0.37 %). Landslide Susceptibility Analysis also revealed that 87 % of the study area is relatively at low to very low risk of landslide event. While only 13 % of the study area is at high to very high risk of landslide event. The study revealed that the susceptibility of landslide event is very low in the study area. However, possible landslide event in the hot spots could be pronounced and could destabilize the natural and man-made environmental systems of the study area.

Impacts of river-bed aggradation and lahar activity downstream of Santiaguito Volcano, Guatemala: a Landsat Thematic Mapper perspective

NASA Astrophysics Data System (ADS)

Flynn, L. P.; Harris, A. J.; Davies, M. A.; Vallence, J. W.; Rose, W. I.

2002-12-01

Lava extrusion at Santiaguito volcano, Guatemala and rainfall runoff cause lahars and river-bed aggradation downstream of the volcano. We present a method that uses vegetation indices extracted from Landsat Thematic Mapper (TM) data to identify zones of impact. The method differentiates vegetation-free and vegetated pixels, constrains areas affected by aggradation, and generates catchment-wide aggradation maps. Application of the technique to 22 TM images acquired between 1987 and 2000 helped us to measure, map and track temporal and spatial variations in the area of lahar impact and river aggradation. To verify our TM-based analyses we carried out 3 field campaigns between 2000 and 2002, during which we focused on a segment of aggraded river beds ~8 km from Santiaguito. We then used our TM and field-based studies to document and validate changes at this location, as follows: (1) Time varying effects of aggradation. The main river to head at Santiaguito is R¡o Nima II. The TM analysis indicated development of a new channel cutting across farm land on the western edge of R¡o Nima II between 1996 and 2000. Field checking showed that development of an aggraded, convex, bed profile caused channels to flow westward away from the aggraded river-channel system. (2) Emplacement of lava flows. The TM time series indicated that a new lava flow extended into the upper reaches of the Rio Nima I during 1996 and triggered aggradation. Field checking confirmed that a new supply of volcaniclastic material had extended aggradation into this previously unaffected drainage. (3) River capture. Capture of R¡o Nima I by R¡o Samal has increased aggradation of along new sections of R¡o Samal , an effect evident in our TM mapping. Field checking showed that, although R¡o Samala does not head at Santiaguito, the new supply of material from R¡o Nima I triggered rapid aggradation of R¡o Samal after 1996.

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.

EnviroAtlas - Austin, TX - 15m Riparian Buffer Vegetated Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 15-m riparian buffer that is vegetated. Vegetated cover is defined as Trees & Forest and Grass & Herbaceous. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. 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 - Des Moines, IA - 51m Riparian Buffer Vegetated Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. Vegetated cover is defined as Trees & Forest and Grass & Herbaceous. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://enviroatlas.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 - New York, NY - 51m Riparian Buffer Vegetated Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. In this community, vegetated cover is defined as Trees & Forest and Grass & Herbaceous. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. 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 - Austin, TX - 51m Riparian Buffer Vegetated Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. Vegetated cover is defined as Trees & Forest and Grass & Herbaceous. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. 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 - Cleveland, OH - 51m Riparian Buffer Vegetated Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. In this community, vegetated cover is defined as Trees & Forest, Grass & Herbaceous, Woody Wetlands, and Emergent Wetlands. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. 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 - Paterson, NJ - 51m Riparian Buffer Vegetated Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. EnviroAtlas defines vegetated buffer for this community as trees and forest and grass and herbaceous. 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).

Going beyond the green: senesced vegetation material predicts basal area and biomass in remote sensing of tree cover conditions in an African tropical dry forest (miombo woodland) landscape

NASA Astrophysics Data System (ADS)

Mayes, Marc; Mustard, John; Melillo, Jerry; Neill, Christopher; Nyadzi, Gerson

2017-08-01

In sub-Saharan Africa (SSA), tropical dry forests and savannas cover over 2.5 million km2 and support livelihoods for millions in fast-growing nations. Intensifying land use pressures have driven rapid changes in tree cover structure (basal area, biomass) that remain poorly characterized at regional scales. Here, we posed the hypothesis that tree cover structure related strongly to senesced and non-photosynthetic (NPV) vegetation features in a SSA tropical dry forest landscape, offering improved means for satellite remote sensing of tree cover structure compared to vegetation greenness-based methods. Across regrowth miombo woodland sites in Tanzania, we analyzed relationships among field data on tree structure, land cover, and satellite indices of green and NPV features based on spectral mixture analyses and normalized difference vegetation index calculated from Landsat 8 data. From satellite-field data relationships, we mapped regional basal area and biomass using NPV and greenness-based metrics, and compared map performances at landscape scales. Total canopy cover related significantly to stem basal area (r 2 = 0.815, p < 0.01) and biomass (r 2 = 0.635, p < 0.01), and NPV dominated ground cover (> 60%) at all sites. From these two conditions emerged a key inverse relationship: skyward exposure of NPV ground cover was high at sites with low tree basal area and biomass, and decreased with increasing stem basal area and biomass. This pattern scaled to Landsat NPV metrics, which showed strong inverse correlations to basal area (Pearson r = -0.85, p < 0.01) and biomass (r = -0.86, p < 0.01). Biomass estimates from Landsat NPV-based maps matched field data, and significantly differentiated landscape gradients in woody biomass that greenness metrics failed to track. The results suggest senesced vegetation metrics at Landsat scales are a promising means for improved monitoring of tree structure across disturbance and ecological gradients in African and other tropical dry forests.

Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery

NASA Astrophysics Data System (ADS)

Monteiro, Sildomar Takahashi; Minekawa, Yohei; Kosugi, Yukio; Akazawa, Tsuneya; Oda, Kunio

Hyperspectral image data provides a powerful tool for non-destructive crop analysis. This paper investigates a hyperspectral image data-processing method to predict the sweetness and amino acid content of soybean crops. Regression models based on artificial neural networks were developed in order to calculate the level of sucrose, glucose, fructose, and nitrogen concentrations, which can be related to the sweetness and amino acid content of vegetables. A performance analysis was conducted comparing regression models obtained using different preprocessing methods, namely, raw reflectance, second derivative, and principal components analysis. This method is demonstrated using high-resolution hyperspectral data of wavelengths ranging from the visible to the near infrared acquired from an experimental field of green vegetable soybeans. The best predictions were achieved using a nonlinear regression model of the second derivative transformed dataset. Glucose could be predicted with greater accuracy, followed by sucrose, fructose and nitrogen. The proposed method provides the possibility to provide relatively accurate maps predicting the chemical content of soybean crop fields.

Status of vegetation cover after 25 years since the last wildfire (Río Verde, Spain)

NASA Astrophysics Data System (ADS)

Martinez-Murillo, Juan F.; Remond, Ricardo; Ruiz-Sinoga, José D.

2016-04-01

Climatic conditions play an important role in the post-fire vegetation recovery as well as other factors like topography, soil, and pre and post-fire land use (Shakesby, 2011; Robichaud et al., 2013). This study deals with the characterization of the vegetation cover status in an area affected by a wildfire 25 years ago. Namely, the objectives are to: i) compare the current and previous vegetation cover to wildfire; and ii) evaluate whether the current vegetation has recovered the previous cover to wildfire. The study area is mainly located in the Rio Verde watershed (Sierra de las Nieves, South of Spain). It corresponds to an area affected by a wildfire in August 8th, 1991. The burned area was equal to 8,156 ha. The burn severity was spatially very high. The main geographic features of the burned area are: mountainous topography (altitudes ranging from 250 m to 1700 m; slope gradient >25%; exposure mainly southfacing); igneous (peridotites), metamorphic (gneiss) and calcareous rocks (limestones); and predominant forest land use (Pinus pinaster sp. woodlands, 10%; pinus opened forest + shrubland, 40%; shrubland, 35%; and bare soil + grassland, 15%). Remote sensing techniques and GIS analysis has been applied to achieve the objectives. Landsat 5 and Landsat 8 images were used: July 13th, 1991 and July 1st, 2013, for the previous wildfire situation and 22-years after, respectively. The 1990 CORINE land cover was also considered to map 1991 land uses prior the wildfire. The Andalucía Regional Government wildfire historic records were used to select the burned area and its geographical limit. 1991 and 2013 land cover maps were obtained by means of object-oriented classifications. Also, NDVI index were calculated and mapped for both years in order to compare the status of vegetation cover. According to the results, the combination of remote sensing and GIS analysis let map the most recovered areas affected by the wildfire in 1991. The vegetation indexes indicated that the vegetation cover in 2013 was still lower than that mapped just before the 1991 wildfire in most of the burned area after 25-years: 33% of the burned area showed a regression in the vegetation from pine to shrubland or to grassland; 54% showed a similar status than in 1991; and only 11% presented a better vegetation cover nowadays than in 1991. References Robichaud, P., Lewis, S.A., Wagenbrenner, J.W., Ashmun, L.E., Brown, R.E. 2013. Post-fire mulching for runoff and erosion mitigation. Part I: Effectiveness at reducing hillslope erosion rates. Catena 105, 75-92. Shakesby, R.A. 2011. Post-wildfire soil erosion in the Mediterranean: Review and future research directions. Earth-Science Reviews 105, 71-100.

The feasibility of using a universal Random Forest model to map tree height across different locations and vegetation types

NASA Astrophysics Data System (ADS)

Su, Y.; Guo, Q.; Jin, S.; Gao, S.; Hu, T.; Liu, J.; Xue, B. L.

2017-12-01

Tree height is an important forest structure parameter for understanding forest ecosystem and improving the accuracy of global carbon stock quantification. Light detection and ranging (LiDAR) can provide accurate tree height measurements, but its use in large-scale tree height mapping is limited by the spatial availability. Random Forest (RF) has been one of the most commonly used algorithms for mapping large-scale tree height through the fusion of LiDAR and other remotely sensed datasets. However, how the variances in vegetation types, geolocations and spatial scales of different study sites influence the RF results is still a question that needs to be addressed. In this study, we selected 16 study sites across four vegetation types in United States (U.S.) fully covered by airborne LiDAR data, and the area of each site was 100 km2. The LiDAR-derived canopy height models (CHMs) were used as the ground truth to train the RF algorithm to predict canopy height from other remotely sensed variables, such as Landsat TM imagery, terrain information and climate surfaces. To address the abovementioned question, 22 models were run under different combinations of vegetation types, geolocations and spatial scales. The results show that the RF model trained at one specific location or vegetation type cannot be used to predict tree height in other locations or vegetation types. However, by training the RF model using samples from all locations and vegetation types, a universal model can be achieved for predicting canopy height across different locations and vegetation types. Moreover, the number of training samples and the targeted spatial resolution of the canopy height product have noticeable influence on the RF prediction accuracy.

An approach for using soil surveys to guide the placement of water quality buffers

Treesearch

M.G. Dosskey; M.J. Helmers; D.E. Eisenhauer

2006-01-01

Vegetative buffers may function better for filtering agricultural runoff in some locations than in others because of intrinsic characteristics of the land on which they are placed. The objective of this study was to develop a method based on soil survey attributes that can be used to compare soil map units for how effectively a buffer installed in them could remove...

Habitat types of the Tenderfoot Creek Experimental Forest

Treesearch

David M. Ondov

1975-01-01

In May 1974, a review draft of the Forest Habitat Types of Montana (Pfister et al. 1974) was released for use by Forest Service personnel and others requiring a method of ecosystem classification as a means to stratify forest environments in Montana. With the use of this review draft in mind, an objective was outlined to develop a vegetation map of the Tenderfoot Creek...

Developing Remote Sensing Methodology to Characterize Savanna Vegetation Structure and Composition for Rangeland Monitoring and Conservation Applications

NASA Astrophysics Data System (ADS)

Tsalyuk, M.; Kelly, M.; Getz, W.

2012-12-01

Rangeland ecosystems cover more than fifty percent of earth's land surface, host considerable biodiversity and provide vital ecosystem services. However, rangelands around the world face degradation due to climate change, land use change and overgrazing. Human-driven changes to fire and grazing regimes enhance degradation processes. The purpose of this research is to develop a remote sensing methodology to characterize the structure and composition of savanna vegetation, in order to improve the ability of conservation managers to monitor and address such degradation processes. Our study site, Etosha National Park, is a 22,270 km^2 semi-arid savanna located in north-central Namibia. Fencing and provision of artificial water sources for wildlife have changed the natural grazing patterns, which has caused bush encroachment and vegetation degradation across the park. We used MODIS and Landsat ETM+ 7 satellite imagery to map the vegetation type, dominant species, density, cover and biomass of herbaceous and woody vegetation in Etosha. We used imagery for 2007-2012 together with extensive field sampling, both in the wet and the dry seasons. At each sampling point, we identified the dominant species and measured the density, canopy size, height and diameter of the trees and shrubs. At only 31% of the sampling points, the identified vegetation type matched the class assigned at the 1996 classification. This may indicate significant habitat modifications in Etosha. We used two parallel analytical approaches to correlate between radiometric and field data. First, we show that traditional supervised classification identifies well five classes: bare soil, grassland, steppe, shrub savanna and tree savanna. We then refined this classification to enable us to identify the species composition in an area utilizing the phenological differences in timing and duration of greenness of the dominant tree and shrub species in Etosha. Specifically, using multi-date images we were able to identify additional six sub-classes based on the dominant species in each class: Colophospermum mopane woodland, Colophospermum mopane shrubland, Cataphractes alexandri woodland, Acacia nebrownii shrubland, mixed Combretum species woodland and Terminalia prunioides woodland. Second, we used quantitative methods to relate satellite-based vegetation indices to the biometric properties measured on the ground. We found a correlation among measured height, diameter and canopy cover of woody vegetation and used this to improve the correlation between cover and Normalized Difference Vegetation Index (NDVI). We showed that the Soil Adjusted Total Vegetation Index (SATVI) and Normalized Difference Water Index (NDWI) were related to both greenness and density at a site. In order to measure grass biomass in the field, we calibrated Disc Pasture Mater by clipping, weighing and drying grass in 1m^2 plots, in the dry and wet seasons, with resulting R^2 of 0.87 and 0.83, respectively. MODIS-derived leaf area index (LAI) data was best correlated with dry grass biomass. We used these correlations to produce detailed maps of each vegetation parameter for the whole park. These maps will provide a baseline to employ historical imagery to better understand the effects of the park's management and changing grazing pressure on vegetation structure.

Mapping gullies, dunes, lava fields, and landslides via surface roughness

NASA Astrophysics Data System (ADS)

Korzeniowska, Karolina; Pfeifer, Norbert; Landtwing, Stephan

2018-01-01

Gully erosion is a widespread and significant process involved in soil and land degradation. Mapping gullies helps to quantify past, and anticipate future, soil losses. Digital terrain models offer promising data for automatically detecting and mapping gullies especially in vegetated areas, although methods vary widely measures of local terrain roughness are the most varied and debated among these methods. Rarely do studies test the performance of roughness metrics for mapping gullies, limiting their applicability to small training areas. To this end, we systematically explored how local terrain roughness derived from high-resolution Light Detection And Ranging (LiDAR) data can aid in the unsupervised detection of gullies over a large area. We also tested expanding this method for other landforms diagnostic of similarly abrupt land-surface changes, including lava fields, dunes, and landslides, as well as investigating the influence of different roughness thresholds, resolutions of kernels, and input data resolution, and comparing our method with previously published roughness algorithms. Our results show that total curvature is a suitable metric for recognising analysed gullies and lava fields from LiDAR data, with comparable success to that of more sophisticated roughness metrics. Tested dunes or landslides remain difficult to distinguish from the surrounding landscape, partly because they are not easily defined in terms of their topographic signature.

Colony mapping: A new technique for monitoring crevice-nesting seabirds

USGS Publications Warehouse

Renner, H.M.; Renner, M.; Reynolds, J.H.; Harping, A.M.A.; Jones, I.L.; Irons, D.B.; Byrd, G.V.

2006-01-01

Monitoring populations of auklets and other crevice-nesting seabirds remains problematic, although numerous methods have been attempted since the mid-1960s. Anecdotal evidence suggests several large auklet colonies have recently decreased in both abundance and extent, concurrently with vegetation encroachment and succession. Quantifying changes in the geographical extent of auklet colonies may be a useful alternative to monitoring population size directly. We propose a standardized method for colony mapping using a randomized systematic grid survey with two components: a simple presence/absence survey and an auklet evidence density survey. A quantitative auklet evidence density index was derived from the frequency of droppings and feathers. This new method was used to map the colony on St. George Island in the southeastern Bering Sea and results were compared to previous colony mapping efforts. Auklet presence was detected in 62 of 201 grid cells (each grid cell = 2500 m2) by sampling a randomly placed 16 m2 plot in each cell; estimated colony area = 155 000 m2. The auklet evidence density index varied by two orders of magnitude across the colony and was strongly correlated with means of replicated counts of birds socializing on the colony surface. Quantitatively mapping all large auklet colonies is logistically feasible using this method and would provide an important baseline for monitoring colony status. Regularly monitoring select colonies using this method may be the best means of detecting changes in distribution and population size of crevice-nesting seabirds. ?? The Cooper Ornithological Society 2006.

Initial Development of Riparian and Marsh Vegetation on Dredged-material Islands in the Sacramento-San Joaquin River Delta, California

Treesearch

A. Sidney England; Mark K. Sogge; Roy A. Woodward

1989-01-01

Natural vegetation establishment and development were monitored for 3 1/2 years on a new, dredged-material island located within the breached levees at Donlon Island in the Sacramento-San Joaquin River Delta. Vegetation measurements and maps prepared annually indicate that marsh and riparian vegetation types have developed rapidly. Topographic data for the island has...

Historical and current forest landscapes of eastern Oregon and Washington Part I: Vegetation pattern and insect and disease hazards.

Treesearch

J.F. Lehmkuhl; P.F. Hessburg; R.L. Everett; M.H. Huff; R.D. Ottmar

1994-01-01

We analyzed historical and current vegetation composition and structure in 49 sample watersheds, primarily on National Forests, within six river basins in eastern Oregon and Washington. Vegetation patterns were mapped from aerial photographs taken from 1932 to 1959, and from 1985 to 1992. We described vegetation attributes, landscape patterns, the range of historical...

Application of remote sensing in the study of vegetation and soils in Idaho

NASA Technical Reports Server (NTRS)

Tisdale, E. W. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Comparison of ERTS-1 imagery and USGS 1:250,000 scale maps of study areas with known ground points revealed significant map errors. These errors were sufficient to render impractical the projection of ERTS-1 imagery directly onto maps of the area. Marked differences were found in the delineation of ground features by different MSS bands. Generally, Band 4 was least useful, while Band 5 proved valuable for indicating patterns of native vegetation, cultivated areas - both dry and irrigated, lava fields, drainage basins, and deep bodies of water. Band 6 was better for landforms and drainages and for shallow bodies of water than Band 5 but inferior for indicating patterns in native vegetation and most types of cultivated land. Band 7 was best of all for indicating lava flows, water bodies, and landform features. Use of a additive color viewer-projector aided greatly in separation of images. A combination of Bands 5 and 7 with appropriate color filters proved best for separating most types of native vegetation and cultivated crops. Landform features and water bodies also showed well with this combination. The addition of Band 4 imagery to these further enhanced the identification of semi-dormant vegetation.

Long Term Land Cover and Seagrass Mapping using Landsat and Object-based Image Analysis from 1972 - 2010 in the Coastal Environment of South East Queensland, Australia

NASA Astrophysics Data System (ADS)

Lyons, M. B.; Phinn, S. R.; Roelfsema, C. M.

2011-12-01

Long term global archives of high-moderate spatial resolution, multi-spectral satellite imagery are now readily accessible, but are not being fully utilised by management agencies due to the lack of appropriate methods to consistently produce accurate and timely management ready information. This work developed an object-based approach to map land cover and seagrass distribution in an Australian coastal environment for a 38 year Landsat image time-series archive. Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery were used without in-situ field data input to produce land and seagrass cover maps every year data was available, resulting in over 60 individual map products over the 38 year archive. Land cover was mapped annually and included several vegetation, bare ground, urban and agricultural classes. Seagrass distribution was also mapped annually, and in some years monthly, via horizontal projective foliage cover classes, sand and deepwater. Land cover products were validated using aerial photography and seagrass was validated with field survey data, producing several measures of accuracy. An average overall accuracy of 65% and 81% was reported for seagrass and land cover respectively, which is consistent with other studies in the area. This study is the first to show moderate spatial resolution, long term annual changes in land cover and seagrass in an Australian environment, without the use of in-situ data; and only one of a few similar studies globally. The land cover products identify several long term trends; such as significant increases in South East Queensland's urban density, vegetation clearing in rural and rural-residential areas, and inter-annual variation in dry vegetation types in western South East Queensland. The seagrass cover products show that there has been a minimal overall change in seagrass extent, but that seagrass cover level distribution is extremely dynamic; evidenced by large scale migrations of higher seagrass cover levels and several events of sudden, significant changes in cover level. These mapping products will allow management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes.

A LAI inversion algorithm based on the unified model of canopy bidirectional reflectance distribution function for the Heihe River Basin

NASA Astrophysics Data System (ADS)

Ma, B.; Li, J.; Fan, W.; Ren, H.; Xu, X.

2017-12-01

Leaf area index (LAI) is one of the important parameters of vegetation canopy structure, which can represent the growth condition of vegetation effectively. The accuracy, availability and timeliness of LAI data can be improved greatly, which is of great importance to vegetation-related research, such as the study of atmospheric, land surface and hydrological processes to obtain LAI by remote sensing method. Heihe River Basin is the inland river basin in northwest China. There are various types of vegetation and all kinds of terrain conditions in the basin, so it is helpful for testing the accuracy of the model under the complex surface and evaluating the correctness of the model to study LAI in this area. On the other hand, located in west arid area of China, the ecological environment of Heihe Basin is fragile, LAI is an important parameter to represent the vegetation growth condition, and can help us understand the status of vegetation in the Heihe River Basin. Different from the previous LAI inversion models, the BRDF (bidirectional reflectance distribution function) unified model can be applied for both continuous vegetation and discrete vegetation, it is appropriate to the complex vegetation distribution. LAI is the key input parameter of the model. We establish the inversion algorithm that can exactly retrieve LAI using remote sensing image based on the unified model. First, we determine the vegetation type through the vegetation classification map to obtain the corresponding G function, leaf and surface reflectivity. Then, we need to determine the leaf area index (LAI), the aggregation index (ζ) and the sky scattered light ratio (β) range and the value of the interval, entering all the parameters into the model to calculate the corresponding reflectivity ρ and establish the lookup table of different vegetation. Finally, we can invert LAI on the basis of the established lookup table. The principle of inversion is least squares method. We have produced 1 km LAI products from 2000 to 2014, once every 8 days. The results show that the algorithm owns good stability and can effectively invert LAI in areas with very complex vegetation and terrain conditions.

Mapping of Vegetation with the Geoinformation System and Determining of Carrying Capacity of the Pre-Urals Steppe area for a Newly Establishing Population of the Przewalski Horse Equus ferus przewalskii at the Orenburg State Nature Reserve

NASA Astrophysics Data System (ADS)

Fedorov, N. I.; Mikhailenko, O. I.; Zharkikh, T. L.; Bakirova, R. T.

2018-01-01

Mapping of the vegetation (1:25000) of the Pre-Urals Steppe area at the Orenburg State Nature Reserve was completed in 2016. A map created with the geoinformation system contains 1931 simple and complex polygons for 25 types of vegetation. In a drought year, the average stock of palatable vegetation of the whole area is estimated at 8380 tons dry weight. The estimation is based on the size of areas covered by different types of vegetation, their grass production, the correction coefficients for decreasing of pasture forage stocks in winter and decreasing of production of grass communities in dry years. Based on pasture forage stocks the area could tolerate the maximum population size of 1769 individuals of the Przewalski horse, their average density could be 0.11 horse per ha. Yet, as watering places for animals are limited in Pre-Urals Steppe, grazing pressures on the vegetation next to the water sources may increase in dry years. That is why the above-mentioned calculated maximum population size and density must be reduced at least by half until some additional watering places are established and monitoring of the grazing effect on the vegetation next to the places is carried out regularly. Thus, the maximum size of the population is estimated at 800 to 900 individuals, which is almost 1.5 times more than necessary to establish a self-sustained population of the Przewalski horse.

Sensitivity of volatile organic compounds (VOCs) and ozone to land surface processes and vegetation distributions in California

NASA Astrophysics Data System (ADS)

Zhao, C.; Huang, M.; Fast, J. D.; Berg, L. K.; Qian, Y.; Guenther, A. B.; Gu, D.; Shrivastava, M. B.; Liu, Y.; Walters, S.; Jin, J.

2014-12-01

Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect secondary organic aerosol (SOA) formation and ultimately aerosol radiative forcing. These uncertainties result from many factors, including coupling strategy between biogenic emissions and land-surface schemes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (VOCs). In this study, sensitivity experiments are conducted using the Weather Research and Forecasting model with chemistry (WRF-Chem) to examine the sensitivity of simulated VOCs and ozone to land surface processes and vegetation distributions in California. The measurements collected during the California Nexus of Air Quality and Climate Experiment (CalNex) and the Carbonaceous Aerosol and Radiative Effects Study (CARES) conducted during May and June of 2010 provide a good opportunity to evaluate the simulations. First, the biogenic VOC emissions in the WRF-Chem simulations with the two land surface schemes, Noah and CLM4, are estimated by the Model of Emissions of Gases and Aerosols from Nature version one (MEGANv1), which has been publicly released and widely used with WRF-Chem. The impacts of land surface processes on estimating biogenic VOC emissions and simulating VOCs and ozone are investigated. Second, in this study, a newer version of MEGAN (MEGANv2.1) is coupled with CLM4 as part of WRF-Chem to examine the sensitivity of biogenic VOC emissions to the MEGAN schemes used and determine the importance of using a consistent vegetation map between a land surface scheme and the biogenic VOC emission scheme. Specifically, MEGANv2.1 is embedded into the CLM4 scheme and shares a consistent vegetation map for estimating biogenic VOC emissions. This is unlike MEGANv1 in WRF-Chem that uses a standalone vegetation map that differs from what is used in land surface schemes. Furthermore, we examine the impact of vegetation distribution on simulating VOCs and ozone by comparing coupled WRF-Chem-CLM-MEGANv2.1 simulations using multiple vegetation maps.

Mapping Landscape Phenology Preference of Yellow-billed Cuckoo with AVHRR data

NASA Astrophysics Data System (ADS)

Wallace, C.; Villarreal, M. L.; Van Riper, C., III

2011-12-01

The yellow-billed cuckoo (Coccycus americanus occidentalis) is a neo-tropical migrant bird that travels north from South America into the southwestern United States during the summer to nest. In Arizona, favored riparian forest and woodland nesting habitat has declined in recent decades, due primarily to human activities and the prolonged drought conditions. As a result, western yellow-billed cuckoos have been petitioned for possible listing under the Endangered Species Act. In this study, we map yellow-billed cuckoo habitat in the state of Arizona using the temporal greenness dynamics of the landscape, or the landscape phenology. Landscape phenometrics were derived from Advanced Very High Resolution Radiometer (AVHRR) satellite Normalized Difference Vegetation Index (NDVI) composite data using Fourier harmonic analysis. Applying Fourier analysis to the waveform composed of the 26 annual composite NDVI values produces phenometrics related to the overall vegetation amount, variability and timing. Field data on Cuckoo presence were obtained from 1998 surveys conducted by Northern Arizona University (NAU), the Arizona Game and Fish Department (AGFD) and the U.S. Geological Survey (USGS). To focus the research within probable landscapes, an AGFD vegetation map (derived from the Arizona GAP program) was used to extract polygons of riparian vegetation and cottonwood-willow riparian vegetation. To create the models, we coupled the satellite phenometrics with field data of cuckoo presence or absence and with points sampling the entirety of mapped riparian and cottonwood-willow vegetation types. Statistical tests reveal that locations with cuckoos present are landscapes with greenness that is significantly more variable and that peaks significantly later than locations in average riparian vegetation, average cottonwood-willow vegetation, or with cuckoos absent. Interestingly, the mean peak greenness date of July 3 for survey locations with cuckoos present coincides with the first day of the 1998 monsoon season recorded for Tucson in southern Arizona. Models developed from the 1998 parameters and applied to 1999 data were effective at predicting cuckoo presence for survey locations visited in 1999, with up to 64 percent of cuckoos located in the highest preference class.

Calculation of upper confidence bounds on not-sampled vegetation types using a systematic grid sample: An application to map unit definition for existing vegetation maps

Treesearch

Paul L. Patterson; Mark Finco

2009-01-01

This paper explores the information FIA data can produce regarding forest types that were not sampled and develops the equations necessary to define the upper confidence bounds on not-sampled forest types. The problem is reduced to a Bernoulli variable. This simplification allows the upper confidence bounds to be calculated based on Cochran (1977). Examples are...

Harmonization of forest disturbance datasets of the conterminous USA from 1986 to 2011

USGS Publications Warehouse

Soulard, Christopher E.; Acevedo, William; Cohen, Warren B.; Yang, Zhiqiang; Stehman, Stephen V.; Taylor, Janis L.

2017-01-01

Several spatial forest disturbance datasets exist for the conterminous USA. The major problem with forest disturbance mapping is that variability between map products leads to uncertainty regarding the actual rate of disturbance. In this article, harmonized maps were produced from multiple data sources (i.e., Global Forest Change, LANDFIRE Vegetation Disturbance, National Land Cover Database, Vegetation Change Tracker, and Web-Enabled Landsat Data). The harmonization process involved fitting common class ontologies and determining spatial congruency to produce forest disturbance maps for four time intervals (1986–1992, 1992–2001, 2001–2006, and 2006–2011). Pixels mapped as disturbed for two or more datasets were labeled as disturbed in the harmonized maps. The primary advantage gained by harmonization was improvement in commission error rates relative to the individual disturbance products. Disturbance omission errors were high for both harmonized and individual forest disturbance maps due to underlying limitations in mapping subtle disturbances with Landsat classification algorithms. To enhance the value of the harmonized disturbance products, we used fire perimeter maps to add information on the cause of disturbance.

Harmonization of forest disturbance datasets of the conterminous USA from 1986 to 2011.

PubMed

Soulard, Christopher E; Acevedo, William; Cohen, Warren B; Yang, Zhiqiang; Stehman, Stephen V; Taylor, Janis L

2017-04-01

Several spatial forest disturbance datasets exist for the conterminous USA. The major problem with forest disturbance mapping is that variability between map products leads to uncertainty regarding the actual rate of disturbance. In this article, harmonized maps were produced from multiple data sources (i.e., Global Forest Change, LANDFIRE Vegetation Disturbance, National Land Cover Database, Vegetation Change Tracker, and Web-Enabled Landsat Data). The harmonization process involved fitting common class ontologies and determining spatial congruency to produce forest disturbance maps for four time intervals (1986-1992, 1992-2001, 2001-2006, and 2006-2011). Pixels mapped as disturbed for two or more datasets were labeled as disturbed in the harmonized maps. The primary advantage gained by harmonization was improvement in commission error rates relative to the individual disturbance products. Disturbance omission errors were high for both harmonized and individual forest disturbance maps due to underlying limitations in mapping subtle disturbances with Landsat classification algorithms. To enhance the value of the harmonized disturbance products, we used fire perimeter maps to add information on the cause of disturbance.

Monitoring restoration impacts to endemic plant communities in soil inclusions of arid environments

USGS Publications Warehouse

Louhaichi, Mounir; Pyke, David A.; Shaff, Scott E.; Johnson, Douglas E.

2013-01-01

Soil inclusions are small patches of soil with different properties than the surrounding, dominant soil. In arid areas of western North America, soil inclusions called slickspot soils are saltier than adjacent soil and support different types of native vegetation. Traditional sagebrush restoration efforts, such as using drills to plant seeds or herbicides to control invasive vegetation, may damage sensitive slickspot soil and supporting vegetation. USGS scientists David Pyke and Scott Shaff and collaborators monitored slickspot size and cover of endangered slickspot peppergrass for two years to see if they were affected by the application of the herbicide glyphosate or by a minimum-till drill in the Snake River Plain, ID. The researchers examined the use of aerial photographs versus on-the-ground measurements and concluded that slickspot sizes were not affected by these treatments. Remote sensing using aerial photographs proved a useful method for mapping slickspot soils.

Assessing land cover performance in Senegal, West Africa using 1-km integrated NDVI and local variance analysis

USGS Publications Warehouse

Budde, M.E.; Tappan, G.; Rowland, James; Lewis, J.; Tieszen, L.L.

2004-01-01

The researchers calculated seasonal integrated normalized difference vegetation index (NDVI) for each of 7 years using a time-series of 1-km data from the Advanced Very High Resolution Radiometer (AVHRR) (1992-93, 1995) and SPOT Vegetation (1998-2001) sensors. We used a local variance technique to identify each pixel as normal or either positively or negatively anomalous when compared to its surroundings. We then summarized the number of years that a given pixel was identified as an anomaly. The resulting anomaly maps were analysed using Landsat TM imagery and extensive ground knowledge to assess the results. This technique identified anomalies that can be linked to numerous anthropogenic impacts including agricultural and urban expansion, maintenance of protected areas and increased fallow. Local variance analysis is a reliable method for assessing vegetation degradation resulting from human pressures or increased land productivity from natural resource management practices. ?? 2004 Published by Elsevier Ltd.

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.

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.

Mapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data

PubMed Central

Li, Ching-Feng; Thies, Boris; Chang, Shih-Chieh; Bendix, Jörg

2017-01-01

Up until now montane cloud forest (MCF) in Taiwan has only been mapped for selected areas of vegetation plots. This paper presents the first comprehensive map of MCF distribution for the entire island. For its creation, a Random Forest model was trained with vegetation plots from the National Vegetation Database of Taiwan that were classified as “MCF” or “non-MCF”. This model predicted the distribution of MCF from a raster data set of parameters derived from a digital elevation model (DEM), Landsat channels and texture measures derived from them as well as ground fog frequency data derived from the Moderate Resolution Imaging Spectroradiometer. While the DEM parameters and Landsat data predicted much of the cloud forest’s location, local deviations in the altitudinal distribution of MCF linked to the monsoonal influence as well as the Massenerhebung effect (causing MCF in atypically low altitudes) were only captured once fog frequency data was included. Therefore, our study suggests that ground fog data are most useful for accurately mapping MCF. PMID:28245279

Integrating Statistical and Expert Knowledge to Develop Phenoregions for the Continental United States

NASA Astrophysics Data System (ADS)

Hoffman, F. M.; Kumar, J.; Hargrove, W. W.

2013-12-01

Vegetated ecosystems typically exhibit unique phenological behavior over the course of a year, suggesting that remotely sensed land surface phenology may be useful for characterizing land cover and ecoregions. However, phenology is also strongly influenced by temperature and water stress; insect, fire, and storm disturbances; and climate change over seasonal, interannual, decadal and longer time scales. Normalized difference vegetation index (NDVI), a remotely sensed measure of greenness, provides a useful proxy for land surface phenology. We used NDVI for the conterminous United States (CONUS) derived from the Moderate Resolution Spectroradiometer (MODIS) at 250 m resolution to develop phenological signatures of emergent ecological regimes called phenoregions. By applying a unsupervised, quantitative data mining technique to NDVI measurements for every eight days over the entire MODIS record, annual maps of phenoregions were developed. This technique produces a prescribed number of prototypical phenological states to which every location belongs in any year. To reduce the impact of short-term disturbances, we derived a single map of the mode of annual phenological states for the CONUS, assigning each map cell to the state with the largest integrated NDVI in cases where multiple states tie for the highest frequency. Since the data mining technique is unsupervised, individual phenoregions are not associated with an ecologically understandable label. To add automated supervision to the process, we applied the method of Mapcurves, developed by Hargrove and Hoffman, to associate individual phenoregions with labeled polygons in expert-derived maps of biomes, land cover, and ecoregions. Utilizing spatial overlays with multiple expert-derived maps, this "label-stealing"' technique exploits the knowledge contained in a collection of maps to identify biome characteristics of our statistically derived phenoregions. Generalized land cover maps were produced by combining phenoregions according to their degree of spatial coincidence with expert-developed land cover or biome regions. Goodness-of-fit maps, which show the strength the spatial correspondence, were also generated.

Neural networks for satellite remote sensing and robotic sensor interpretation

NASA Astrophysics Data System (ADS)

Martens, Siegfried

Remote sensing of forests and robotic sensor fusion can be viewed, in part, as supervised learning problems, mapping from sensory input to perceptual output. This dissertation develops ARTMAP neural networks for real-time category learning, pattern recognition, and prediction tailored to remote sensing and robotics applications. Three studies are presented. The first two use ARTMAP to create maps from remotely sensed data, while the third uses an ARTMAP system for sensor fusion on a mobile robot. The first study uses ARTMAP to predict vegetation mixtures in the Plumas National Forest based on spectral data from the Landsat Thematic Mapper satellite. While most previous ARTMAP systems have predicted discrete output classes, this project develops new capabilities for multi-valued prediction. On the mixture prediction task, the new network is shown to perform better than maximum likelihood and linear mixture models. The second remote sensing study uses an ARTMAP classification system to evaluate the relative importance of spectral and terrain data for map-making. This project has produced a large-scale map of remotely sensed vegetation in the Sierra National Forest. Network predictions are validated with ground truth data, and maps produced using the ARTMAP system are compared to a map produced by human experts. The ARTMAP Sierra map was generated in an afternoon, while the labor intensive expert method required nearly a year to perform the same task. The robotics research uses an ARTMAP system to integrate visual information and ultrasonic sensory information on a B14 mobile robot. The goal is to produce a more accurate measure of distance than is provided by the raw sensors. ARTMAP effectively combines sensory sources both within and between modalities. The improved distance percept is used to produce occupancy grid visualizations of the robot's environment. The maps produced point to specific problems of raw sensory information processing and demonstrate the benefits of using a neural network system for sensor fusion.

Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact.

PubMed

Khanna, Shruti; Santos, Maria J; Ustin, Susan L; Shapiro, Kristen; Haverkamp, Paul J; Lay, Mui

2018-02-12

Oil spills from offshore drilling and coastal refineries often cause significant degradation of coastal environments. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and mortality due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and mortality after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5 m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5 m to 30 m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15 m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date (cloud-free, etc.) is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills.

Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact

PubMed Central

Santos, Maria J.; Ustin, Susan L.; Haverkamp, Paul J.; Lay, Mui

2018-01-01

Oil spills from offshore drilling and coastal refineries often cause significant degradation of coastal environments. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and mortality due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and mortality after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5 m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5 m to 30 m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15 m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date (cloud-free, etc.) is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills. PMID:29439504

Mapping forest structure, species gradients and growth in an urban area using lidar and hyperspectral imagery

NASA Astrophysics Data System (ADS)

Gu, Huan

Urban forests play an important role in the urban ecosystem by providing a range of ecosystem services. Characterization of forest structure, species variation and growth in urban forests is critical for understanding the status, function and process of urban ecosystems, and helping maximize the benefits of urban ecosystems through management. The development of methods and applications to quantify urban forests using remote sensing data has lagged the study of natural forests due to the heterogeneity and complexity of urban ecosystems. In this dissertation, I quantify and map forest structure, species gradients and forest growth in an urban area using discrete-return lidar, airborne imaging spectroscopy and thermal infrared data. Specific objectives are: (1) to demonstrate the utility of leaf-off lidar originally collected for topographic mapping to characterize and map forest structure and associated uncertainties, including aboveground biomass, basal area, diameter, height and crown size; (2) to map species gradients using forest structural variables estimated from lidar and foliar functional traits, vegetation indices derived from AVIRIS hyperspectral imagery in conjunction with field-measured species data; and (3) to identify factors related to relative growth rates in aboveground biomass in the urban forests, and assess forest growth patterns across areas with varying degree of human interactions. The findings from this dissertation are: (1) leaf-off lidar originally acquired for topographic mapping provides a robust, potentially low-cost approach to quantify spatial patterns of forest structure and carbon stock in urban areas; (2) foliar functional traits and vegetation indices from hyperspectral data capture gradients of species distributions in the heterogeneous urban landscape; (3) species gradients, stand structure, foliar functional traits and temperature are strongly related to forest growth in the urban forests; and (4) high uncertainties in our ability to map forest structure, species gradient and growth rate occur in residential neighborhoods and along forest edges. Maps generated from this dissertation provide estimates of broad-scale spatial variations in forest structure, species distributions and growth to the city forest managers. The associated maps of uncertainty help managers understand the limitations of the maps and identify locations where the maps are more reliable and where more data are needed.

Mapping national scale land cover disturbance for the continental United States, 2006 to 2010

NASA Astrophysics Data System (ADS)

Hansen, M. C.; Potapov, P. V.; Egorov, A.; Roy, D. P.; Loveland, T. R.

2011-12-01

Data from the Web-Enabled Landsat Data (WELD) project were used to quantify forest cover loss and bare ground gain dynamics for the continental United States at a 30 meter spatial resolution from 2006 to 2010. Results illustrate the land cover dynamics associated with forestry, urbanization and other medium to long-term cover conversion processes. Ephemeral changes, such as crop rotations and fallows or inundation, were not quantified. Forest disturbance is pervasive at the national-scale, while increasing bare ground is found in growing urban areas as well as in mining regions. The methods applied are an outgrowth of the Vegetation Continuous Field (VCF) method, initially employed with MODIS data and then WELD data to map percent cover variables. As in our previous work with MODIS in mapping forest change, we applied the VCF method to characterize forest cover loss and bare ground gain probability per pixel. Additional themes will be added to provide a more comprehensive picture of national-scale land dynamics based on these initial results using WELD.

Unmanned Aerial Vehicles Produce High-Resolution Seasonally-Relevant Imagery for Classifying Wetland Vegetation

NASA Astrophysics Data System (ADS)

Marcaccio, J. V.; Markle, C. E.; Chow-Fraser, P.

2015-08-01

With recent advances in technology, personal aerial imagery acquired with unmanned aerial vehicles (UAVs) has transformed the way ecologists can map seasonal changes in wetland habitat. Here, we use a multi-rotor (consumer quad-copter, the DJI Phantom 2 Vision+) UAV to acquire a high-resolution (< 8 cm) composite photo of a coastal wetland in summer 2014. Using validation data collected in the field, we determine if a UAV image and SWOOP (Southwestern Ontario Orthoimagery Project) image (collected in spring 2010) differ in their classification of type of dominant vegetation type and percent cover of three plant classes: submerged aquatic vegetation, floating aquatic vegetation, and emergent vegetation. The UAV imagery was more accurate than available SWOOP imagery for mapping percent cover of submergent and floating vegetation categories, but both were able to accurately determine the dominant vegetation type and percent cover of emergent vegetation. Our results underscore the value and potential for affordable UAVs (complete quad-copter system < 3,000 CAD) to revolutionize the way ecologists obtain imagery and conduct field research. In Canada, new UAV regulations make this an easy and affordable way to obtain multiple high-resolution images of small (< 1.0 km2) wetlands, or portions of larger wetlands throughout a year.

Mapping mountain torrent hazards in the Hexi Corridor using an evidential reasoning approach

NASA Astrophysics Data System (ADS)

Ran, Youhua; Liu, Jinpeng; Tian, Feng; Wang, Dekai

2017-02-01

The Hexi Corridor is an important part of the Silk Road Economic Belt and a crucial channel for westward development in China. Many important national engineering projects pass through the corridor, such as highways, railways, and the West-to-East Gas Pipeline. The frequent torrent disasters greatly impact the security of infrastructure and human safety. In this study, an evidential reasoning approach based on Dempster-Shafer theory is proposed for mapping mountain torrent hazards in the Hexi Corridor. A torrent hazard map for the Hexi Corridor was generated by integrating the driving factors of mountain torrent disasters including precipitation, terrain, flow concentration processes, and the vegetation fraction. The results show that the capability of the proposed method is satisfactory. The torrent hazard map shows that there is high potential torrent hazard in the central and southeastern Hexi Corridor. The results are useful for engineering planning support and resource protection in the Hexi Corridor. Further efforts are discussed for improving torrent hazard mapping and prediction.

Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning

USGS Publications Warehouse

Anderson, Kyle E.; Glenn, Nancy F.; Spaete, Lucas P.; Shinneman, Douglas; Pilliod, David S.; Arkle, Robert; McIlroy, Susan; Derryberry, DeWayne R.

2018-01-01

Terrestrial laser scanning (TLS) has been shown to enable an efficient, precise, and non-destructive inventory of vegetation structure at ranges up to hundreds of meters. We developed a method that leverages TLS collections with machine learning techniques to model and map canopy cover and biomass of several classes of short-stature vegetation across large plots. We collected high-definition TLS scans of 26 1-ha plots in desert grasslands and big sagebrush shrublands in southwest Idaho, USA. We used the Random Forests machine learning algorithm to develop decision tree models predicting the biomass and canopy cover of several vegetation classes from statistical descriptors of the aboveground heights of TLS points. Manual measurements of vegetation characteristics collected within each plot served as training and validation data. Models based on five or fewer TLS descriptors of vegetation heights were developed to predict the canopy cover fraction of shrubs (R2 = 0.77, RMSE = 7%), annual grasses (R2 = 0.70, RMSE = 21%), perennial grasses (R2 = 0.36, RMSE = 12%), forbs (R2 = 0.52, RMSE = 6%), bare earth or litter (R2 = 0.49, RMSE = 19%), and the biomass of shrubs (R2 = 0.71, RMSE = 175 g) and herbaceous vegetation (R2 = 0.61, RMSE = 99 g) (all values reported are out-of-bag). Our models explained much of the variability between predictions and manual measurements, and yet we expect that future applications could produce even better results by reducing some of the methodological sources of error that we encountered. Our work demonstrates how TLS can be used efficiently to extend manual measurement of vegetation characteristics from small to large plots in grasslands and shrublands, with potential application to other similarly structured ecosystems. Our method shows that vegetation structural characteristics can be modeled without classifying and delineating individual plants, a challenging and time-consuming step common in previous methods applying TLS to vegetation inventory. Improving application of TLS to studies of shrub-steppe ecosystems will serve immediate management needs by enhancing vegetation inventories, environmental modeling studies, and the ability to train broader datasets collected from air and space.

A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data.

PubMed

Sharma, Ram C; Hara, Keitarou; Hirayama, Hidetake

2017-01-01

This paper presents the performance and evaluation of a number of machine learning classifiers for the discrimination between the vegetation physiognomic classes using the satellite based time-series of the surface reflectance data. Discrimination of six vegetation physiognomic classes, Evergreen Coniferous Forest, Evergreen Broadleaf Forest, Deciduous Coniferous Forest, Deciduous Broadleaf Forest, Shrubs, and Herbs, was dealt with in the research. Rich-feature data were prepared from time-series of the satellite data for the discrimination and cross-validation of the vegetation physiognomic types using machine learning approach. A set of machine learning experiments comprised of a number of supervised classifiers with different model parameters was conducted to assess how the discrimination of vegetation physiognomic classes varies with classifiers, input features, and ground truth data size. The performance of each experiment was evaluated by using the 10-fold cross-validation method. Experiment using the Random Forests classifier provided highest overall accuracy (0.81) and kappa coefficient (0.78). However, accuracy metrics did not vary much with experiments. Accuracy metrics were found to be very sensitive to input features and size of ground truth data. The results obtained in the research are expected to be useful for improving the vegetation physiognomic mapping in Japan.

NASA/BLM Applications Pilot Test (APT), phase 2. Volume 1: Executive summary. [vegetation mapping and production estimation in northwestern Arizona

NASA Technical Reports Server (NTRS)

1981-01-01

Data from LANDSAT, low altitude color aerial photography, and ground visits were combined and used to produce vegetation cover maps and to estimate productivity of range, woodland, and forest resources in northwestern Arizona. A planning session, two workshops, and four status reviews were held to assist technology transfer from NASA. Computer aided digital classification of LANDSAT data was selected as a major source of input data. An overview is presented of the data processing, data collection, productivity estimation, and map verification techniques used. Cost analysis and digital LANDSAT digital products are also considered.

Program Merges SAR Data on Terrain and Vegetation Heights

NASA Technical Reports Server (NTRS)

Siqueira, Paul; Hensley, Scott; Rodriguez, Ernesto; Simard, Marc

2007-01-01

X/P Merge is a computer program that estimates ground-surface elevations and vegetation heights from multiple sets of data acquired by the GeoSAR instrument [a terrain-mapping synthetic-aperture radar (SAR) system that operates in the X and bands]. X/P Merge software combines data from X- and P-band digital elevation models, SAR backscatter magnitudes, and interferometric correlation magnitudes into a simplified set of output topographical maps of ground-surface elevation and tree height.

Hiawatha National Forest Riparian Inventory: A Case Study

NASA Astrophysics Data System (ADS)

Abood, S. A.

2014-12-01

Riparian areas are dynamic, transitional ecotones between aquatic and terrestrial ecosystems with well-defined vegetation and soil characteristics. Riparian areas offers wildlife habitat and stream water quality, offers bank stability and protects against erosions, provides aesthetics and recreational value, and other numerous valuable ecosystem functions. Quantifying and delineating riparian areas is an essential step in riparian monitoring, riparian management/planning and policy decisions, and in preserving its valuable ecological functions. Previous approaches to riparian areas mapping have primarily utilized fixed width buffers. However, these methodologies only take the watercourse into consideration and ignore critical geomorphology, associated vegetation and soil characteristics. Other approaches utilize remote sensing technologies such as aerial photos interpretation or satellite imagery riparian vegetation classification. Such techniques requires expert knowledge, high spatial resolution data, and expensive when mapping riparian areas on a landscape scale. The goal of this study is to develop a cost effective robust workflow to consistently map the geographic extent and composition of riparian areas within the Hiawatha National Forest boundary utilizing the Riparian Buffer Delineation Model (RBDM) v3.0 and open source geospatial data. This approach recognizes the dynamic and transitional natures of riparian areas by accounting for hydrologic, geomorphic and vegetation data as inputs into the delineation process and the results would suggests incorporating functional variable width riparian mapping within watershed management planning to improve protection and restoration of valuable riparian functionality and biodiversity.

Riparian vegetation as an indicator of riparian condition: Detecting departures from historic condition across the North American West.

PubMed

Macfarlane, William W; Gilbert, Jordan T; Jensen, Martha L; Gilbert, Joshua D; Hough-Snee, Nate; McHugh, Peter A; Wheaton, Joseph M; Bennett, Stephen N

2017-11-01

Floodplain riparian ecosystems support unique vegetation communities and high biodiversity relative to terrestrial landscapes. Accordingly, estimating riparian ecosystem health across landscapes is critical for sustainable river management. However, methods that identify local riparian vegetation condition, an effective proxy for riparian health, have not been applied across broad, regional extents. Here we present an index to assess reach-scale (500 m segment) riparian vegetation condition across entire drainage networks within large, physiographically-diverse regions. We estimated riparian vegetation condition for 53,250 km of perennial streams and rivers, 25,685 km in Utah, and 27,565 km in twelve watersheds of the interior Columbia River Basin (CRB), USA. We used nationally available, existing land cover classification derived from 30 m Landsat imagery (LANDFIRE EVT) and a modeled estimate of pre-European settlement land cover (LANDFIRE BpS). The index characterizes riparian vegetation condition as the ratio of existing native riparian vegetation cover to pre-European settlement riparian vegetation cover at a given reach. Roughly 62% of Utah and 48% of CRB watersheds showed significant (>33%) to large (>66%) departure from historic condition. Riparian vegetation change was predominantly caused by human land-use impacts (development and agriculture), or vegetation change (native riparian to invasive or upland vegetation types) that likely resulted from flow and disturbance regime alteration. Through comparisons to ground-based classification results, we estimate the existing vegetation component of the index to be 85% accurate. Our assessments yielded riparian condition maps that will help resource managers better prioritize sites and treatments for reach-scale conservation and restoration activities. Copyright © 2016 Elsevier Ltd. All rights reserved.

Assessment of geostatistical features for object-based image classification of contrasted landscape vegetation cover

NASA Astrophysics Data System (ADS)

de Oliveira Silveira, Eduarda Martiniano; de Menezes, Michele Duarte; Acerbi Júnior, Fausto Weimar; Castro Nunes Santos Terra, Marcela; de Mello, José Márcio

2017-07-01

Accurate mapping and monitoring of savanna and semiarid woodland biomes are needed to support the selection of areas of conservation, to provide sustainable land use, and to improve the understanding of vegetation. The potential of geostatistical features, derived from medium spatial resolution satellite imagery, to characterize contrasted landscape vegetation cover and improve object-based image classification is studied. The study site in Brazil includes cerrado sensu stricto, deciduous forest, and palm swamp vegetation cover. Sentinel 2 and Landsat 8 images were acquired and divided into objects, for each of which a semivariogram was calculated using near-infrared (NIR) and normalized difference vegetation index (NDVI) to extract the set of geostatistical features. The features selected by principal component analysis were used as input data to train a random forest algorithm. Tests were conducted, combining spectral and geostatistical features. Change detection evaluation was performed using a confusion matrix and its accuracies. The semivariogram curves were efficient to characterize spatial heterogeneity, with similar results using NIR and NDVI from Sentinel 2 and Landsat 8. Accuracy was significantly greater when combining geostatistical features with spectral data, suggesting that this method can improve image classification results.

Evaluating a small footprint, waveform-resolving lidar over coastal vegetation communities

USGS Publications Warehouse

Nayegandhl, A.; Brock, J.C.; Wright, C.W.; O'Connell, M. J.

2006-01-01

NASA's Experimental Advanced Airborne Research Lidar (EAARL) is a raster-scanning, waveform-resolving, green-wavelength (532 nm) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor records the time history of the return waveform within a small footprint (20 cm diameter) for each laser pulse, enabling characterization of vegetation canopy structure and "bare earth" topography under a variety of vegetation types. A collection of individual waveforms combined within a synthesized large footprint was used to define three metrics: canopy height (CH), canopy reflection ratio (CRR), and height of median energy (HOME). Bare Earth Elevation (BEE) metric was derived using the individual small-footprint waveforms. All four metrics were tested for reproducibility, which resulted in an average of 95 percent correspondence within two standard deviations of the mean. CH and BEE values were also tested for accuracy using ground-truth data. The results presented in this paper show that combining several individual small-footprint laser pulses to define a composite "large-footprint" waveform is a possible method to depict the vertical structure of a vegetation canopy. ?? 2006 American Society for Photogrammetry and Remote Sensing.

National-scale cropland mapping based on spectral-temporal features and outdated land cover information.

PubMed

Waldner, François; Hansen, Matthew C; Potapov, Peter V; Löw, Fabian; Newby, Terence; Ferreira, Stefanus; Defourny, Pierre

2017-01-01

The lack of sufficient ground truth data has always constrained supervised learning, thereby hindering the generation of up-to-date satellite-derived thematic maps. This is all the more true for those applications requiring frequent updates over large areas such as cropland mapping. Therefore, we present a method enabling the automated production of spatially consistent cropland maps at the national scale, based on spectral-temporal features and outdated land cover information. Following an unsupervised approach, this method extracts reliable calibration pixels based on their labels in the outdated map and their spectral signatures. To ensure spatial consistency and coherence in the map, we first propose to generate seamless input images by normalizing the time series and deriving spectral-temporal features that target salient cropland characteristics. Second, we reduce the spatial variability of the class signatures by stratifying the country and by classifying each stratum independently. Finally, we remove speckle with a weighted majority filter accounting for per-pixel classification confidence. Capitalizing on a wall-to-wall validation data set, the method was tested in South Africa using a 16-year old land cover map and multi-sensor Landsat time series. The overall accuracy of the resulting cropland map reached 92%. A spatially explicit validation revealed large variations across the country and suggests that intensive grain-growing areas were better characterized than smallholder farming systems. Informative features in the classification process vary from one stratum to another but features targeting the minimum of vegetation as well as short-wave infrared features were consistently important throughout the country. Overall, the approach showed potential for routinely delivering consistent cropland maps over large areas as required for operational crop monitoring.

National-scale cropland mapping based on spectral-temporal features and outdated land cover information

PubMed Central

Hansen, Matthew C.; Potapov, Peter V.; Löw, Fabian; Newby, Terence; Ferreira, Stefanus; Defourny, Pierre

2017-01-01

The lack of sufficient ground truth data has always constrained supervised learning, thereby hindering the generation of up-to-date satellite-derived thematic maps. This is all the more true for those applications requiring frequent updates over large areas such as cropland mapping. Therefore, we present a method enabling the automated production of spatially consistent cropland maps at the national scale, based on spectral-temporal features and outdated land cover information. Following an unsupervised approach, this method extracts reliable calibration pixels based on their labels in the outdated map and their spectral signatures. To ensure spatial consistency and coherence in the map, we first propose to generate seamless input images by normalizing the time series and deriving spectral-temporal features that target salient cropland characteristics. Second, we reduce the spatial variability of the class signatures by stratifying the country and by classifying each stratum independently. Finally, we remove speckle with a weighted majority filter accounting for per-pixel classification confidence. Capitalizing on a wall-to-wall validation data set, the method was tested in South Africa using a 16-year old land cover map and multi-sensor Landsat time series. The overall accuracy of the resulting cropland map reached 92%. A spatially explicit validation revealed large variations across the country and suggests that intensive grain-growing areas were better characterized than smallholder farming systems. Informative features in the classification process vary from one stratum to another but features targeting the minimum of vegetation as well as short-wave infrared features were consistently important throughout the country. Overall, the approach showed potential for routinely delivering consistent cropland maps over large areas as required for operational crop monitoring. PMID:28817618

Species distribution modelling for plant communities: Stacked single species or multivariate modelling approaches?

Treesearch

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

2014-01-01

Landscape management and conservation planning require maps of vegetation composition and structure over large regions. Species distribution models (SDMs) are often used for individual species, but projects mapping multiple species are rarer. We compare maps of plant community composition assembled by stacking results from many SDMs with multivariate maps constructed...

Mineral and Vegetation Maps of the Bodie Hills, Sweetwater Mountains, and Wassuk Range, California/Nevada, Generated from ASTER Satellite Data

USGS Publications Warehouse

Rockwell, Barnaby W.

2010-01-01

Multispectral remote sensing data acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were analyzed to identify and map minerals, vegetation groups, and volatiles (water and snow) in support of geologic studies of the Bodie Hills, Sweetwater Mountains, and Wassuk Range, California/Nevada. Digital mineral and vegetation mapping results are presented in both portable document format (PDF) and ERDAS Imagine format (.img). The ERDAS-format files are suitable for integration with other geospatial data in Geographic Information Systems (GIS) such as ArcGIS. The ERDAS files showing occurrence of 1) iron-bearing minerals, vegetation, and water, and 2) clay, sulfate, mica, carbonate, Mg-OH, and hydrous quartz minerals have been attributed according to identified material, so that the material detected in a pixel can be queried with the interactive attribute identification tools of GIS and image processing software packages (for example, the Identify Tool of ArcMap and the Inquire Cursor Tool of ERDAS Imagine). All raster data have been orthorectified to the Universal Transverse Mercator (UTM) projection using a projective transform with ground-control points selected from orthorectified Landsat Thematic Mapper data and a digital elevation model from the U.S. Geological Survey (USGS) National Elevation Dataset (1/3 arc second, 10 m resolution). Metadata compliant with Federal Geographic Data Committee (FGDC) standards for all ERDAS-format files have been included, and contain important information regarding geographic coordinate systems, attributes, and cross-references. Documentation regarding spectral analysis methodologies employed to make the maps is included in these cross-references.

High-resolution mapping and spatial variability of soil organic carbon storage of permafrost-affected soils

NASA Astrophysics Data System (ADS)

Siewert, Matthias; Hugelius, Gustaf

2017-04-01

Permafrost-affected soils store large amounts of soil organic carbon (SOC). Mapping of this SOC provides a first order spatial input variable for research that relates carbon stored in permafrost regions to carbon cycle dynamics. High-resolution satellite imagery is becoming increasingly available even in circum-polar regions. The presented research highlights findings of high-resolution mapping efforts of SOC from five study areas in the northern circum-polar permafrost region. These study areas are located in Siberia (Kytalyk, Spasskaya Pad /Neleger, Lena delta), Northern Sweden (Abisko) and Northwestern Canada (Herschel Island). Our high spatial resolution analyses show how geomorphology has a strong influence on the distribution of SOC. This is organized at different spatial scales. Periglacial landforms and processes dictate local scale SOC distribution due to patterned ground. Such landforms are non-sorted circles and ice-wedge polygons of different age and scale. Palsas and peat plateaus are formed and can cover larger areas in Sub-Arctic environments. Study areas that have not been affected by Pleistocene glaciation feature ice-rich Yedoma sediments that dominate the local relief through thermokarst formation and create landscape scale macro environments that dictate the distribution of SOC. A general trend indicates higher SOC storage in Arctic tundra soils compared to forested Boreal or Sub-Arctic taiga soils. Yet, due to the shallower active layer depth in the Arctic, much of the SOC may be permanently frozen and thus not be available to ecosystem processes. Significantly more SOC is stored in soils compared to vegetation, indicating that vegetation growth and incorporation of the carbon into the plant phytomass alone will not be able to offset SOC released from permafrost. This contribution also addresses advances in thematic mapping methods and digital soil mapping of SOC in permafrost terrain. In particular machine-learning methods, such as support vector machines, artificial neural networks and random forests show promising results as a toolbox for mapping permafrost-affected soils. Yet, these new methods do not decrease our dependency from soil pedon data from the field. In contrary, soil pedon data represents an urgent research priority. Statistical analyses are provided as an indication for best practice of soil pedon sampling for the quantification and the model representation of SOC stored in permafrost-affected soils.

Skylab/EREP application to ecological, geological, and oceanographic investigations of Delaware Bay

NASA Technical Reports Server (NTRS)

Klemas, V.; Bartlett, D. S.; Philpot, W. D.; Rogers, R. H.; Reed, L. E.

1978-01-01

Skylab/EREP S190A and S190B film products were optically enhanced and visually interpreted to extract data suitable for; (1) mapping coastal land use; (2) inventorying wetlands vegetation; (3) monitoring tidal conditions; (4) observing suspended sediment patterns; (5) charting surface currents; (6) locating coastal fronts and water mass boundaries; (7) monitoring industrial and municipal waste dumps in the ocean; (8) determining the size and flow direction of river, bay and man-made discharge plumes; and (9) observing ship traffic. Film products were visually analyzed to identify and map ten land-use and vegetation categories at a scale of 1:125,000. Digital tapes from the multispectral scanner were used to prepare thematic maps of land use. Classification accuracies obtained by comparison of derived thematic maps of land-use with USGS-CARETS land-use maps in southern Delaware ranged from 44 percent to 100 percent.

Indicator Development for Potential Presence of Schistosomiasis Japonicum's Vector in Lake and Marshland Regions- A Case Study of Poyang Lake, Jiangxi Province, P.R. China

NASA Astrophysics Data System (ADS)

Marie, Tiphanie; Yesou, Herve; Huber, Claire; De Fraipont, Paul; Uribe, Carlos; Lacaux, Jean-Pierre; Lafaye, Murielle; Lai, Xijun; Desnos, Yves-Louis

2013-01-01

Earth observation data and bibliography on environmental parameters were used for mapping Oncomelania hupensis distribution, the Schistosomiasis japonicum’s intermediate host snail, within Poyang Lake (Jiangxi Province, P.R. China). Areas suitable for the development of O. hupensis, the vector of schistosomiasis, were derived from submersion time parameters and vegetation community indicators. ENVISAT time series data acquired from 2000 to 2009 were used for submersion times mapping, and 5 Beijing-1 data acquired during the dry season between 2006 and 2008 were used to map suitable vegetation for vector development. Yearly maps obtained indicate four principally potential endemic areas: the Gan Delta, the bank of the Fu He River, the Dalianzi Hu sector and the Poyang Lake Nature Reserve. Monthly maps from December 2005 to December 2008 show the dynamic of potential O. hupensis presence areas.

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.

Mapping Urban Tree Canopy Cover Using Fused Airborne LIDAR and Satellite Imagery Data

NASA Astrophysics Data System (ADS)

Parmehr, Ebadat G.; Amati, Marco; Fraser, Clive S.

2016-06-01

Urban green spaces, particularly urban trees, play a key role in enhancing the liveability of cities. The availability of accurate and up-to-date maps of tree canopy cover is important for sustainable development of urban green spaces. LiDAR point clouds are widely used for the mapping of buildings and trees, and several LiDAR point cloud classification techniques have been proposed for automatic mapping. However, the effectiveness of point cloud classification techniques for automated tree extraction from LiDAR data can be impacted to the point of failure by the complexity of tree canopy shapes in urban areas. Multispectral imagery, which provides complementary information to LiDAR data, can improve point cloud classification quality. This paper proposes a reliable method for the extraction of tree canopy cover from fused LiDAR point cloud and multispectral satellite imagery data. The proposed method initially associates each LiDAR point with spectral information from the co-registered satellite imagery data. It calculates the normalised difference vegetation index (NDVI) value for each LiDAR point and corrects tree points which have been misclassified as buildings. Then, region growing of tree points, taking the NDVI value into account, is applied. Finally, the LiDAR points classified as tree points are utilised to generate a canopy cover map. The performance of the proposed tree canopy cover mapping method is experimentally evaluated on a data set of airborne LiDAR and WorldView 2 imagery covering a suburb in Melbourne, Australia.

Mapping paddy rice distribution using multi-temporal Landsat imagery in the Sanjiang Plain, northeast China

PubMed Central

XIAO, Xiangming; DONG, Jinwei; QIN, Yuanwei; WANG, Zongming

2016-01-01

Information of paddy rice distribution is essential for food production and methane emission calculation. Phenology-based algorithms have been utilized in the mapping of paddy rice fields by identifying the unique flooding and seedling transplanting phases using multi-temporal moderate resolution (500 m to 1 km) images. In this study, we developed simple algorithms to identify paddy rice at a fine resolution at the regional scale using multi-temporal Landsat imagery. Sixteen Landsat images from 2010–2012 were used to generate the 30 m paddy rice map in the Sanjiang Plain, northeast China—one of the major paddy rice cultivation regions in China. Three vegetation indices, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI), were used to identify rice fields during the flooding/transplanting and ripening phases. The user and producer accuracies of paddy rice on the resultant Landsat-based paddy rice map were 90% and 94%, respectively. The Landsat-based paddy rice map was an improvement over the paddy rice layer on the National Land Cover Dataset, which was generated through visual interpretation and digitalization on the fine-resolution images. The agricultural census data substantially underreported paddy rice area, raising serious concern about its use for studies on food security. PMID:27695637

Terrain, vegetation, and landscape evolution of the R4D research site, Brooks Range Foothills, Alaska

USGS Publications Warehouse

Walker, D.A.; Binnian, Emily F.; Evans, B. M.; Lederer, N.D.; Nordstrand, E.A.; Webber, P.J.

1989-01-01

Maps of the vegetation and terrain of a 22 km2 area centered on the Department of Energy (DOE) R4D (Response, Resistance, Resilience to and Recovery from Disturbance in Arctic Ecosystems) study site in the Southern Foothills Physiographic Province of Alaska were made using integrated geobotanical mapping procedures and a geographic-information system. Typical land forms and surface f orms include hillslope water tracks, Sagavanirktok-age till deposits, nonsorted stone stripes, and colluvial-basin deposits. Thirty-two plant communities are described; the dominant vegetation (51% of the mapped area) is moist tussock-sedge, dwarf-shrub tundra dominated by Eriophorum vaginatum or Carex bigelowii. Much of the spatial variation in the mapped geobotanical characters reflects different-aged glaciated surfaces. Shannon-Wienerin dices indicate that the more mature landscapes, represented by retransported hillslope deposits and basin colluvium, are less heterogeneous than newer landscapes such as surficial till deposits and floodplains. A typical toposequence on a mid-Pleistocene-age surface is discussed with respect to evolution of the landscape. Thick Sphagnum moss layers occur on lower hillslopes, and the patterns of moss-layer development, heat flux, active layer thickness, and ground-ice are seen as keys to developing thermokarst-susceptibility maps.

Arid land monitoring using Landsat albedo difference images

USGS Publications Warehouse

Robinove, Charles J.; Chavez, Pat S.; Gehring, Dale G.; Holmgren, Ralph

1981-01-01

The Landsat albedo, or percentage of incoming radiation reflected from the ground in the wavelength range of 0.5 [mu]m to 1.1 [mu]m, is calculated from an equation using the Landsat digital brightness values and solar irradiance values, and correcting for atmospheric scattering, multispectral scanner calibration, and sun angle. The albedo calculated for each pixel is used to create an albedo image, whose grey scale is proportional to the albedo. Differencing sequential registered images and mapping selected values of the difference is used to create quantitative maps of increased or decreased albedo values of the terrain. All maps and other output products are in black and white rather than color, thus making the method quite economical. Decreases of albedo in arid regions may indicate improvement of land quality; increases may indicate degradation. Tests of the albedo difference mapping method in the Desert Experimental Range in southwestern Utah (a cold desert with little long-term terrain change) for a four-year period show that mapped changes can be correlated with erosion from flash floods, increased or decreased soil moisture, and increases or decreases in the density of desert vegetation, both perennial shrubs and annual plants. All terrain changes identified in this test were related to variations in precipitation. Although further tests of this method in hot deserts showing severe "desertification" are needed, the method is nevertheless recommended for experimental use in monitoring terrain change in other arid and semiarid regions of the world.

Characterizing sub-arctic peatland vegeation using height estimates from structure from motion and an unmanned aerial system (UAS)

NASA Astrophysics Data System (ADS)

Palace, M. W.; DelGreco, J.; Herrick, C.; Sullivan, F.; Varner, R. K.

2017-12-01

The collapse of permafrost, due to thawing, changes landscape topography, hydrology and vegetation. Changes in plant species composition influence methane production pathways and methane emission rates. The complex spatial heterogeneity of vegetation composition across peatlands proves important in quantifying methane emissions. Effort to characterize vegetation across these permafrost peatlands has been conducted with varied success, with difficulty seen in estimating some cover types that are at opposite ends of the permafrost collapse transition, ie palsa/tall shrub and tall graminoid. This is because some of the species are the same (horsetail) and some of the species have similar structure (horsetail/Carex spp.). High resolution digital elevation maps, developed with airborne LIght Detection And Ranging (lidar) have provided insight into some wetland attributes, but lidar collection is costly and requires extensive data processing effort. Lidar information also lacks the spectral information that optical sensors provide. We used an inexpensive Unmanned Aerial Vehicle (UAV) with an optical sensor to image a mire in northern Sweden (Stordalen Mire) in 2015. We collected 700 overlapping images that were stitched together using Structure from Motion (SfM). SfM analysis also provided, due to parallax, the ability to develop a height map of vegetation. This height map was used, along with textural analysis, to develop an artificial neural network to predict five vegetation cover types. Using 200 training points, we found improvements in our prediction of these cover types. We suggest that using the digital height model from SfM provides useful information in remotely sensing vegetation across a permafrost collapsing region that exhibit resulting changes in vegetation composition. The ability to rapidly and inexpensively deploy such a UAV system provides the opportunity to examine multiple sites with limited personnel effort in remote areas.

Monitoring Wetlands Area Using Microwave, Optical And In-Situ Data

NASA Astrophysics Data System (ADS)

Dabrowska, Katarzyna; Zielinska, Maria Budzynska

2011-01-01

The study of Wetlands has been continue within the PECS Project: “Study and implement remote sensing techniques for the assessment of carbon balances for different biomasses and soil moistures within various ecosystems”. The research has been conducted in Biebrza valley, one of the largest wetland in Europe, since 2003. Recently, to existing data base of wetlands monitoring Carbon flux measurements using the Chamber Method and Eddy Correlation Method have been included. The study aims at monitoring and mapping various soil-vegetation variables and the assessment of the level of carbon balance using optical and microwave satellite data along with ground truth observations. Optical images have been used for classification of wetlands vegetation and calculation of LAI and biomass. For the assessment of water balance, energy budget approach has been applied. Microwave images have been used for the assessment of soil moisture and biomass.

Analysis of smoke and cloud impact on seasonal and interannual variations in normalized difference vegetation index in Amazon

NASA Astrophysics Data System (ADS)

Kobayashi, H.; Dye, D. G.

2004-12-01

Normalized difference vegetation index (NDVI) derived from National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) is a unique measurement of long-term variations in global vegetation dynamics. The NDVI data have been used for the detection of the seasonal and interannual variations in vegetation. However, as reported in several studies, NDVI decreases with the increase in clouds and/or smoke aerosol contaminated in the pixels. This study assesses the smoke and clouds effect on long-term Global Inventory Modeling and Mapping Studies (GIMMS) and Pathfinder AVHRR Land (PAL) NDVI data in Amazon. This knowledge will help developing the correction method in the tropics in the future. To assess the smoke and cloud effects on GIMMS and PAL, we used another satellite-derived data sets; NDVI derived from SPOT/VEGETATION (VGT) data and Aerosol Index (AI) derived from Total Ozone Mapping Spectrometer (TOMS). Since April 1998, VGT has measured the earth surface globally including in Amazon. The advantage of the VGT is that it has blue channel where the smoke and cloud can be easily detected. By analyzing the VGT NDVI and comparing with the AVHRR-based NDVI, we inferred smoke and cloud effect on the AVHRR-based NDVI. From the results of the VGT analysis, we found the large NDVI seasonality in South and Southeastern Amazon. In these areas, the NDVI gradually increased from April to July and decreased from August to October. However the sufficient NDVI data were not existed from August to November when the smoke and cloud pixels were masked using blue reflectance. Thus it is said that the smoke and clouds mainly cause the large decreases in NDVI between August and November and NDVI has little vegetation signature in these months. Also we examined the interannual variations in NDVI and smoke aerosol. Then the decrease in NDVI is well consistent with the increase in the increase in AI. Our results suggest that the months between April and July are the most reliable season to monitor the vegetation.

Vegetation Coverage Mapping and Soil Effect Correction in Estimating Vegetation Water Content and Dry Biomass from Satellites

NASA Astrophysics Data System (ADS)

Huang, J.; Chen, D.

2005-12-01

Vegetation water content (VWC) attracts great research interests in hydrology research in recent years. As an important parameter describing the horizontal expansion of vegetation, vegetation coverage is essential to implement soil effect correction for partially vegetated fields to estimate VWC accurately. Ground measurements of corn and soybeans in SMEX02 resulted in an identical expolinear relationship between vegetation coverage and leaf area index (LAI), which is used for vegetation coverage mapping. Results illustrated two parts of LAI growth quantitatively: the horizontal expansion of leaf coverage and the vertical accumulation of leaf layers. It is believed that the former part contributes significantly to LAI growth at initial vegetation growth stage and the latter is more dominant after vegetation coverage reaches a certain level. The Normalized Difference Water Index (NDWI) using short-wave infrared bands is convinced for its late saturation at high LAI values, in contrast to the Normalized Difference Vegetation Index (NDVI). NDWI is then utilized to estimate LAI, via another expolinear relationship, which is evidenced having vegetation species independency in study of corn and soybeans in SMEX02 sites. It is believed that the surface reflectance measured at satellites spectral bands are the mixed results of signals reflected from vegetation and bare soil, especially at partially vegetated fields. A simple linear mixture model utilizing vegetation coverage information is proposed to correct soil effect in such cases. Surface reflectance fractions for -rpure- vegetation are derived from the model. Comparing with ground measurements, empirical models using soil effect corrected vegetation indices to estimate VWC and dry biomass (DB) are generated. The study enhanced the in-depth understanding of the mechanisms how vegetation growth takes effect on satellites spectral reflectance with and without soil effect, which are particularly useful for modeling in hydrology, agriculture, forestry and meteorology etc.

Evolution of regional to global paddy rice mapping methods: A review

NASA Astrophysics Data System (ADS)

Dong, Jinwei; Xiao, Xiangming

2016-09-01

Paddy rice agriculture plays an important role in various environmental issues including food security, water use, climate change, and disease transmission. However, regional and global paddy rice maps are surprisingly scarce and sporadic despite numerous efforts in paddy rice mapping algorithms and applications. With the increasing need for regional to global paddy rice maps, this paper reviewed the existing paddy rice mapping methods from the literatures ranging from the 1980s to 2015. In particular, we illustrated the evolution of these paddy rice mapping efforts, looking specifically at the future trajectory of paddy rice mapping methodologies. The biophysical features and growth phases of paddy rice were analyzed first, and feature selections for paddy rice mapping were analyzed from spectral, polarimetric, temporal, spatial, and textural aspects. We sorted out paddy rice mapping algorithms into four categories: (1) Reflectance data and image statistic-based approaches, (2) vegetation index (VI) data and enhanced image statistic-based approaches, (3) VI or RADAR backscatter-based temporal analysis approaches, and (4) phenology-based approaches through remote sensing recognition of key growth phases. The phenology-based approaches using unique features of paddy rice (e.g., transplanting) for mapping have been increasingly used in paddy rice mapping. Current applications of these phenology-based approaches generally use coarse resolution MODIS data, which involves mixed pixel issues in Asia where smallholders comprise the majority of paddy rice agriculture. The free release of Landsat archive data and the launch of Landsat 8 and Sentinel-2 are providing unprecedented opportunities to map paddy rice in fragmented landscapes with higher spatial resolution. Based on the literature review, we discussed a series of issues for large scale operational paddy rice mapping.

Connectivity processes and riparian vegetation of the upper Paraná River, Brazil

NASA Astrophysics Data System (ADS)

Stevaux, José C.; Corradini, Fabrício A.; Aquino, Samia

2013-10-01

In fluvial systems, the relationship between a dominant variable (e.g. flood pulse) and its dependent ones (e.g. riparian vegetation) is called connectivity. This paper analyzes the connectivity elements and processes controlling riparian vegetation for a reach of the upper Paraná River (Brazil) and estimates the future changes in channel-vegetation relationship as a consequence of the managing of a large dam. The studied reach is situated 30 km downstream from the Porto Primavera Dam (construction finished in 1999). Through aerial photography (1:25,000, 1996), RGB-CBERS satellite imagery and a previous field botany survey it was possible to elaborate a map with the five major morpho-vegetation units: 1) Tree-dominated natural levee, 2) Shrubby upper floodplain, 3) Shrub-herbaceous mid floodplain, 4) Grass-herbaceous lower floodplain and 5) Shrub-herbaceous flood runoff channel units. By use of a detailed topographic survey and statistical tools each morpho-vegetation type was analyzed according to its connectivity parameters (frequency, recurrence, permanence, seasonality, potamophase, limnophase and FCQ index) in the pre- and post-dam closure periods of the historical series. Data showed that most of the morpho-vegetation units were predicted to present changes in connectivity parameters values after dam closing and the new regime could affect, in different intensity, the river ecology and particularly the riparian vegetation. The methods used in this study can be useful for dam impact studies in other South American tropical rivers.

A scheme for the uniform mapping and monitoring of earth resources and environmental complexes: An assessment of natural vegetation, environmental, and crop analogs. [Sierra-Lahontan and Colorado Plateaus, Northern Great Valley (CA), and Louisiana Coastal Plain

NASA Technical Reports Server (NTRS)

Poulton, C. E.; Welch, R. I. (Principal Investigator)

1975-01-01

The author has identified the following significant results. A study was performed to develop and test a procedure for the uniform mapping and monitoring of natural ecosystems in the semi-arid and wood regions of the Sierra-Lahontan and Colorado Plateau areas, and for the estimating of rice crop production in the Northern Great Valley (Ca.) and the Louisiana Coastal Plain. ERTS-1 and high flight and low flight aerial photos were used in a visual photointerpretation scheme to identify vegetation complexes, map acreages, and evaluate crop vigor and stress. Results indicated that the vegetation analog concept is valid; that depending on the kind of vegetation and its density, analogs are interpretable at different levels in the hierarchical classification from second to the fourth level. The second level uses physiognomic growth form-structural criteria, and the fourth level uses floristic or taxonomic criteria, usually at generic level. It is recommended that analog comparisons should be made in relatively small test areas where large homogeneous examples can be found of each analog.

Linking imaging spectroscopy and trait data to better understand spatial and temporal variability in functional traits

NASA Astrophysics Data System (ADS)

Townsend, Philip; Kruger, Eric; Wang, Zhihui; Singh, Aditya

2017-04-01

Imaging spectroscopy exhibits great potential for mapping foliar functional traits that are impractical or expensive to regularly measure on the ground, and are essentially impossible to characterize comprehensively across space. Specifically, the high information content in spectroscopic data enables us to identify narrow spectral feature that are associated with vegetation primary and secondary biochemistry (nutrients, pigments, defensive compounds), leaf structure (e.g., leaf mass per area), canopy structure, and physiological capacity. Ultimately, knowledge of the variability in such traits is critical to understanding vegetation productivity, as well as responses to climatic variability, disturbances, pests and pathogens. The great challenge to the use of imaging spectroscopy to supplement trait databases is the development of trait retrieval approaches that are broadly applicable within and between ecosystem types. Here, we outline how we are using the US National Ecological Observatory Network (NEON) to prototype the scaling and comparison of trait distributions derived from field measurements and imagery. We find that algorithms to map traits from imagery are robust across ecosystem types, when controlling for physiognomy and vegetation percent cover, and that among all vegetation types, the chemometric algorithms utilize similar features for mapping of traits.

EnviroAtlas - Minneapolis/St. Paul, MN - 51m Riparian Buffer Vegetated Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. In this community, vegetated cover is defined as Trees and Forest, Grass and Herbaceous, Woody Wetlands, and Emergent Wetlands. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. 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).

A case study of carbon fluxes from land change in the southwest Brazilian Amazon

USGS Publications Warehouse

Barrett, K.; Rogan, J.; Eastman, J.R.

2009-01-01

Worldwide, land change is responsible for one-fifth of anthropogenic carbon emissions. In Brazil, three-quarters of carbon emissions originate from land change. This study represents a municipal-scale study of carbon fluxes from vegetation in Rio Branco, Brazil. Land-cover maps of pasture, forest, and secondary growth from 1993, 1996, 1999, and 2003 were produced using an unsupervised classification method (overall accuracy = 89%). Carbon fluxes from land change over the decade of imagery were estimated from transitions between land-cover categories for each time interval. This article presents new methods for estimating emissions reductions from carbon stored in the vegetation that replaces forests (e.g., pasture) and sequestration by new (>10-15 years) forests, which reduced gross emissions by 16, 15, and 22% for the period of 1993-1996, 1996-1999, and 1999-2003, respectively. The methods used in the analysis are broadly applicable and provide a comprehensive characterization of regional-scale carbon fluxes from land change.

Land use maps of the Tanana and Purcell Mountain areas, Alaska, based on Earth Resources Technology Satellite imagery

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1974-01-01

The author has identified the following significant results. ERTS imagery in photographic format was used to make land use maps of two areas of special interest to native corporations under terms of the Alaska Native Claims Settlement Act. Land selections are to be made in these areas, and the maps should facilitate decisions because of their comprehensive presentation of resource distribution information. The ERTS images enabled mapping broadly-defined land use classes in large areas in a comparatively short time. Some aerial photography was used to identify colors and shades of gray on the various images. The 14 mapped land use categories are identified according to the classification system under development by the U.S. Geological Survey. These maps exemplify a series of about a dozen diverse Alaskan areas. The principal resource depicted is vegetation, and clearly shown are vegetation units of special importance, including stands possibly containing trees of commercial grade and stands constituting wildlife habitat.

Accuracy assessment/validation methodology and results of 2010–11 land-cover/land-use data for Pools 13, 26, La Grange, and Open River South, Upper Mississippi River System

USGS Publications Warehouse

Jakusz, J.W.; Dieck, J.J.; Langrehr, H.A.; Ruhser, J.J.; Lubinski, S.J.

2016-01-11

Similar to an AA, validation involves generating random points based on the total area for each map class. However, instead of collecting field data, two or three individuals not involved with the photo-interpretative mapping separately review each of the points onscreen and record a best-fit vegetation type(s) for each site. Once the individual analyses are complete, results are joined together and a comparative analysis is performed. The objective of this initial analysis is to identify areas where the validation results were in agreement (matches) and areas where validation results were in disagreement (mismatches). The two or three individuals then perform an analysis, looking at each mismatched site, and agree upon a final validation class. (If two vegetation types at a specific site appear to be equally prevalent, the validation team is permitted to assign the site two best-fit vegetation types.) Following the validation team’s comparative analysis of vegetation assignments, the data are entered into a database and compared to the mappers’ vegetation assignments. Agreements and disagreements between the map and validation classes are identified, and a contingency table is produced. This document presents the AA processes/results for Pools 13 and La Grange, as well as the validation process/results for Pools 13 and 26 and Open River South.

Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data

NASA Astrophysics Data System (ADS)

Pullanagari, R. R.; Kereszturi, G.; Yule, I. J.

2017-06-01

New Zealand farming relies heavily on grazed pasture for feeding livestock; therefore it is important to provide high quality palatable grass in order to maintain profitable and sustainable grassland management. The presence of non-photosynthetic vegetation (NPV) such as dead vegetation in pastures severely limits the quality and productivity of pastures. Quantifying the fraction of dead vegetation in mixed pastures is a great challenge even with remote sensing approaches. In this study, a high spatial resolution with pixel resolution of 1 m and spectral resolution of 3.5-5.6 nm imaging spectroscopy data from AisaFENIX (380-2500 nm) was used to assess the fraction of dead vegetation component in mixed pastures on a hill country farm in New Zealand. We used different methods to retrieve dead vegetation fraction from the spectra; narrow band vegetation indices, full spectrum based partial least squares (PLS) regression and feature selection based PLS regression. Among all approaches, feature selection based PLS model exhibited better performance in terms of prediction accuracy (R2CV = 0.73, RMSECV = 6.05, RPDCV = 2.25). The results were consistent with validation data, and also performed well on the external test data (R2 = 0.62, RMSE = 8.06, RPD = 2.06). In addition, statistical tests were conducted to ascertain the effect of topographical variables such as slope and aspect on the accumulation of the dead vegetation fraction. Steep slopes (>25°) had a significantly (p < 0.05) higher amount of dead vegetation. In contrast, aspect showed non-significant impact on dead vegetation accumulation. The results from the study indicate that AisaFENIX imaging spectroscopy data could be a useful tool for mapping the dead vegetation fraction accurately.

Assessing wildfire exposure in the Wildland-Urban Interface area of the mountains of central Argentina.

PubMed

Argañaraz, J P; Radeloff, V C; Bar-Massada, A; Gavier-Pizarro, G I; Scavuzzo, C M; Bellis, L M

2017-07-01

Wildfires are a major threat to people and property in Wildland Urban Interface (WUI) communities worldwide, but while the patterns of the WUI in North America, Europe and Oceania have been studied before, this is not the case in Latin America. Our goals were to a) map WUI areas in central Argentina, and b) assess wildfire exposure for WUI communities in relation to historic fires, with special emphasis on large fires and estimated burn probability based on an empirical model. We mapped the WUI in the mountains of central Argentina (810,000 ha), after digitizing the location of 276,700 buildings and deriving vegetation maps from satellite imagery. The areas where houses and wildland vegetation intermingle were classified as Intermix WUI (housing density > 6.17 hu/km 2 and wildland vegetation cover > 50%), and the areas where wildland vegetation abuts settlements were classified as Interface WUI (housing density > 6.17 hu/km 2 , wildland vegetation cover < 50%, but within 600 m of a vegetated patch larger than 5 km 2 ). We generated burn probability maps based on historical fire data from 1999 to 2011; as well as from an empirical model of fire frequency. WUI areas occupied 15% of our study area and contained 144,000 buildings (52%). Most WUI area was Intermix WUI, but most WUI buildings were in the Interface WUI. Our findings suggest that central Argentina has a WUI fire problem. WUI areas included most of the buildings exposed to wildfires and most of the buildings located in areas of higher burn probability. Our findings can help focus fire management activities in areas of higher risk, and ultimately provide support for landscape management and planning aimed at reducing wildfire risk in WUI communities. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Application of Remote Sensing Data to GIS Studies of Land Use, Land Cover, and Vegetation Mapping in the State of Hawaii

NASA Technical Reports Server (NTRS)

Hogan, Christine A.

1996-01-01

A land cover-vegetation map with a base classification system for remote sensing use in a tropical island environment was produced of the island of Hawaii for the State of Hawaii to evaluate whether or not useful land cover information can be derived from Landsat TM data. In addition, an island-wide change detection mosaic combining a previously created 1977 MSS land classification with the TM-based classification was produced. In order to reach the goal of transferring remote sensing technology to State of Hawaii personnel, a pilot project was conducted while training State of Hawaii personnel in remote sensing technology and classification systems. Spectral characteristics of young island land cover types were compared to determine if there are differences in vegetation types on lava, vegetation types on soils, and barren lava from soils, and if they can be detected remotely, based on differences in pigments detecting plant physiognomic type, health, stress at senescence, heat, moisture level, and biomass. Geographic information systems (GIS) and global positioning systems (GPS) were used to assist in image rectification and classification. GIS was also used to produce large-format color output maps. An interactive GIS program was written to provide on-line access to scanned photos taken at field sites. The pilot project found Landsat TM to be a credible source of land cover information for geologically young islands, and TM data bands are effective in detecting spectral characteristics of different land cover types through remote sensing. Large agriculture field patterns were resolved and mapped successfully from wildland vegetation, but small agriculture field patterns were not. Additional processing was required to work with the four TM scenes from two separate orbits which span three years, including El Nino and drought dates. Results of the project emphasized the need for further land cover and land use processing and research. Change in vegetation composition was noted in the change detection image.

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

FOGWELL, T.W.

During the biological survey and inventory of the Hanford Site conducted in the mid-1990s (1995 and 1996), preliminary surveys of the riparian vegetation were conducted along the Hanford Reach. These preliminary data were reported to The Nature Conservancy (TNC), but were not included in any TNC reports to DOE or stakeholders. During the latter part of FY2001, PNNL contracted with SEE Botanical, the parties that performed the original surveys in the mid 1990s, to complete the data summaries and mapping associated with the earlier survey data. Those data sets were delivered to PNNL and the riparian mapping by vegetation typemore » for the Hanford Reach is being digitized during the first quarter of FY2002. These mapping efforts provide the information necessary to create subsequent spatial data layers to describe the riparian zone according to plant functional types (trees, shrubs, grasses, sedges, forbs). Quantification of the riparian zone by vegetation types is important to a number of DOE'S priority issues including modeling contaminant transport and uptake in the near-riverine environment and the determination of ecological risk. This work included the identification of vegetative zones along the Reach by changes in dominant plant species covering the shoreline from just to the north of the 300 Area to China Bar near Vernita. Dominant and indicator species included Agropyron dasytachyudA. smithii, Apocynum cannabinum, Aristida longiseta, Artemisia campestris ssp. borealis var scouleriana, Artemisa dracunculus, Artemisia lindleyana, Artemisia tridentata, Bromus tectorum, Chrysothamnus nauseosus, Coreopsis atkinsoniana. Eleocharis palustris, Elymus cinereus, Equisetum hyemale, Eriogonum compositum, Juniperus trichocarpa, Phalaris arundinacea, Poa compressa. Salk exigua, Scirpus acutus, Solidago occidentalis, Sporobolus asper,and Sporobolus cryptandrus. This letter report documents the data received, the processing by PNNL staff, and additional data gathered in FY2002 to support development of a complete data layer describing riparian vegetation cover types on the Columbia River adjacent to the Hanford Site boundaries. Included with this report are the preliminary riparian vegetation maps and the associated metadata for that GIS layer.« less

Estimating plant available water content from remotely sensed evapotranspiration

NASA Astrophysics Data System (ADS)

van Dijk, A. I. J. M.; Warren, G.; Doody, T.

2012-04-01

Plant available water content (PAWC) is an emergent soil property that is a critical variable in hydrological modelling. PAWC determines the active soil water storage and, in water-limited environments, is the main cause of different ecohydrological behaviour between (deep-rooted) perennial vegetation and (shallow-rooted) seasonal vegetation. Conventionally, PAWC is estimated for a combination of soil and vegetation from three variables: maximum rooting depth and the volumetric water content at field capacity and permanent wilting point, respectively. Without elaborate local field observation, large uncertainties in PAWC occur due to the assumptions associated with each of the three variables. We developed an alternative, observation-based method to estimate PAWC from precipitation observations and CSIRO MODIS Reflectance-based Evapotranspiration (CMRSET) estimates. Processing steps include (1) removing residual systematic bias in the CMRSET estimates, (2) making spatially appropriate assumptions about local water inputs and surface runoff losses, (3) using mean seasonal patterns in precipitation and CMRSET to estimate the seasonal pattern in soil water storage changes, (4) from these, calculating the mean seasonal storage range, which can be treated as an estimate of PAWC. We evaluate the resulting PAWC estimates against those determined in field experiments for 180 sites across Australia. We show that the method produces better estimates of PAWC than conventional techniques. In addition, the method provides detailed information with full continental coverage at moderate resolution (250 m) scale. The resulting maps can be used to identify likely groundwater dependent ecosystems and to derive PAWC distributions for each combination of soil and vegetation type.

The Karakum and Kyzylkum sand seas dynamics; mapping and palaeoclimatic interpretations

NASA Astrophysics Data System (ADS)

Maman, Shimrit; Blumberg, Dan G.; Tsoar, Haim; Porat, Naomi

2015-04-01

Sand seas are large basins in deserts that are mantled by wind-swept sand and that exhibit varying degrees of vegetation cover. Wilson (1973) was the first to globally map and classify sand seas. Beyond Wilson's maps, however, little research has been published regarding the Karakum and Kyzylkum sand seas of Central Asia. Wilson's maps delineate active ergs from inactive ergs based solely on precipitation. His assumption of annual average rainfall as a factor determining mobility vs. stability of sand seas is too simplistic and does not take into consideration other factors such as biogenic soil crusts and wind power, both of which are known to have major effects on the dynamics of sand dunes. Literature related to mapping and classifying the Central Asian ergs by remote sensing or sand sea classification state (stable/active) is lacking. Moreover, the palaeoclimatic significance of dunes in Central Asia is difficult to assess, as there has been few studies of dune stratigraphy and numerical ages are lacking. Optically stimulated luminescence (OSL) is a firm optical dating method that is used to determine the elapsed time since quartz grains were last exposed to sunlight, thus, their burial. Yet, absolute ages indicating mobilization and stabilization of these sands, are still inadequately known and are here under discussion. The broad concern of this research was to determine the dynamics of the Central Asian sand seas and study the palaeoclimatic changes that brought to their stabilization. As there are no reliable maps or aeolian discussion of these sands, establishment of a digital data base was initially conducted, focusing on identifying and mapping these sand seas. The vast area and inaccessibility make traditional mapping methods virtually impossible. A variety of space-borne imagery both optical and radar, with varying spectral and spatial resolutions was used. These images provided the basis for mapping sand distribution, dune forms, and vegetation cover. GIS analysis was performed in parallel with field work to obtain validation and verification. The remote sensing and GIS results show that these ergs are mostly stabilized, with the estimated sand mantled area for the Karakum desert ~260,000 km2, and for the Kyzylkum it is ~195,500 km2. Meteorological analysis of wind and precipitation data indicate a low wind power environment (DP< 200) and sufficient rainfall (>100 mm) to support vegetation. Thus, these sands are indicative of past periods during which the climate in this region was different than today, enabling aeolian sand activity. Optically stimulated luminescence ages derived from the upper meter of the interdune of 14 exposed sections from both ergs, indicate sand stabilization during the mid-Holocene. This stabilization is understood to reflect a transition to a warmer, wetter, and less windy climate that generally persisted until today. The OSL ages, coupled with a compilation of regional palaeoclimatic data, corroborate and reinforce the previously proposed Mid-Holocene Liavliakan phase, known to reflect a warmer, wetter, and less windy climate that persists until today and resulted in dune stabilization around the Mid-Holocene.

Analysis of regional-scale vegetation dynamics of Mexico using stratified AVHRR NDVI data. [Normalized Difference Vegetaion Index

NASA Technical Reports Server (NTRS)

Turcotte, Kevin M.; Kramber, William J.; Venugopal, Gopalan; Lulla, Kamlesh

1989-01-01

Previous studies have shown that a good relationship exists between AVHRR Normalized Difference Vegetation Index (NDVI) measurements, and both regional-scale patterns of vegetation seasonality and productivity. Most of these studies used known samples of vegetation types. An alternative approach, and the objective was to examine the above relationships by analyzing one year of AVHRR NDVI data that was stratified using a small-scale vegetation map of Mexico. The results show that there is a good relationship between AVHRR NDVI measurements and regional-scale vegetation dynamics of Mexico.

Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing

USGS Publications Warehouse

Kokaly, R.F.; Rockwell, B.W.; Haire, S.L.; King, T.V.V.

2007-01-01

Forest fires leave behind a changed ecosystem with a patchwork of surface cover that includes ash, charred organic matter, soils and soil minerals, and dead, damaged, and living vegetation. The distributions of these materials affect post-fire processes of erosion, nutrient cycling, and vegetation regrowth. We analyzed high spatial resolution (2.4??m pixel size) Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data collected over the Cerro Grande fire, to map post-fire surface cover into 10 classes, including ash, soil minerals, scorched conifer trees, and green vegetation. The Cerro Grande fire occurred near Los Alamos, New Mexico, in May 2000. The AVIRIS data were collected September 3, 2000. The surface cover map revealed complex patterns of ash, iron oxide minerals, and clay minerals in areas of complete combustion. Scorched conifer trees, which retained dry needles heated by the fire but not fully combusted by the flames, were found to cover much of the post-fire landscape. These scorched trees were found in narrow zones at the edges of completely burned areas. A surface cover map was also made using Landsat Enhanced Thematic Mapper plus (ETM+) data, collected September 5, 2000, and a maximum likelihood, supervised classification. When compared to AVIRIS, the Landsat classification grossly overestimated cover by dry conifer and ash classes and severely underestimated soil and green vegetation cover. In a comparison of AVIRIS surface cover to the Burned Area Emergency Rehabilitation (BAER) map of burn severity, the BAER high burn severity areas did not capture the variable patterns of post-fire surface cover by ash, soil, and scorched conifer trees seen in the AVIRIS map. The BAER map, derived from air photos, also did not capture the distribution of scorched trees that were observed in the AVIRIS map. Similarly, the moderate severity class of Landsat-derived burn severity maps generated from the differenced Normalized Burn Ratio (dNBR) calculation had low agreement with the AVIRIS classes of scorched conifer trees. Burn severity and surface cover images were found to contain complementary information, with the dNBR map presenting an image of degree of change caused by fire and the AVIRIS-derived map showing specific surface cover resulting from fire.

Recovery and archiving key Arctic Alaska vegetation map and plot data for the Arctic-Boreal Vulnerability Field Experiment (ABoVE)

NASA Astrophysics Data System (ADS)

Walker, D. A.; Breen, A. L.; Broderson, D.; Epstein, H. E.; Fisher, W.; Grunblatt, J.; Heinrichs, T.; Raynolds, M. K.; Walker, M. D.; Wirth, L.

2013-12-01

Abundant ground-based information will be needed to inform remote-sensing and modeling studies of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE). A large body of plot and map data collected by the Alaska Geobotany Center (AGC) and collaborators from the Arctic regions of Alaska and the circumpolar Arctic over the past several decades is being archived and made accessible to scientists and the public via the Geographic Information Network of Alaska's (GINA's) 'Catalog' display and portal system. We are building two main types of data archives: Vegetation Plot Archive: For the plot information we use a Turboveg database to construct the Alaska portion of the international Arctic Vegetation Archive (AVA) http://www.geobotany.uaf.edu/ava/. High quality plot data and non-digital legacy datasets in danger of being lost have highest priority for entry into the archive. A key aspect of the database is the PanArctic Species List (PASL-1), developed specifically for the AVA to provide a standard of species nomenclature for the entire Arctic biome. A wide variety of reports, documents, and ancillary data are linked to each plot's geographic location. Geoecological Map Archive: This database includes maps and remote sensing products and links to other relevant data associated with the maps, mainly those produced by the Alaska Geobotany Center. Map data include GIS shape files of vegetation, land-cover, soils, landforms and other categorical variables and digital raster data of elevation, multispectral satellite-derived data, and data products and metadata associated with these. The map archive will contain all the information that is currently in the hierarchical Toolik-Arctic Geobotanical Atlas (T-AGA) in Alaska http://www.arcticatlas.org, plus several additions that are in the process of development and will be combined with GINA's already substantial holdings of spatial data from northern Alaska. The Geoecological Atlas Portal uses GINA's Catalog tool to develop a web interface to view and access the plot and map data. The mapping portal allows visualization of GIS data, sample-point locations and imagery and access to the map data. Catalog facilitates the discovery and dissemination of science-based information products in support of analysis and decision-making concerned with development and climate change and is currently used by GINA in several similar archive/distribution portals.

From local spectral measurements to maps of vegetation cover and biomass on the Qinghai-Tibet-Plateau: Do we need hyperspectral information?

NASA Astrophysics Data System (ADS)

Meyer, Hanna; Lehnert, Lukas W.; Wang, Yun; Reudenbach, Christoph; Nauss, Thomas; Bendix, Jörg

2017-03-01

Though the relevance of pasture degradation on the Qinghai-Tibet Plateau (QTP) is widely postulated, its extent is still unknown. Due to the enormous spatial extent, remote sensing provides the only possibility to investigate pasture degradation via frequently used proxies such as vegetation cover and aboveground biomass (AGB). However, unified remote sensing approaches are still lacking. This study tests the applicability of hyper- and multispectral in situ measurements to map vegetation cover and AGB on regional scales. Using machine learning techniques, it is tested whether the full hyperspectral information is needed or if multispectral information is sufficient to accurately estimate pasture degradation proxies. To regionalize pasture degradation proxies, the transferability of the locally derived ML-models to high resolution multispectral satellite data is assessed. 1183 hyperspectral measurements and vegetation records were performed at 18 locations on the QTP. Random Forests models with recursive feature selection were trained to estimate vegetation cover and AGB using narrow-band indices (NBI) as predictors. Separate models were calculated using NBI from hyperspectral data as well as from the same data resampled to WorldView-2, QuickBird and RapidEye channels. The hyperspectral results were compared to the multispectral results. Finally, the models were applied to satellite data to map vegetation cover and AGB on a regional scale. Vegetation cover was accurately predicted by Random Forest if hyperspectral measurements were used (cross validated R2 = 0.89). In contrast, errors in AGB estimations were considerably higher (cross validated R2 = 0.32). Only small differences in accuracy were observed between the models based on hyperspectral compared to multispectral data. The application of the models to satellite images generally resulted in an increase of the estimation error. Though this reflects the challenge of applying in situ measurements to satellite data, the results still show a high potential to map pasture degradation proxies on the QTP. Thus, this study presents robust methodology to remotely detect and monitor pasture degradation at high spatial resolutions.

Multistage, multiband and sequential imagery to identify and quantify non-forest vegetation resources

NASA Technical Reports Server (NTRS)

Driscoll, R. S.; Francis, R. C.

1972-01-01

Earth Resources photographs from Apollo 6, 7, and 9 and photographs taken during Gemini 4, were used in the research along with high altitude and conventional aerial photography. A unified land use and resource analysis system was devised and used to develop a mapping legend. The natural vegetation, land use, macrorelief, and landforms of northern Maricopa County, Arizona, were analyzed and inventoried. This inventory was interpreted in relation to the critical problem of urban expansion and agricultural production in the study area. The central thrust of the research program has been to develop methods for use of space and small-scale, high-altitude aerial photography to develop information for land use planning and resource allocation decisions.

Vegetative resistance to flow in South Florida; summary of vegetation sampling at sites NESRS3 and P33, Shark River slough, April 1996

USGS Publications Warehouse

Carter, Virginia; Ruhl, H.; Rybicki, N.B.; Reel, J.T.; Gammon, P.T.

1999-01-01

The U.S. Geological Survey is one of many agencies participating in the effort to restore the south Florida Everglades. We are sampling and characterizing the vegetation at selected sites in the Everglades as part of a study to quantify vegetative flow resistance. The objectives of the vegetative sampling are (1) to provide detailed information on species composition, vegetative characteristics, vegetative structure, and biomass for quantification of vegetative resistance to flow, and (2) to use this information to classify the vegetation and to improve existing vegetation maps for use with numerical models of surface-water flow. Vegetative sampling was conducted in the Shark River Slough in April, 1996. The data collected and presented here include live, dead, and periphyton biomass, vegetation characteristics and structure, and leaf area index.

Mapping the Climate of Puerto Rico, Vieques and Culebra.

Treesearch

CHRISTOPHER DALY; E. H. HELMER; MAYA QUINONES

2003-01-01

Spatially explicit climate data contribute to watershed resource management, mapping vegetation type with satellite imagery, mapping present and hypothetical future ecological zones, and predicting species distributions. The regression based Parameter-elevation Regressions on Independent Slopes Model (PRISM) uses spatial data sets, a knowledge base and expert...

An ERTS multispectral scanner experiment for mapping iron compounds

NASA Technical Reports Server (NTRS)

Vincent, R. K. (Principal Investigator)

1972-01-01

There are no author-identified significant results in this report. An experimental plan for enhancing spectral features related to the chemical composition of geological targets in ERTS multispectral scanner data is described. The experiment is designed to produce visible-reflective infrared ratio images from ERTS-1 data. Iron compounds are promising remote sensing targets because they display prominent spectral features in the visible-reflective infrared wavelength region and are geologically significant. The region selected for this ERTS experiment is the southern end of the Wind River Range in Wyoming. If this method proves successful it should prove useful for regional geologic mapping, mineralogical exploration, and soil mapping. It may also be helpful to ERTS users in scientific disciplines other than geology, especially to those concerned with targets composed of mixtures of live vegetation and soil or rock.

Multipolarization radar images for geologic mapping and vegetation discrimination

NASA Technical Reports Server (NTRS)

Evans, D. L.; Farr, T. G.; Ford, J. P.; Thompson, T. W.; Werner, C. L.

1986-01-01

NASA has developed an airborne SAR that simultaneously yields image data in four linear polarizations in L-band with 10-m resolution over a swath of about 10 km. Signal data are recorded both optically and digitally and annotated in each of the channels to facilitate completely automated digital correlation. Comparison of the relative intensities of the different polarizations furnishes discriminatory mapping information. Local intensity variations in like-polarization images result from topographic effects, while strong cross polarization responses denote the effects of vegetation cover and, in some cases, possible scattering from the subsurface. In each of the areas studied, multiple polarization data led to the discrimination and mapping of unique surface unit features.

Spatio - Temporal Variation of Aerosol and its relation to vegetation cover over mega-city New Delhi

NASA Astrophysics Data System (ADS)

Pandey, Alok; Pravesh Kumar, Ram; Berwal, Shivesh; Kumar, Krishan; Kumar, Ritesh

2016-07-01

MODerate resolution Imaging Spectro-radiometer (MODIS) on board NASA's Terra and Aqua satellite Level 2 Aerosol optical depth (AOD) data is used for aerosol study and LISS III sensor on board Indian Remote Sensing (IRS) Satellite procured from the National Remote Sensing Center (NRSC), Hyderabad, India was used for vegetation cover estimation over New Delhi and its surrounding regions. Lowest AOD was found in the spring and winter season where highest AOD in summer months. Different dates representing different seasons LISS III imageries were used for generation of land-cover maps for vegetation study. The land cover maps reveal that most of the surrounding areas of Delhi are covered with vegetation in the month of March. By the month of May-June herbs are cut or dry from most of the region surrounding Delhi and the land cover in the surrounding areas changes to bare soil. During the rainy season (July to September) the vegetation cover over Delhi and the surrounding areas increases significantly. In November - December there is dispersed vegetation cover over New Delhi and its surrounding regions depending upon the age of the newly sown crop and ornamental plants. We found that, there is statistically significant negative correlation between AOD and Vegetation in every season over New Delhi.

Application of Skylab imagery to some geological and environmental problems in Italy. [and Sicily

NASA Technical Reports Server (NTRS)

Cassinis, R.; Lechi, G. M.; Tonelli, A. M.

1975-01-01

Four topics are considered: regional geology of Sicily, volcanic surveillance in southern Italy, hydrogeology (with special regard given to the discovery and mapping of paleoriverbeds), and crop investigation. The discovery of unknown lineaments and structures in Sicily contributes to the geological knowledge of this region and in particular to the mechanical phenomena involving the upper part of the crust. An attempt was made to relate the status of vegetation surrounding Etna volcano to the magmatic gas escapes filtering through the soil. False-color Skylab images were used to analyze the vigor of the Etnean forestal belt vegetation canopy in order to map possible gas-vent ways as well as the 'active' microfractures. In northern Italy, buried channels were mapped in the Venetian Plain, and a tentative cost-benefit evaluation was done in the field of vegetational studies, both disease detection and species inventory were performed in the Po River Delta and in northwestern Italy.

Mapping Successional Stages in a Wet Tropical Forest Using Landsat ETM+ and Forest Inventory Data

NASA Technical Reports Server (NTRS)

Goncalves, Fabio G.; Yatskov, Mikhail; dos Santos, Joao Roberto; Treuhaft, Robert N.; Law, Beverly E.

2010-01-01

In this study, we test whether an existing classification technique based on the integration of Landsat ETM+ and forest inventory data enables detailed characterization of successional stages in a wet tropical forest site. The specific objectives were: (1) to map forest age classes across the La Selva Biological Station in Costa Rica; and (2) to quantify uncertainties in the proposed approach in relation to field data and existing vegetation maps. Although significant relationships between vegetation height entropy (a surrogate for forest age) and ETM+ data were detected, the classification scheme tested in this study was not suitable for characterizing spatial variation in age at La Selva, as evidenced by the error matrix and the low Kappa coefficient (12.9%). Factors affecting the performance of the classification at this particular study site include the smooth transition in vegetation structure between intermediate and advanced successional stages, and the low sensitivity of NDVI to variations in vertical structure at high biomass levels.

EnviroAtlas - Tampa, FL - 51m Riparian Buffer Vegetated Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. 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).

Radar response to vegetation. [soil moisture mapping via microwave backscattering

NASA Technical Reports Server (NTRS)

Ulaby, F. T.

1975-01-01

Active microwave measurements of vegetation backscatter were conducted to determine the utility of radar in mapping soil moisture through vegetation and mapping crop types. Using a truck-mounted boom, spectral response data were obtained for four crop types (corn, milo, soybeans, and alfalfa) over the 4-8 GHz frequency band, at incidence angles of 0 to 70 degrees in 10-degree steps, and for all four linear polarization combinations. Based on a total of 125 data sets covering a wide range of soil moisture, content, system design criteria are proposed for each of the aforementioned objectives. Quantitative soil moisture determination was best achieved at the lower frequency end of the 4-8 GHz band using HH polarized waves in the 5- to 15-degree incidence angle range. A combination of low and high frequency measurements are suggested for classifying crop types. For crop discrimination, a dual-frequency dual-polarization (VV and cross) system operating at incidence angles above 40 degrees is suggested.