Ningaloo Reef: Shallow Marine Habitats Mapped Using a Hyperspectral Sensor

PubMed Central

Kobryn, Halina T.; Wouters, Kristin; Beckley, Lynnath E.; Heege, Thomas

2013-01-01

Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km2 of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km2). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas. PMID:23922921

Monitoring technologies for ocean disposal of radioactive waste

NASA Astrophysics Data System (ADS)

Triplett, M. B.; Solomon, K. A.; Bishop, C. B.; Tyce, R. C.

1982-01-01

The feasibility of using carefully selected subseabed locations to permanently isolate high level radioactive wastes at ocean depths greater than 4000 meters is discussed. Disposal at several candidate subseabed areas is being studied because of the long term geologic stability of the sediments, remoteness from human activity, and lack of useful natural resources. While the deep sea environment is remote, it also poses some significant challenges for the technology required to survey and monitor these sites, to identify and pinpoint container leakage should it occur, and to provide the environmental information and data base essential to determining the probable impacts of any such occurrence. Objectives and technical approaches to aid in the selective development of advanced technologies for the future monitoring of nuclear low level and high level waste disposal in the deep seabed are presented. Detailed recommendations for measurement and sampling technology development needed for deep seabed nuclear waste monitoring are also presented.

A geomorphological seabed classification for the Weddell Sea, Antarctica

NASA Astrophysics Data System (ADS)

Jerosch, Kerstin; Kuhn, Gerhard; Krajnik, Ingo; Scharf, Frauke Katharina; Dorschel, Boris

2016-06-01

Sea floor morphology plays an important role in many scientific disciplines such as ecology, hydrology and sedimentology since geomorphic features can act as physical controls for e.g. species distribution, oceanographically flow-path estimations or sedimentation processes. In this study, we provide a terrain analysis of the Weddell Sea based on the 500 m × 500 m resolution bathymetry data provided by the mapping project IBCSO. Seventeen seabed classes are recognized at the sea floor based on a fine and broad scale Benthic Positioning Index calculation highlighting the diversity of the glacially carved shelf. Beside the morphology, slope, aspect, terrain rugosity and hillshade were calculated and supplied to the data archive PANGAEA. Applying zonal statistics to the geomorphic features identified unambiguously the shelf edge of the Weddell Sea with a width of 45-70 km and a mean depth of about 1200 m ranging from 270 m to 4300 m. A complex morphology of troughs, flat ridges, pinnacles, steep slopes, seamounts, outcrops, and narrow ridges, structures with approx. 5-7 km width, build an approx. 40-70 km long swath along the shelf edge. The study shows where scarps and depressions control the connection between shelf and abyssal and where high and low declination within the scarps e.g. occur. For evaluation purpose, 428 grain size samples were added to the seabed class map. The mean values of mud, sand and gravel of those samples falling into a single seabed class was calculated, respectively, and assigned to a sediment texture class according to a common sediment classification scheme.

An adaptable walking-skid for seabed ROV under strong current disturbance

NASA Astrophysics Data System (ADS)

Si, Jianting; Chin, Chengsiong

2014-09-01

This paper proposed a new concept of an adaptable multi-legged skid design for retro-fitting to a remotely-operated vehicle (ROV) during high tidal current underwater pipeline inspection. The sole reliance on propeller-driven propulsion for ROV is replaced with a proposed low cost biomimetic solution in the form of an attachable hexapod walking skid. The advantage of this adaptable walking skid is the high stability in positioning and endurances to strong current on the seabed environment. The computer simulation flow studies using Solidworks Flow Simulation shown that the skid attachment in different compensation postures caused at least four times increase in overall drag, and negative lift forces on the seabed ROV to achieve a better maneuvering and station keeping under the high current condition (from 0.5 m/s to 5.0 m/s). A graphical user interface is designed to interact with the user during robot-in-the-loop testing and kinematics simulation in the pool.

Contamination tracer testing with seabed drills: IODP Expedition 357

NASA Astrophysics Data System (ADS)

Orcutt, Beth N.; Bergenthal, Markus; Freudenthal, Tim; Smith, David; Lilley, Marvin D.; Schnieders, Luzie; Green, Sophie; Früh-Green, Gretchen L.

2017-11-01

IODP Expedition 357 utilized seabed drills for the first time in the history of the ocean drilling program, with the aim of collecting intact sequences of shallow mantle core from the Atlantis Massif to examine serpentinization processes and the deep biosphere. This novel drilling approach required the development of a new remote seafloor system for delivering synthetic tracers during drilling to assess for possible sample contamination. Here, we describe this new tracer delivery system, assess the performance of the system during the expedition, provide an overview of the quality of the core samples collected for deep biosphere investigations based on tracer concentrations, and make recommendations for future applications of the system.

Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps?

NASA Astrophysics Data System (ADS)

Turner, Joseph A.; Babcock, Russell C.; Hovey, Renae; Kendrick, Gary A.

2018-05-01

Numerous machine-learning classifiers are available for benthic habitat map production, which can lead to different results. This study highlights the performance of the Random Forest (RF) classifier, which was significantly better than Classification Trees (CT), Naïve Bayes (NB), and a multi-model ensemble in terms of overall accuracy, Balanced Error Rate (BER), Kappa, and area under the curve (AUC) values. RF accuracy was often higher than 90% for each substratum class, even at the most detailed level of the substratum classification and AUC values also indicated excellent performance (0.8-1). Total agreement between classifiers was high at the broadest level of classification (75-80%) when differentiating between hard and soft substratum. However, this sharply declined as the number of substratum categories increased (19-45%) including a mix of rock, gravel, pebbles, and sand. The model ensemble, produced from the results of all three classifiers by majority voting, did not show any increase in predictive performance when compared to the single RF classifier. This study shows how a single classifier may be sufficient to produce benthic seabed maps and model ensembles of multiple classifiers.

A synthetic map of the north-west European Shelf sedimentary environment for applications in marine science

NASA Astrophysics Data System (ADS)

Wilson, Robert J.; Speirs, Douglas C.; Sabatino, Alessandro; Heath, Michael R.

2018-01-01

Seabed sediment mapping is important for a wide range of marine policy, planning and scientific issues, and there has been considerable national and international investment around the world in the collation and synthesis of sediment datasets. However, in Europe at least, much of this effort has been directed towards seabed classification and mapping of discrete habitats. Scientific users often have to resort to reverse engineering these classifications to recover continuous variables, such as mud content and median grain size, that are required for many ecological and biophysical studies. Here we present a new set of 0.125° by 0.125° resolution synthetic maps of continuous properties of the north-west European sedimentary environment, extending from the Bay of Biscay to the northern limits of the North Sea and the Faroe Islands. The maps are a blend of gridded survey data, statistically modelled values based on distributions of bed shear stress due to tidal currents and waves, and bathymetric properties. Recent work has shown that statistical models can predict sediment composition in British waters and the North Sea with high accuracy, and here we extend this to the entire shelf and to the mapping of other key seabed parameters. The maps include percentage compositions of mud, sand and gravel; porosity and permeability; median grain size of the whole sediment and of the sand and the gravel fractions; carbon and nitrogen content of sediments; percentage of seabed area covered by rock; mean and maximum depth-averaged tidal velocity and wave orbital velocity at the seabed; and mean monthly natural disturbance rates. A number of applications for these maps exist, including species distribution modelling and the more accurate representation of sea-floor biogeochemistry in ecosystem models. The data products are available from https://doi.org/10.15129/1e27b806-1eae-494d-83b5-a5f4792c46fc.

Applying multibeam sonar and mathematical modeling for mapping seabed substrate and biota of offshore shallows

NASA Astrophysics Data System (ADS)

Herkül, Kristjan; Peterson, Anneliis; Paekivi, Sander

2017-06-01

Both basic science and marine spatial planning are in a need of high resolution spatially continuous data on seabed habitats and biota. As conventional point-wise sampling is unable to cover large spatial extents in high detail, it must be supplemented with remote sensing and modeling in order to fulfill the scientific and management needs. The combined use of in situ sampling, sonar scanning, and mathematical modeling is becoming the main method for mapping both abiotic and biotic seabed features. Further development and testing of the methods in varying locations and environmental settings is essential for moving towards unified and generally accepted methodology. To fill the relevant research gap in the Baltic Sea, we used multibeam sonar and mathematical modeling methods - generalized additive models (GAM) and random forest (RF) - together with underwater video to map seabed substrate and epibenthos of offshore shallows. In addition to testing the general applicability of the proposed complex of techniques, the predictive power of different sonar-based variables and modeling algorithms were tested. Mean depth, followed by mean backscatter, were the most influential variables in most of the models. Generally, mean values of sonar-based variables had higher predictive power than their standard deviations. The predictive accuracy of RF was higher than that of GAM. To conclude, we found the method to be feasible and with predictive accuracy similar to previous studies of sonar-based mapping.

Global Seabed Materials and Habitats Mapped: The Computational Methods

NASA Astrophysics Data System (ADS)

Jenkins, C. J.

2016-02-01

What the seabed is made of has proven difficult to map on the scale of whole ocean-basins. Direct sampling and observation can be augmented with proxy-parameter methods such as acoustics. Both avenues are essential to obtain enough detail and coverage, and also to validate the mapping methods. We focus on the direct observations such as samplings, photo and video, probes, diver and sub reports, and surveyed features. These are often in word-descriptive form: over 85% of the records for site materials are in this form, whether as sample/view descriptions or classifications, or described parameters such as consolidation, color, odor, structures and components. Descriptions are absolutely necessary for unusual materials and for processes - in other words, for research. This project dbSEABED not only has the largest collection of seafloor materials data worldwide, but it uses advanced computing math to obtain the best possible coverages and detail. Included in those techniques are linguistic text analysis (e.g., Natural Language Processing, NLP), fuzzy set theory (FST), and machine learning (ML, e.g., Random Forest). These techniques allow efficient and accurate import of huge datasets, thereby optimizing the data that exists. They merge quantitative and qualitative types of data for rich parameter sets, and extrapolate where the data are sparse for best map production. The dbSEABED data resources are now very widely used worldwide in oceanographic research, environmental management, the geosciences, engineering and survey.

Assessing Deep Sea Communities Through Seabed Imagery

NASA Astrophysics Data System (ADS)

Matkin, A. G.; Cross, K.; Milititsky, M.

2016-02-01

The deep sea still remains virtually unexplored. Human activity, such as oil and gas exploration and deep sea mining, is expanding further into the deep sea, increasing the need to survey and map extensive areas of this habitat in order to assess ecosystem health and value. The technology needed to explore this remote environment has been advancing. Seabed imagery can cover extensive areas of the seafloor and investigate areas where sampling with traditional coring methodologies is just not possible (e.g. cold water coral reefs). Remotely operated vehicles (ROVs) are an expensive option, so drop or towed camera systems can provide a more viable and affordable alternative, while still allowing for real-time control. Assessment of seabed imagery in terms of presence, abundance and density of particular species can be conducted by bringing together a variety of analytical tools for a holistic approach. Sixteen deep sea transects located offshore West Africa were investigated with a towed digital video telemetry system (DTS). Both digital stills and video footage were acquired. An extensive data set was obtained from over 13,000 usable photographs, allowing for characterisation of the different habitats present in terms of community composition and abundance. All observed fauna were identified to the lowest taxonomic level and enumerated when possible, with densities derived after the seabed area was calculated for each suitable photograph. This methodology allowed for consistent assessment of the different habitat types present, overcoming constraints, such as specific taxa that cannot be enumerated, such as sponges, corals or bryozoans, the presence of mobile and sessile species, or the level of taxonomic detail. Although this methodology will not enable a full characterisation of a deep sea community, in terms of species composition for instance, itt will allow a robust assessment of large areas of the deep sea in terms of sensitive habitats present and community characteristics of each habitat. Such data can be readily utilised for planning and licensing purposes and be potentially revisited in the future when taxonomic resolution increases, for a more detailed characterisation or monitoring of this poorly described environment.

U.S. Geological Survey ArcMap Sediment Classification tool

USGS Publications Warehouse

O'Malley, John

2007-01-01

The U.S. Geological Survey (USGS) ArcMap Sediment Classification tool is a custom toolbar that extends the Environmental Systems Research Institute, Inc. (ESRI) ArcGIS 9.2 Desktop application to aid in the analysis of seabed sediment classification. The tool uses as input either a point data layer with field attributes containing percentage of gravel, sand, silt, and clay or four raster data layers representing a percentage of sediment (0-100%) for the various sediment grain size analysis: sand, gravel, silt and clay. This tool is designed to analyze the percent of sediment at a given location and classify the sediments according to either the Folk (1954, 1974) or Shepard (1954) as modified by Schlee(1973) classification schemes. The sediment analysis tool is based upon the USGS SEDCLASS program (Poppe, et al. 2004).

Surface area and the seabed area, volume, depth, slope, and topographic variation for the world's seas, oceans, and countries.

PubMed

Costello, Mark John; Cheung, Alan; De Hauwere, Nathalie

2010-12-01

Depth and topography directly and indirectly influence most ocean environmental conditions, including light penetration and photosynthesis, sedimentation, current movements and stratification, and thus temperature and oxygen gradients. These parameters are thus likely to influence species distribution patterns and productivity in the oceans. They may be considered the foundation for any standardized classification of ocean ecosystems and important correlates of metrics of biodiversity (e.g., species richness and composition, fisheries). While statistics on ocean depth and topography are often quoted, how they were derived is rarely cited, and unless calculated using the same spatial resolution the resulting statistics will not be strictly comparable. We provide such statistics using the best available resolution (1-min) global bathymetry, and open source digital maps of the world's seas and oceans and countries' Exclusive Economic Zones, using a standardized methodology. We created a terrain map and calculated sea surface and seabed area, volume, and mean, standard deviation, maximum, and minimum, of both depth and slope. All the source data and our database are freely available online. We found that although the ocean is flat, and up to 71% of the area has a < 1 degree slope. It had over 1 million approximately circular features that may be seamounts or sea-hills as well as prominent mountain ranges or ridges. However, currently available global data significantly underestimate seabed slopes. The 1-min data set used here predicts there are 68,669 seamounts compared to the 30,314 previously predicted using the same method but lower spatial resolution data. The ocean volume exceeds 1.3 billion km(3) (or 1.3 sextillion liters), and sea surface and seabed areas over 354 million km(2). We propose the coefficient of variation of slope as an index of topographic heterogeneity. Future studies may improve on this database, for example by using a more detailed bathymetry, and in situ measured data. The database could be used to classify ocean features, such as abyssal plains, ridges, and slopes, and thus provide the basis for a standards based classification of ocean topography.

Conjugate-Gradient Neural Networks in Classification of Multisource and Very-High-Dimensional Remote Sensing Data

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Swain, P. H.; Ersoy, O. K.

1993-01-01

Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data, but do not compare as well with statistical methods in classification of very-high-dimentional data.

MAREANO: The national seafloor mapping programme of Norway - providing new knowledge for making informed management decisions

NASA Astrophysics Data System (ADS)

Thorsnes, T.; Bjarnadóttir, L. R.

2017-12-01

MAREANO (Marine AREA database for NOrwegian waters) is a state funded programme that has been mapping the seabed in Norwegian waters since 2005. Core datasets include detailed bathymetric data, video transect data and physical samples of the seabed. Integrated knowledge of the geology, habitats and the environmental status of the seabed is gained from the combined datasets and all results are presented on www.mareano.no. The results from MAREANO serve as a baseline of scientific information for decision-makers and which is actively used by ocean management agencies. Since 2005 the programme has grown and matured a great deal. Funding has increased twentyfold (now about 13 mill. USD), and the size of seabed mapped is now tenfold (about 22000 km2 annually). With this expansion the programme has evolved a more complex structure, regulating its activities more strictly and adhering to long-term plans. During this time the number of products has also increased, and so has the need for reviewing and improving methods. In 2015 MAREANO prepared a comprehensive report which documented and evaluated current methods and reviewed sampling/mapping standards based on management needs. Whilst the methods adopted by MAREANO to date have largely proved effective, several recent advances in technology within the various fields of seabed mapping offer great potential for improvements. Since 2014 MAREANO has been testing out some of this new technology such as acquisition of seabed data with improved resolution and autonomy in data collection, using AUVs equipped with synthetic aperture sonar and ROVs with underwater hyperspectral-sensors. Recently, MAREANO scientists have also been exploring new, more automated methods for data interpretation, classification and modelling. Preliminary results are promising and these new methods are expected to help to streamline the map production workflow in the future, thereby reducing production costs, while making even better maps that are both reproducible and more statistically robust. With its vast experience within seafloor mapping, MAREANO strives to prioritize dissemination of results through multiple channels, nationally and internationally. Currently MAREANO is also reaching out to the global community through the MAREAGLO initiative in order to share the MAREANO method.

Objected-oriented remote sensing image classification method based on geographic ontology model

NASA Astrophysics Data System (ADS)

Chu, Z.; Liu, Z. J.; Gu, H. Y.

2016-11-01

Nowadays, with the development of high resolution remote sensing image and the wide application of laser point cloud data, proceeding objected-oriented remote sensing classification based on the characteristic knowledge of multi-source spatial data has been an important trend on the field of remote sensing image classification, which gradually replaced the traditional method through improving algorithm to optimize image classification results. For this purpose, the paper puts forward a remote sensing image classification method that uses the he characteristic knowledge of multi-source spatial data to build the geographic ontology semantic network model, and carries out the objected-oriented classification experiment to implement urban features classification, the experiment uses protégé software which is developed by Stanford University in the United States, and intelligent image analysis software—eCognition software as the experiment platform, uses hyperspectral image and Lidar data that is obtained through flight in DaFeng City of JiangSu as the main data source, first of all, the experiment uses hyperspectral image to obtain feature knowledge of remote sensing image and related special index, the second, the experiment uses Lidar data to generate nDSM(Normalized DSM, Normalized Digital Surface Model),obtaining elevation information, the last, the experiment bases image feature knowledge, special index and elevation information to build the geographic ontology semantic network model that implement urban features classification, the experiment results show that, this method is significantly higher than the traditional classification algorithm on classification accuracy, especially it performs more evidently on the respect of building classification. The method not only considers the advantage of multi-source spatial data, for example, remote sensing image, Lidar data and so on, but also realizes multi-source spatial data knowledge integration and application of the knowledge to the field of remote sensing image classification, which provides an effective way for objected-oriented remote sensing image classification in the future.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

PubMed

Li, Linyi; Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

PubMed Central

Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440

Mapping and classifying the seabed of the West Greenland continental shelf

NASA Astrophysics Data System (ADS)

Gougeon, S.; Kemp, K. M.; Blicher, M. E.; Yesson, C.

2017-03-01

Marine benthic habitats support a diversity of marine organisms that are both economically and intrinsically valuable. Our knowledge of the distribution of these habitats is largely incomplete, particularly in deeper water and at higher latitudes. The western continental shelf of Greenland is one example of a deep (more than 500 m) Arctic region with limited information available. This study uses an adaptation of the EUNIS seabed classification scheme to document benthic habitats in the region of the West Greenland shrimp trawl fishery from 60°N to 72°N in depths of 61-725 m. More than 2000 images collected at 224 stations between 2011 and 2015 were grouped into 7 habitat classes. A classification model was developed using environmental proxies to make habitat predictions for the entire western shelf (200-700 m below 72°N). The spatial distribution of habitats correlates with temperature and latitude. Muddy sediments appear in northern and colder areas whereas sandy and rocky areas dominate in the south. Southern regions are also warmer and have stronger currents. The Mud habitat is the most widespread, covering around a third of the study area. There is a general pattern that deep channels and basins are dominated by muddy sediments, many of which are fed by glacial sedimentation and outlets from fjords, while shallow banks and shelf have a mix of more complex habitats. This first habitat classification map of the West Greenland shelf will be a useful tool for researchers, management and conservationists.

Seabed sediment classification for monitoring underwater nourishments using time series of multi-beam echo-soundings

NASA Astrophysics Data System (ADS)

Gaida, T. C.; Snellen, M.; van Dijk, T. A. G. P.; Simons, D. G.

2017-12-01

Coastal erosion induced by natural processes, such as wind, waves, tidal currents, or human interferences endangers human beings, infrastructure, fauna and flora at the oceans and rivers all over the world. In The Netherlands, in particular the North Sea islands are strongly affected by sediment erosion. To protect and stabilize the coastline, beach and shoreface nourishments are frequently performed. Thereby, sediment reservoirs are created that replace the eroded sediments. Increasing the long-term efficiency of coastal protection requires monitoring of the temporal and spatial development of the coastal nourishments. Multi-beam echo-sounders (MBES) allow for detailed and comprehensive investigations of the seabed composition and structure. To investigate the potential of using MBES for monitoring nourishments in a tidal inlet, four MBES surveys per year are carried out at the Dutch Wadden island Ameland. A pre-nourishment MBES survey was performed in April 2017 and the subsequent post-nourishment survey will take place in September 2017. Both surveys are equipped with a Kongsberg EM 2040C dual-head MBES and are supported with extensive grab sampling. In this study the use of MBES backscatter and bathymetry data are considered as an approach for monitoring coastal nourishments. The aim is to develop a monitoring procedure that allows for comparing MBES data taken during different surveys, i.e., with variations in environmental conditions, MBES characteristics and acquisition procedures. Different unsupervised and supervised acoustic seafloor classification techniques are applied to the processed MBES data to classify the seabed sediments. The analysis of the pre-nourishment MBES data indicates that the backscatter and consequently the classification are highly driven by the abundancy of shell fragments. These results will be used as a baseline to investigate the accumulation of the underwater nourishments. Independent grab samples will be used to select the optimal method for monitoring the development of underwater nourishments. This work will contribute to conventional and also to modern coastal protection strategies, e.g. using nature-based solutions, where natural processes (tides, waves) are used to redistribute coastal nourishments.

A low-cost drone based application for identifying and mapping of coastal fish nursery grounds

NASA Astrophysics Data System (ADS)

Ventura, Daniele; Bruno, Michele; Jona Lasinio, Giovanna; Belluscio, Andrea; Ardizzone, Giandomenico

2016-03-01

Acquiring seabed, landform or other topographic data in the field of marine ecology has a pivotal role in defining and mapping key marine habitats. However, accessibility for this kind of data with a high level of detail for very shallow and inaccessible marine habitats has been often challenging, time consuming. Spatial and temporal coverage often has to be compromised to make more cost effective the monitoring routine. Nowadays, emerging technologies, can overcome many of these constraints. Here we describe a recent development in remote sensing based on a small unmanned drone (UAVs) that produce very fine scale maps of fish nursery areas. This technology is simple to use, inexpensive, and timely in producing aerial photographs of marine areas. Both technical details regarding aerial photos acquisition (drone and camera settings) and post processing workflow (3D model generation with Structure From Motion algorithm and photo-stitching) are given. Finally by applying modern algorithm of semi-automatic image analysis and classification (Maximum Likelihood, ECHO and Object-based Image Analysis) we compared the results of three thematic maps of nursery area for juvenile sparid fishes, highlighting the potential of this method in mapping and monitoring coastal marine habitats.

Highly dynamic biological seabed alterations revealed by side scan sonar tracking of Lanice conchilega beds offshore the island of Sylt (German Bight)

NASA Astrophysics Data System (ADS)

Heinrich, C.; Feldens, P.; Schwarzer, K.

2017-06-01

Hydroacoustic surveys are common tools for habitat investigation and monitoring that aid in the realisation of the aims of the EU Marine Directives. However, the creation of habitat maps is difficult, especially when benthic organisms densely populate the seafloor. This study assesses the sensitivity of entropy and homogeneity image texture parameters derived from backscatter strength data to benthic habitats dominated by the tubeworm Lanice conchilega. Side scan sonar backscatter surveys were carried out in 2010 and 2011 in the German Bight (southern North Sea) at two sites approx. 20 km offshore of the island of Sylt. Abiotic and biotic seabed facies, such as sorted bedforms, areas of fine to medium sand and L. conchilega beds with different tube densities, were identified and characterised based on manual expert analysis and image texture analysis. Ground truthing was performed by grab sampling and underwater video observations. Compared to the manual expert analysis, the k- means classification of image textures proves to be a semi-automated method to investigate small-scale differences in a biologically altered seabed from backscatter data. The texture parameters entropy and homogeneity appear linearly interrelated with tube density, the former positively and the latter negatively. Reinvestigation of one site after 1 year showed an extensive change in the distribution of the L. conchilega-altered seabed. Such marked annual fluctuations in L. conchilega tube cover demonstrate the need for dense time series and high spatial coverage to meaningfully monitor ecological patterns on the seafloor with acoustic backscatter methods in the study region and similar settings worldwide, particularly because the sand mason plays a pivotal role in promoting biodiversity. In this context, image texture analysis provides a cost-effective and reproducible method to track biologically altered seabeds from side scan sonar backscatter signatures.

Constraints on a shallow offshore gas environment determined by a multidisciplinary geophysical approach: The Malin Sea, NW Ireland

NASA Astrophysics Data System (ADS)

Garcia, Xavier; Monteys, Xavier; Evans, Rob L.; Szpak, Michal

2014-04-01

During the Irish National Seabed Survey (INSS) in 2003, a gas related pockmark field was discovered and extensively mapped in the Malin Shelf region (NW Ireland). In summer 2006, additional complementary data involving core sample analysis, multibeam and single-beam backscatter classification, and a marine controlled-source electromagnetic survey were obtained in specific locations.This multidisciplinary approach allowed us to map the upper 20 m of the seabed in an unprecedented way and to correlate the main geophysical parameters with the geological properties of the seabed. The EM data provide us with information about sediment conductivity, which can be used as a proxy for porosity and also to identify the presence of fluid and fluid migration pathways. We conclude that, as a whole, the central part of the Malin basin is characterized by higher conductivities, which we interpret as a lithological change. Within the basin several areas are characterized by conductive anomalies associated with fluid flow processes and potentially the presence of microbial activity, as suggested by previous work. Pockmark structures show a characteristic electrical signature, with high-conductivity anomalies on the edges and less conductive, homogeneous interiors with several high-conductivity anomalies, potentially associated with gas-driven microbial activity.

Remote Sensing Information Classification

NASA Technical Reports Server (NTRS)

Rickman, Douglas L.

2008-01-01

This viewgraph presentation reviews the classification of Remote Sensing data in relation to epidemiology. Classification is a way to reduce the dimensionality and precision to something a human can understand. Classification changes SCALAR data into NOMINAL data.

A Quantitative, Non-Destructive Methodology for Habitat Characterisation and Benthic Monitoring at Offshore Renewable Energy Developments

PubMed Central

Sheehan, Emma V.; Stevens, Timothy F.; Attrill, Martin J.

2010-01-01

Following governments' policies to tackle global climate change, the development of offshore renewable energy sites is likely to increase substantially over coming years. All such developments interact with the seabed to some degree and so a key need exists for suitable methodology to monitor the impacts of large-scale Marine Renewable Energy Installations (MREIs). Many of these will be situated on mixed or rocky substrata, where conventional methods to characterise the habitat are unsuitable. Traditional destructive sampling is also inappropriate in conservation terms, particularly as safety zones around (MREIs) could function as Marine Protected Areas, with positive benefits for biodiversity. Here we describe a technique developed to effectively monitor the impact of MREIs and report the results of its field testing, enabling large areas to be surveyed accurately and cost-effectively. The methodology is based on a high-definition video camera, plus LED lights and laser scale markers, mounted on a “flying array” that maintains itself above the seabed grounded by a length of chain, thus causing minimal damage. Samples are taken by slow-speed tows of the gear behind a boat (200 m transects). The HD video and randomly selected frame grabs are analysed to quantify species distribution. The equipment was tested over two years in Lyme Bay, UK (25 m depth), then subsequently successfully deployed in demanding conditions at the deep (>50 m) high-energy Wave Hub site off Cornwall, UK, and a potential tidal stream energy site in Guernsey, Channel Islands (1.5 ms−1 current), the first time remote samples from such a habitat have been achieved. The next stage in the monitoring development process is described, involving the use of Remote Operated Vehicles to survey the seabed post-deployment of MREI devices. The complete methodology provides the first quantitative, relatively non-destructive method for monitoring mixed-substrate benthic communities beneath MPAs and MREIs pre- and post-device deployment. PMID:21206748

A quantitative, non-destructive methodology for habitat characterisation and benthic monitoring at offshore renewable energy developments.

PubMed

Sheehan, Emma V; Stevens, Timothy F; Attrill, Martin J

2010-12-29

Following governments' policies to tackle global climate change, the development of offshore renewable energy sites is likely to increase substantially over coming years. All such developments interact with the seabed to some degree and so a key need exists for suitable methodology to monitor the impacts of large-scale Marine Renewable Energy Installations (MREIs). Many of these will be situated on mixed or rocky substrata, where conventional methods to characterise the habitat are unsuitable. Traditional destructive sampling is also inappropriate in conservation terms, particularly as safety zones around (MREIs) could function as Marine Protected Areas, with positive benefits for biodiversity. Here we describe a technique developed to effectively monitor the impact of MREIs and report the results of its field testing, enabling large areas to be surveyed accurately and cost-effectively. The methodology is based on a high-definition video camera, plus LED lights and laser scale markers, mounted on a "flying array" that maintains itself above the seabed grounded by a length of chain, thus causing minimal damage. Samples are taken by slow-speed tows of the gear behind a boat (200 m transects). The HD video and randomly selected frame grabs are analysed to quantify species distribution. The equipment was tested over two years in Lyme Bay, UK (25 m depth), then subsequently successfully deployed in demanding conditions at the deep (>50 m) high-energy Wave Hub site off Cornwall, UK, and a potential tidal stream energy site in Guernsey, Channel Islands (1.5 ms⁻¹ current), the first time remote samples from such a habitat have been achieved. The next stage in the monitoring development process is described, involving the use of Remote Operated Vehicles to survey the seabed post-deployment of MREI devices. The complete methodology provides the first quantitative, relatively non-destructive method for monitoring mixed-substrate benthic communities beneath MPAs and MREIs pre- and post-device deployment.

Understanding Nearshore Processes Of a Large Arctic Delta Using Combined Seabed Mapping, In Situ Observations, Remote Sensing and Modeling

NASA Astrophysics Data System (ADS)

Solomon, S. M.; Couture, N. J.; Forbes, D. L.; Hoque, A.; Jenner, K. A.; Lintern, G.; Mulligan, R. P.; Perrie, W. A.; Stevens, C. W.; Toulany, B.; Whalen, D.

2009-12-01

The Mackenzie River Delta and the adjacent continental shelf in the southeastern Beaufort Sea are known to host significant quantities of hydrocarbons. Recent environmental reviews of proposed hydrocarbon development have highlighted the need for a better understanding of the processes that control sediment transport and coastal stability. Over the past several years field surveys have been undertaken in winter, spring and summer to acquire data on seabed morphology, sediment properties, sea ice, river-ocean interaction and nearshore oceanography. These data are being used to improve conceptual models of nearshore processes and to develop and validate numerical models of waves, circulation and sediment transport. The timing and location of sediment erosion, transport and deposition is complex, driven by a combination of open water season storms and spring floods. Unlike temperate counterparts, the interaction between the Mackenzie River and the Beaufort Sea during spring freshet is mediated by the presence of ice cover. Increasing discharge exceeds the under-ice flow capacity leading to flooding of the ice surface, followed by vortex drainage through the ice and scour of the seabed below (“strudel” drainage and scour). During winter months, nearshore circulation slows beneath a thickening ice canopy. Recent surveys have shown that the low gradient inner shelf is composed of extensive shoals where ice freezes to the seabed and intervening zones which are slightly deeper than the ice is thick. The duration of ice contact with the bed determines the thermal characteristics of the seabed. Analysis of cores shows that the silts comprising the shoals are up to 6 m thick. The predominantly well sorted and cross-laminated nature of the silts at the top of the cores suggests an active delta front environment. Measurements of waves, currents, conductivity, temperature and sediment concentration during spring and late summer have been acquired. During moderate August storm events, waves attenuate rapidly inshore of the 3 m isobath. Entrainment of fine material and rapid flocculation due to the presence of brackish water may induce the transient formation of high density suspensions near the seabed which contributes to this rapid attenuation. The relatively poor performance of shallow water wave models (e.g. SWAN) in very shallow depths during storm simulations appears to be related to inaccurate formulations for wave attenuation in this environment.

Introduction of geospatial perspective to the ecology of fish-habitat relationships in Indonesian coral reefs: A remote sensing approach

NASA Astrophysics Data System (ADS)

Sawayama, Shuhei; Nurdin, Nurjannah; Akbar AS, Muhammad; Sakamoto, Shingo X.; Komatsu, Teruhisa

2015-06-01

Coral reef ecosystems worldwide are now being harmed by various stresses accompanying the degradation of fish habitats and thus knowledge of fish-habitat relationships is urgently required. Because conventional research methods were not practical for this purpose due to the lack of a geospatial perspective, we attempted to develop a research method integrating visual fish observation with a seabed habitat map and to expand knowledge to a two-dimensional scale. WorldView-2 satellite imagery of Spermonde Archipelago, Indonesia obtained in September 2012 was analyzed and classified into four typical substrates: live coral, dead coral, seagrass and sand. Overall classification accuracy of this map was 81.3% and considered precise enough for subsequent analyses. Three sub-areas (CC: continuous coral reef, BC: boundary of coral reef and FC: few live coral zone) around reef slopes were extracted from the map. Visual transect surveys for several fish species were conducted within each sub-area in June 2013. As a result, Mean density (Ind. / 300 m2) of Chaetodon octofasciatus, known as an obligate feeder of corals, was significantly higher at BC than at the others (p < 0.05), implying that this species' density is strongly influenced by spatial configuration of its habitat, like the "edge effect." This indicates that future conservation procedures for coral reef fishes should consider not only coral cover but also its spatial configuration. The present study also indicates that the introduction of a geospatial perspective derived from remote sensing has great potential to progress conventional ecological studies on coral reef fishes.

Remote-sensing-based analysis of landscape change in the desiccated seabed of the Aral Sea--a potential tool for assessing the hazard degree of dust and salt storms.

PubMed

Löw, F; Navratil, P; Kotte, K; Schöler, H F; Bubenzer, O

2013-10-01

With the recession of the Aral Sea in Central Asia, once the world's fourth largest lake, a huge new saline desert emerged which is nowadays called the Aralkum. Saline soils in the Aralkum are a major source for dust and salt storms in the region. The aim of this study was to analyze the spatio-temporal land cover change dynamics in the Aralkum and discuss potential implications for the recent and future dust and salt storm activity in the region. MODIS satellite time series were classified from 2000-2008 and change of land cover was quantified. The Aral Sea desiccation accelerated between 2004 and 2008. The area of sandy surfaces and salt soils, which bear the greatest dust and salt storm generation potential increased by more than 36 %. In parts of the Aralkum desalinization of soils was found to take place within 4-8 years. The implication of the ongoing regression of the Aral Sea is that the expansion of saline surfaces will continue. Knowing the spatio-temporal dynamics of both the location and the surface characteristics of the source areas for dust and salt storms allows drawing conclusions about the potential hazard degree of the dust load. The remote-sensing-based land cover assessment presented in this study could be coupled with existing knowledge on the location of source areas for an early estimation of trends in shifting dust composition. Opportunities, limits, and requirements of satellite-based land cover classification and change detection in the Aralkum are discussed.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

NASA Astrophysics Data System (ADS)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

Nearshore Hydroacoustic Seafloor Mapping In The German Bight (North Sea): Hydroacoustic Interpretation With And Without Classification

NASA Astrophysics Data System (ADS)

Hass, H. C.; Mielck, F.; Papenmeier, S.

2016-12-01

Nearshore habitats are in constant dynamic change. They need regular assessment and appropriate monitoring of areas of special interest. To accomplish this, hydroacoustic seabed characterization tools are applied to allow for cost-effective and efficient mapping of the seafloor. In this context single beam echosounders (SBES) systems provide a comprehensive view by analyzing the hardness and roughness characteristics of the seafloor. Interpolation between transect lines becomes necessary when gapless maps are needed. This study presents a simple method to process and visualize data recorded with RoxAnn (Sonavision, Edinburgh, UK) and similar SBES. Both, hardness and roughness data are merged to one combined parameter that receives a color code (RGB) according to the acoustic properties of the seafloor. This color information is then interpolated to obtain an area-wide map that provides unclassified and thus unbiased seabed information. The RGB color data can subsequently be used for classification and modeling purposes. Four surveys are shown from a morphologically complex nearshore area west of the island of Helgoland (SE North Sea). The area has complex textural and dynamic characteristics reaching from outcropping bedrock via sandy to muddy areas with mostly gradual transitions. RoxAnn data allow to discriminate all seafloor types that were suggested by ground-truth information (seafloor samples, video). The area appears to be fluctuating within certain limits. Sediment import (sand and fluid mud) paths can be reconstructed. Manually, six RoxAnn zones (RZ) were identified and left without hard boundaries to better match the seafloor types of the study site. The k-means fuzzy cluster analysis employed yields best results with 3 classes. We show that interpretations on the basis of largely non-classified, color-coded and interpolated data provide the best gain of information in the highest possible resolution. Classification with hard boundaries is necessary for stakeholders but may cause reduction of information important to science. It becomes apparent that the type of classification addressing stakeholder issues is not always compatible with scientific objectives.

Gonadal function in males with autoimmune Addison's disease and autoantibodies to steroidogenic enzymes

PubMed Central

Dalla Costa, M; Bonanni, G; Masiero, S; Faggian, D; Chen, S; Furmaniak, J; Rees Smith, B; Perniola, R; Radetti, G; Garelli, S; Chiarelli, S; Albergoni, M P; Plebani, M; Betterle, C

2014-01-01

Steroidogenic enzyme autoantibodies (SEAbs) are frequently present and are markers of autoimmune premature ovarian failure (POF) in females with autoimmune Addison's disease (AAD). The prevalence and significance of SEAbs in males with AAD have not yet been defined. We studied the prevalence of SEAbs in a large cohort of males with AAD and assessed the relationship between SEAbs positivity and testicular function. A total of 154 males with AAD (mean age 34 years) were studied. SEAbs included autoantibodies to steroid-producing cells (StCA), detected by immunofluorescence, and steroid 17α-hydroxylase (17α-OHAbs) and side chain cleavage enzyme (SCCAbs) measured by immunoprecipitation assays. Gonadal function was evaluated by measuring follicle-stimulating hormone (FSH), luteinizing hormone (LH), total testosterone (TT), sex hormone-binding globulin (SHGB), anti-müllerian hormone (AMH) and inhibin-B (I-B). Twenty-six males, 10 SEAbs(+) and 16 SEAbs(–), were followed-up for a mean period of 7·6 years to assess the behaviour of SEAbs and testicular function. SEAbs were found in 24·7% of males with AAD, with the highest frequency in patients with autoimmune polyendocrine syndrome type 1 (APS-1). The levels of reproductive hormones in 30 SEAbs(+) males were in the normal range according to age and were not significantly different compared to 55 SEAbs(–) males (P > 0·05). During follow-up, both SEAbs(+) and SEAbs(–) patients maintained normal testicular function. SEAbs were found with high frequency in males with AAD; however, they were not associated with testicular failure. This study suggests that the diagnostic value of SEAbs in males with AAD differs compared to females, and this may be related to the immunoprivileged status of the testis. PMID:24666377

Gonadal function in males with autoimmune Addison's disease and autoantibodies to steroidogenic enzymes.

PubMed

Dalla Costa, M; Bonanni, G; Masiero, S; Faggian, D; Chen, S; Furmaniak, J; Rees Smith, B; Perniola, R; Radetti, G; Garelli, S; Chiarelli, S; Albergoni, M P; Plebani, M; Betterle, C

2014-06-01

Steroidogenic enzyme autoantibodies (SEAbs) are frequently present and are markers of autoimmune premature ovarian failure (POF) in females with autoimmune Addison's disease (AAD). The prevalence and significance of SEAbs in males with AAD have not yet been defined. We studied the prevalence of SEAbs in a large cohort of males with AAD and assessed the relationship between SEAbs positivity and testicular function. A total of 154 males with AAD (mean age 34 years) were studied. SEAbs included autoantibodies to steroid-producing cells (StCA), detected by immunofluorescence, and steroid 17α-hydroxylase (17α-OHAbs) and side chain cleavage enzyme (SCCAbs) measured by immunoprecipitation assays. Gonadal function was evaluated by measuring follicle-stimulating hormone (FSH), luteinizing hormone (LH), total testosterone (TT), sex hormone-binding globulin (SHGB), anti-müllerian hormone (AMH) and inhibin-B (I-B). Twenty-six males, 10 SEAbs((+)) and 16 SEAbs((-)), were followed-up for a mean period of 7·6 years to assess the behaviour of SEAbs and testicular function. SEAbs were found in 24·7% of males with AAD, with the highest frequency in patients with autoimmune polyendocrine syndrome type 1 (APS-1). The levels of reproductive hormones in 30 SEAbs((+)) males were in the normal range according to age and were not significantly different compared to 55 SEAbs((-)) males (P > 0·05). During follow-up, both SEAbs((+)) and SEAbs((-)) patients maintained normal testicular function. SEAbs were found with high frequency in males with AAD; however, they were not associated with testicular failure. This study suggests that the diagnostic value of SEAbs in males with AAD differs compared to females, and this may be related to the immunoprivileged status of the testis. © 2014 British Society for Immunology.

Application of Convolutional Neural Network in Classification of High Resolution Agricultural Remote Sensing Images

NASA Astrophysics Data System (ADS)

Yao, C.; Zhang, Y.; Zhang, Y.; Liu, H.

2017-09-01

With the rapid development of Precision Agriculture (PA) promoted by high-resolution remote sensing, it makes significant sense in management and estimation of agriculture through crop classification of high-resolution remote sensing image. Due to the complex and fragmentation of the features and the surroundings in the circumstance of high-resolution, the accuracy of the traditional classification methods has not been able to meet the standard of agricultural problems. In this case, this paper proposed a classification method for high-resolution agricultural remote sensing images based on convolution neural networks(CNN). For training, a large number of training samples were produced by panchromatic images of GF-1 high-resolution satellite of China. In the experiment, through training and testing on the CNN under the toolbox of deep learning by MATLAB, the crop classification finally got the correct rate of 99.66 % after the gradual optimization of adjusting parameter during training. Through improving the accuracy of image classification and image recognition, the applications of CNN provide a reference value for the field of remote sensing in PA.

Autonomous bed-sediment imaging-systems for revealing temporal variability of grain size

USGS Publications Warehouse

Buscombe, Daniel; Rubin, David M.; Lacy, Jessica R.; Storlazzi, Curt D.; Hatcher, Gerald; Chezar, Henry; Wyland, Robert; Sherwood, Christopher R.

2014-01-01

We describe a remotely operated video microscope system, designed to provide high-resolution images of seabed sediments. Two versions were developed, which differ in how they raise the camera from the seabed. The first used hydraulics and the second used the energy associated with wave orbital motion. Images were analyzed using automated frequency-domain methods, which following a rigorous partially supervised quality control procedure, yielded estimates to within 20% of the true size as determined by on-screen manual measurements of grains. Long-term grain-size variability at a sandy inner shelf site offshore of Santa Cruz, California, USA, was investigated using the hydraulic system. Eighteen months of high frequency (min to h), high-resolution (μm) images were collected, and grain size distributions compiled. The data constitutes the longest known high-frequency record of seabed-grain size at this sample frequency, at any location. Short-term grain-size variability of sand in an energetic surf zone at Praa Sands, Cornwall, UK was investigated using the ‘wave-powered’ system. The data are the first high-frequency record of grain size at a single location of a highly mobile and evolving bed in a natural surf zone. Using this technology, it is now possible to measure bed-sediment-grain size at a time-scale comparable with flow conditions. Results suggest models of sediment transport at sandy, wave-dominated, nearshore locations should allow for substantial changes in grain-size distribution over time-scales as short as a few hours.

Supervised Classification of Underwater Optical Imagery for Improved Detection and Characterization of Underwater Military Munitions

DTIC Science & Technology

2015-06-01

Atmospheric Administration (NOAA) at the “ Ordnance Reef” site off of Waianae, Hawaii by divers using a hand-held high -definition video (HDV) camera...for the Ordnance Reef dataset as well, though less dramatic than the Miami data because the 2-D results were high to begin with at Ordnance Reef...generally > 80% accuracy. Discrimination of environments was high for the major seabed types. For example, sand and mixed sand-seagrass were classified with

Random-Forest Classification of High-Resolution Remote Sensing Images and Ndsm Over Urban Areas

NASA Astrophysics Data System (ADS)

Sun, X. F.; Lin, X. G.

2017-09-01

As an intermediate step between raw remote sensing data and digital urban maps, remote sensing data classification has been a challenging and long-standing research problem in the community of remote sensing. In this work, an effective classification method is proposed for classifying high-resolution remote sensing data over urban areas. Starting from high resolution multi-spectral images and 3D geometry data, our method proceeds in three main stages: feature extraction, classification, and classified result refinement. First, we extract color, vegetation index and texture features from the multi-spectral image and compute the height, elevation texture and differential morphological profile (DMP) features from the 3D geometry data. Then in the classification stage, multiple random forest (RF) classifiers are trained separately, then combined to form a RF ensemble to estimate each sample's category probabilities. Finally the probabilities along with the feature importance indicator outputted by RF ensemble are used to construct a fully connected conditional random field (FCCRF) graph model, by which the classification results are refined through mean-field based statistical inference. Experiments on the ISPRS Semantic Labeling Contest dataset show that our proposed 3-stage method achieves 86.9% overall accuracy on the test data.

Do deep convolutional neural networks really need to be deep when applied for remote scene classification?

NASA Astrophysics Data System (ADS)

Luo, Chang; Wang, Jie; Feng, Gang; Xu, Suhui; Wang, Shiqiang

2017-10-01

Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, for remote scene classification, there are not sufficient images to train a very deep CNN from scratch. From two viewpoints of generalization power, we propose two promising kinds of deep CNNs for remote scenes and try to find whether deep CNNs need to be deep for remote scene classification. First, we transfer successful pretrained deep CNNs to remote scenes based on the theory that depth of CNNs brings the generalization power by learning available hypothesis for finite data samples. Second, according to the opposite viewpoint that generalization power of deep CNNs comes from massive memorization and shallow CNNs with enough neural nodes have perfect finite sample expressivity, we design a lightweight deep CNN (LDCNN) for remote scene classification. With five well-known pretrained deep CNNs, experimental results on two independent remote-sensing datasets demonstrate that transferred deep CNNs can achieve state-of-the-art results in an unsupervised setting. However, because of its shallow architecture, LDCNN cannot obtain satisfactory performance, regardless of whether in an unsupervised, semisupervised, or supervised setting. CNNs really need depth to obtain general features for remote scenes. This paper also provides baseline for applying deep CNNs to other remote sensing tasks.

Land use/cover classification in the Brazilian Amazon using satellite images.

PubMed

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

2012-09-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Land use/cover classification in the Brazilian Amazon using satellite images

PubMed Central

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

2013-01-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

Research on Remote Sensing Image Classification Based on Feature Level Fusion

NASA Astrophysics Data System (ADS)

Yuan, L.; Zhu, G.

2018-04-01

Remote sensing image classification, as an important direction of remote sensing image processing and application, has been widely studied. However, in the process of existing classification algorithms, there still exists the phenomenon of misclassification and missing points, which leads to the final classification accuracy is not high. In this paper, we selected Sentinel-1A and Landsat8 OLI images as data sources, and propose a classification method based on feature level fusion. Compare three kind of feature level fusion algorithms (i.e., Gram-Schmidt spectral sharpening, Principal Component Analysis transform and Brovey transform), and then select the best fused image for the classification experimental. In the classification process, we choose four kinds of image classification algorithms (i.e. Minimum distance, Mahalanobis distance, Support Vector Machine and ISODATA) to do contrast experiment. We use overall classification precision and Kappa coefficient as the classification accuracy evaluation criteria, and the four classification results of fused image are analysed. The experimental results show that the fusion effect of Gram-Schmidt spectral sharpening is better than other methods. In four kinds of classification algorithms, the fused image has the best applicability to Support Vector Machine classification, the overall classification precision is 94.01 % and the Kappa coefficients is 0.91. The fused image with Sentinel-1A and Landsat8 OLI is not only have more spatial information and spectral texture characteristics, but also enhances the distinguishing features of the images. The proposed method is beneficial to improve the accuracy and stability of remote sensing image classification.

Deep-sea faunal communities associated with a lost intermodal shipping container in the Monterey Bay National Marine Sanctuary, CA.

PubMed

Taylor, Josi R; DeVogelaere, Andrew P; Burton, Erica J; Frey, Oren; Lundsten, Lonny; Kuhnz, Linda A; Whaling, P J; Lovera, Christopher; Buck, Kurt R; Barry, James P

2014-06-15

Carrying assorted cargo and covered with paints of varying toxicity, lost intermodal containers may take centuries to degrade on the deep seafloor. In June 2004, scientists from Monterey Bay Aquarium Research Institute (MBARI) discovered a recently lost container during a Remotely Operated Vehicle (ROV) dive on a sediment-covered seabed at 1281 m depth in Monterey Bay National Marine Sanctuary (MBNMS). The site was revisited by ROV in March 2011. Analyses of sediment samples and high-definition video indicate that faunal assemblages on the container's exterior and the seabed within 10 m of the container differed significantly from those up to 500 m. The container surface provides hard substratum for colonization by taxa typically found in rocky habitats. However, some key taxa that dominate rocky areas were absent or rare on the container, perhaps related to its potential toxicity or limited time for colonization and growth. Ecological effects appear to be restricted to the container surface and the benthos within ∼10 m. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Uncertainty quantification of seabed parameters for large data volumes along survey tracks with a tempered particle filter

NASA Astrophysics Data System (ADS)

Dettmer, J.; Quijano, J. E.; Dosso, S. E.; Holland, C. W.; Mandolesi, E.

2016-12-01

Geophysical seabed properties are important for the detection and classification of unexploded ordnance. However, current surveying methods such as vertical seismic profiling, coring, or inversion are of limited use when surveying large areas with high spatial sampling density. We consider surveys based on a source and receiver array towed by an autonomous vehicle which produce large volumes of seabed reflectivity data that contain unprecedented and detailed seabed information. The data are analyzed with a particle filter, which requires efficient reflection-coefficient computation, efficient inversion algorithms and efficient use of computer resources. The filter quantifies information content of multiple sequential data sets by considering results from previous data along the survey track to inform the importance sampling at the current point. Challenges arise from environmental changes along the track where the number of sediment layers and their properties change. This is addressed by a trans-dimensional model in the filter which allows layering complexity to change along a track. Efficiency is improved by likelihood tempering of various particle subsets and including exchange moves (parallel tempering). The filter is implemented on a hybrid computer that combines central processing units (CPUs) and graphics processing units (GPUs) to exploit three levels of parallelism: (1) fine-grained parallel computation of spherical reflection coefficients with a GPU implementation of Levin integration; (2) updating particles by concurrent CPU processes which exchange information using automatic load balancing (coarse grained parallelism); (3) overlapping CPU-GPU communication (a major bottleneck) with GPU computation by staggering CPU access to the multiple GPUs. The algorithm is applied to spherical reflection coefficients for data sets along a 14-km track on the Malta Plateau, Mediterranean Sea. We demonstrate substantial efficiency gains over previous methods. [This research was supported in part by the U.S. Dept of Defense, thought the Strategic Environmental Research and Development Program (SERDP).

Classification of high resolution remote sensing image based on geo-ontology and conditional random fields

NASA Astrophysics Data System (ADS)

Hong, Liang

2013-10-01

The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

An Annotated Bibliography of Patents Related to Coastal Engineering. Volume I. 1967-1970. Appendix.

DTIC Science & Technology

1979-11-01

HYDRAULIC MODEL BASIN PILE, STRUCTURE CONNECTION ICE PROTECTION PILE, WOOD 16 -- mi • . ... -- POLLUTANT ABSORPTION SEISMIC ACOUSTIC TRANSMITTER ARRAY...NT SEABED PIPELINE PLACEMENT WIND MEASUREMENT SEABED PROPERTY MEASUREMENT WOOD PRESERVATIVE SEABED SCOUR PROTECTION SEABED SITE SURVEY SEABED SOIL...concrete, wood , or thin steel piling to aid driving. PILE EXTRACTOR - A means of removing a pile from the earth. PILE FOOTING - A means of increasing a

Integrated Remote Sensing Modalities for Classification at a Legacy Test Site

NASA Astrophysics Data System (ADS)

Lee, D. J.; Anderson, D.; Craven, J.

2016-12-01

Detecting, locating, and characterizing suspected underground nuclear test sites is of interest to the worldwide nonproliferation monitoring community. Remote sensing provides both cultural and surface geological information over a large search area in a non-intrusive manner. We have characterized a legacy nuclear test site at the Nevada National Security Site (NNSS) using an aerial system based on RGB imagery, light detection and ranging, and hyperspectral imaging. We integrate these different remote sensing modalities to perform pattern recognition and classification tasks on the test site. These tasks include detecting cultural artifacts and exotic materials. We evaluate if the integration of different remote sensing modalities improves classification performance.

Autonomous target recognition using remotely sensed surface vibration measurements

NASA Astrophysics Data System (ADS)

Geurts, James; Ruck, Dennis W.; Rogers, Steven K.; Oxley, Mark E.; Barr, Dallas N.

1993-09-01

The remotely measured surface vibration signatures of tactical military ground vehicles are investigated for use in target classification and identification friend or foe (IFF) systems. The use of remote surface vibration sensing by a laser radar reduces the effects of partial occlusion, concealment, and camouflage experienced by automatic target recognition systems using traditional imagery in a tactical battlefield environment. Linear Predictive Coding (LPC) efficiently represents the vibration signatures and nearest neighbor classifiers exploit the LPC feature set using a variety of distortion metrics. Nearest neighbor classifiers achieve an 88 percent classification rate in an eight class problem, representing a classification performance increase of thirty percent from previous efforts. A novel confidence figure of merit is implemented to attain a 100 percent classification rate with less than 60 percent rejection. The high classification rates are achieved on a target set which would pose significant problems to traditional image-based recognition systems. The targets are presented to the sensor in a variety of aspects and engine speeds at a range of 1 kilometer. The classification rates achieved demonstrate the benefits of using remote vibration measurement in a ground IFF system. The signature modeling and classification system can also be used to identify rotary and fixed-wing targets.

Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Treesearch

John Hogland; Nedret Billor; Nathaniel Anderson

2013-01-01

Discriminant analysis, referred to as maximum likelihood classification within popular remote sensing software packages, is a common supervised technique used by analysts. Polytomous logistic regression (PLR), also referred to as multinomial logistic regression, is an alternative classification approach that is less restrictive, more flexible, and easy to interpret. To...

Squared exponential covariance function for prediction of hydrocarbon in seabed logging application

NASA Astrophysics Data System (ADS)

Mukhtar, Siti Mariam; Daud, Hanita; Dass, Sarat Chandra

2016-11-01

Seabed Logging technology (SBL) has progressively emerged as one of the demanding technologies in Exploration and Production (E&P) industry. Hydrocarbon prediction in deep water areas is crucial task for a driller in any oil and gas company as drilling cost is very expensive. Simulation data generated by Computer Software Technology (CST) is used to predict the presence of hydrocarbon where the models replicate real SBL environment. These models indicate that the hydrocarbon filled reservoirs are more resistive than surrounding water filled sediments. Then, as hydrocarbon depth is increased, it is more challenging to differentiate data with and without hydrocarbon. MATLAB is used for data extractions for curve fitting process using Gaussian process (GP). GP can be classified into regression and classification problems, where this work only focuses on Gaussian process regression (GPR) problem. Most popular choice to supervise GPR is squared exponential (SE), as it provides stability and probabilistic prediction in huge amounts of data. Hence, SE is used to predict the presence or absence of hydrocarbon in the reservoir from the data generated.

Research on Remote Sensing Geological Information Extraction Based on Object Oriented Classification

NASA Astrophysics Data System (ADS)

Gao, Hui

2018-04-01

The northern Tibet belongs to the Sub cold arid climate zone in the plateau. It is rarely visited by people. The geological working conditions are very poor. However, the stratum exposures are good and human interference is very small. Therefore, the research on the automatic classification and extraction of remote sensing geological information has typical significance and good application prospect. Based on the object-oriented classification in Northern Tibet, using the Worldview2 high-resolution remote sensing data, combined with the tectonic information and image enhancement, the lithological spectral features, shape features, spatial locations and topological relations of various geological information are excavated. By setting the threshold, based on the hierarchical classification, eight kinds of geological information were classified and extracted. Compared with the existing geological maps, the accuracy analysis shows that the overall accuracy reached 87.8561 %, indicating that the classification-oriented method is effective and feasible for this study area and provides a new idea for the automatic extraction of remote sensing geological information.

An incremental knowledge assimilation system (IKAS) for mine detection

NASA Astrophysics Data System (ADS)

Porway, Jake; Raju, Chaitanya; Varadarajan, Karthik Mahesh; Nguyen, Hieu; Yadegar, Joseph

2010-04-01

In this paper we present an adaptive incremental learning system for underwater mine detection and classification that utilizes statistical models of seabed texture and an adaptive nearest-neighbor classifier to identify varied underwater targets in many different environments. The first stage of processing uses our Background Adaptive ANomaly detector (BAAN), which identifies statistically likely target regions using Gabor filter responses over the image. Using this information, BAAN classifies the background type and updates its detection using background-specific parameters. To perform classification, a Fully Adaptive Nearest Neighbor (FAAN) determines the best label for each detection. FAAN uses an extremely fast version of Nearest Neighbor to find the most likely label for the target. The classifier perpetually assimilates new and relevant information into its existing knowledge database in an incremental fashion, allowing improved classification accuracy and capturing concept drift in the target classes. Experiments show that the system achieves >90% classification accuracy on underwater mine detection tasks performed on synthesized datasets provided by the Office of Naval Research. We have also demonstrated that the system can incrementally improve its detection accuracy by constantly learning from new samples.

Cluster Method Analysis of K. S. C. Image

NASA Technical Reports Server (NTRS)

Rodriguez, Joe, Jr.; Desai, M.

1997-01-01

Information obtained from satellite-based systems has moved to the forefront as a method in the identification of many land cover types. Identification of different land features through remote sensing is an effective tool for regional and global assessment of geometric characteristics. Classification data acquired from remote sensing images have a wide variety of applications. In particular, analysis of remote sensing images have special applications in the classification of various types of vegetation. Results obtained from classification studies of a particular area or region serve towards a greater understanding of what parameters (ecological, temporal, etc.) affect the region being analyzed. In this paper, we make a distinction between both types of classification approaches although, focus is given to the unsupervised classification method using 1987 Thematic Mapped (TM) images of Kennedy Space Center.

Effect of radiance-to-reflectance transformation and atmosphere removal on maximum likelihood classification accuracy of high-dimensional remote sensing data

NASA Technical Reports Server (NTRS)

Hoffbeck, Joseph P.; Landgrebe, David A.

1994-01-01

Many analysis algorithms for high-dimensional remote sensing data require that the remotely sensed radiance spectra be transformed to approximate reflectance to allow comparison with a library of laboratory reflectance spectra. In maximum likelihood classification, however, the remotely sensed spectra are compared to training samples, thus a transformation to reflectance may or may not be helpful. The effect of several radiance-to-reflectance transformations on maximum likelihood classification accuracy is investigated in this paper. We show that the empirical line approach, LOWTRAN7, flat-field correction, single spectrum method, and internal average reflectance are all non-singular affine transformations, and that non-singular affine transformations have no effect on discriminant analysis feature extraction and maximum likelihood classification accuracy. (An affine transformation is a linear transformation with an optional offset.) Since the Atmosphere Removal Program (ATREM) and the log residue method are not affine transformations, experiments with Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data were conducted to determine the effect of these transformations on maximum likelihood classification accuracy. The average classification accuracy of the data transformed by ATREM and the log residue method was slightly less than the accuracy of the original radiance data. Since the radiance-to-reflectance transformations allow direct comparison of remotely sensed spectra with laboratory reflectance spectra, they can be quite useful in labeling the training samples required by maximum likelihood classification, but these transformations have only a slight effect or no effect at all on discriminant analysis and maximum likelihood classification accuracy.

77 FR 12245 - Deep Seabed Mining: Request for Extension of Exploration Licenses

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-29

... DEPARTMENT OF COMMERCE National Oceanic and Atmospheric Administration Deep Seabed Mining: Request.... Department of Commerce. ACTION: Notice of receipt of application to extend Deep Seabed Mining Exploration... received an application for five-year extensions of Deep Seabed Mining Exploration Licenses USA-1 and USA-4...

Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation

NASA Astrophysics Data System (ADS)

Chen, Y.; Luo, M.; Xu, L.; Zhou, X.; Ren, J.; Zhou, J.

2018-04-01

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

Methods and potentials for using satellite image classification in school lessons

NASA Astrophysics Data System (ADS)

Voss, Kerstin; Goetzke, Roland; Hodam, Henryk

2011-11-01

The FIS project - FIS stands for Fernerkundung in Schulen (Remote Sensing in Schools) - aims at a better integration of the topic "satellite remote sensing" in school lessons. According to this, the overarching objective is to teach pupils basic knowledge and fields of application of remote sensing. Despite the growing significance of digital geomedia, the topic "remote sensing" is not broadly supported in schools. Often, the topic is reduced to a short reflection on satellite images and used only for additional illustration of issues relevant for the curriculum. Without addressing the issue of image data, this can hardly contribute to the improvement of the pupils' methodical competences. Because remote sensing covers more than simple, visual interpretation of satellite images, it is necessary to integrate remote sensing methods like preprocessing, classification and change detection. Dealing with these topics often fails because of confusing background information and the lack of easy-to-use software. Based on these insights, the FIS project created different simple analysis tools for remote sensing in school lessons, which enable teachers as well as pupils to be introduced to the topic in a structured way. This functionality as well as the fields of application of these analysis tools will be presented in detail with the help of three different classification tools for satellite image classification.

Investigations on classification categories for wetlands of Chesapeake Bay using remotely sensed data

NASA Technical Reports Server (NTRS)

Williamson, F. S. L.

1974-01-01

The use of remote sensors to determine the characteristics of the wetlands of the Chesapeake Bay and surrounding areas is discussed. The objectives of the program are stated as follows: (1) to use data and remote sensing techniques developed from studies of Rhode River, West River, and South River salt marshes to develop a wetland classification scheme useful in other regions of the Chesapeake Bay and to evaluate the classification system with respect to vegetation types, marsh physiography, man-induced perturbation, and salinity; and (2) to develop a program using remote sensing techniques, for the extension of the classification to Chesapeake Bay salt marshes and to coordinate this program with the goals of the Chesapeake Research Consortium and the states of Maryland and Virginia. Maps of the Chesapeake Bay areas are developed from aerial photographs to display the wetland structure and vegetation.

Machine processing of remotely sensed data; Proceedings of the Conference, Purdue University, West Lafayette, Ind., October 16-18, 1973

NASA Technical Reports Server (NTRS)

1973-01-01

Topics discussed include the management and processing of earth resources information, special-purpose processors for the machine processing of remotely sensed data, digital image registration by a mathematical programming technique, the use of remote-sensor data in land classification (in particular, the use of ERTS-1 multispectral scanning data), the use of remote-sensor data in geometrical transformations and mapping, earth resource measurement with the aid of ERTS-1 multispectral scanning data, the use of remote-sensor data in the classification of turbidity levels in coastal zones and in the identification of ecological anomalies, the problem of feature selection and the classification of objects in multispectral images, the estimation of proportions of certain categories of objects, and a number of special systems and techniques. Individual items are announced in this issue.

A land use and land cover classification system for use with remote sensor data

USGS Publications Warehouse

Anderson, James R.; Hardy, Ernest E.; Roach, John T.; Witmer, Richard E.

1976-01-01

The framework of a national land use and land cover classification system is presented for use with remote sensor data. The classification system has been developed to meet the needs of Federal and State agencies for an up-to-date overview of land use and land cover throughout the country on a basis that is uniform in categorization at the more generalized first and second levels and that will be receptive to data from satellite and aircraft remote sensors. The proposed system uses the features of existing widely used classification systems that are amenable to data derived from remote sensing sources. It is intentionally left open-ended so that Federal, regional, State, and local agencies can have flexibility in developing more detailed land use classifications at the third and fourth levels in order to meet their particular needs and at the same time remain compatible with each other and the national system. Revision of the land use classification system as presented in U.S. Geological Survey Circular 671 was undertaken in order to incorporate the results of extensive testing and review of the categorization and definitions.

Remote Sensing Image Classification Applied to the First National Geographical Information Census of China

NASA Astrophysics Data System (ADS)

Yu, Xin; Wen, Zongyong; Zhu, Zhaorong; Xia, Qiang; Shun, Lan

2016-06-01

Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN) to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC) and Maximum Likelihood Classification Method (MLC) in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.

Classification of remotely sensed data using OCR-inspired neural network techniques. [Optical Character Recognition

NASA Technical Reports Server (NTRS)

Kiang, Richard K.

1992-01-01

Neural networks have been applied to classifications of remotely sensed data with some success. To improve the performance of this approach, an examination was made of how neural networks are applied to the optical character recognition (OCR) of handwritten digits and letters. A three-layer, feedforward network, along with techniques adopted from OCR, was used to classify Landsat-4 Thematic Mapper data. Good results were obtained. To overcome the difficulties that are characteristic of remote sensing applications and to attain significant improvements in classification accuracy, a special network architecture may be required.

Spectral and spatial resolution analysis of multi sensor satellite data for coral reef mapping: Tioman Island, Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet; Kabiri, Keivan

2012-07-01

This paper describes an assessment of coral reef mapping using multi sensor satellite images such as Landsat ETM, SPOT and IKONOS images for Tioman Island, Malaysia. The study area is known to be one of the best Islands in South East Asia for its unique collection of diversified coral reefs and serves host to thousands of tourists every year. For the coral reef identification, classification and analysis, Landsat ETM, SPOT and IKONOS images were collected processed and classified using hierarchical classification schemes. At first, Decision tree classification method was implemented to separate three main land cover classes i.e. water, rural and vegetation and then maximum likelihood supervised classification method was used to classify these main classes. The accuracy of the classification result is evaluated by a separated test sample set, which is selected based on the fieldwork survey and view interpretation from IKONOS image. Few types of ancillary data in used are: (a) DGPS ground control points; (b) Water quality parameters measured by Hydrolab DS4a; (c) Sea-bed substrates spectrum measured by Unispec and; (d) Landcover observation photos along Tioman island coastal area. The overall accuracy of the final classification result obtained was 92.25% with the kappa coefficient is 0.8940. Key words: Coral reef, Multi-spectral Segmentation, Pixel-Based Classification, Decision Tree, Tioman Island

Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

NASA Astrophysics Data System (ADS)

Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

2016-09-01

It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image classification activities. Currently, the approach is used only on high resolution optical three-band remote sensing imagery. The feasibility using the approach on other kinds of remote sensing images or involving additional bands in classification will be studied in future.

A Improved and Highly Effective Seabed Surface Sand Sampling Device

NASA Astrophysics Data System (ADS)

Liu, Ying

2017-04-01

In marine geology research, it is necessary to obtain a sufficient quantity of seabed surface samples, while also ensuring that the samples are in their original state. Currently, there are a number of seabed surface sampling devices available, but it is very difficult to obtain sand samples using ordinary seabed surface sampling devices, whereas machine-controlled seabed surface sampling devices are unable to dive into deeper regions of water. To obtain larger quantities of samples in their original states, many researchers have tried to improve seabed surface sampling devices, but these efforts have generally produced ambiguous results. To resolve the aforementioned issue, we have designed an improved and highly effective seabed surface sand sampling device, which incorporates the strengths of a variety of sampling devices; it is capable of diving into deeper water regions to obtain sand samples, and is also suited for use in streams, rivers, lakes and seas with varying levels of flow velocities and depth.

a Novel Framework for Remote Sensing Image Scene Classification

NASA Astrophysics Data System (ADS)

Jiang, S.; Zhao, H.; Wu, W.; Tan, Q.

2018-04-01

High resolution remote sensing (HRRS) images scene classification aims to label an image with a specific semantic category. HRRS images contain more details of the ground objects and their spatial distribution patterns than low spatial resolution images. Scene classification can bridge the gap between low-level features and high-level semantics. It can be applied in urban planning, target detection and other fields. This paper proposes a novel framework for HRRS images scene classification. This framework combines the convolutional neural network (CNN) and XGBoost, which utilizes CNN as feature extractor and XGBoost as a classifier. Then, this framework is evaluated on two different HRRS images datasets: UC-Merced dataset and NWPU-RESISC45 dataset. Our framework achieved satisfying accuracies on two datasets, which is 95.57 % and 83.35 % respectively. From the experiments result, our framework has been proven to be effective for remote sensing images classification. Furthermore, we believe this framework will be more practical for further HRRS scene classification, since it costs less time on training stage.

Analysis on the application of background parameters on remote sensing classification

NASA Astrophysics Data System (ADS)

Qiao, Y.

Drawing accurate crop cultivation acreage, dynamic monitoring of crops growing and yield forecast are some important applications of remote sensing to agriculture. During the 8th 5-Year Plan period, the task of yield estimation using remote sensing technology for the main crops in major production regions in China once was a subtopic to the national research task titled "Study on Application of Remote sensing Technology". In 21 century in a movement launched by Chinese Ministry of Agriculture to combine high technology to farming production, remote sensing has given full play to farm crops' growth monitoring and yield forecast. And later in 2001 Chinese Ministry of Agriculture entrusted the Northern China Center of Agricultural Remote Sensing to forecast yield of some main crops like wheat, maize and rice in rather short time to supply information for the government decision maker. Present paper is a report for this task. It describes the application of background parameters in image recognition, classification and mapping with focuses on plan of the geo-science's theory, ecological feature and its cartographical objects or scale, the study of phrenology for image optimal time for classification of the ground objects, the analysis of optimal waveband composition and the application of background data base to spatial information recognition ;The research based on the knowledge of background parameters is indispensable for improving the accuracy of image classification and mapping quality and won a secondary reward of tech-science achievement from Chinese Ministry of Agriculture. Keywords: Spatial image; Classification; Background parameter

Subsea Cable Tracking by Autonomous Underwater Vehicle with Magnetic Sensing Guidance.

PubMed

Xiang, Xianbo; Yu, Caoyang; Niu, Zemin; Zhang, Qin

2016-08-20

The changes of the seabed environment caused by a natural disaster or human activities dramatically affect the life span of the subsea buried cable. It is essential to track the cable route in order to inspect the condition of the buried cable and protect its surviving seabed environment. The magnetic sensor is instrumental in guiding the remotely-operated vehicle (ROV) to track and inspect the buried cable underseas. In this paper, a novel framework integrating the underwater cable localization method with the magnetic guidance and control algorithm is proposed, in order to enable the automatic cable tracking by a three-degrees-of-freedom (3-DOF) under-actuated autonomous underwater vehicle (AUV) without human beings in the loop. The work relies on the passive magnetic sensing method to localize the subsea cable by using two tri-axial magnetometers, and a new analytic formulation is presented to compute the heading deviation, horizontal offset and buried depth of the cable. With the magnetic localization, the cable tracking and inspection mission is elaborately constructed as a straight-line path following control problem in the horizontal plane. A dedicated magnetic line-of-sight (LOS) guidance is built based on the relative geometric relationship between the vehicle and the cable, and the feedback linearizing technique is adopted to design a simplified cable tracking controller considering the side-slip effects, such that the under-actuated vehicle is able to move towards the subsea cable and then inspect its buried environment, which further guides the environmental protection of the cable by setting prohibited fishing/anchoring zones and increasing the buried depth. Finally, numerical simulation results show the effectiveness of the proposed magnetic guidance and control algorithm on the envisioned subsea cable tracking and the potential protection of the seabed environment along the cable route.

Subsea Cable Tracking by Autonomous Underwater Vehicle with Magnetic Sensing Guidance

PubMed Central

Xiang, Xianbo; Yu, Caoyang; Niu, Zemin; Zhang, Qin

2016-01-01

The changes of the seabed environment caused by a natural disaster or human activities dramatically affect the life span of the subsea buried cable. It is essential to track the cable route in order to inspect the condition of the buried cable and protect its surviving seabed environment. The magnetic sensor is instrumental in guiding the remotely-operated vehicle (ROV) to track and inspect the buried cable underseas. In this paper, a novel framework integrating the underwater cable localization method with the magnetic guidance and control algorithm is proposed, in order to enable the automatic cable tracking by a three-degrees-of-freedom (3-DOF) under-actuated autonomous underwater vehicle (AUV) without human beings in the loop. The work relies on the passive magnetic sensing method to localize the subsea cable by using two tri-axial magnetometers, and a new analytic formulation is presented to compute the heading deviation, horizontal offset and buried depth of the cable. With the magnetic localization, the cable tracking and inspection mission is elaborately constructed as a straight-line path following control problem in the horizontal plane. A dedicated magnetic line-of-sight (LOS) guidance is built based on the relative geometric relationship between the vehicle and the cable, and the feedback linearizing technique is adopted to design a simplified cable tracking controller considering the side-slip effects, such that the under-actuated vehicle is able to move towards the subsea cable and then inspect its buried environment, which further guides the environmental protection of the cable by setting prohibited fishing/anchoring zones and increasing the buried depth. Finally, numerical simulation results show the effectiveness of the proposed magnetic guidance and control algorithm on the envisioned subsea cable tracking and the potential protection of the seabed environment along the cable route. PMID:27556465

Towards marine seismological Network: real time small aperture seismic array

NASA Astrophysics Data System (ADS)

Ilinskiy, Dmitry

2017-04-01

Most powerful and dangerous seismic events are generated in underwater subduction zones. Existing seismological networks are based on land seismological stations. Increased demands for accuracy of location, magnitude, rupture process of coming earthquakes and at the same time reduction of data processing time require information from seabed seismic stations located near the earthquake generation area. Marine stations provide important contribution for clarification of the tectonic settings in most active subduction zones of the world. Early warning system for subduction zone area is based on marine seabed array which located near the area of most hazardous seismic zone in the region. Fast track processing for location of the earthquake hypocenter and energy takes place in buoy surface unit. Information about detected and located earthquake reaches the onshore seismological center earlier than the first break waves from the same earthquake will reach the nearest onshore seismological station. Implementation of small aperture array is based on existed and shown a good proven performance and costs effective solutions such as weather moored buoy and self-pop up autonomous seabed seismic nodes. Permanent seabed system for real-time operation has to be installed in deep sea waters far from the coast. Seabed array consists of several self-popup seismological stations which continuously acquire the data, detect the events of certain energy class and send detected event parameters to the surface buoy via acoustic link. Surface buoy unit determine the earthquake location by receiving the event parameters from seabed units and send such information in semi-real time to the onshore seismological center via narrow band satellite link. Upon the request from the cost the system could send wave form of events of certain energy class, bottom seismic station battery status and other environmental parameters. When the battery life of particular seabed unit is close to became empty, the seabed unit is switching into sleep mode and send that information to surface buoy and father to the onshore data center. Then seabed unit can wait for the vessel of opportunity for recovery of seabed unit to sea surface and replacing seabed station to another one with fresh batteries. All collected permanent seismic data by seabed unit could than downloaded for father processing and analysis. In our presentation we will demonstrate the several working prototypes of proposed system such as real time cable broad band seismological station and real time buoy seabed seismological station.

A Method of Spatial Mapping and Reclassification for High-Spatial-Resolution Remote Sensing Image Classification

PubMed Central

Wang, Guizhou; Liu, Jianbo; He, Guojin

2013-01-01

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy. PMID:24453808

Evaluation of seabed mapping methods for fine-scale classification of extremely shallow benthic habitats - Application to the Venice Lagoon, Italy

NASA Astrophysics Data System (ADS)

Montereale Gavazzi, G.; Madricardo, F.; Janowski, L.; Kruss, A.; Blondel, P.; Sigovini, M.; Foglini, F.

2016-03-01

Recent technological developments of multibeam echosounder systems (MBES) allow mapping of benthic habitats with unprecedented detail. MBES can now be employed in extremely shallow waters, challenging data acquisition (as these instruments were often designed for deeper waters) and data interpretation (honed on datasets with resolution sometimes orders of magnitude lower). With extremely high-resolution bathymetry and co-located backscatter data, it is now possible to map the spatial distribution of fine scale benthic habitats, even identifying the acoustic signatures of single sponges. In this context, it is necessary to understand which of the commonly used segmentation methods is best suited to account for such level of detail. At the same time, new sampling protocols for precisely geo-referenced ground truth data need to be developed to validate the benthic environmental classification. This study focuses on a dataset collected in a shallow (2-10 m deep) tidal channel of the Lagoon of Venice, Italy. Using 0.05-m and 0.2-m raster grids, we compared a range of classifications, both pixel-based and object-based approaches, including manual, Maximum Likelihood Classifier, Jenks Optimization clustering, textural analysis and Object Based Image Analysis. Through a comprehensive and accurately geo-referenced ground truth dataset, we were able to identify five different classes of the substrate composition, including sponges, mixed submerged aquatic vegetation, mixed detritic bottom (fine and coarse) and unconsolidated bare sediment. We computed estimates of accuracy (namely Overall, User, Producer Accuracies and the Kappa statistic) by cross tabulating predicted and reference instances. Overall, pixel based segmentations produced the highest accuracies and the accuracy assessment is strongly dependent on the number of classes chosen for the thematic output. Tidal channels in the Venice Lagoon are extremely important in terms of habitats and sediment distribution, particularly within the context of the new tidal barrier being built. However, they had remained largely unexplored until now, because of the surveying challenges. The application of this remote sensing approach, combined with targeted sampling, opens a new perspective in the monitoring of benthic habitats in view of a knowledge-based management of natural resources in shallow coastal areas.

Crop identification technology assessment for remote sensing (CITARS). Volume 6: Data processing at the laboratory for applications of remote sensing

NASA Technical Reports Server (NTRS)

Bauer, M. E.; Cary, T. K.; Davis, B. J.; Swain, P. H.

1975-01-01

The results of classifications and experiments for the crop identification technology assessment for remote sensing are summarized. Using two analysis procedures, 15 data sets were classified. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. Additionally, 20 data sets were classified using training statistics from another segment or date. The classification and proportion estimation results of the local and nonlocal classifications are reported. Data also describe several other experiments to provide additional understanding of the results of the crop identification technology assessment for remote sensing. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, spectral discriminability of corn, soybeans, and other, and analyses of aircraft multispectral data.

Deep learning decision fusion for the classification of urban remote sensing data

NASA Astrophysics Data System (ADS)

Abdi, Ghasem; Samadzadegan, Farhad; Reinartz, Peter

2018-01-01

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

Neural network approaches versus statistical methods in classification of multisource remote sensing data

NASA Technical Reports Server (NTRS)

Benediktsson, Jon A.; Swain, Philip H.; Ersoy, Okan K.

1990-01-01

Neural network learning procedures and statistical classificaiton methods are applied and compared empirically in classification of multisource remote sensing and geographic data. Statistical multisource classification by means of a method based on Bayesian classification theory is also investigated and modified. The modifications permit control of the influence of the data sources involved in the classification process. Reliability measures are introduced to rank the quality of the data sources. The data sources are then weighted according to these rankings in the statistical multisource classification. Four data sources are used in experiments: Landsat MSS data and three forms of topographic data (elevation, slope, and aspect). Experimental results show that two different approaches have unique advantages and disadvantages in this classification application.

An unsupervised classification technique for multispectral remote sensing data.

NASA Technical Reports Server (NTRS)

Su, M. Y.; Cummings, R. E.

1973-01-01

Description of a two-part clustering technique consisting of (a) a sequential statistical clustering, which is essentially a sequential variance analysis, and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum-likelihood classification techniques.

Airborne Laser Remote Sensor for Oil Detection and Classification : Engineering Requirements and Technical Considerations Relevant to a Performance Specification

DOT National Transportation Integrated Search

1975-08-01

This report outlines the engineering requirements for an Airborne Laser Remote Sensor for Oil Detection and Classification System. Detailed engineering requirements are given for the major units of the system. Technical considerations pertinent to a ...

Mapping of the Seagrass Cover Along the Mediterranean Coast of Turkey Using Landsat 8 Oli Images

NASA Astrophysics Data System (ADS)

Bakirman, T.; Gumusay, M. U.; Tuney, I.

2016-06-01

Benthic habitat is defined as ecological environment where marine animals, plants and other organisms live in. Benthic habitat mapping is defined as plotting the distribution and extent of habitats to create a map with complete coverage of the seabed showing distinct boundaries separating adjacent habitats or the use of spatially continuous environmental data sets to represent and predict biological patterns on the seafloor. Seagrass is an essential endemic marine species that prevents coast erosion and regulates carbon dioxide absorption in both undersea and atmosphere. Fishing, mining, pollution and other human activities cause serious damage to seabed ecosystems and reduce benthic biodiversity. According to the latest studies, only 5-10% of the seafloor is mapped, therefore it is not possible to manage resources effectively, protect ecologically important areas. In this study, it is aimed to map seagrass cover using Landsat 8 OLI images in the northern part of Mediterranean coast of Turkey. After pre-processing (e.g. radiometric, atmospheric, water depth correction) of Landsat images, coverage maps are produced with supervised classification using in-situ data which are underwater photos and videos. Result maps and accuracy assessment are presented and discussed.

[Object-oriented segmentation and classification of forest gap based on QuickBird remote sensing image.

PubMed

Mao, Xue Gang; Du, Zi Han; Liu, Jia Qian; Chen, Shu Xin; Hou, Ji Yu

2018-01-01

Traditional field investigation and artificial interpretation could not satisfy the need of forest gaps extraction at regional scale. High spatial resolution remote sensing image provides the possibility for regional forest gaps extraction. In this study, we used object-oriented classification method to segment and classify forest gaps based on QuickBird high resolution optical remote sensing image in Jiangle National Forestry Farm of Fujian Province. In the process of object-oriented classification, 10 scales (10-100, with a step length of 10) were adopted to segment QuickBird remote sensing image; and the intersection area of reference object (RA or ) and intersection area of segmented object (RA os ) were adopted to evaluate the segmentation result at each scale. For segmentation result at each scale, 16 spectral characteristics and support vector machine classifier (SVM) were further used to classify forest gaps, non-forest gaps and others. The results showed that the optimal segmentation scale was 40 when RA or was equal to RA os . The accuracy difference between the maximum and minimum at different segmentation scales was 22%. At optimal scale, the overall classification accuracy was 88% (Kappa=0.82) based on SVM classifier. Combining high resolution remote sensing image data with object-oriented classification method could replace the traditional field investigation and artificial interpretation method to identify and classify forest gaps at regional scale.

Supervised classification of continental shelf sediment off western Donegal, Ireland

NASA Astrophysics Data System (ADS)

Monteys, X.; Craven, K.; McCarron, S. G.

2017-12-01

Managing human impacts on marine ecosystems requires natural regions to be identified and mapped over a range of hierarchically nested scales. In recent years (2000-present) the Irish National Seabed Survey (INSS) and Integrated Mapping for the Sustainable Development of Ireland's Marine Resources programme (INFOMAR) (Geological Survey Ireland and Marine Institute collaborations) has provided unprecedented quantities of high quality data on Ireland's offshore territories. The increasing availability of large, detailed digital representations of these environments requires the application of objective and quantitative analyses. This study presents results of a new approach for sea floor sediment mapping based on an integrated analysis of INFOMAR multibeam bathymetric data (including the derivatives of slope and relative position), backscatter data (including derivatives of angular response analysis) and sediment groundtruthing over the continental shelf, west of Donegal. It applies a Geographic-Object-Based Image Analysis software package to provide a supervised classification of the surface sediment. This approach can provide a statistically robust, high resolution classification of the seafloor. Initial results display a differentiation of sediment classes and a reduction in artefacts from previously applied methodologies. These results indicate a methodology that could be used during physical habitat mapping and classification of marine environments.

Minimum distance classification in remote sensing

NASA Technical Reports Server (NTRS)

Wacker, A. G.; Landgrebe, D. A.

1972-01-01

The utilization of minimum distance classification methods in remote sensing problems, such as crop species identification, is considered. Literature concerning both minimum distance classification problems and distance measures is reviewed. Experimental results are presented for several examples. The objective of these examples is to: (a) compare the sample classification accuracy of a minimum distance classifier, with the vector classification accuracy of a maximum likelihood classifier, and (b) compare the accuracy of a parametric minimum distance classifier with that of a nonparametric one. Results show the minimum distance classifier performance is 5% to 10% better than that of the maximum likelihood classifier. The nonparametric classifier is only slightly better than the parametric version.

Applications of satellite remote sensing to forested ecosystems

Treesearch

Louis R. Iverson; Robin Lambert Graham; Elizabeth A. Cook; Elizabeth A. Cook

1989-01-01

Since the launch of the first civilian earth-observing satellite in 1972, satellite remote sensing has provided increasingly sophisticated information on the structure and function of forested ecosystems. Forest classification and mapping, common uses of satellite data, have improved over the years as a result of more discriminating sensors, better classification...

MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Lin, Daoyu; Fu, Kun; Wang, Yang; Xu, Guangluan; Sun, Xian

2017-11-01

With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks (CNNs). However, due to the limited amount of labeled data available, supervised learning is often difficult to carry out. Therefore, we proposed an unsupervised model called multiple-layer feature-matching generative adversarial networks (MARTA GANs) to learn a representation using only unlabeled data. MARTA GANs consists of both a generative model $G$ and a discriminative model $D$. We treat $D$ as a feature extractor. To fit the complex properties of remote sensing data, we use a fusion layer to merge the mid-level and global features. $G$ can produce numerous images that are similar to the training data; therefore, $D$ can learn better representations of remotely sensed images using the training data provided by $G$. The classification results on two widely used remote sensing image databases show that the proposed method significantly improves the classification performance compared with other state-of-the-art methods.

Contribution of non-negative matrix factorization to the classification of remote sensing images

NASA Astrophysics Data System (ADS)

Karoui, M. S.; Deville, Y.; Hosseini, S.; Ouamri, A.; Ducrot, D.

2008-10-01

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. The classification process requires some pre-processing, especially for data size reduction. The most usual technique is Principal Component Analysis. Another approach consists in regarding each pixel of the multispectral image as a mixture of pure elements contained in the observed area. Using Blind Source Separation (BSS) methods, one can hope to unmix each pixel and to perform the recognition of the classes constituting the observed scene. Our contribution consists in using Non-negative Matrix Factorization (NMF) combined with sparse coding as a solution to BSS, in order to generate new images (which are at least partly separated images) using HRV SPOT images from Oran area, Algeria). These images are then used as inputs of a supervised classifier integrating textural information. The results of classifications of these "separated" images show a clear improvement (correct pixel classification rate improved by more than 20%) compared to classification of initial (i.e. non separated) images. These results show the contribution of NMF as an attractive pre-processing for classification of multispectral remote sensing imagery.

Classification of permafrost active layer depth from remotely sensed and topographic evidence

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

Peddle, D.R.; Franklin, S.E.

1993-04-01

The remote detection of permafrost (perennially frozen ground) has important implications to environmental resource development, engineering studies, natural hazard prediction, and climate change research. In this study, the authors present results from two experiments into the classification of permafrost active layer depth within the zone of discontinuous permafrost in northern Canada. A new software system based on evidential reasoning was implemented to permit the integrated classification of multisource data consisting of landcover, terrain aspect, and equivalent latitude, each of which possessed different formats, data types, or statistical properties that could not be handled by conventional classification algorithms available to thismore » study. In the first experiment, four active layer depth classes were classified using ground based measurements of the three variables with an accuracy of 83% compared to in situ soil probe determination of permafrost active layer depth at over 500 field sites. This confirmed the environmental significance of the variables selected, and provided a baseline result to which a remote sensing classification could be compared. In the second experiment, evidence for each input variable was obtained from image processing of digital SPOT imagery and a photogrammetric digital elevation model, and used to classify active layer depth with an accuracy of 79%. These results suggest the classification of evidence from remotely sensed measures of spectral response and topography may provide suitable indicators of permafrost active layer depth.« less

Theory and analysis of statistical discriminant techniques as applied to remote sensing data

NASA Technical Reports Server (NTRS)

Odell, P. L.

1973-01-01

Classification of remote earth resources sensing data according to normed exponential density statistics is reported. The use of density models appropriate for several physical situations provides an exact solution for the probabilities of classifications associated with the Bayes discriminant procedure even when the covariance matrices are unequal.

Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA (Final)

EPA Science Inventory

EPA announced the availability of the final report,. This report and associated land use/land cover (LULC) coverage is the result o...

Statistical inference for remote sensing-based estimates of net deforestation

Treesearch

Ronald E. McRoberts; Brian F. Walters

2012-01-01

Statistical inference requires expression of an estimate in probabilistic terms, usually in the form of a confidence interval. An approach to constructing confidence intervals for remote sensing-based estimates of net deforestation is illustrated. The approach is based on post-classification methods using two independent forest/non-forest classifications because...

Reduction of Topographic Effect for Curve Number Estimated from Remotely Sensed Imagery

NASA Astrophysics Data System (ADS)

Zhang, Wen-Yan; Lin, Chao-Yuan

2016-04-01

The Soil Conservation Service Curve Number (SCS-CN) method is commonly used in hydrology to estimate direct runoff volume. The CN is the empirical parameter which corresponding to land use/land cover, hydrologic soil group and antecedent soil moisture condition. In large watersheds with complex topography, satellite remote sensing is the appropriate approach to acquire the land use change information. However, the topographic effect have been usually found in the remotely sensed imageries and resulted in land use classification. This research selected summer and winter scenes of Landsat-5 TM during 2008 to classified land use in Chen-You-Lan Watershed, Taiwan. The b-correction, the empirical topographic correction method, was applied to Landsat-5 TM data. Land use were categorized using K-mean classification into 4 groups i.e. forest, grassland, agriculture and river. Accuracy assessment of image classification was performed with national land use map. The results showed that after topographic correction, the overall accuracy of classification was increased from 68.0% to 74.5%. The average CN estimated from remotely sensed imagery decreased from 48.69 to 45.35 where the average CN estimated from national LULC map was 44.11. Therefore, the topographic correction method was recommended to normalize the topographic effect from the satellite remote sensing data before estimating the CN.

Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

NASA Astrophysics Data System (ADS)

Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

2018-04-01

In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

A remote sensing based vegetation classification logic for global land cover analysis

USGS Publications Warehouse

Running, Steven W.; Loveland, Thomas R.; Pierce, Lars L.; Nemani, R.R.; Hunt, E. Raymond

1995-01-01

This article proposes a simple new logic for classifying global vegetation. The critical features of this classification are that 1) it is based on simple, observable, unambiguous characteristics of vegetation structure that are important to ecosystem biogeochemistry and can be measured in the field for validation, 2) the structural characteristics are remotely sensible so that repeatable and efficient global reclassifications of existing vegetation will be possible, and 3) the defined vegetation classes directly translate into the biophysical parameters of interest by global climate and biogeochemical models. A first test of this logic for the continental United States is presented based on an existing 1 km AVHRR normalized difference vegetation index database. Procedures for solving critical remote sensing problems needed to implement the classification are discussed. Also, some inferences from this classification to advanced vegetation biophysical variables such as specific leaf area and photosynthetic capacity useful to global biogeochemical modeling are suggested.

76 FR 63613 - Secretary of Energy Advisory Board Natural Gas Subcommittee

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-13

... DEPARTMENT OF ENERGY Secretary of Energy Advisory Board Natural Gas Subcommittee AGENCY... Secretary of Energy Advisory Board (SEAB) Natural Gas Subcommittee. SEAB was reestablished pursuant to the... recommendations to the SEAB on how to improve the safety and environmental performance of natural gas hydraulic...

Towards automatic lithological classification from remote sensing data using support vector machines

NASA Astrophysics Data System (ADS)

Yu, Le; Porwal, Alok; Holden, Eun-Jung; Dentith, Michael

2010-05-01

Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14 principal component bands, 14 independent component bands, 3 band ratios, 3 DEM derivatives: slope/curvatureroughness and 2 aeromagnetic derivatives: mean and variance of susceptibility) extracted from the ASTER, DEM and aeromagnetic data, in order to determine the optimal inputs that provide the highest classification accuracy. It was found that a combination of ASTER-derived independent components, principal components and band ratios, DEM-derived slope, curvature and roughness, and aeromagnetic-derived mean and variance of magnetic susceptibility provide the highest classification accuracy of 93.4% on independent test samples. A comparison of the classification results of the SVM with those of maximum likelihood (84.9%) and minimum distance (38.4%) classifiers clearly show that the SVM algorithm returns much higher classification accuracy. Therefore, the SVM method can be used to produce quick and reliable geological maps from scarce geological information, which is still the case with many under-developed frontier regions of the world.

Classification accuracy for stratification with remotely sensed data

Treesearch

Raymond L. Czaplewski; Paul L. Patterson

2003-01-01

Tools are developed that help specify the classification accuracy required from remotely sensed data. These tools are applied during the planning stage of a sample survey that will use poststratification, prestratification with proportional allocation, or double sampling for stratification. Accuracy standards are developed in terms of an “error matrix,” which is...

Seabed geodiversity in a glaciated shelf area, the Baltic Sea

NASA Astrophysics Data System (ADS)

Kaskela, Anu Marii; Kotilainen, Aarno Tapio

2017-10-01

Geodiversity describes the heterogeneity of the physical terrain. We have performed basin-wide geodiversity analysis on a glaciated epicontinental seabed to assess geodiversity measures and patterns, locate areas with high geodiversity, and draw conclusions on contributing processes. Geodiversity quantification is a rather new topic and is mainly practiced in land areas. We applied geodiversity methods developed for terrestrial studies to a seabed environment. Three geodiversity parameters, including the richness, patchiness, and geodiversity index, of the Baltic Sea were assessed in a GIS environment based on broad-scale datasets on seabed substrates, structures, and bedrock. A set of environmental and geological variables, which were considered to reflect geological processes under seabed conditions, were compared with the geodiversity to identify some of its drivers. We observed differences in the geodiversity levels of the Baltic subbasins, which are mainly due to basement type/bedrock, roughness, shore density, and glacier-derived processes. The geodiversity of the Baltic Sea generally increases from South to North and from open-sea to high-shore density areas (archipelagos). Crystalline bedrock areas provide more diverse seabed environments than sedimentary rock areas. The analysis helps to inform scientists, marine spatial planners, and managers about abiotic conservation values, the dynamics of the seabed environment, and potential areas with elevated biodiversity.

The use of remote sensing and linear wave theory to model local wave energy around Alphonse Atoll, Seychelles

NASA Astrophysics Data System (ADS)

Hamylton, S.

2011-12-01

This paper demonstrates a practical step-wise method for modelling wave energy at the landscape scale using GIS and remote sensing techniques at Alphonse Atoll, Seychelles. Inputs are a map of the benthic surface (seabed) cover, a detailed bathymetric model derived from remotely sensed Compact Airborne Spectrographic Imager (CASI) data and information on regional wave heights. Incident energy at the reef crest around the atoll perimeter is calculated as a function of its deepwater value with wave parameters (significant wave height and period) hindcast in the offshore zone using the WaveWatch III application developed by the National Oceanographic and Atmospheric Administration. Energy modifications are calculated at constant intervals as waves transform over the forereef platform along a series of reef profile transects running into the atoll centre. Factors for shoaling, refraction and frictional attenuation are calculated at each interval for given changes in bathymetry and benthic coverage type and a nominal reduction in absolute energy is incorporated at the reef crest to account for wave breaking. Overall energy estimates are derived for a period of 5 years and related to spatial patterning of reef flat surface cover (sand and seagrass patches).

The roles of resuspension, diffusion and biogeochemical processes on oxygen dynamics offshore of the Rhône River, France: a numerical modeling study

NASA Astrophysics Data System (ADS)

Moriarty, Julia M.; Harris, Courtney K.; Fennel, Katja; Friedrichs, Marjorie A. M.; Xu, Kehui; Rabouille, Christophe

2017-04-01

Observations indicate that resuspension and associated fluxes of organic material and porewater between the seabed and overlying water can alter biogeochemical dynamics in some environments, but measuring the role of sediment processes on oxygen and nutrient dynamics is challenging. A modeling approach offers a means of quantifying these fluxes for a range of conditions, but models have typically relied on simplifying assumptions regarding seabed-water-column interactions. Thus, to evaluate the role of resuspension on biogeochemical dynamics, we developed a coupled hydrodynamic, sediment transport, and biogeochemical model (HydroBioSed) within the Regional Ocean Modeling System (ROMS). This coupled model accounts for processes including the storage of particulate organic matter (POM) and dissolved nutrients within the seabed; fluxes of this material between the seabed and the water column via erosion, deposition, and diffusion at the sediment-water interface; and biogeochemical reactions within the seabed. A one-dimensional version of HydroBioSed was then implemented for the Rhône subaqueous delta in France. To isolate the role of resuspension on biogeochemical dynamics, this model implementation was run for a 2-month period that included three resuspension events; also, the supply of organic matter, oxygen, and nutrients to the model was held constant in time. Consistent with time series observations from the Rhône Delta, model results showed that erosion increased the diffusive flux of oxygen into the seabed by increasing the vertical gradient of oxygen at the seabed-water interface. This enhanced supply of oxygen to the seabed, as well as resuspension-induced increases in ammonium availability in surficial sediments, allowed seabed oxygen consumption to increase via nitrification. This increase in nitrification compensated for the decrease in seabed oxygen consumption due to aerobic remineralization that occurred as organic matter was entrained into the water column. Additionally, entrainment of POM into the water column during resuspension events, and the associated increase in remineralization there, also increased oxygen consumption in the region of the water column below the pycnocline. During these resuspension events, modeled rates of oxygen consumption increased by factors of up to ˜ 2 and ˜ 8 in the seabed and below the pycnocline, respectively. When averaged over 2 months, the intermittent cycles of erosion and deposition led to a ˜ 16 % increase of oxygen consumption in the seabed, as well as a larger increase of ˜ 140 % below the pycnocline. These results imply that observations collected during quiescent periods, and biogeochemical models that neglect resuspension or use typical parameterizations for resuspension, may underestimate net oxygen consumption at sites like the Rhône Delta. Local resuspension likely has the most pronounced effect on oxygen dynamics at study sites with a high oxygen concentration in bottom waters, only a thin seabed oxic layer, and abundant labile organic matter.

Global analysis of depletion and recovery of seabed biota after bottom trawling disturbance.

PubMed

Hiddink, Jan Geert; Jennings, Simon; Sciberras, Marija; Szostek, Claire L; Hughes, Kathryn M; Ellis, Nick; Rijnsdorp, Adriaan D; McConnaughey, Robert A; Mazor, Tessa; Hilborn, Ray; Collie, Jeremy S; Pitcher, C Roland; Amoroso, Ricardo O; Parma, Ana M; Suuronen, Petri; Kaiser, Michel J

2017-08-01

Bottom trawling is the most widespread human activity affecting seabed habitats. Here, we collate all available data for experimental and comparative studies of trawling impacts on whole communities of seabed macroinvertebrates on sedimentary habitats and develop widely applicable methods to estimate depletion and recovery rates of biota after trawling. Depletion of biota and trawl penetration into the seabed are highly correlated. Otter trawls caused the least depletion, removing 6% of biota per pass and penetrating the seabed on average down to 2.4 cm, whereas hydraulic dredges caused the most depletion, removing 41% of biota and penetrating the seabed on average 16.1 cm. Median recovery times posttrawling (from 50 to 95% of unimpacted biomass) ranged between 1.9 and 6.4 y. By accounting for the effects of penetration depth, environmental variation, and uncertainty, the models explained much of the variability of depletion and recovery estimates from single studies. Coupled with large-scale, high-resolution maps of trawling frequency and habitat, our estimates of depletion and recovery rates enable the assessment of trawling impacts on unprecedented spatial scales.

Global analysis of depletion and recovery of seabed biota after bottom trawling disturbance

PubMed Central

Hiddink, Jan Geert; Jennings, Simon; Sciberras, Marija; Szostek, Claire L.; Hughes, Kathryn M.; Ellis, Nick; Rijnsdorp, Adriaan D.; McConnaughey, Robert A.; Mazor, Tessa; Hilborn, Ray; Collie, Jeremy S.; Pitcher, C. Roland; Amoroso, Ricardo O.; Parma, Ana M.; Suuronen, Petri; Kaiser, Michel J.

2017-01-01

Bottom trawling is the most widespread human activity affecting seabed habitats. Here, we collate all available data for experimental and comparative studies of trawling impacts on whole communities of seabed macroinvertebrates on sedimentary habitats and develop widely applicable methods to estimate depletion and recovery rates of biota after trawling. Depletion of biota and trawl penetration into the seabed are highly correlated. Otter trawls caused the least depletion, removing 6% of biota per pass and penetrating the seabed on average down to 2.4 cm, whereas hydraulic dredges caused the most depletion, removing 41% of biota and penetrating the seabed on average 16.1 cm. Median recovery times posttrawling (from 50 to 95% of unimpacted biomass) ranged between 1.9 and 6.4 y. By accounting for the effects of penetration depth, environmental variation, and uncertainty, the models explained much of the variability of depletion and recovery estimates from single studies. Coupled with large-scale, high-resolution maps of trawling frequency and habitat, our estimates of depletion and recovery rates enable the assessment of trawling impacts on unprecedented spatial scales. PMID:28716926

Image interpretation for a multilevel land use classification system

NASA Technical Reports Server (NTRS)

1973-01-01

The potential use is discussed of three remote sensors for developing a four level land use classification system. Three types of imagery for photointerpretation are presented: ERTS-1 satellite imagery, high altitude photography, and medium altitude photography. Suggestions are given as to which remote sensors and imagery scales may be most effectively employed to provide data on specific types of land use.

Purification of Training Samples Based on Spectral Feature and Superpixel Segmentation

NASA Astrophysics Data System (ADS)

Guan, X.; Qi, W.; He, J.; Wen, Q.; Chen, T.; Wang, Z.

2018-04-01

Remote sensing image classification is an effective way to extract information from large volumes of high-spatial resolution remote sensing images. Generally, supervised image classification relies on abundant and high-precision training data, which is often manually interpreted by human experts to provide ground truth for training and evaluating the performance of the classifier. Remote sensing enterprises accumulated lots of manually interpreted products from early lower-spatial resolution remote sensing images by executing their routine research and business programs. However, these manually interpreted products may not match the very high resolution (VHR) image properly because of different dates or spatial resolution of both data, thus, hindering suitability of manually interpreted products in training classification models, or small coverage area of these manually interpreted products. We also face similar problems in our laboratory in 21st Century Aerospace Technology Co. Ltd (short for 21AT). In this work, we propose a method to purify the interpreted product to match newly available VHRI data and provide the best training data for supervised image classifiers in VHR image classification. And results indicate that our proposed method can efficiently purify the input data for future machine learning use.

A stereo remote sensing feature selection method based on artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

2014-05-01

To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost

PubMed Central

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited. PMID:26528811

Object-Oriented Classification of Sugarcane Using Time-Series Middle-Resolution Remote Sensing Data Based on AdaBoost.

PubMed

Zhou, Zhen; Huang, Jingfeng; Wang, Jing; Zhang, Kangyu; Kuang, Zhaomin; Zhong, Shiquan; Song, Xiaodong

2015-01-01

Most areas planted with sugarcane are located in southern China. However, remote sensing of sugarcane has been limited because useable remote sensing data are limited due to the cloudy climate of this region during the growing season and severe spectral mixing with other crops. In this study, we developed a methodology for automatically mapping sugarcane over large areas using time-series middle-resolution remote sensing data. For this purpose, two major techniques were used, the object-oriented method (OOM) and data mining (DM). In addition, time-series Chinese HJ-1 CCD images were obtained during the sugarcane growing period. Image objects were generated using a multi-resolution segmentation algorithm, and DM was implemented using the AdaBoost algorithm, which generated the prediction model. The prediction model was applied to the HJ-1 CCD time-series image objects, and then a map of the sugarcane planting area was produced. The classification accuracy was evaluated using independent field survey sampling points. The confusion matrix analysis showed that the overall classification accuracy reached 93.6% and that the Kappa coefficient was 0.85. Thus, the results showed that this method is feasible, efficient, and applicable for extrapolating the classification of other crops in large areas where the application of high-resolution remote sensing data is impractical due to financial considerations or because qualified images are limited.

Classification of high-resolution multispectral satellite remote sensing images using extended morphological attribute profiles and independent component analysis

NASA Astrophysics Data System (ADS)

Wu, Yu; Zheng, Lijuan; Xie, Donghai; Zhong, Ruofei

2017-07-01

In this study, the extended morphological attribute profiles (EAPs) and independent component analysis (ICA) were combined for feature extraction of high-resolution multispectral satellite remote sensing images and the regularized least squares (RLS) approach with the radial basis function (RBF) kernel was further applied for the classification. Based on the major two independent components, the geometrical features were extracted using the EAPs method. In this study, three morphological attributes were calculated and extracted for each independent component, including area, standard deviation, and moment of inertia. The extracted geometrical features classified results using RLS approach and the commonly used LIB-SVM library of support vector machines method. The Worldview-3 and Chinese GF-2 multispectral images were tested, and the results showed that the features extracted by EAPs and ICA can effectively improve the accuracy of the high-resolution multispectral image classification, 2% larger than EAPs and principal component analysis (PCA) method, and 6% larger than APs and original high-resolution multispectral data. Moreover, it is also suggested that both the GURLS and LIB-SVM libraries are well suited for the multispectral remote sensing image classification. The GURLS library is easy to be used with automatic parameter selection but its computation time may be larger than the LIB-SVM library. This study would be helpful for the classification application of high-resolution multispectral satellite remote sensing images.

Exploring diversity in ensemble classification: Applications in large area land cover mapping

NASA Astrophysics Data System (ADS)

Mellor, Andrew; Boukir, Samia

2017-07-01

Ensemble classifiers, such as random forests, are now commonly applied in the field of remote sensing, and have been shown to perform better than single classifier systems, resulting in reduced generalisation error. Diversity across the members of ensemble classifiers is known to have a strong influence on classification performance - whereby classifier errors are uncorrelated and more uniformly distributed across ensemble members. The relationship between ensemble diversity and classification performance has not yet been fully explored in the fields of information science and machine learning and has never been examined in the field of remote sensing. This study is a novel exploration of ensemble diversity and its link to classification performance, applied to a multi-class canopy cover classification problem using random forests and multisource remote sensing and ancillary GIS data, across seven million hectares of diverse dry-sclerophyll dominated public forests in Victoria Australia. A particular emphasis is placed on analysing the relationship between ensemble diversity and ensemble margin - two key concepts in ensemble learning. The main novelty of our work is on boosting diversity by emphasizing the contribution of lower margin instances used in the learning process. Exploring the influence of tree pruning on diversity is also a new empirical analysis that contributes to a better understanding of ensemble performance. Results reveal insights into the trade-off between ensemble classification accuracy and diversity, and through the ensemble margin, demonstrate how inducing diversity by targeting lower margin training samples is a means of achieving better classifier performance for more difficult or rarer classes and reducing information redundancy in classification problems. Our findings inform strategies for collecting training data and designing and parameterising ensemble classifiers, such as random forests. This is particularly important in large area remote sensing applications, for which training data is costly and resource intensive to collect.

Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

USGS Publications Warehouse

Corcoran, Jennifer M.; Knight, Joseph F.; Gallant, Alisa L.

2013-01-01

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

A stochastic atmospheric model for remote sensing applications

NASA Technical Reports Server (NTRS)

Turner, R. E.

1983-01-01

There are many factors which reduce the accuracy of classification of objects in the satellite remote sensing of Earth's surface. One important factor is the variability in the scattering and absorptive properties of the atmospheric components such as particulates and the variable gases. For multispectral remote sensing of the Earth's surface in the visible and infrared parts of the spectrum the atmospheric particulates are a major source of variability in the received signal. It is difficult to design a sensor which will determine the unknown atmospheric components by remote sensing methods, at least to the accuracy needed for multispectral classification. The problem of spatial and temporal variations in the atmospheric quantities which can affect the measured radiances are examined. A method based upon the stochastic nature of the atmospheric components was developed, and, using actual data the statistical parameters needed for inclusion into a radiometric model was generated. Methods are then described for an improved correction of radiances. These algorithms will then result in a more accurate and consistent classification procedure.

Remote sensing application to regional activities

NASA Technical Reports Server (NTRS)

Shahrokhi, F.; Jones, N. L.; Sharber, L. A.

1976-01-01

Two agencies within the State of Tennessee were identified whereby the transfer of aerospace technology, namely remote sensing, could be applied to their stated problem areas. Their stated problem areas are wetland and land classification and strip mining studies. In both studies, LANDSAT data was analyzed with the UTSI video-input analog/digital automatic analysis and classification facility. In the West Tennessee area three land-use classifications could be distinguished; cropland, wetland, and forest. In the East Tennessee study area, measurements were submitted to statistical tests which verified the significant differences due to natural terrain, stripped areas, various stages of reclamation, water, etc. Classifications for both studies were output in the form of maps of symbols and varying shades of gray.

Post-Drilling Changes in Seabed Landscape and Megabenthos in a Deep-Sea Hydrothermal System, the Iheya North Field, Okinawa Trough

PubMed Central

Nakajima, Ryota; Yamamoto, Hiroyuki; Kawagucci, Shinsuke; Takaya, Yutaro; Nozaki, Tatsuo; Chen, Chong; Fujikura, Katsunori; Miwa, Tetsuya; Takai, Ken

2015-01-01

There has been an increasing interest in seafloor exploitation such as mineral mining in deep-sea hydrothermal fields, but the environmental impact of anthropogenic disturbance to the seafloor is poorly known. In this study, the effect of such anthropogenic disturbance by scientific drilling operations (IODP Expedition 331) on seabed landscape and megafaunal habitation was surveyed for over 3 years using remotely operated vehicle video observation in a deep-sea hydrothermal field, the Iheya North field, in the Okinawa Trough. We focused on observations from a particular drilling site (Site C0014) where the most dynamic change of landscape and megafaunal habitation was observed among the drilling sites of IODP Exp. 331. No visible hydrothermal fluid discharge had been observed at the sedimentary seafloor at Site C0014, where Calyptogena clam colonies were known for more than 10 years, before the drilling event. After drilling commenced, the original Calyptogena colonies were completely buried by the drilling deposits. Several months after the drilling, diffusing high-temperature hydrothermal fluid began to discharge from the sedimentary subseafloor in the area of over 20 m from the drill holes, ‘artificially’ creating a new hydrothermal vent habitat. Widespread microbial mats developed on the seafloor with the diffusing hydrothermal fluids and the galatheid crab Shinkaia crosnieri endemic to vents dominated the new vent community. The previously soft, sedimentary seafloor was hardened probably due to barite/gypsum mineralization or silicification, becoming rough and undulated with many fissures after the drilling operation. Although the effects of the drilling operation on seabed landscape and megafaunal composition are probably confined to an area of maximally 30 m from the drill holes, the newly established hydrothermal vent ecosystem has already lasted 2 years and is like to continue to exist until the fluid discharge ceases and thus the ecosystem in the area has been altered for long-term. PMID:25902075

Post-drilling changes in seabed landscape and megabenthos in a deep-sea hydrothermal system, the Iheya North field, Okinawa Trough.

PubMed

Nakajima, Ryota; Yamamoto, Hiroyuki; Kawagucci, Shinsuke; Takaya, Yutaro; Nozaki, Tatsuo; Chen, Chong; Fujikura, Katsunori; Miwa, Tetsuya; Takai, Ken

2015-01-01

There has been an increasing interest in seafloor exploitation such as mineral mining in deep-sea hydrothermal fields, but the environmental impact of anthropogenic disturbance to the seafloor is poorly known. In this study, the effect of such anthropogenic disturbance by scientific drilling operations (IODP Expedition 331) on seabed landscape and megafaunal habitation was surveyed for over 3 years using remotely operated vehicle video observation in a deep-sea hydrothermal field, the Iheya North field, in the Okinawa Trough. We focused on observations from a particular drilling site (Site C0014) where the most dynamic change of landscape and megafaunal habitation was observed among the drilling sites of IODP Exp. 331. No visible hydrothermal fluid discharge had been observed at the sedimentary seafloor at Site C0014, where Calyptogena clam colonies were known for more than 10 years, before the drilling event. After drilling commenced, the original Calyptogena colonies were completely buried by the drilling deposits. Several months after the drilling, diffusing high-temperature hydrothermal fluid began to discharge from the sedimentary subseafloor in the area of over 20 m from the drill holes, 'artificially' creating a new hydrothermal vent habitat. Widespread microbial mats developed on the seafloor with the diffusing hydrothermal fluids and the galatheid crab Shinkaia crosnieri endemic to vents dominated the new vent community. The previously soft, sedimentary seafloor was hardened probably due to barite/gypsum mineralization or silicification, becoming rough and undulated with many fissures after the drilling operation. Although the effects of the drilling operation on seabed landscape and megafaunal composition are probably confined to an area of maximally 30 m from the drill holes, the newly established hydrothermal vent ecosystem has already lasted 2 years and is like to continue to exist until the fluid discharge ceases and thus the ecosystem in the area has been altered for long-term.

Accuracy of Remotely Sensed Classifications For Stratification of Forest and Nonforest Lands

Treesearch

Raymond L. Czaplewski; Paul L. Patterson

2001-01-01

We specify accuracy standards for remotely sensed classifications used by FIA to stratify landscapes into two categories: forest and nonforest. Accuracy must be highest when forest area approaches 100 percent of the landscape. If forest area is rare in a landscape, then accuracy in the nonforest stratum must be very high, even at the expense of accuracy in the forest...

Remote sensing imagery classification using multi-objective gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2016-10-01

Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

a Rough Set Decision Tree Based Mlp-Cnn for Very High Resolution Remotely Sensed Image Classification

NASA Astrophysics Data System (ADS)

Zhang, C.; Pan, X.; Zhang, S. Q.; Li, H. P.; Atkinson, P. M.

2017-09-01

Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

A Improved Seabed Surface Sand Sampling Device

NASA Astrophysics Data System (ADS)

Luo, X.

2017-12-01

In marine geology research it is necessary to obtain a suf fcient quantity of seabed surface samples, while also en- suring that the samples are in their original state. Currently,there are a number of seabed surface sampling devices available, but we fnd it is very diffcult to obtain sand samples using these devices, particularly when dealing with fne sand. Machine-controlled seabed surface sampling devices are also available, but generally unable to dive into deeper regions of water. To obtain larger quantities of seabed surface sand samples in their original states, many researchers have tried to improve upon sampling devices,but these efforts have generally produced ambiguous results, in our opinion.To resolve this issue, we have designed an improved andhighly effective seabed surface sand sampling device that incorporates the strengths of a variety of sampling devices. It is capable of diving into deepwater to obtain fne sand samples and is also suited for use in streams, rivers, lakes and seas with varying levels of depth (up to 100 m). This device can be used for geological mapping, underwater prospecting, geological engineering and ecological, environmental studies in both marine and terrestrial waters.

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.

Variance approximations for assessments of classification accuracy

Treesearch

R. L. Czaplewski

1994-01-01

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

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

NASA Astrophysics Data System (ADS)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

[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.

Sub-metric Resolution FWI of Ultra-High-Frequency Marine Reflection Seismograms. A Remote Sensing Tool for the Characterisation of Shallow Marine Geohazard

NASA Astrophysics Data System (ADS)

Provenzano, G.; Vardy, M. E.; Henstock, T.; Zervos, A.

2017-12-01

A quantitative high-resolution physical model of the top 100 meters of the sub-seabed is of key importance for a wide range of shallow geohazard scenarios: identification of potential shallow landsliding, monitoring of gas storage sites, and assessment of offshore structures stability. Cur- rently, engineering-scale sediment characterisation relies heavily on direct sampling of the seabed and in-situ measurements. Such an approach is expensive and time-consuming, as well as liable to alter the sediment properties during the coring process. As opposed to reservoir-scale seismic exploration, ultra-high-frequency (UHF, 0.2-4.0 kHz) multi-channel marine reflection seismic data are most often limited to a to semi-quantitative interpretation of the reflection amplitudes and facies geometries, leaving largely unexploited its intrinsic value as a remote characterisation tool. In this work, we develop a seismic inversion methodology to obtain a robust sub-metric resolution elastic model from limited-offset, limited-bandwidth UHF seismic reflection data, with minimal pre-processing and limited a priori information. The Full Waveform Inversion is implemented as a stochastic optimiser based upon a Genetic Algorithm, modified in order to improve the robustness against inaccurate starting model populations. Multiple independent runs are used to create a robust posterior model distribution and quantify the uncertainties on the solution. The methodology has been applied to complex synthetic examples and to real datasets acquired in areas prone to shallow landsliding. The inverted elastic models show a satisfactory match with the ground-truths and a good sensitivity to relevant variations in the sediment texture and saturation state. We apply the methodology to a range of synthetic consolidating slopes under different loading conditions and sediment properties distributions. Our work demonstrates that the seismic inversion of UHF data has the potential to become an important practical tool for marine ground model building in spatially heterogeneous areas, reducing the reliance on expensive and time-consuming coring campaigns.

Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks.

PubMed

Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping

2018-04-26

With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction.

Multi-Agent Information Classification Using Dynamic Acquaintance Lists.

ERIC Educational Resources Information Center

Mukhopadhyay, Snehasis; Peng, Shengquan; Raje, Rajeev; Palakal, Mathew; Mostafa, Javed

2003-01-01

Discussion of automated information services focuses on information classification and collaborative agents, i.e. intelligent computer programs. Highlights include multi-agent systems; distributed artificial intelligence; thesauri; document representation and classification; agent modeling; acquaintances, or remote agents discovered through…

Using hyperspectral remote sensing for land cover classification

NASA Astrophysics Data System (ADS)

Zhang, Wendy W.; Sriharan, Shobha

2005-01-01

This project used hyperspectral data set to classify land cover using remote sensing techniques. Many different earth-sensing satellites, with diverse sensors mounted on sophisticated platforms, are currently in earth orbit. These sensors are designed to cover a wide range of the electromagnetic spectrum and are generating enormous amounts of data that must be processed, stored, and made available to the user community. The Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) collects data in 224 bands that are approximately 9.6 nm wide in contiguous bands between 0.40 and 2.45 mm. Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-IR, and thermal IR portions of the spectrum. The unsupervised image classification procedure automatically categorizes the pixels in an image into land cover classes or themes. Experiments on using hyperspectral remote sensing for land cover classification were conducted during the 2003 and 2004 NASA Summer Faculty Fellowship Program at Stennis Space Center. Research Systems Inc.'s (RSI) ENVI software package was used in this application framework. In this application, emphasis was placed on: (1) Spectrally oriented classification procedures for land cover mapping, particularly, the supervised surface classification using AVIRIS data; and (2) Identifying data endmembers.

Possible return of Acropora cervicornis at Pulaski Shoal, Dry Tortugas National Park, Florida

USGS Publications Warehouse

Lidz, Barbara H.; Zawada, David G.

2013-01-01

Seabed classification is essential to assessing environmental associations and physical status in coral reef ecosystems. At Pulaski Shoal in Dry Tortugas National Park, Florida, nearly continuous underwater-image coverage was acquired in 15.5 hours in 2009 along 70.2 km of transect lines spanning ~0.2 km2. The Along-Track Reef-Imaging System (ATRIS), a boat-based, high-speed, digital imaging system, was used. ATRIS-derived benthic classes were merged with a QuickBird satellite image to create a habitat map that defines areas of senile coral reef, carbonate sand, seagrasses, and coral rubble. This atypical approach of starting with extensive, high-resolution in situ imagery and extrapolating between transect lines using satellite imagery leverages the strengths of each remote-sensing modality. The ATRIS images also captured the spatial distribution of two species once common on now-degraded Florida-Caribbean coral reefs: the stony staghorn coral Acropora cervicornis, a designated threatened species, and the long-spined urchin Diadema antillarum. This article documents the utility of ATRIS imagery for quantifying number and estimating age of A. cervicornis colonies (n = 400, age range, 5–11 y) since the severe hypothermic die-off in the Dry Tortugas in 1976–77. This study is also the first to document the largest number of new colonies of A. cervicornis tabulated in an area of the park where coral-monitoring stations maintained by the Fish and Wildlife Research Institute have not been established. The elevated numbers provide an updated baseline for tracking revival of this species at Pulaski Shoal.

Contextual classification on a CDC Flexible Processor system. [for photomapped remote sensing data

NASA Technical Reports Server (NTRS)

Smith, B. W.; Siegel, H. J.; Swain, P. H.

1981-01-01

A potential hardware organization for the Flexible Processor Array is presented. An algorithm that implements a contextual classifier for remote sensing data analysis is given, along with uniprocessor classification algorithms. The Flexible Processor algorithm is provided, as are simulated timings for contextual classifiers run on the Flexible Processor Array and another system. The timings are analyzed for context neighborhoods of sizes three and nine.

Unsupervised classification of remote multispectral sensing data

NASA Technical Reports Server (NTRS)

Su, M. Y.

1972-01-01

The new unsupervised classification technique for classifying multispectral remote sensing data which can be either from the multispectral scanner or digitized color-separation aerial photographs consists of two parts: (a) a sequential statistical clustering which is a one-pass sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. Applications of the technique using an IBM-7094 computer on multispectral data sets over Purdue's Flight Line C-1 and the Yellowstone National Park test site have been accomplished. Comparisons between the classification maps by the unsupervised technique and the supervised maximum liklihood technique indicate that the classification accuracies are in agreement.

On the use of drift bottle and seabed drifter data in coastal management

NASA Technical Reports Server (NTRS)

Welch, C. S.; Norcross, J. J.

1973-01-01

The use of drift bottle and seabed drifter information for use in coastal management is discussed. The drift bottle/seabed drifter portion of VIMS project MACONS (Mid Atlantic Continental Shelf) is described as an example of how a comprehensive survey using drift bottles and seabed drifters provides data useful for coastal management. The data from MACONS are analyzed to answer specific questions of interest to several different coastal managers: a manager siting a deep oil port, one siting a sewage outfall, a manager responsible for setting up emergency beach protection procedures before an accident occurs, and a manager responsible for the environmental quality of a particular small section of coastline.

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.)

Lossless Compression of Classification-Map Data

NASA Technical Reports Server (NTRS)

Hua, Xie; Klimesh, Matthew

2009-01-01

A lossless image-data-compression algorithm intended specifically for application to classification-map data is based on prediction, context modeling, and entropy coding. The algorithm was formulated, in consideration of the differences between classification maps and ordinary images of natural scenes, so as to be capable of compressing classification- map data more effectively than do general-purpose image-data-compression algorithms. Classification maps are typically generated from remote-sensing images acquired by instruments aboard aircraft (see figure) and spacecraft. A classification map is a synthetic image that summarizes information derived from one or more original remote-sensing image(s) of a scene. The value assigned to each pixel in such a map is the index of a class that represents some type of content deduced from the original image data for example, a type of vegetation, a mineral, or a body of water at the corresponding location in the scene. When classification maps are generated onboard the aircraft or spacecraft, it is desirable to compress the classification-map data in order to reduce the volume of data that must be transmitted to a ground station.

[Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

PubMed

Li, Hongyi; Shi, Zhou; Sha, Jinming; Cheng, Jieliang

2006-08-01

In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.

Seafloor heterogeneity influences the biodiversity–ecosystem functioning relationships in the deep sea

PubMed Central

Zeppilli, Daniela; Pusceddu, Antonio; Trincardi, Fabio; Danovaro, Roberto

2016-01-01

Theoretical ecology predicts that heterogeneous habitats allow more species to co-exist in a given area. In the deep sea, biodiversity is positively linked with ecosystem functioning, suggesting that deep-seabed heterogeneity could influence ecosystem functions and the relationships between biodiversity and ecosystem functioning (BEF). To shed light on the BEF relationships in a heterogeneous deep seabed, we investigated variations in meiofaunal biodiversity, biomass and ecosystem efficiency within and among different seabed morphologies (e.g., furrows, erosional troughs, sediment waves and other depositional structures, landslide scars and deposits) in a narrow geo-morphologically articulated sector of the Adriatic Sea. We show that distinct seafloor morphologies are characterized by highly diverse nematode assemblages, whereas areas sharing similar seabed morphologies host similar nematode assemblages. BEF relationships are consistently positive across the entire region, but different seabed morphologies are characterised by different slope coefficients of the relationship. Our results suggest that seafloor heterogeneity, allowing diversified assemblages across different habitats, increases diversity and influence ecosystem processes at the regional scale, and BEF relationships at smaller spatial scales. We conclude that high-resolution seabed mapping and a detailed analysis of the species distribution at the habitat scale are crucial for improving management of goods and services delivered by deep-sea ecosystems. PMID:27211908

Seabed photographs, sediment texture analyses, and sun-illuminated sea floor topography in the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts

USGS Publications Warehouse

Valentine, Page C.; Gallea, Leslie B.; Blackwood, Dann S.; Twomey, Erin R.

2010-01-01

The U.S. Geological Survey, in collaboration with National Oceanic and Atmospheric Administration's National Marine Sanctuary Program, conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary region from 1993 to 2004. The mapped area is approximately 3,700 km (1,100 nmi) in size and was subdivided into 18 quadrangles. An extensive series of sea-floor maps of the region based on multibeam sonar surveys has been published as paper maps and online in digital format (PDF, EPS, PS). In addition, 2,628 seabed-sediment samples were collected and analyzed and are in the usSEABED: Atlantic Coast Offshore Surficial Sediment Data Release. This report presents for viewing and downloading the more than 10,600 still seabed photographs that were acquired during the project. The digital images are provided in thumbnail, medium (1536 x 1024 pixels), and high (3071 x 2048) resolution. The images can be viewed by quadrangle on the U.S. Geological Survey Woods Hole Coastal and Marine Science Center's photograph database. Photograph metadata are embedded in each image in Exchangeable Image File Format and also provided in spreadsheet format. Published digital topographic maps and descriptive text for seabed features are included here for downloading and serve as context for the photographs. An interactive topographic map for each quadrangle shows locations of photograph stations, and each location is linked to the photograph database. This map also shows stations where seabed sediment was collected for texture analysis; the results of grain-size analysis and associated metadata are presented in spreadsheet format.

Multitask SVM learning for remote sensing data classification

NASA Astrophysics Data System (ADS)

Leiva-Murillo, Jose M.; Gómez-Chova, Luis; Camps-Valls, Gustavo

2010-10-01

Many remote sensing data processing problems are inherently constituted by several tasks that can be solved either individually or jointly. For instance, each image in a multitemporal classification setting could be taken as an individual task but relation to previous acquisitions should be properly considered. In such problems, different modalities of the data (temporal, spatial, angular) gives rise to changes between the training and test distributions, which constitutes a difficult learning problem known as covariate shift. Multitask learning methods aim at jointly solving a set of prediction problems in an efficient way by sharing information across tasks. This paper presents a novel kernel method for multitask learning in remote sensing data classification. The proposed method alleviates the dataset shift problem by imposing cross-information in the classifiers through matrix regularization. We consider the support vector machine (SVM) as core learner and two regularization schemes are introduced: 1) the Euclidean distance of the predictors in the Hilbert space; and 2) the inclusion of relational operators between tasks. Experiments are conducted in the challenging remote sensing problems of cloud screening from multispectral MERIS images and for landmine detection.

Nineteen hundred seventy three significant accomplishments. [Landsat satellite data applications

NASA Technical Reports Server (NTRS)

1974-01-01

Data collected by the Skylab remote sensing satellites was used to develop applications techniques and to combine automatic data classification with statistical clustering methods. Continuing research was concentrated in the correlation and registration of data products and in the definition of the atmospheric effects on remote sensing. The causes of errors encountered in the automated classification of agricultural data are identified. Other applications in forestry, geography, environmental geology, and land use are discussed.

Deep neural network-based domain adaptation for classification of remote sensing images

NASA Astrophysics Data System (ADS)

Ma, Li; Song, Jiazhen

2017-10-01

We investigate the effectiveness of deep neural network for cross-domain classification of remote sensing images in this paper. In the network, class centroid alignment is utilized as a domain adaptation strategy, making the network able to transfer knowledge from the source domain to target domain on a per-class basis. Since predicted labels of target data should be used to estimate the centroid of each class, we use overall centroid alignment as a coarse domain adaptation method to improve the estimation accuracy. In addition, rectified linear unit is used as the activation function to produce sparse features, which may improve the separation capability. The proposed network can provide both aligned features and an adaptive classifier, as well as obtain label-free classification of target domain data. The experimental results using Hyperion, NCALM, and WorldView-2 remote sensing images demonstrated the effectiveness of the proposed approach.

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

NASA Astrophysics Data System (ADS)

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

2008-03-01

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

A comparison of PCA/ICA for data preprocessing in remote sensing imagery classification

NASA Astrophysics Data System (ADS)

He, Hui; Yu, Xianchuan

2005-10-01

In this paper a performance comparison of a variety of data preprocessing algorithms in remote sensing image classification is presented. These selected algorithms are principal component analysis (PCA) and three different independent component analyses, ICA (Fast-ICA (Aapo Hyvarinen, 1999), Kernel-ICA (KCCA and KGV (Bach & Jordan, 2002), EFFICA (Aiyou Chen & Peter Bickel, 2003). These algorithms were applied to a remote sensing imagery (1600×1197), obtained from Shunyi, Beijing. For classification, a MLC method is used for the raw and preprocessed data. The results show that classification with the preprocessed data have more confident results than that with raw data and among the preprocessing algorithms, ICA algorithms improve on PCA and EFFICA performs better than the others. The convergence of these ICA algorithms (for data points more than a million) are also studied, the result shows EFFICA converges much faster than the others. Furthermore, because EFFICA is a one-step maximum likelihood estimate (MLE) which reaches asymptotic Fisher efficiency (EFFICA), it computers quite small so that its demand of memory come down greatly, which settled the "out of memory" problem occurred in the other algorithms.

Evaluation criteria for software classification inventories, accuracies, and maps

NASA Technical Reports Server (NTRS)

Jayroe, R. R., Jr.

1976-01-01

Statistical criteria are presented for modifying the contingency table used to evaluate tabular classification results obtained from remote sensing and ground truth maps. This classification technique contains information on the spatial complexity of the test site, on the relative location of classification errors, on agreement of the classification maps with ground truth maps, and reduces back to the original information normally found in a contingency table.

PPS GPS: What Is It? And How Do I Get It

DTIC Science & Technology

1994-06-01

Positioning Service, Selective Availabilit B.PRICE CODIE 17. SECURITY CLASSIFICATION II. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20...the TEC Water Detection Response Team which operates in remote areas of the world. These activities, require the GPS receiver to be capable of removing

Abstracting of suspected illegal land use in urban areas using case-based classification of remote sensing images

NASA Astrophysics Data System (ADS)

Chen, Fulong; Wang, Chao; Yang, Chengyun; Zhang, Hong; Wu, Fan; Lin, Wenjuan; Zhang, Bo

2008-11-01

This paper proposed a method that uses a case-based classification of remote sensing images and applied this method to abstract the information of suspected illegal land use in urban areas. Because of the discrete cases for imagery classification, the proposed method dealt with the oscillation of spectrum or backscatter within the same land use category, and it not only overcame the deficiency of maximum likelihood classification (the prior probability of land use could not be obtained) but also inherited the advantages of the knowledge-based classification system, such as artificial intelligence and automatic characteristics. Consequently, the proposed method could do the classifying better. Then the researchers used the object-oriented technique for shadow removal in highly dense city zones. With multi-temporal SPOT 5 images whose resolution was 2.5×2.5 meters, the researchers found that the method can abstract suspected illegal land use information in urban areas using post-classification comparison technique.

Classification of Active Microwave and Passive Optical Data Based on Bayesian Theory and Mrf

NASA Astrophysics Data System (ADS)

Yu, F.; Li, H. T.; Han, Y. S.; Gu, H. Y.

2012-08-01

A classifier based on Bayesian theory and Markov random field (MRF) is presented to classify the active microwave and passive optical remote sensing data, which have demonstrated their respective advantages in inversion of surface soil moisture content. In the method, the VV, VH polarization of ASAR and all the 7 TM bands are taken as the input of the classifier to get the class labels of each pixel of the images. And the model is validated for the necessities of integration of TM and ASAR, it shows that, the total precision of classification in this paper is 89.4%. Comparing with the classification with single TM, the accuracy increase 11.5%, illustrating that synthesis of active and passive optical remote sensing data is efficient and potential in classification.

Benthic impacts of intertidal oyster culture, with consideration of taxonomic sufficiency.

PubMed

Forrest, Barrie M; Creese, Robert G

2006-01-01

An investigation of the impacts from elevated intertidal Pacific oyster culture in a New Zealand estuary showed enhanced sedimentation beneath culture racks compared with other sites. Seabed elevation beneath racks was generally lower than between them, suggesting that topographic patterns more likely result from a local effect of rack structures on hydrodynamic processes than from enhanced deposition. Compared with control sites, seabed sediments within the farm had a greater silt/clay and organic content, and a lower redox potential and shear strength. While a marked trend in macrofaunal species richness was not evident, species composition and dominance patterns were consistent with a disturbance gradient, with farm effects not evident 35 m from the perimeter of the racks. Of the environmental variables measured, sediment shear strength was most closely associated with the distribution and density of macrofauna, suggesting that human-induced disturbance from farming operations may have contributed to the biological patterns. To evaluate the taxonomic sufficiency needed to document impacts, aggregation to the family level based on Linnean classification was compared with an aggregation scheme based on ;general groups' identifiable with limited taxonomic expertise. Compared with species-level analyses, spatial patterns of impact were equally discernible at both aggregation levels used, provided density rather than presence/absence data were used. Once baseline conditions are established and the efficacy of taxonomic aggregation demonstrated, a ;general group' scheme provides an appropriate and increasingly relevant tool for routine monitoring.

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.

15 CFR 971.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS General... the effect of harassing, persons conducting deep seabed mining activities authorized by law...

15 CFR 971.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS General... the effect of harassing, persons conducting deep seabed mining activities authorized by law...

15 CFR 971.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS General... the effect of harassing, persons conducting deep seabed mining activities authorized by law...

15 CFR 971.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS General... the effect of harassing, persons conducting deep seabed mining activities authorized by law...

15 CFR 971.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS General... the effect of harassing, persons conducting deep seabed mining activities authorized by law...

A patch-based convolutional neural network for remote sensing image classification.

PubMed

Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di

2017-11-01

Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas. Copyright © 2017 Elsevier Ltd. All rights reserved.

Status of Vegetation Classification in Redwood Ecosystems

Treesearch

Thomas M. Mahony; John D. Stuart

2007-01-01

Vegetation classifications, based primarily on physiognomic variability and canopy dominants and derived principally from remotely sensed imagery, have been completed for the entire redwood range (Eyre 1980, Fox 1989). However, systematic, quantitative, floristic-based vegetation classifications in old-growth redwood forests have not been completed for large portions...

Developing INFOMAR's Seabed Mapping Data to Support a Sustainable Marine Economy

NASA Astrophysics Data System (ADS)

Judge, M. T.; Guinan, J.

2016-02-01

As Ireland's national seabed mapping programme, INFOMAR1 (INtegrated mapping FOr the sustainable development of Ireland's MARine resource) enters its eleventh year it continues to provide pivotal seabed mapping data products, e.g. databases, charts and physical habitat maps to support Ireland's Integrated Marine Plan. The programme, jointly coordinated by the Geological Survey of Ireland and the Marine Institute, has gained a world class reputation for developing seabed mapping technologies, infrastructure and expertise. In the government's current Integrated Marine Plan, the programme's critical role in marine spatial planning enabling infrastructural development, research and education has been cited2. INFOMAR's free data policy supports a thriving maritime economy by promoting easy access to seabed mapping datasets that underpin; maritime safety, security and surveillance, governance, business development, research and technology innovation and infrastructure. The first hydrographic surveys of the national marine mapping programme mapped the extent of Ireland's deepest offshore area, whilst in recent years the focus has been to map the coastal and shallow areas. Targeted coastal areas include 26 bays and 3 priority areas for which specialised equipment, techniques and vessels are required. This talk will discuss how the INFOMAR programme has evolved to address the scientific and technological challenges of seabed mapping across a range of water depths; particularly the challenges associated with addressing inshore data gaps. It will describe how the data converts to bathymetric and geological maps detailing seabed characteristics and habitats. We will expand on how maps are: incorporated into collaborative marine projects such as EMODnet, commercialised to identify marine resources and used as marine decision support tools that drive policy and promote protection of the vastly under discovered marine area.

The USGS role in mapping the nation's submerged lands

USGS Publications Warehouse

Schwab, Bill; Haines, John

2004-01-01

The seabed provides habitat for a diverse marine life having commercial, recreational, and intrinsic value. The habitat value of the seabed is largely a function of the geological structure and related geological, biological, oceanologic, and geochemical processes. Of equal importance, the nation's submerged lands contain energy and mineral resources and are utilized for the siting of offshore infrastructure and waste disposal. Seabed character and processes influence the safety and viability of offshore operations. Seabed and subseabed characterization is a prerequisite for the assessment, protection, and utilization of both living and non-living marine resources. A comprehensive program to characterize and understand the nation's submerged lands requires scientific expertise in the fields of geology, biology, hydrography, and oceanography. The U.S. Geological Survey (USGS) has long experience as the Federal agency charged with conducting geologic research and mapping in both coastal and offshore regions. The USGS Coastal and Marine Geology Program (CMGP) leads the nation in expertise related to characterization of seabed and subseabed geology, geological processes, seabed dynamics, and (in collaboration with the National Oceanic and Atmospheric Administration (NOAA) and international partners) habitat geoscience. Numerous USGS studies show that sea-floor geology and processes determine the character and distribution of biological habitats, control coastal evolution, influence the coastal response to storm events and human alterations, and determine the occurrence and concentration of natural resources.

Multi-source remotely sensed data fusion for improving land cover classification

NASA Astrophysics Data System (ADS)

Chen, Bin; Huang, Bo; Xu, Bing

2017-02-01

Although many advances have been made in past decades, land cover classification of fine-resolution remotely sensed (RS) data integrating multiple temporal, angular, and spectral features remains limited, and the contribution of different RS features to land cover classification accuracy remains uncertain. We proposed to improve land cover classification accuracy by integrating multi-source RS features through data fusion. We further investigated the effect of different RS features on classification performance. The results of fusing Landsat-8 Operational Land Imager (OLI) data with Moderate Resolution Imaging Spectroradiometer (MODIS), China Environment 1A series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data integrating temporal, spectral, angular, and topographic features achieved better land cover classification accuracy than the original RS data. Compared with the topographic feature, the temporal and angular features extracted from the fused data played more important roles in classification performance, especially those temporal features containing abundant vegetation growth information, which markedly increased the overall classification accuracy. In addition, the multispectral and hyperspectral fusion successfully discriminated detailed forest types. Our study provides a straightforward strategy for hierarchical land cover classification by making full use of available RS data. All of these methods and findings could be useful for land cover classification at both regional and global scales.

15 CFR 970.207 - Antitrust information.

Code of Federal Regulations, 2014 CFR

2014-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Applications Contents § 970... license, provided that said agreement relates to deep seabed hard mineral resource exploration or mining...

15 CFR 970.207 - Antitrust information.

Code of Federal Regulations, 2012 CFR

2012-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Applications Contents § 970... license, provided that said agreement relates to deep seabed hard mineral resource exploration or mining...

15 CFR 970.207 - Antitrust information.

Code of Federal Regulations, 2013 CFR

2013-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Applications Contents § 970... license, provided that said agreement relates to deep seabed hard mineral resource exploration or mining...

15 CFR 970.207 - Antitrust information.

Code of Federal Regulations, 2010 CFR

2010-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Applications Contents § 970... license, provided that said agreement relates to deep seabed hard mineral resource exploration or mining...

15 CFR 970.207 - Antitrust information.

Code of Federal Regulations, 2011 CFR

2011-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Applications Contents § 970... license, provided that said agreement relates to deep seabed hard mineral resource exploration or mining...

Postprocessing classification images

NASA Technical Reports Server (NTRS)

Kan, E. P.

1979-01-01

Program cleans up remote-sensing maps. It can be used with existing image-processing software. Remapped images closely resemble familiar resource information maps and can replace or supplement classification images not postprocessed by this program.

Development and application of a new comprehensive image-based classification scheme for coastal and benthic environments along the southeast Florida continental shelf

NASA Astrophysics Data System (ADS)

Makowski, Christopher

The coastal (terrestrial) and benthic environments along the southeast Florida continental shelf show a unique biophysical succession of marine features from a highly urbanized, developed coastal region in the north (i.e. northern Miami-Dade County) to a protective marine sanctuary in the southeast (i.e. Florida Keys National Marine Sanctuary). However, the establishment of a standard bio-geomorphological classification scheme for this area of coastal and benthic environments is lacking. The purpose of this study was to test the hypothesis and answer the research question of whether new parameters of integrating geomorphological components with dominant biological covers could be developed and applied across multiple remote sensing platforms for an innovative way to identify, interpret, and classify diverse coastal and benthic environments along the southeast Florida continental shelf. An ordered manageable hierarchical classification scheme was developed to incorporate the categories of Physiographic Realm, Morphodynamic Zone, Geoform, Landform, Dominant Surface Sediment, and Dominant Biological Cover. Six different remote sensing platforms (i.e. five multi-spectral satellite image sensors and one high-resolution aerial orthoimagery) were acquired, delineated according to the new classification scheme, and compared to determine optimal formats for classifying the study area. Cognitive digital classification at a nominal scale of 1:6000 proved to be more accurate than autoclassification programs and therefore used to differentiate coastal marine environments based on spectral reflectance characteristics, such as color, tone, saturation, pattern, and texture of the seafloor topology. In addition, attribute tables were created in conjugation with interpretations to quantify and compare the spatial relationships between classificatory units. IKONOS-2 satellite imagery was determined to be the optimal platform for applying the hierarchical classification scheme. However, each remote sensing platform had beneficial properties depending on research goals, logistical restrictions, and financial support. This study concluded that a new hierarchical comprehensive classification scheme for identifying coastal marine environments along the southeast Florida continental shelf could be achieved by integrating geomorphological features with biological coverages. This newly developed scheme, which can be applied across multiple remote sensing platforms with GIS software, establishes an innovative classification protocol to be used in future research studies.

ANALYSIS OF A CLASSIFICATION ERROR MATRIX USING CATEGORICAL DATA TECHNIQUES.

USGS Publications Warehouse

Rosenfield, George H.; Fitzpatrick-Lins, Katherine

1984-01-01

Summary form only given. A classification error matrix typically contains tabulation results of an accuracy evaluation of a thematic classification, such as that of a land use and land cover map. The diagonal elements of the matrix represent the counts corrected, and the usual designation of classification accuracy has been the total percent correct. The nondiagonal elements of the matrix have usually been neglected. The classification error matrix is known in statistical terms as a contingency table of categorical data. As an example, an application of these methodologies to a problem of remotely sensed data concerning two photointerpreters and four categories of classification indicated that there is no significant difference in the interpretation between the two photointerpreters, and that there are significant differences among the interpreted category classifications. However, two categories, oak and cottonwood, are not separable in classification in this experiment at the 0. 51 percent probability. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each of the interpreted categories. A conditional coefficient of agreement for the individual categories is compared to other methods for expressing category accuracy which have already been presented in the remote sensing literature.

Use of remote sensing for land use policy formulation

NASA Technical Reports Server (NTRS)

1987-01-01

The overall objectives and strategies of the Center for Remote Sensing remain to provide a center for excellence for multidisciplinary scientific expertise to address land-related global habitability and earth observing systems scientific issues. Specific research projects that were underway during the final contract period include: digital classification of coniferous forest types in Michigan's northern lower peninsula; a physiographic ecosystem approach to remote classification and mapping; land surface change detection and inventory; analysis of radiant temperature data; and development of methodologies to assess possible impacts of man's changes of land surface on meteorological parameters. Significant progress in each of the five project areas has occurred. Summaries on each of the projects are provided.

Identification of Terrestrial Reflectance From Remote Sensing

NASA Technical Reports Server (NTRS)

Alter-Gartenberg, Rachel; Nolf, Scott R.; Stacy, Kathryn (Technical Monitor)

2000-01-01

Correcting for atmospheric effects is an essential part of surface-reflectance recovery from radiance measurements. Model-based atmospheric correction techniques enable an accurate identification and classification of terrestrial reflectances from multi-spectral imagery. Successful and efficient removal of atmospheric effects from remote-sensing data is a key factor in the success of Earth observation missions. This report assesses the performance, robustness and sensitivity of two atmospheric-correction and reflectance-recovery techniques as part of an end-to-end simulation of hyper-spectral acquisition, identification and classification.

The Application of Remote Sensing Techniques to Urban Data Acquisition

NASA Technical Reports Server (NTRS)

Horton, F. E.

1971-01-01

The application of remote sensing techniques useful in acquiring data concerning housing quality is discussed. Conclusions reached from the investigation were: (1) Use of individuals with a higher degree of training in photointerpretation should significantly increase the percentage of successful classifications. (2) Small area classification of urban housing quality can definitely be accomplished via high resolution aerial photography. Such surveys, at the levels of accuracy demonstrated, can be of major utility in quick look surveys. (3) Survey costs should be significantly reduced.

Application of remotely sensed multispectral data to automated analysis of marshland vegetation. Inference to the location of breeding habitats of the salt marsh mosquito (Aedes Sollicitans)

NASA Technical Reports Server (NTRS)

Cibula, W. G.

1976-01-01

The techniques used for the automated classification of marshland vegetation and for the color-coded display of remotely acquired data to facilitate the control of mosquito breeding are presented. A multispectral scanner system and its mode of operation are described, and the computer processing techniques are discussed. The procedures for the selection of calibration sites are explained. Three methods for displaying color-coded classification data are presented.

International Models and Methods of Remote Sensing Education and Training.

ERIC Educational Resources Information Center

Anderson, Paul S.

A classification of remote sensing courses throughout the world, the world-wide need for sensing instruction, and alternative instructional methods for meeting those needs are discussed. Remote sensing involves aerial photointerpretation or the use of satellite and other non-photographic imagery; its focus is to interpret what is in the photograph…

Calibration of remotely sensed proportion or area estimates for misclassification error

Treesearch

Raymond L. Czaplewski; Glenn P. Catts

1992-01-01

Classifications of remotely sensed data contain misclassification errors that bias areal estimates. Monte Carlo techniques were used to compare two statistical methods that correct or calibrate remotely sensed areal estimates for misclassification bias using reference data from an error matrix. The inverse calibration estimator was consistently superior to the...

Layered classification techniques for remote sensing applications

NASA Technical Reports Server (NTRS)

Swain, P. H.; Wu, C. L.; Landgrebe, D. A.; Hauska, H.

1975-01-01

The single-stage method of pattern classification utilizes all available features in a single test which assigns the unknown to a category according to a specific decision strategy (such as the maximum likelihood strategy). The layered classifier classifies the unknown through a sequence of tests, each of which may be dependent on the outcome of previous tests. Although the layered classifier was originally investigated as a means of improving classification accuracy and efficiency, it was found that in the context of remote sensing data analysis, other advantages also accrue due to many of the special characteristics of both the data and the applications pursued. The layered classifier method and several of the diverse applications of this approach are discussed.

15 CFR 970.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970... United States or any other nation; and any other activity designed to harass deep seabed mining...

15 CFR 970.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970... United States or any other nation; and any other activity designed to harass deep seabed mining...

15 CFR 970.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970... United States or any other nation; and any other activity designed to harass deep seabed mining...

15 CFR 970.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970... United States or any other nation; and any other activity designed to harass deep seabed mining...

15 CFR 970.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970... United States or any other nation; and any other activity designed to harass deep seabed mining...

Support Vector Machines for Hyperspectral Remote Sensing Classification

NASA Technical Reports Server (NTRS)

Gualtieri, J. Anthony; Cromp, R. F.

1998-01-01

The Support Vector Machine provides a new way to design classification algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a difficult classification problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class problem respectively. These results are somewhat better than other recent results on the same data. A key feature of this classifier is its ability to use high-dimensional data without the usual recourse to a feature selection step to reduce the dimensionality of the data. For this application, this is important, as hyperspectral data consists of several hundred contiguous spectral channels for each exemplar. We provide an introduction to this new approach, and demonstrate its application to classification of an agriculture scene.

Regional yield predictions of malting barley by remote sensing and ancillary data

NASA Astrophysics Data System (ADS)

Weissteiner, Christof J.; Braun, Matthias; Kuehbauch, Walter

2004-02-01

Yield forecasts are of high interest to the malting and brewing industry in order to allow the most convenient purchasing policy of raw materials. Within this investigation, malting barley yield forecasts (Hordeum vulgare L.) were performed for typical growing regions in South-Western Germany. Multisensoral and multitemporal Remote Sensing data on one hand and ancillary meteorological, agrostatistical, topographical and pedological data on the other hand were used as input data for prediction models, which were based on an empirical-statistical modeling approach. Since spring barley production is depending on acreage and on the yield per area, classification is needed, which was performed by a supervised multitemporal classification algorithm, utilizing optical Remote Sensing data (LANDSAT TM/ETM+). Comparison between a pixel-based and an object-oriented classification algorithm was carried out. The basic version of the yield estimation model was conducted by means of linear correlation of Remote Sensing data (NOAA-AVHRR NDVI), CORINE land cover data and agrostatistical data. In an extended version meteorological data (temperature, precipitation, etc.) and soil data was incorporated. Both, basic and extended prediction systems, led to feasible results, depending on the selection of the time span for NDVI accumulation.

Analysis of multispectral signatures and investigation of multi-aspect remote sensing techniques

NASA Technical Reports Server (NTRS)

Malila, W. A.; Hieber, R. H.; Sarno, J. E.

1974-01-01

Two major aspects of remote sensing with multispectral scanners (MSS) are investigated. The first, multispectral signature analysis, includes the effects on classification performance of systematic variations found in the average signals received from various ground covers as well as the prediction of these variations with theoretical models of physical processes. The foremost effects studied are those associated with the time of day airborne MSS data are collected. Six data collection runs made over the same flight line in a period of five hours are analyzed, it is found that the time span significantly affects classification performance. Variations associated with scan angle also are studied. The second major topic of discussion is multi-aspect remote sensing, a new concept in remote sensing with scanners. Here, data are collected on multiple passes by a scanner that can be tilted to scan forward of the aircraft at different angles on different passes. The use of such spatially registered data to achieve improved classification of agricultural scenes is investigated and found promising. Also considered are the possibilities of extracting from multi-aspect data, information on the condition of corn canopies and the stand characteristics of forests.

A Review of Wetland Remote Sensing.

PubMed

Guo, Meng; Li, Jing; Sheng, Chunlei; Xu, Jiawei; Wu, Li

2017-04-05

Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers.

A Review of Wetland Remote Sensing

PubMed Central

Guo, Meng; Li, Jing; Sheng, Chunlei; Xu, Jiawei; Wu, Li

2017-01-01

Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers. PMID:28379174

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, Jin AU; Shin, Robert T.; Nghiem, Son V.; Yueh, Herng-Aung; Han, Hsiu C.; Lim, Harold H.; Arnold, David V.

1990-01-01

Remote sensing of earth terrain is examined. The layered random medium model is used to investigate the fully polarimetric scattering of electromagnetic waves from vegetation. The model is used to interpret the measured data for vegetation fields such as rice, wheat, or soybean over water or soil. Accurate calibration of polarimetric radar systems is essential for the polarimetric remote sensing of earth terrain. A polarimetric calibration algorithm using three arbitrary in-scene reflectors is developed. In the interpretation of active and passive microwave remote sensing data from the earth terrain, the random medium model was shown to be quite successful. A multivariate K-distribution is proposed to model the statistics of fully polarimetric radar returns from earth terrain. In the terrain cover classification using the synthetic aperture radar (SAR) images, the applications of the K-distribution model will provide better performance than the conventional Gaussian classifiers. The layered random medium model is used to study the polarimetric response of sea ice. Supervised and unsupervised classification procedures are also developed and applied to synthetic aperture radar polarimetric images in order to identify their various earth terrain components for more than two classes. These classification procedures were applied to San Francisco Bay and Traverse City SAR images.

Automated training site selection for large-area remote-sensing image analysis

NASA Astrophysics Data System (ADS)

McCaffrey, Thomas M.; Franklin, Steven E.

1993-11-01

A computer program is presented to select training sites automatically from remotely sensed digital imagery. The basic ideas are to guide the image analyst through the process of selecting typical and representative areas for large-area image classifications by minimizing bias, and to provide an initial list of potential classes for which training sites are required to develop a classification scheme or to verify classification accuracy. Reducing subjectivity in training site selection is achieved by using a purely statistical selection of homogeneous sites which then can be compared to field knowledge, aerial photography, or other remote-sensing imagery and ancillary data to arrive at a final selection of sites to be used to train the classification decision rules. The selection of the homogeneous sites uses simple tests based on the coefficient of variance, the F-statistic, and the Student's i-statistic. Comparisons of site means are conducted with a linear growing list of previously located homogeneous pixels. The program supports a common pixel-interleaved digital image format and has been tested on aerial and satellite optical imagery. The program is coded efficiently in the C programming language and was developed under AIX-Unix on an IBM RISC 6000 24-bit color workstation.

Applications of remote sensing, volume 1

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1977-01-01

The author has identified the following significant results. ECHO successfully exploits the redundancy of states characteristics of sampled imagery of ground scenes to achieve better classification accuracy, reduce the number of classifications required, and reduce the variability of classification results. The information required to produce ECHO classifications are cell size, cell homogeneity, cell-to-field annexation parameters, input data, and a class conditional marginal density statistics deck.

15 CFR 971.428 - Other necessary permits.

Code of Federal Regulations, 2010 CFR

2010-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Issuance/Transfer.... Each permit will provide that securing the deep seabed mining permit for activities described in the... Federal, State, and local permits. ...

[Remote sensing monitoring and screening for urban black and odorous water body: A review.

PubMed

Shen, Qian; Zhu, Li; Cao, Hong Ye

2017-10-01

Continuous improvement of urban water environment and overall control of black and odorous water body are not merely national strategic needs with the action plan for prevention and treatment of water pollution, but also the hot issues attracting the attention of people. Most previous researches concentrated on the study of cause, evaluation and treatment measures of this phenomenon, and there are few researches on the monitoring using remote sensing, which is often a strain to meet the national needs of operational monitoring. This paper mainly summarized the urgent research problems, mainly including the identification and classification standard, research on the key technologies, and the frame of remote sensing screening systems for the urban black and odorous water body. The main key technologies were concluded too, including the high spatial resolution image preprocessing and extraction technique for black and odorous water body, the extraction of water information in city zones, the classification of the black and odorous water, and the identification and classification technique based on satellite-sky-ground remote sensing. This paper summarized the research progress and put forward research ideas of monitoring and screening urban black and odorous water body via high spatial resolution remote sensing technology, which would be beneficial to having an overall grasp of spatial distribution and improvement progress of black and odorous water body, and provide strong technical support for controlling urban black and odorous water body.

Seabed maps showing topography, ruggedness, backscatter intensity, sediment mobility, and the distribution of geologic substrates in Quadrangle 6 of the Stellwagen Bank National Marine Sanctuary Region offshore of Boston, Massachusetts

USGS Publications Warehouse

Valentine, Page C.; Gallea, Leslie B.

2015-11-10

The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration's National Marine Sanctuary Program, has conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary (SBNMS) region since 1993. The area is approximately 3,700 square kilometers (km2) and is subdivided into 18 quadrangles. Seven maps, at a scale of 1:25,000, of quadrangle 6 (211 km2) depict seabed topography, backscatter, ruggedness, geology, substrate mobility, mud content, and areas dominated by fine-grained or coarse-grained sand. Interpretations of bathymetric and seabed backscatter imagery, photographs, video, and grain-size analyses were used to create the geology-based maps. In all, data from 420 stations were analyzed, including sediment samples from 325 locations. The seabed geology map shows the distribution of 10 substrate types ranging from boulder ridges to immobile, muddy sand to mobile, rippled sand. Mapped substrate types are defined on the basis of sediment grain-size composition, surface morphology, sediment layering, the mobility or immobility of substrate surfaces, and water depth range. This map series is intended to portray the major geological elements (substrates, topographic features, processes) of environments within quadrangle 6. Additionally, these maps will be the basis for the study of the ecological requirements of invertebrate and vertebrate species that utilize these substrates and guide seabed management in the region.

Enhanced Detection of Sea-Disposed Man-Made Objects in Backscatter Data

NASA Astrophysics Data System (ADS)

Edwards, M.; Davis, R. B.

2016-12-01

The Hawai'i Undersea Military Munitions Assessment (HUMMA) project developed software to increase data visualization capabilities applicable to seafloor reflectivity datasets acquired by a variety of bottom-mapping sonar systems. The purpose of these improvements is to detect different intensity values within an arbitrary amplitude range that may be associated with relative target reflectivity as well as extend the overall amplitude range across which detailed dynamic contrast may be effectively displayed. The backscatter dataset used to develop this software imaged tens of thousands of reflective targets resting on the seabed that were systematically sea disposed south of Oahu, Hawaii, around the end of World War II in waters ranging from 300-600 meters depth. Human-occupied and remotely operated vehicles conducted ground-truth video and photographic reconnaissance of thousands of these reflective targets, documenting and geo-referencing long curvilinear trials of items including munitions, paint cans, airplane parts, scuttled ships, cars and bundled anti-submarine nets. Edwards et al. [2012] determined that most individual trails consist of objects of one particular type. The software described in this presentation, in combination with the ground-truth images, was developed to help recognize different types of objects based on reflectivity, size, and shape from altitudes of tens of meters above the seabed. The fundamental goal of the software is to facilitate rapid underway detection and geo-location of specific sea-disposed objects so their impact on the environment can be assessed.

Geomechanical Characterization and Stability Analysis of the Bedrock Underlying the Costa Concordia Cruise Ship

NASA Astrophysics Data System (ADS)

Dotta, Giulia; Gigli, Giovanni; Ferrigno, Federica; Gabbani, Giuliano; Nocentini, Massimiliano; Lombardi, Luca; Agostini, Andrea; Nolesini, Teresa; Casagli, Nicola

2017-09-01

The shipwreck of the Costa Concordia cruise ship, which ran aground on 13 January 2012 on the northwestern coast of Giglio Island (Italy), required continuous monitoring of the position and movement of the vessel to guarantee the security of workers and rescuers operating around and within the wreck and to support shipwreck removal operations. Furthermore, understanding the geomechanical properties and stability behaviour of the coastal rock mass and rocky seabed underlying the ship was of similar importance. To assess the stability conditions of the ship, a ground-based monitoring system was installed in front of the wreck. The network included a terrestrial laser scanner (TLS) device, which was used to perform remote semiautomatic geomechanical characterization of the observed rock mass. Using TLS survey techniques, three main discontinuity sets were identified in the granitic rock mass of Giglio Island. Furthermore, a multibeam bathymetric survey was used to qualitatively characterize the seabed. To integrate the processed TLS data and quantitatively describe the rock mass quality, a subsequent field survey was carried out to provide a rock mass geomechanical evaluation (from very good to moderate quality). Based on the acquired information, kinematic and stability analyses were performed to create a spatial prediction of rock failure mechanisms in the study area. The obtained kinematic hazard index values were generally low; only the plane failure index reached slightly higher values. The general stability of the rock mass was confirmed by the stability analysis, which yielded a high safety factor value (approximately 12).

Regimes for the ocean, outer space, and weather

NASA Technical Reports Server (NTRS)

Brown, S.; Cornell, N. W.; Fabian, L. L.; Weiss, E. B.

1977-01-01

The allocation of resources among users of the oceans, outer space and the weather is discussed. Attention is given to the international management of maritime navigation, the control of fisheries, offshore oil and gas exploitation, mineral exploitation in the deep seabed (especially the mining of manganese nodules), and the regulation of oceanographic studies. The management of outer space is considered, with special reference to remote sensing by satellites, television broadcasting, the technical requirements of maritime satellites, and problems associated with satellite frequency and orbit allocation. Rainmaking and typhoon modification, as well as the distribution of weather modification capabilities in the world, are also mentioned. The United Nations, international agencies and tribunals, and multi- or bilateral agreements are some of the implements suggested for use in the regulation of the oceans, outer space and the weather.

Methods of Determining Playa Surface Conditions Using Remote Sensing

DTIC Science & Technology

1987-10-08

NO. 11. TITLE (include Security Classification) METHODS OF DETERMINING PLAYA SURFACE CONDITIONS USING REMOTE SENSING 12. PERSONAL AUTHOR(S) J. PONDER...PLAYA SURFACE CONDITIONS USING REMOTE SENSING J. Ponder Henley U. S. Army Engineer Topographic Laboratories Fort Belvoir, Virginia 22060-5546 "ABSTRACT...geochemistry, hydrology and remote sensing but all of these are important to the understanding of these unique geomorphic features. There is a large body

Variance estimates and confidence intervals for the Kappa measure of classification accuracy

Treesearch

M. A. Kalkhan; R. M. Reich; R. L. Czaplewski

1997-01-01

The Kappa statistic is frequently used to characterize the results of an accuracy assessment used to evaluate land use and land cover classifications obtained by remotely sensed data. This statistic allows comparisons of alternative sampling designs, classification algorithms, photo-interpreters, and so forth. In order to make these comparisons, it is...

Collaborative classification of hyperspectral and visible images with convolutional neural network

NASA Astrophysics Data System (ADS)

Zhang, Mengmeng; Li, Wei; Du, Qian

2017-10-01

Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.

Integration of heterogeneous features for remote sensing scene classification

NASA Astrophysics Data System (ADS)

Wang, Xin; Xiong, Xingnan; Ning, Chen; Shi, Aiye; Lv, Guofang

2018-01-01

Scene classification is one of the most important issues in remote sensing (RS) image processing. We find that features from different channels (shape, spectral, texture, etc.), levels (low-level and middle-level), or perspectives (local and global) could provide various properties for RS images, and then propose a heterogeneous feature framework to extract and integrate heterogeneous features with different types for RS scene classification. The proposed method is composed of three modules (1) heterogeneous features extraction, where three heterogeneous feature types, called DS-SURF-LLC, mean-Std-LLC, and MS-CLBP, are calculated, (2) heterogeneous features fusion, where the multiple kernel learning (MKL) is utilized to integrate the heterogeneous features, and (3) an MKL support vector machine classifier for RS scene classification. The proposed method is extensively evaluated on three challenging benchmark datasets (a 6-class dataset, a 12-class dataset, and a 21-class dataset), and the experimental results show that the proposed method leads to good classification performance. It produces good informative features to describe the RS image scenes. Moreover, the integration of heterogeneous features outperforms some state-of-the-art features on RS scene classification tasks.

[An object-based information extraction technology for dominant tree species group types].

PubMed

Tian, Tian; Fan, Wen-yi; Lu, Wei; Xiao, Xiang

2015-06-01

Information extraction for dominant tree group types is difficult in remote sensing image classification, howevers, the object-oriented classification method using high spatial resolution remote sensing data is a new method to realize the accurate type information extraction. In this paper, taking the Jiangle Forest Farm in Fujian Province as the research area, based on the Quickbird image data in 2013, the object-oriented method was adopted to identify the farmland, shrub-herbaceous plant, young afforested land, Pinus massoniana, Cunninghamia lanceolata and broad-leave tree types. Three types of classification factors including spectral, texture, and different vegetation indices were used to establish a class hierarchy. According to the different levels, membership functions and the decision tree classification rules were adopted. The results showed that the method based on the object-oriented method by using texture, spectrum and the vegetation indices achieved the classification accuracy of 91.3%, which was increased by 5.7% compared with that by only using the texture and spectrum.

Comparison of support vector machine classification to partial least squares dimension reduction with logistic descrimination of hyperspectral data

NASA Astrophysics Data System (ADS)

Wilson, Machelle; Ustin, Susan L.; Rocke, David

2003-03-01

Remote sensing technologies with high spatial and spectral resolution show a great deal of promise in addressing critical environmental monitoring issues, but the ability to analyze and interpret the data lags behind the technology. Robust analytical methods are required before the wealth of data available through remote sensing can be applied to a wide range of environmental problems for which remote detection is the best method. In this study we compare the classification effectiveness of two relatively new techniques on data consisting of leaf-level reflectance from plants that have been exposed to varying levels of heavy metal toxicity. If these methodologies work well on leaf-level data, then there is some hope that they will also work well on data from airborne and space-borne platforms. The classification methods compared were support vector machine classification of exposed and non-exposed plants based on the reflectance data, and partial east squares compression of the reflectance data followed by classification using logistic discrimination (PLS/LD). PLS/LD was performed in two ways. We used the continuous concentration data as the response during compression, and then used the binary response required during logistic discrimination. We also used a binary response during compression followed by logistic discrimination. The statistics we used to compare the effectiveness of the methodologies was the leave-one-out cross validation estimate of the prediction error.

PI2GIS: processing image to geographical information systems, a learning tool for QGIS

NASA Astrophysics Data System (ADS)

Correia, R.; Teodoro, A.; Duarte, L.

2017-10-01

To perform an accurate interpretation of remote sensing images, it is necessary to extract information using different image processing techniques. Nowadays, it became usual to use image processing plugins to add new capabilities/functionalities integrated in Geographical Information System (GIS) software. The aim of this work was to develop an open source application to automatically process and classify remote sensing images from a set of satellite input data. The application was integrated in a GIS software (QGIS), automating several image processing steps. The use of QGIS for this purpose is justified since it is easy and quick to develop new plugins, using Python language. This plugin is inspired in the Semi-Automatic Classification Plugin (SCP) developed by Luca Congedo. SCP allows the supervised classification of remote sensing images, the calculation of vegetation indices such as NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) and other image processing operations. When analysing SCP, it was realized that a set of operations, that are very useful in teaching classes of remote sensing and image processing tasks, were lacking, such as the visualization of histograms, the application of filters, different image corrections, unsupervised classification and several environmental indices computation. The new set of operations included in the PI2GIS plugin can be divided into three groups: pre-processing, processing, and classification procedures. The application was tested consider an image from Landsat 8 OLI from a North area of Portugal.

Machine processing of remotely sensed data; Proceedings of the Fifth Annual Symposium, Purdue University, West Lafayette, Ind., June 27-29, 1979

NASA Technical Reports Server (NTRS)

Tendam, I. M. (Editor); Morrison, D. B.

1979-01-01

Papers are presented on techniques and applications for the machine processing of remotely sensed data. Specific topics include the Landsat-D mission and thematic mapper, data preprocessing to account for atmospheric and solar illumination effects, sampling in crop area estimation, the LACIE program, the assessment of revegetation on surface mine land using color infrared aerial photography, the identification of surface-disturbed features through a nonparametric analysis of Landsat MSS data, the extraction of soil data in vegetated areas, and the transfer of remote sensing computer technology to developing nations. Attention is also given to the classification of multispectral remote sensing data using context, the use of guided clustering techniques for Landsat data analysis in forest land cover mapping, crop classification using an interactive color display, and future trends in image processing software and hardware.

Implementation of Multispectral Image Classification on a Remote Adaptive Computer

NASA Technical Reports Server (NTRS)

Figueiredo, Marco A.; Gloster, Clay S.; Stephens, Mark; Graves, Corey A.; Nakkar, Mouna

1999-01-01

As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms its justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of m a,gnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application, that, can benefit from implementation on an FPGA - based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm implemented on a typical general - purpose computer).

The Irish Seabed Mapping Programme: INFOMAR - Integrated Mapping Survey for the Sustainable Developments of Ireland's Marine Resources. Progress to Date.

NASA Astrophysics Data System (ADS)

Sacchetti, F.; Benetti, S.; Fitzpatrick, F.

2006-12-01

During the last six years, the Geological Survey of Ireland and the Marine Institute of Ireland worked together on the multimillion Irish National Seabed Survey project with the purpose of mapping the Irish marine territory using a suite of remote sensing equipment, from multibeam to seismic, achieving 87% coverage of the marine zone. Ireland was the first country in the world to carry out an extensive mapping project of their extended Exclusive Economic Zone. The Irish National Seabed Survey is now succeeded by the multiyear INFOMAR Programme. INFOMAR will concentrate initially on mapping twenty-six selected priority bays, three sea areas and the fisheries-protection "Biologically Sensitive Area", and then will complete 100% mapping of the remainder of the EEZ. Designed to incorporate all elements of an integrated mapping programme, the key data acquisition will include hydrography, oceanographic, geological and heritage data. These data sets discharge Ireland's obligations under international treaties to which she is signatory and the uses of these data are vast and multipurpose: from management plans for inshore fishing, aquaculture, coastal protection and engineering works, to environmental impact assessments related to licensing activity and support to the evolving needs of integrated coastal zone management. INFOMAR also includes a data management, exchange and integration programme for the establishment of a National Marine Data Discovery and Exchange Service; providing improved dissemination of information to researchers, policy makers, the public and private sector and the adoption of standard operating procedures in data management to facilitate inter-agency data integration. During the first year of activity, INFOMAR carried out an integrated survey from the national research vessel, the RV Celtic Explorer, acquiring hydrographic, geophysical and groundtruthing data from Bantry and Dunmanus Bays, located off the South West coast of Ireland. Airborne LiDAR (Light Detection And Ranging) and small-vessel mapping surveys have also been carried out, giving detailed bathymetric, topographic and habitat information for the shoaler waters and inshore areas. This presentation will focus both on the general framework and scope of INFOMAR and the initial results and experiences of this year's survey.

Response of megabenthic assemblages to different scales of habitat heterogeneity on the Mauritanian slope

NASA Astrophysics Data System (ADS)

Jones, Daniel O. B.; Brewer, Michael E.

2012-09-01

The topographically complex deep seabed on the Mauritanian slope, from 990 to 1460 m water depth, was imaged with video in an extensive quantitative survey of 17,199 m2 of seafloor using a Remote Operated Vehicle (ROV). This study investigated the influence of habitat heterogeneity at two scales on the megafaunal assemblages observed by ROV. Changes in megafaunal assemblages on the Mauritanian slope were assessed at a broad scale, within depth zones, and at a finer scale, in response to changes in local geomorphology associated with submarine landslides. Geomorphology was determined by classification of habitat parameters (slope, aspect, bathymetric position, curvature, fractal dimension and ruggedness) derived from an autonomous underwater vehicle-based multibeam bathymetry survey. Habitat parameters were classified by Iterative Self Organizing Clustering into six major geomorphological groups, four of which were assessed in the ROV video survey. A total of 29 megafaunal taxa were observed along the entire survey, with an overall average faunal density of 0.344 ind m-2. Megafaunal assemblage density, species richness and evenness varied significantly across the depth range of the survey in the most common geomorphological zone (sedimentary plains of low slope and complexity). Characteristic species inhabited the shallow areas (asteroid, ophiuroid, anemone, small macrourid), intermediate areas (Benthothuria funabris, black cerianthid, squat lobster) and deeper areas (the holothurians Enypniastes eximia and Elipidia echinata). Megafaunal density, species richness and evenness were not significantly different between geomorphogical groups within one depth zone (1300-1400 m). However, the steepest zone, on the edge of a major headwall feature, had four unique taxa (Parapagurus pilosimanus, a comatulid crinoid, a gorgonian and its associated ophiuroid).

Remote sensing-based predictors improve distribution models of rare, early successional and boradleaf tree species in Utah

Treesearch

N. E. Zimmermann; T. C. Edwards; G. G. Moisen; T. S. Frescino; J. A. Blackard

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species...

Analysis of wind-driven ambient noise in a shallow water environment with a sandy seabed.

PubMed

Knobles, D P; Joshi, S M; Gaul, R D; Graber, H C; Williams, N J

2008-09-01

On the New Jersey continental shelf ambient sound levels were recorded during tropical storm Ernesto that produced wind speeds up to 40 knots in early September 2006. The seabed at the position of the acoustic measurements can be approximately described as coarse sand. Differences between the ambient noise levels for the New Jersey shelf measurements and deep water reference measurements are modeled using both normal mode and ray methods. The analysis is consistent with a nonlinear frequency dependent seabed attenuation for the New Jersey site.

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes.

PubMed

Yates, Katherine L; Mellin, Camille; Caley, M Julian; Radford, Ben T; Meeuwig, Jessica J

2016-01-01

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability.

Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes

PubMed Central

Yates, Katherine L.; Mellin, Camille; Caley, M. Julian; Radford, Ben T.; Meeuwig, Jessica J.

2016-01-01

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability. PMID:27333202

An assessment of the effectiveness of a random forest classifier for land-cover classification

NASA Astrophysics Data System (ADS)

Rodriguez-Galiano, V. F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J. P.

2012-01-01

Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level.

[Object-oriented remote sensing image classification in epidemiological studies of visceral leishmaniasis in urban areas].

PubMed

Almeida, Andréa Sobral de; Werneck, Guilherme Loureiro; Resendes, Ana Paula da Costa

2014-08-01

This study explored the use of object-oriented classification of remote sensing imagery in epidemiological studies of visceral leishmaniasis (VL) in urban areas. To obtain temperature and environmental information, an object-oriented classification approach was applied to Landsat 5 TM scenes from the city of Teresina, Piauí State, Brazil. For 1993-1996, VL incidence rates correlated positively with census tracts covered by dense vegetation, grass/pasture, and bare soil and negatively with areas covered by water and densely populated areas. In 2001-2006, positive correlations were found with dense vegetation, grass/pasture, bare soil, and densely populated areas and negative correlations with occupied urban areas with some vegetation. Land surface temperature correlated negatively with VL incidence in both periods. Object-oriented classification can be useful to characterize landscape features associated with VL in urban areas and to help identify risk areas in order to prioritize interventions.

Technology for Waste Treatment at Remote Army Sites

DTIC Science & Technology

1986-09-01

Management "AD-A.17 6 801 i echnology for Waste Treatment at Remote Army Sites by * Richard J. Scholze James E. Alleinan Steve R. Struss EdD. Smith This...62720 IA896 A 1039 IT TITLE (include Security Classification) Technology for Waste Treatment at Remote Army Sites (Unclassified) 12 PERSONAL...management human wastes 13 02 waste treatment remote sites I I wastes (sanitary engineering)~ 19 ABSTRACT (Continue on reverse if necessary and identify by

Crop Identification Technology Assessment for Remote Sensing (CITARS)

NASA Technical Reports Server (NTRS)

Bauer, M. E.; Cary, T. K.; Davis, B. J.; Swain, P. H.

1975-01-01

The results of classifications and experiments performed for the Crop Identification Technology Assessment for Remote Sensing (CITARS) project are summarized. Fifteen data sets were classified using two analysis procedures. One procedure used class weights while the other assumed equal probabilities of occurrence for all classes. In addition, 20 data sets were classified using training statistics from another segment or date. The results of both the local and non-local classifications in terms of classification and proportion estimation are presented. Several additional experiments are described which were performed to provide additional understanding of the CITARS results. These experiments investigated alternative analysis procedures, training set selection and size, effects of multitemporal registration, the spectral discriminability of corn, soybeans, and other, and analysis of aircraft multispectral data.

A Lesson in Complexity: Seabed Minerals and Easter Island.

ERIC Educational Resources Information Center

Druker, Kristen

1984-01-01

This high school-level classroom activity presents a hypothetical situation based on scientific fact concerning the likelihood that seabed mineral deposits lie off Easter Island. Activity goals, instructional strategies, and instructions for students are included. (JN)

Seafloor doming driven by degassing processes unveils sprouting volcanism in coastal areas.

PubMed

Passaro, Salvatore; Tamburrino, Stella; Vallefuoco, Mattia; Tassi, Franco; Vaselli, Orlando; Giannini, Luciano; Chiodini, Giovanni; Caliro, Stefano; Sacchi, Marco; Rizzo, Andrea Luca; Ventura, Guido

2016-03-01

We report evidences of active seabed doming and gas discharge few kilometers offshore from the Naples harbor (Italy). Pockmarks, mounds, and craters characterize the seabed. These morphologies represent the top of shallow crustal structures including pagodas, faults and folds affecting the present-day seabed. They record upraise, pressurization, and release of He and CO2 from mantle melts and decarbonation reactions of crustal rocks. These gases are likely similar to those that feed the hydrothermal systems of the Ischia, Campi Flegrei and Somma-Vesuvius active volcanoes, suggesting the occurrence of a mantle source variously mixed to crustal fluids beneath the Gulf of Naples. The seafloor swelling and breaching by gas upraising and pressurization processes require overpressures in the order of 2-3 MPa. Seabed doming, faulting, and gas discharge are manifestations of non-volcanic unrests potentially preluding submarine eruptions and/or hydrothermal explosions.

Seafloor doming driven by degassing processes unveils sprouting volcanism in coastal areas

PubMed Central

Passaro, Salvatore; Tamburrino, Stella; Vallefuoco, Mattia; Tassi, Franco; Vaselli, Orlando; Giannini, Luciano; Chiodini, Giovanni; Caliro, Stefano; Sacchi, Marco; Rizzo, Andrea Luca; Ventura, Guido

2016-01-01

We report evidences of active seabed doming and gas discharge few kilometers offshore from the Naples harbor (Italy). Pockmarks, mounds, and craters characterize the seabed. These morphologies represent the top of shallow crustal structures including pagodas, faults and folds affecting the present-day seabed. They record upraise, pressurization, and release of He and CO2 from mantle melts and decarbonation reactions of crustal rocks. These gases are likely similar to those that feed the hydrothermal systems of the Ischia, Campi Flegrei and Somma-Vesuvius active volcanoes, suggesting the occurrence of a mantle source variously mixed to crustal fluids beneath the Gulf of Naples. The seafloor swelling and breaching by gas upraising and pressurization processes require overpressures in the order of 2–3 MPa. Seabed doming, faulting, and gas discharge are manifestations of non-volcanic unrests potentially preluding submarine eruptions and/or hydrothermal explosions. PMID:26925957

Morphostructural Analysis and Seabed Shelf Typing

NASA Astrophysics Data System (ADS)

Nikiforov, S. L.; Sorokhtin, N. O.; Koshel', S. M.; Lobkovsky, L. I.

2018-03-01

Analysis of the morphometric characteristics from a study of the Barents Sea seabed has shown that the existing troughs are consistent with geodynamic conclusions, allowing morphological typing into structural slopes and reconstruction of their origin. Thus, the Norwegian-Mezenskaya rift system and Svyataya Anna and Victoria troughs were formed due to stretching of the lithosphere. The South Barents and Medvezinsko- Edzinskaya depressions formed at the generation stage of lithospheric plates due to the collision of several island arcs between outliers of the ancient oceanic crust. The choice of the geomorphic method for studying the seabed is because the science of geomorphology comprehensively studies bottom relief (morphology), its origin, and age. Adequate reconstruction of the causal relationships of exogenous and endogenous processes aids in substantiating the prediction of probable catastrophic seabed events. The results of mathematical calculations have confirmed the geodynamic conclusions within the Barents Sea region.

Basic forest cover mapping using digitized remote sensor data and automated data processing techniques

NASA Technical Reports Server (NTRS)

Coggeshall, M. E.; Hoffer, R. M.

1973-01-01

Remote sensing equipment and automatic data processing techniques were employed as aids in the institution of improved forest resource management methods. On the basis of automatically calculated statistics derived from manually selected training samples, the feature selection processor of LARSYS selected, upon consideration of various groups of the four available spectral regions, a series of channel combinations whose automatic classification performances (for six cover types, including both deciduous and coniferous forest) were tested, analyzed, and further compared with automatic classification results obtained from digitized color infrared photography.

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

NASA Astrophysics Data System (ADS)

Guo, Yiqing; Jia, Xiuping; Paull, David

2018-06-01

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

usSEABED: Pacific coast (California, Oregon, Washington) offshore surficial-sediment data release

USGS Publications Warehouse

Reid, Jane A.; Reid, Jamey M.; Jenkins, Chris J.; Zimmermann, Mark; Williams, S. Jeffress; Field, Michael E.

2006-01-01

Over the past 50 years there has been an explosion in scientific interest, research effort, and information gathered on the geologic sedimentary character of the continental margin of the United States. Data and information from thousands of publications have greatly increased our scientific understanding of the geologic origins of the margin surface but rarely have those data been combined and integrated. This publication is the first release of the Pacific coast data from the usSEABED database. The report contains a compilation of published and unpublished sediment texture and other geologic data about the sea floor from diverse sources. usSEABED is an innovative database system developed to unify assorted data, with the data processed by the dbSEABED system. Examples of maps displaying attributes such as grain size and sediment color are included. This database contains information that is a scientific foundation for the U.S. Geological Survey (USGS) Sea floor Mapping and Benthic Habitats project and the Marine Aggregate Resources and Processes assessment project, and will be useful to the marine science community for other studies of the Pacific coast continental margin. The publication is divided into 10 sections: Home, Introduction, Content, usSEABED (data), dbSEABED (processing), Data Catalog, References, Contacts, Acknowledgments, and Frequently Asked Questions. Use the navigation bar on the left to navigate to specific sections of this report. Underlined topics throughout the publication are links to more information. Links to specific and detailed information on processing and to those to pages outside this report will open in a new browser window.

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

76 FR 80884 - The Department of Commerce will submit to the Office of Management and Budget (OMB) for clearance...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-27

...: National Oceanic and Atmospheric Administration (NOAA). Title: Deep Seabed Mining Regulations for... the issuing and monitoring of exploration licenses under the Deep Seabed Hard Mineral Resources Act...

Applications of remote sensing in resource management in Nebraska

NASA Technical Reports Server (NTRS)

Drew, J. V.

1974-01-01

The project is reported for studying the application of remote sensing in land use classification and delineation of major tectonic lineaments in Nebraska. Other research reported include the use of aircraft and ERTS-1 satellite imagery in detecting and estimating the acreage of irrigated land, and the application of remote sensing in estimating evapotranspiration in the Platte River Basin.

Post-classification approaches to estimating change in forest area using remotely sense auxiliary data.

Treesearch

Ronald E. McRoberts

2014-01-01

Multiple remote sensing-based approaches to estimating gross afforestation, gross deforestation, and net deforestation are possible. However, many of these approaches have severe data requirements in the form of long time series of remotely sensed data and/or large numbers of observations of land cover change to train classifiers and assess the accuracy of...

Development of new exploration tools for seabed mineral resources - Result of R/V YOKOSUKA research cruise YK09-09 -

NASA Astrophysics Data System (ADS)

Harada, M.; Sayanagi, K.; Kasaya, T.; Sawa, T.; Goto, T.; Tada, N.; Ichihara, H.; Asada, M.; Nakajima, T.; Isezaki, N.

2009-12-01

Detailed information on subsurface structure under seafloor is necessary for the estimation of seabed resources such as the hydrothermal deposit and methane hydrate. Although advantages of geophysical exploration near seafloor are expected for the seabed resource survey, efficient method has not been well-established. The authors started a project to develop exploration tools for seabed resources under the financial support of MEXT-Japan. We carry out research and development mainly regarding measurement of the magnetic field with high-resolution and high-sampling rate electric exploration devices with accurately controlled active source signals. Developed tools will be mounted underwater platforms such as deep-tow system, ROV (remotely operated vehicle), and AUV (autonomous undersea vehicle). We carried out the research cruise (vessel: JAMSTEC R/V YOKOSUKA YK09-09, cruise period: 19-29 July 2009, area surveyed: Kumano-nada, off Kii Peninsula, Japan) to investigate the performance of developed equipments for magnetic exploration. We mounted an Overhauser and two flux-gate magnetometers on the deep-tow and the AUV URASHIMA. To inspect the efficiency of equipments, it is better to measure the magnetic anomaly which is caused by known magnetic source. Therefore, we made a magnetic target which is consisted of 50 neodymium magnets. Before the navigation, the magnetic target was put under water and its position was measured by the acoustic method. The depth of target is about 2,050 meters, and the measurement was performed in the circle of a radius of about 300 meters. The vehicles were navigated at heights of 25 meters for AUV, and about 15 meters for deep-tow. Each of underwater navigation was practiced for two times. Both performances were carried out successfully, which means that we detected the significant magnetic anomalies caused by the target. We will be able to estimate three-dimensional distribution of anomalous magnetic field, and the source property of magnetic target. However, we have to resolve a lot of problems; (1) elimination of noises caused by the vehicles themselves, and their attitude, and (2) precise estimation of the position of vehicles. We will introduce the results of the research cruise and data processing in the presentation. Acknowledgement: We are grateful to captain Mr. E. Ukekura, chief officer Mr. S. Kusaka, chief AUV/DT operator Mr. T. Sakurai, and operation team, who made our difficult trials in the navigation possible by their professional skill. We also thank to the YOKOSUKA marine crew for overall support, and the engineers who take part in the development of equipments. This study is financially supported by the Ministry of Education, Culture, Sports, Science and Technology, Japan.

Pollen Bearing Honey Bee Detection in Hive Entrance Video Recorded by Remote Embedded System for Pollination Monitoring

NASA Astrophysics Data System (ADS)

Babic, Z.; Pilipovic, R.; Risojevic, V.; Mirjanic, G.

2016-06-01

Honey bees have crucial role in pollination across the world. This paper presents a simple, non-invasive, system for pollen bearing honey bee detection in surveillance video obtained at the entrance of a hive. The proposed system can be used as a part of a more complex system for tracking and counting of honey bees with remote pollination monitoring as a final goal. The proposed method is executed in real time on embedded systems co-located with a hive. Background subtraction, color segmentation and morphology methods are used for segmentation of honey bees. Classification in two classes, pollen bearing honey bees and honey bees that do not have pollen load, is performed using nearest mean classifier, with a simple descriptor consisting of color variance and eccentricity features. On in-house data set we achieved correct classification rate of 88.7% with 50 training images per class. We show that the obtained classification results are not far behind from the results of state-of-the-art image classification methods. That favors the proposed method, particularly having in mind that real time video transmission to remote high performance computing workstation is still an issue, and transfer of obtained parameters of pollination process is much easier.

A review and analysis of neural networks for classification of remotely sensed multispectral imagery

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1993-01-01

A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.

15 CFR 970.100 - Purpose.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970.100 Purpose. (a) General... recognition that the deep seabed mining industry is still evolving and that more information must be developed...

15 CFR 970.100 - Purpose.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970.100 Purpose. (a) General... recognition that the deep seabed mining industry is still evolving and that more information must be developed...

15 CFR 970.100 - Purpose.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970.100 Purpose. (a) General... recognition that the deep seabed mining industry is still evolving and that more information must be developed...

15 CFR 970.100 - Purpose.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970.100 Purpose. (a) General... recognition that the deep seabed mining industry is still evolving and that more information must be developed...

15 CFR 970.100 - Purpose.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970.100 Purpose. (a) General... recognition that the deep seabed mining industry is still evolving and that more information must be developed...

Quality Evaluation of Land-Cover Classification Using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Dang, Y.; Zhang, J.; Zhao, Y.; Luo, F.; Ma, W.; Yu, F.

2018-04-01

Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can't be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image's membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation of DLG-DOM coupling land-cover classification or other kinds of labelled remote sensing data can be further studied.

Exploring the impact of wavelet-based denoising in the classification of remote sensing hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco

2016-10-01

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

Information extraction with object based support vector machines and vegetation indices

NASA Astrophysics Data System (ADS)

Ustuner, Mustafa; Abdikan, Saygin; Balik Sanli, Fusun

2016-07-01

Information extraction through remote sensing data is important for policy and decision makers as extracted information provide base layers for many application of real world. Classification of remotely sensed data is the one of the most common methods of extracting information however it is still a challenging issue because several factors are affecting the accuracy of the classification. Resolution of the imagery, number and homogeneity of land cover classes, purity of training data and characteristic of adopted classifiers are just some of these challenging factors. Object based image classification has some superiority than pixel based classification for high resolution images since it uses geometry and structure information besides spectral information. Vegetation indices are also commonly used for the classification process since it provides additional spectral information for vegetation, forestry and agricultural areas. In this study, the impacts of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE) on the classification accuracy of RapidEye imagery were investigated. Object based Support Vector Machines were implemented for the classification of crop types for the study area located in Aegean region of Turkey. Results demonstrated that the incorporation of NDRE increase the classification accuracy from 79,96% to 86,80% as overall accuracy, however NDVI decrease the classification accuracy from 79,96% to 78,90%. Moreover it is proven than object based classification with RapidEye data give promising results for crop type mapping and analysis.

Hyperspectral Image Classification for Land Cover Based on an Improved Interval Type-II Fuzzy C-Means Approach

PubMed Central

Li, Zhao-Liang

2018-01-01

Few studies have examined hyperspectral remote-sensing image classification with type-II fuzzy sets. This paper addresses image classification based on a hyperspectral remote-sensing technique using an improved interval type-II fuzzy c-means (IT2FCM*) approach. In this study, in contrast to other traditional fuzzy c-means-based approaches, the IT2FCM* algorithm considers the ranking of interval numbers and the spectral uncertainty. The classification results based on a hyperspectral dataset using the FCM, IT2FCM, and the proposed improved IT2FCM* algorithms show that the IT2FCM* method plays the best performance according to the clustering accuracy. In this paper, in order to validate and demonstrate the separability of the IT2FCM*, four type-I fuzzy validity indexes are employed, and a comparative analysis of these fuzzy validity indexes also applied in FCM and IT2FCM methods are made. These four indexes are also applied into different spatial and spectral resolution datasets to analyze the effects of spectral and spatial scaling factors on the separability of FCM, IT2FCM, and IT2FCM* methods. The results of these validity indexes from the hyperspectral datasets show that the improved IT2FCM* algorithm have the best values among these three algorithms in general. The results demonstrate that the IT2FCM* exhibits good performance in hyperspectral remote-sensing image classification because of its ability to handle hyperspectral uncertainty. PMID:29373548

Mapping Sub-Antarctic Cushion Plants Using Random Forests to Combine Very High Resolution Satellite Imagery and Terrain Modelling

PubMed Central

Bricher, Phillippa K.; Lucieer, Arko; Shaw, Justine; Terauds, Aleks; Bergstrom, Dana M.

2013-01-01

Monitoring changes in the distribution and density of plant species often requires accurate and high-resolution baseline maps of those species. Detecting such change at the landscape scale is often problematic, particularly in remote areas. We examine a new technique to improve accuracy and objectivity in mapping vegetation, combining species distribution modelling and satellite image classification on a remote sub-Antarctic island. In this study, we combine spectral data from very high resolution WorldView-2 satellite imagery and terrain variables from a high resolution digital elevation model to improve mapping accuracy, in both pixel- and object-based classifications. Random forest classification was used to explore the effectiveness of these approaches on mapping the distribution of the critically endangered cushion plant Azorella macquariensis Orchard (Apiaceae) on sub-Antarctic Macquarie Island. Both pixel- and object-based classifications of the distribution of Azorella achieved very high overall validation accuracies (91.6–96.3%, κ = 0.849–0.924). Both two-class and three-class classifications were able to accurately and consistently identify the areas where Azorella was absent, indicating that these maps provide a suitable baseline for monitoring expected change in the distribution of the cushion plants. Detecting such change is critical given the threats this species is currently facing under altering environmental conditions. The method presented here has applications to monitoring a range of species, particularly in remote and isolated environments. PMID:23940805

Some new classification methods for hyperspectral remote sensing

NASA Astrophysics Data System (ADS)

Du, Pei-jun; Chen, Yun-hao; Jones, Simon; Ferwerda, Jelle G.; Chen, Zhi-jun; Zhang, Hua-peng; Tan, Kun; Yin, Zuo-xia

2006-10-01

Hyperspectral Remote Sensing (HRS) is one of the most significant recent achievements of Earth Observation Technology. Classification is the most commonly employed processing methodology. In this paper three new hyperspectral RS image classification methods are analyzed. These methods are: Object-oriented FIRS image classification, HRS image classification based on information fusion and HSRS image classification by Back Propagation Neural Network (BPNN). OMIS FIRS image is used as the example data. Object-oriented techniques have gained popularity for RS image classification in recent years. In such method, image segmentation is used to extract the regions from the pixel information based on homogeneity criteria at first, and spectral parameters like mean vector, texture, NDVI and spatial/shape parameters like aspect ratio, convexity, solidity, roundness and orientation for each region are calculated, finally classification of the image using the region feature vectors and also using suitable classifiers such as artificial neural network (ANN). It proves that object-oriented methods can improve classification accuracy since they utilize information and features both from the point and the neighborhood, and the processing unit is a polygon (in which all pixels are homogeneous and belong to the class). HRS image classification based on information fusion, divides all bands of the image into different groups initially, and extracts features from every group according to the properties of each group. Three levels of information fusion: data level fusion, feature level fusion and decision level fusion are used to HRS image classification. Artificial Neural Network (ANN) can perform well in RS image classification. In order to promote the advances of ANN used for HIRS image classification, Back Propagation Neural Network (BPNN), the most commonly used neural network, is used to HRS image classification.

Upper Kalamazoo watershed land cover inventory. [based on remote sensing

NASA Technical Reports Server (NTRS)

Richason, B., III; Enslin, W.

1973-01-01

Approximately 1000 square miles of the eastern portion of the watershed were inventoried based on remote sensing imagery. The classification scheme, imagery and interpretation procedures, and a cost analysis are discussed. The distributions of land cover within the area are tabulated.

Monitoring Rangeland Health by Remote Sensing

USDA-ARS?s Scientific Manuscript database

Based on a land-cover classification from NASA’s MODerate resolution Imaging Spectroradiometer (MODIS), rangelands cover 48% of the Earth’s land surface, not including Antarctica. Nearly all analyses imply the most economical means of monitoring large areas of rangelands worldwide is with remote s...

A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon.

PubMed

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E; Moran, Emilio

2008-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.

Optimal land use/cover classification using remote sensing imagery for hydrological modelling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2007-10-01

Land use/cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/cover. This paper presents different approaches to attain an optimal land use/cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/cover map was not sufficient for the delineation of HRUs, since the agricultural land use/cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Therefore we adopted a visual classification approach using optical data alone and also fused with ENVISAT ASAR data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modelling.

A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon

PubMed Central

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E.; Moran, Emilio

2009-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin. PMID:19789716

Identification of pests and diseases of Dalbergia hainanensis based on EVI time series and classification of decision tree

NASA Astrophysics Data System (ADS)

Luo, Qiu; Xin, Wu; Qiming, Xiong

2017-06-01

In the process of vegetation remote sensing information extraction, the problem of phenological features and low performance of remote sensing analysis algorithm is not considered. To solve this problem, the method of remote sensing vegetation information based on EVI time-series and the classification of decision-tree of multi-source branch similarity is promoted. Firstly, to improve the time-series stability of recognition accuracy, the seasonal feature of vegetation is extracted based on the fitting span range of time-series. Secondly, the decision-tree similarity is distinguished by adaptive selection path or probability parameter of component prediction. As an index, it is to evaluate the degree of task association, decide whether to perform migration of multi-source decision tree, and ensure the speed of migration. Finally, the accuracy of classification and recognition of pests and diseases can reach 87%--98% of commercial forest in Dalbergia hainanensis, which is significantly better than that of MODIS coverage accuracy of 80%--96% in this area. Therefore, the validity of the proposed method can be verified.

A framework for farmland parcels extraction based on image classification

NASA Astrophysics Data System (ADS)

Liu, Guoying; Ge, Wenying; Song, Xu; Zhao, Hongdan

2018-03-01

It is very important for the government to build an accurate national basic cultivated land database. In this work, farmland parcels extraction is one of the basic steps. However, during the past years, people had to spend much time on determining an area is a farmland parcel or not, since they were bounded to understand remote sensing images only from the mere visual interpretation. In order to overcome this problem, in this study, a method was proposed to extract farmland parcels by means of image classification. In the proposed method, farmland areas and ridge areas of the classification map are semantically processed independently and the results are fused together to form the final results of farmland parcels. Experiments on high spatial remote sensing images have shown the effectiveness of the proposed method.

76 FR 34070 - Secretary of Energy Advisory Board, Natural Gas Subcommittee

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-10

... DEPARTMENT OF ENERGY Secretary of Energy Advisory Board, Natural Gas Subcommittee AGENCY... the Secretary of Energy Advisory Board (SEAB), Natural Gas Subcommittee. SEAB was reestablished... directed by the Secretary. The Natural Gas Subcommittee was established to provide advice and...

15 CFR 971.602 - Significant adverse environmental effects.

Code of Federal Regulations, 2010 CFR

2010-01-01

... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS... testing of recovery equipment, the recovery of manganese nodules in commercial quantities from the deep seabed, and the construction and operation of commercial-scale processing facilities as activities which...

Conceptual design study: Cold water pipe systems for self-mounted OTEC powerplants

NASA Astrophysics Data System (ADS)

1981-02-01

The conceptual design and installation aspects of cold water pipes (CWP) systems for shelf mounted OTEC power plants in Puerto Rico and Hawaii are considered. The CWP systems using Fiberglass Reinforced Plastic (FRP) and steel were designed; the FRP, can be controlled by varying the core thickness; and steel is used as a structural material in offshore applications. A marine railway approach was chosen for installation of the CWP. Two methods for pulling the track for the railway down the pipe fairway to its final location are presented. The track is permanently fastened to the sloping seabed with piles installed by a remotely controlled cart that rides on the track itself. Both the marine railway and the shelf mounted platform that houses the OTEC power plant require an anodic or equivalent corrosion protection system.

Rip current evidence by hydrodynamic simulations, bathymetric surveys and UAV observation

NASA Astrophysics Data System (ADS)

Benassai, Guido; Aucelli, Pietro; Budillon, Giorgio; De Stefano, Massimo; Di Luccio, Diana; Di Paola, Gianluigi; Montella, Raffaele; Mucerino, Luigi; Sica, Mario; Pennetta, Micla

2017-09-01

The prediction of the formation, spacing and location of rip currents is a scientific challenge that can be achieved by means of different complementary methods. In this paper the analysis of numerical and experimental data, including RPAS (remotely piloted aircraft systems) observations, allowed us to detect the presence of rip currents and rip channels at the mouth of Sele River, in the Gulf of Salerno, southern Italy. The dataset used to analyze these phenomena consisted of two different bathymetric surveys, a detailed sediment analysis and a set of high-resolution wave numerical simulations, completed with Google EarthTM images and RPAS observations. The grain size trend analysis and the numerical simulations allowed us to identify the rip current occurrence, forced by topographically constrained channels incised on the seabed, which were compared with observations.

Proceedings of the Eleventh International Symposium on Remote Sensing of Environment, volume 2. [application and processing of remotely sensed data

NASA Technical Reports Server (NTRS)

1977-01-01

Application and processing of remotely sensed data are discussed. Areas of application include: pollution monitoring, water quality, land use, marine resources, ocean surface properties, and agriculture. Image processing and scene analysis are described along with automated photointerpretation and classification techniques. Data from infrared and multispectral band scanners onboard LANDSAT satellites are emphasized.

usSEABED: Gulf of Mexico and Caribbean (Puerto Rico and U.S. Virgin Islands) offshore surficial sediment data release

USGS Publications Warehouse

Buczkowski, Brian J.; Reid, Jane A.; Jenkins, Chris J.; Reid, Jamey M.; Williams, S. Jeffress; Flocks, James G.

2006-01-01

Over the past 50 years there has been an explosion in scientific interest, research effort and information gathered on the geologic sedimentary character of the United States continental margin. Data and information from thousands of publications have greatly increased our scientific understanding of the geologic origins of the shelf surface but rarely have those data been combined and integrated. This publication is the first release of the Gulf of Mexico and Caribbean (Puerto Rico and U.S. Virgin Islands) coastal and offshore data from the usSEABED database. The report contains a compilation of published and previously unpublished sediment texture and other geologic data about the sea floor from diverse sources. usSEABED is an innovative database system developed to bring assorted data together in a unified database. The dbSEABED system is used to process the data. Examples of maps displaying attributes such as grain size and sediment color are included. This database contains information that is a scientific foundation for the USGS Marine Aggregate Resources and Processes Assessment and Benthic Habitats projects, and will be useful to the marine science community for other studies of the Gulf of Mexico and Caribbean continental margins. This publication is divided into ten sections: Home, Introduction, Content, usSEABED (data), dbSEABED (processing), Data Catalog, References, Contacts, Acknowledgments and Frequently Asked Questions. Use the navigation bar on the left to navigate to specific sections of this report. Underlined topics throughout the publication are links to more information. Links to specific and detailed information on processing and those to pages outside this report will open in a new browser window.

Incorporating ecosystem services into environmental management of deep-seabed mining

NASA Astrophysics Data System (ADS)

Le, Jennifer T.; Levin, Lisa A.; Carson, Richard T.

2017-03-01

Accelerated exploration of minerals in the deep sea over the past decade has raised the likelihood that commercial mining of the deep seabed will commence in the near future. Environmental concerns create a growing urgency for development of environmental regulations under commercial exploitation. Here, we consider an ecosystem services approach to the environmental policy and management of deep-sea mineral resources. Ecosystem services link the environment and human well-being, and can help improve sustainability and stewardship of the deep sea by providing a quantitative basis for decision-making. This paper briefly reviews ecosystem services provided by habitats targeted for deep-seabed mining (hydrothermal vents, seamounts, nodule provinces, and phosphate-rich margins), and presents practical steps to incorporate ecosystem services into deep-seabed mining regulation. The linkages and translation between ecosystem structure, ecological function (including supporting services), and ecosystem services are highlighted as generating human benefits. We consider criteria for identifying which ecosystem services are vulnerable to potential mining impacts, the role of ecological functions in providing ecosystem services, development of ecosystem service indicators, valuation of ecosystem services, and implementation of ecosystem services concepts. The first three steps put ecosystem services into a deep-seabed mining context; the last two steps help to incorporate ecosystem services into a management and decision-making framework. Phases of environmental planning discussed in the context of ecosystem services include conducting strategic environmental assessments, collecting baseline data, monitoring, establishing marine protected areas, assessing cumulative impacts, identifying thresholds and triggers, and creating an environmental damage compensation regime. We also identify knowledge gaps that need to be addressed in order to operationalize ecosystem services concepts in deep-seabed mining regulation and propose potential tools to fill them.

The legal regime for moon resource utilization, with particular emphasis on environmental protection, and comparable solutions adopted for deep seabed activities

NASA Astrophysics Data System (ADS)

Viikari, L.

This paper will examine the resource utilization regime as established by the body of international space law and by the 1979 Moon Treaty in particular, as well as the current problems pertaining to it. A particular area of interest is environmental protection vis-à-vis resource utilization. A potential source of fruitful analogy is provided by the deep seabed mineral utilization regime, as established by the 1982 United Nations Convention on the Law of the Sea, the 1994 New York Agreement amending it, and the recent 2000 Mining Code as the first part of more detailed regulations that will eventually govern exploration for and exploitation of all deep seabed minerals. Such comparison seems advantageous, because several developments in the field of using the space environment are showing obvious similarities with previous developments in the law of the sea regarding deep seabed resource management. The Moon and the deep seabed (and their natural resources) are also the only environs explicitly proclaimed as the common heritage of mankind. On the other hand, both domains are increasingly affected by commercializat ion and privatization, too. A recent new (legally non-binding) instrument for space activities is the 1996 Declaration on International Cooperation in the Exploration and Use of Outer Space for the Benefit and in the Interests of All States, Taking into Particular Account the Needs of Developing Countries. It attempts at an important compromise regarding the Common Heritage provision, offering a means to share benefits while recognizing market principles. These principles very much resemble the previous solutions adopted by the 1994 New York Agreement for the deep seabed. The paper attempts to reflect in particular upon the experience available from such developments.

IMPACTS OF PATCH SIZE AND LANDSCAPE HETEROGENEITY ON THEMATIC IMAGE CLASSIFICATION ACCURACY

EPA Science Inventory

Impacts of Patch Size and Landscape Heterogeneity on Thematic Image Classification Accuracy.
Currently, most thematic accuracy assessments of classified remotely sensed images oily account for errors between the various classes employed, at particular pixels of interest, thu...

Classification of forest land attributes using multi-source remotely sensed data

NASA Astrophysics Data System (ADS)

Pippuri, Inka; Suvanto, Aki; Maltamo, Matti; Korhonen, Kari T.; Pitkänen, Juho; Packalen, Petteri

2016-02-01

The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008-2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.

Multiple Scale Remote Sensing for Monitoring Rangelands

USDA-ARS?s Scientific Manuscript database

Based on a land-cover classification from NASA’s MODerate resolution Imaging Spectroradiometer (MODIS), rangelands cover 48% of the Earth’s land surface, not including Antarctica. Nearly all analyses imply the most economical means of monitoring large areas of rangelands worldwide is with remote se...

Remote sensing of wildland resources: A state-of-the-art review

Treesearch

Robert C. Aldrich

1979-01-01

A review, with literature citations, of current remote sensing technology, applications, and costs for wildland resource management, including collection, interpretation, and processing of data gathered through photographic and nonphotographic techniques for classification and mapping, interpretive information for specific applications, measurement of resource...

Semantic Segmentation of Convolutional Neural Network for Supervised Classification of Multispectral Remote Sensing

NASA Astrophysics Data System (ADS)

Xue, L.; Liu, C.; Wu, Y.; Li, H.

2018-04-01

Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the classification of roads, vegetation, buildings and water from remote Sensing Imagery is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for ground object segmentation and the results could be further improved. This paper used convolution neural network named U-Net, its structure has a contracting path and an expansive path to get high resolution output. In the network , We added BN layers, which is more conducive to the reverse pass. Moreover, after upsampling convolution , we add dropout layers to prevent overfitting. They are promoted to get more precise segmentation results. To verify this network architecture, we used a Kaggle dataset. Experimental results show that U-Net achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.

Ocean Space and Seabed Mining

ERIC Educational Resources Information Center

Earney, Fillmore C. F.

1975-01-01

The purpose of this paper is to briefly explore some of the questions with which this and the next generation will be faced concerning the wise and equitable use of ocean space and seabed minerals and to look at contemporary efforts to resolve some of these questions. (Author)

15 CFR 970.701 - Significant adverse environmental effects.

Code of Federal Regulations, 2014 CFR

2014-01-01

... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES... effects of deep seabed mining which cumulatively during commercial recovery have the potential for significant effect. These three effects also occur during mining system tests that may be conducted under a...

15 CFR 970.701 - Significant adverse environmental effects.

Code of Federal Regulations, 2013 CFR

2013-01-01

... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES... effects of deep seabed mining which cumulatively during commercial recovery have the potential for significant effect. These three effects also occur during mining system tests that may be conducted under a...

15 CFR 970.701 - Significant adverse environmental effects.

Code of Federal Regulations, 2012 CFR

2012-01-01

... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES... effects of deep seabed mining which cumulatively during commercial recovery have the potential for significant effect. These three effects also occur during mining system tests that may be conducted under a...

76 FR 34071 - Secretary of Energy Advisory Board, Natural Gas Subcommittee; Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-10

... DEPARTMENT OF ENERGY Secretary of Energy Advisory Board, Natural Gas Subcommittee; Meeting AGENCY... the Secretary of Energy Advisory Board (SEAB), Natural Gas Subcommittee. SEAB was reestablished... directed by the Secretary. The Natural Gas Subcommittee was established to provide advice and...

76 FR 34070 - Secretary of Energy Advisory Board Natural Gas Subcommittee

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-10

... DEPARTMENT OF ENERGY Secretary of Energy Advisory Board Natural Gas Subcommittee AGENCY... the Secretary of Energy Advisory Board (SEAB) Natural Gas Subcommittee. SEAB was reestablished... Natural Gas Subcommittee was established to provide advice and recommendations to the Full Board on how to...

15 CFR 970.300 - Purposes and definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Procedures for Applications... procedures which the Administrator will apply to applications filed with NOAA covering areas of the deep... the Administrator and a reciprocating state; and (ii) In which the deep seabed areas applied for...

15 CFR 970.300 - Purposes and definitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Procedures for Applications... procedures which the Administrator will apply to applications filed with NOAA covering areas of the deep... the Administrator and a reciprocating state; and (ii) In which the deep seabed areas applied for...

15 CFR 970.300 - Purposes and definitions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Procedures for Applications... procedures which the Administrator will apply to applications filed with NOAA covering areas of the deep... the Administrator and a reciprocating state; and (ii) In which the deep seabed areas applied for...

15 CFR 970.701 - Significant adverse environmental effects.

Code of Federal Regulations, 2010 CFR

2010-01-01

... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES... effects of deep seabed mining which cumulatively during commercial recovery have the potential for significant effect. These three effects also occur during mining system tests that may be conducted under a...

15 CFR 970.701 - Significant adverse environmental effects.

Code of Federal Regulations, 2011 CFR

2011-01-01

... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES... effects of deep seabed mining which cumulatively during commercial recovery have the potential for significant effect. These three effects also occur during mining system tests that may be conducted under a...

15 CFR 970.300 - Purposes and definitions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... the Administrator and a reciprocating state; and (ii) In which the deep seabed areas applied for... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Procedures for Applications... procedures which the Administrator will apply to applications filed with NOAA covering areas of the deep...

Can the benefits of physical seabed restoration justify the costs? An assessment of a disused aggregate extraction site off the Thames Estuary, UK.

PubMed

Cooper, Keith; Burdon, Daryl; Atkins, Jonathan P; Weiss, Laura; Somerfield, Paul; Elliott, Michael; Turner, Kerry; Ware, Suzanne; Vivian, Chris

2013-10-15

Physical and biological seabed impacts can persist long after the cessation of marine aggregate dredging. Whilst small-scale experimental studies have shown that it may be possible to mitigate such impacts, it is unclear whether the costs of restoration are justified on an industrial scale. Here we explore this question using a case study off the Thames Estuary, UK. By understanding the nature and scale of persistent impacts, we identify possible techniques to restore the physical properties of the seabed, and the costs and the likelihood of success. An analysis of the ecosystem services and goods/benefits produced by the site is used to determine whether intervention is justified. Whilst a comparison of costs and benefits at this site suggests restoration would not be warranted, the analysis is site-specific. We emphasise the need to better define what is, and is not, an acceptable seabed condition post-dredging. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.

Ground Truth Sampling and LANDSAT Accuracy Assessment

NASA Technical Reports Server (NTRS)

Robinson, J. W.; Gunther, F. J.; Campbell, W. J.

1982-01-01

It is noted that the key factor in any accuracy assessment of remote sensing data is the method used for determining the ground truth, independent of the remote sensing data itself. The sampling and accuracy procedures developed for nuclear power plant siting study are described. The purpose of the sampling procedure was to provide data for developing supervised classifications for two study sites and for assessing the accuracy of that and the other procedures used. The purpose of the accuracy assessment was to allow the comparison of the cost and accuracy of various classification procedures as applied to various data types.

A simulation of remote sensor systems and data processing algorithms for spectral feature classification

NASA Technical Reports Server (NTRS)

Arduini, R. F.; Aherron, R. M.; Samms, R. W.

1984-01-01

A computational model of the deterministic and stochastic processes involved in multispectral remote sensing was designed to evaluate the performance of sensor systems and data processing algorithms for spectral feature classification. Accuracy in distinguishing between categories of surfaces or between specific types is developed as a means to compare sensor systems and data processing algorithms. The model allows studies to be made of the effects of variability of the atmosphere and of surface reflectance, as well as the effects of channel selection and sensor noise. Examples of these effects are shown.

Innovative R.E.A. tools for integrated bathymetric survey

NASA Astrophysics Data System (ADS)

Demarte, Maurizio; Ivaldi, Roberta; Sinapi, Luigi; Bruzzone, Gabriele; Caccia, Massimo; Odetti, Angelo; Fontanelli, Giacomo; Masini, Andrea; Simeone, Emilio

2017-04-01

The REA (Rapid Environmental Assessment) concept is a methodology finalized to acquire environmental information, process them and return in standard paper-chart or standard digital format. Acquired data become thus available for the ingestion or the valorization of the Civilian Protection Emergency Organization or the Rapid Response Forces. The use of Remotely Piloted Aircraft Systems (RPAS) with the miniaturization of multispectral camera or Hyperspectral camera gives to the operator the capability to react in a short time jointly with the capacity to collect a big amount of different data and to deliver a very large number of products. The proposed methodology incorporates data collected from remote and autonomous sensors that acquire data over areas in a cost-effective manner. The hyperspectral sensors are able to map seafloor morphology, seabed structure, depth of bottom surface and an estimate of sediment development. The considerable spectral portions are selected using an appropriate configuration of hyperspectral cameras to maximize the spectral resolution. Data acquired by hyperspectral camera are geo-referenced synchronously to an Attitude and Heading Reference Systems (AHRS) sensor. The data can be subjected to a first step on-board processing of the unmanned vehicle before be transferred through the Ground Control Station (GCS) to a Processing Exploitation Dissemination (PED) system. The recent introduction of Data Distribution Systems (DDS) capabilities in PED allow a cooperative distributed approach to modern decision making. Two platforms are used in our project, a Remote Piloted Aircraft (RPAS) and an Unmanned Surface Vehicle (USV). The two platforms mutually interact to cover a surveyed area wider than the ones that could be covered by the single vehicles. The USV, especially designed to work in very shallow water, has a modular structure and an open hardware and software architecture allowing for an easy installation and integration of various sensors useful for seabed analysis. The very stable platform located on the top of the USV allows for taking-off and landing of the RPAS. By exploiting its higher power autonomy and load capability, the USV will be used as a mothership for the RPAS. In particular, during the missions the USV will be able to furnish recharging possibility for the RPAS and it will be able to function as a bridge for the communication between the RPAS and its control station. The main advantage of the system is the remote acquisition of high-resolution bathymetric data from RPAS in areas where the possibility to have a systematic and traditional survey are few or none. These tools (USV carrying an RPAS with Hyperspectral camera) constitute an innovative and powerful system that gives to the Emergency Response Unit the right instruments to react quickly. The developing of this support could be solve the classical conflict between resolution, needed to capture the fine scale variability and coverage, needed for the large environmental phenomena, with very high variability over a wide range of spatial and temporal scales as the coastal environment.

[Value of a novel categorization of congenital double-outlet right ventricle on guiding the choice of surgical approaches].

PubMed

Pang, Kunjing; Meng, Hong; Wang, Hao; Hu, Shengshou; Hua, Zhongdong; Pan, Xiangbin; Li, Shoujun

2015-11-01

To explore the feasibility and value of a new categorization of double outlet right ventricular (DORV) on guiding the optimal choices of surgical approaches. Five hundred and twenty one DORV patients diagnosed by echocardiography, angiocardiography and CT at Fuwai Hospital from May 2003 to September 2014 were enrolled in this retrospective study. Congenital DORV was categorized according to three basic factors as follows: the positional relationships of great arteries (normal relation or abnormal relation), the relationships of the ventricular septal defect (VSD) to the great arteries (committed VSD or remote VSD), the presence or absence of pulmonary outflow tract obstruction (POTO). Eight types of DORV were established: type I (normal relation, committed VSD, without POTO), type II (normal relation, committed VSD, POTO), type III (normal relation, remote VSD, without POTO), type IV (normal relation, remote VSD, POTO), type V (abnormal relation, committed VSD, without POTO), type VI (abnormal relation, committed VSD, POTO), type VII (abnormal relation, remote VSD, without POTO), type VIII (abnormal relation, remote VSD, POTO). Feasibility of this classification and the value of this classification on guiding the choice of surgical approaches were analyzed. Among the five hundred and twenty one patients, there were 90 patients (17.3%) with type I DORV, 94 patients (18.0%) with type II, 33 patients (6.3%) with type III, 34 patients (6.5%) with type IV, 64 patients (12.3%) with type V, 61 patients (11.7%) with type VI, 33 patients (6.3%) with type VII, 112 patients (21.5%) with type VIII. Thus, all patients could be typed by this classification method. The echocardiography diagnosis was consistent with the intra-operative and or cardiac catheterization/CT findings. Excluding the contraindications of bi-ventricular repair, different surgical approaches were performed in every subtype of DORV according the classification, which indicated that this novel categorization could accurately guide the clinic managements. This novel DORV categorization can accurately diagnose DORV lesions, and guide the clinic therapy choice.

Coastline change and marine geo-hazards in the Yellow River Delta (China)

NASA Astrophysics Data System (ADS)

Zhou, L.; Liu, J.; Liu, X.

2003-04-01

COASTLINE CHANGE AND MARINE GEO-HAZARDS IN THE YELLOW RIVER DELTA (CHINA) Zhou Liangyong(1,2), Liu Jian(1,3), Liu Xiqing(1) (1)Qingdao Institute of Marine Geology,(2)Ocean University of China,(3)Research Centre for Coastal Geology, CGS qdzliangyong@cgs.gov.cn/Fax: +86-532-5720553 Satellite remote sensing, bathymetry and high-resolution seismic data have been used to examine the coastline change during the period from 1976 to 2001 and the offshore marine geo-hazards in the modern Yellow River Delta. Trends in the temporal sequence of the eight coastlines derived from Landsat images were used in the definition of erosional classes of the coastline. Four classes were distinguished, including rapid erosion (>100 m/yr), moderate erosion (20-100 m/yr), no detectable erosion (-1 - 20 m/yr), and accretion (-200--1 m/yr). We revealed the subtle variations in sea floor morphology and sediment geometries using high-resolution acoustic survey. Many kinds of geo-hazards were identified in the active subaqueous delta lobe and abandoned delta lobes, such as seabed erosions, gas-charged sediments, listric faults, synsedimentary rises, incised palaeo-valleys, infilled gullies, diapirs, active slope failures and sediment collapses. The resultant map of geo-envrionment and geo-hazards presents the coastline change and distribution of geo-hazards mentioned above in the Yellow River Delta. The gas-charged sediment distributes mainly in the abandoned delta lobes. The synsedimentary rise outside of the modern river mouth is a new evidence for the seabed mass-movement which modifies the progradational subaquaeous slopes of modern Yellow River Delta.

TEMPORAL CORRELATION OF CLASSIFICATIONS IN REMOTE SENSING

EPA Science Inventory

A bivariate binary model is developed for estimating the change in land cover from satellite images obtained at two different times. The binary classifications of a pixel at the two times are modeled as potentially correlated random variables, conditional on the true states of th...

Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

Treesearch

Weiqi Zhou; Austin Troy; Morgan Grove

2008-01-01

Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...

Monitoring land cover dynamics in the Aral Sea region by remote sensing

NASA Astrophysics Data System (ADS)

Kozhoridze, Giorgi; Orlovsky, Leah; Orlovsky, Nikolai

2012-10-01

The Aral Sea ecological crisis resulted from the USSR government decision in 1960s to deploy agricultural project for cotton production in Central Asia. Consequently water flow in the Aral Sea decreased drastically due to the regulation of Amydarya and Syrdarya Rivers for irrigation purposes from 55-60 km3 in 1950s to 43 km3 in 1970s, 4 km3 in 1980s and 9-10 km3 in 2000s. Expert land cover classification approach gives the opportunity to use the unlimited variable for classification purposes. The band algebra (band5/band4 and Band4/Band3) and remote sensing indices (Normalized differential Salinity Index (NDSI), Salt Pan Index (SPI), Salt Index (SI), Normalized difference Vegetation Index (NDVI), Albedo, Crust Index) utilized for the land cover classification has shown satisfactory result with classification overall accuracy 86.9 % and kappa coefficient 0.85. Developed research algorithm and obtained results can support monitoring system, contingency planning development, and improvement of natural resources rational management.

15 CFR 970.603 - Conservation of resources.

Code of Federal Regulations, 2012 CFR

2012-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.603 Conservation of resources. (a) With respect to the exploration phase of seabed mining, the... provisions only as the Administrator deems necessary. (b) NOAA views license phase mining system tests as an...

15 CFR 970.603 - Conservation of resources.

Code of Federal Regulations, 2014 CFR

2014-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.603 Conservation of resources. (a) With respect to the exploration phase of seabed mining, the... provisions only as the Administrator deems necessary. (b) NOAA views license phase mining system tests as an...

15 CFR 970.603 - Conservation of resources.

Code of Federal Regulations, 2013 CFR

2013-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.603 Conservation of resources. (a) With respect to the exploration phase of seabed mining, the... provisions only as the Administrator deems necessary. (b) NOAA views license phase mining system tests as an...

15 CFR 970.603 - Conservation of resources.

Code of Federal Regulations, 2010 CFR

2010-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.603 Conservation of resources. (a) With respect to the exploration phase of seabed mining, the... provisions only as the Administrator deems necessary. (b) NOAA views license phase mining system tests as an...

15 CFR 970.603 - Conservation of resources.

Code of Federal Regulations, 2011 CFR

2011-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.603 Conservation of resources. (a) With respect to the exploration phase of seabed mining, the... provisions only as the Administrator deems necessary. (b) NOAA views license phase mining system tests as an...

The use of multimedia and programmed teaching machines for remote sensing education

NASA Technical Reports Server (NTRS)

Ulliman, J. J.

1980-01-01

The advantages, limitations, and uses of various audio visual equipments and techniques used in various universities for individualized and group instruction in the interpretation and classification of remotely sensed data are considered as well as systems for programmed and computer-assisted instruction.

Watershed-scale land-use mapping with satellite imagery

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing data has many advantages compared with other data sources, such as field methods and aerial photography, for land cover classification. In particular,it is useful in evaluating temporal and spatial effects. In addition, remote sensing can offer a cost-effective means of prov...

Storm-wave-induced seabed deformation: Results from in situ observation in the Yellow River subaqueous delta

NASA Astrophysics Data System (ADS)

Jia, Y.; Wang, Z. Mr; Liu, X.; Shan, H.

2017-12-01

Submarine landslides move large volumes of sediment and are often hazardous to offshore installations. Current research into submarine landslides mainly relies on marine surveying techniques. In contrast, in situ observations of the submarine landslide process, specifically seabed deformation, are sparse, and therefore restrict our understanding of submarine landslide mechanisms and the establishment of a disaster warning scheme. The submarine landslide monitoring (SLM) system, which has been designed to partly overcome these pitfalls, can monitor storm-wave-induced submarine landslides in situ and over a long time period. The SLM system comprises two parts: (1) a hydrodynamic monitoring tripod for recording hydrodynamic data and (2) a shape accel array for recording seabed deformation at different depths. This study recorded the development of the SLM system and the results of in situ observation in the Yellow River Delta, China, during the boreal winter of 2014-2015. The results show an abrupt small-scale storm-wave-induced seabed shear deformation; the shear interface is in at least 1.5-m depth and the displacement of sediments at 1.23-m depth is more than 13 mm. The performance of the SLM system confirms the feasibility and stability of this approach. Further, the in situ observations, as well as the laboratory tests, helped reveal the profound mechanism of storm-wave-induced seabed deformation.

The decision tree approach to classification

NASA Technical Reports Server (NTRS)

Wu, C.; Landgrebe, D. A.; Swain, P. H.

1975-01-01

A class of multistage decision tree classifiers is proposed and studied relative to the classification of multispectral remotely sensed data. The decision tree classifiers are shown to have the potential for improving both the classification accuracy and the computation efficiency. Dimensionality in pattern recognition is discussed and two theorems on the lower bound of logic computation for multiclass classification are derived. The automatic or optimization approach is emphasized. Experimental results on real data are reported, which clearly demonstrate the usefulness of decision tree classifiers.

Spectrally based mapping of riverbed composition

USGS Publications Warehouse

Legleiter, Carl; Stegman, Tobin K.; Overstreet, Brandon T.

2016-01-01

Remote sensing methods provide an efficient means of characterizing fluvial systems. This study evaluated the potential to map riverbed composition based on in situ and/or remote measurements of reflectance. Field spectra and substrate photos from the Snake River, Wyoming, USA, were used to identify different sediment facies and degrees of algal development and to quantify their optical characteristics. We hypothesized that accounting for the effects of depth and water column attenuation to isolate the reflectance of the streambed would enhance distinctions among bottom types and facilitate substrate classification. A bottom reflectance retrieval algorithm adapted from coastal research yielded realistic spectra for the 450 to 700 nm range; but bottom reflectance-based substrate classifications, generated using a random forest technique, were no more accurate than classifications derived from above-water field spectra. Additional hypothesis testing indicated that a combination of reflectance magnitude (brightness) and indices of spectral shape provided the most accurate riverbed classifications. Convolving field spectra to the response functions of a multispectral satellite and a hyperspectral imaging system did not reduce classification accuracies, implying that high spectral resolution was not essential. Supervised classifications of algal density produced from hyperspectral data and an inferred bottom reflectance image were not highly accurate, but unsupervised classification of the bottom reflectance image revealed distinct spectrally based clusters, suggesting that such an image could provide additional river information. We attribute the failure of bottom reflectance retrieval to yield more reliable substrate maps to a latent correlation between depth and bottom type. Accounting for the effects of depth might have eliminated a key distinction among substrates and thus reduced discriminatory power. Although further, more systematic study across a broader range of fluvial environments is needed to substantiate our initial results, this case study suggests that bed composition in shallow, clear-flowing rivers potentially could be mapped remotely.

UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING

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

Nancy F. Glenn; Jessica J. Mitchell; Matthew O. Anderson

2012-06-01

UAV-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Idaho State University, Boise Center Aerospace Lab, were recently tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. Overall, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. Future mapping efforts that leverage ground reference data, ultra-high spatial resolution photos and time series analysis shouldmore » be able to effectively distinguish native grasses such as Sandberg bluegrass (Poa secunda), from invasives such as burr buttercup (Ranunculus testiculatus) and cheatgrass (Bromus tectorum).« less

Development of remote sensing based site specific weed management for Midwest mint production

NASA Astrophysics Data System (ADS)

Gumz, Mary Saumur Paulson

Peppermint and spearmint are high value essential oil crops in Indiana, Michigan, and Wisconsin. Although the mints are profitable alternatives to corn and soybeans, mint production efficiency must improve in order to allow industry survival against foreign produced oils and synthetic flavorings. Weed control is the major input cost in mint production and tools to increase efficiency are necessary. Remote sensing-based site-specific weed management offers potential for decreasing weed control costs through simplified weed detection and control from accurate site specific weed and herbicide application maps. This research showed the practicability of remote sensing for weed detection in the mints. Research was designed to compare spectral response curves of field grown mint and weeds, and to use these data to develop spectral vegetation indices for automated weed detection. Viability of remote sensing in mint production was established using unsupervised classification, supervised classification, handheld spectroradiometer readings and spectral vegetation indices (SVIs). Unsupervised classification of multispectral images of peppermint production fields generated crop health maps with 92 and 67% accuracy in meadow and row peppermint, respectively. Supervised classification of multispectral images identified weed infestations with 97% and 85% accuracy for meadow and row peppermint, respectively. Supervised classification showed that peppermint was spectrally distinct from weeds, but the accuracy of these measures was dependent on extensive ground referencing which is impractical and too costly for on-farm use. Handheld spectroradiometer measurements of peppermint, spearmint, and several weeds and crop and weed mixtures were taken over three years from greenhouse grown plants, replicated field plots, and production peppermint and spearmint fields. Results showed that mints have greater near infrared (NIR) and lower green reflectance and a steeper red edge slope than all weed species. These distinguishing characteristics were combined to develop narrow band and broadband spectral vegetation indices (SVIs, ratios of NIR/green reflectance), that were effective in differentiating mint from key weed species. Hyperspectral images of production peppermint and spearmint fields were then classified using SVI-based classification. Narrowband and broadband SVIs classified early season peppermint and spearmint with 64 to 100% accuracy compared to 79 to 100% accuracy for supervised classification of multispectral images of the same fields. Broadband SVIs have potential for use as an automated spectral indicator for weeds in the mints since they require minimal ground referencing and can be calculated from multispectral imagery which is cheaper and more readily available than hyperspectral imagery. This research will allow growers to implement remote sensing based site specific weed management in mint resulting in reduced grower input costs and reduced herbicide entry into the environment and will have applications in other specialty and meadow crops.

76 FR 31318 - Secretary of Energy Advisory Board Natural Gas Subcommittee

Federal Register 2010, 2011, 2012, 2013, 2014

2011-05-31

...This notice announces an open meeting of the Secretary of Energy Advisory Board (SEAB) Natural Gas Subcommittee. SEAB was reestablished pursuant to the Federal Advisory Committee Act (Pub. L. 92-463, 86 Stat. 770) (the Act). This notice is provided in accordance with the Act.

76 FR 18208 - Secretary of Energy Advisory Board; Notice of open meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-01

... DEPARTMENT OF ENERGY Secretary of Energy Advisory Board; Notice of open meeting AGENCY: Department... Secretary of Energy Advisory Board (SEAB). SEAB was reestablished pursuant to the Federal Advisory Committee...: Background: The Board was reestablished to provide advice and recommendations to the Secretary on the...

76 FR 65180 - Proposed Information Collection; Comment Request; Deep Seabed Mining Exploration Licenses

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-20

... Collection; Comment Request; Deep Seabed Mining Exploration Licenses AGENCY: National Oceanic and Atmospheric... documentation electronically when feasible. III. Data OMB Control Number: 0648-0145. Form Number: None. Type of... information on respondents, including through the use of automated collection techniques or other forms of...

77 FR 40586 - Coastal Programs Division

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-10

... approval of extension of deep sea hard mineral exploration licenses and amended exploration plan. SUMMARY... FR 12245 on the request of Lockheed Martin Corp. to extend the deep seabed hard mineral exploration licenses USA-1 and USA-4 issued under the Deep Seabed Hard Mineral Resources Act (DSHMRA; 30 U.S.C. 1401...

Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy.

PubMed

Baldeck, Claire A; Asner, Gregory P; Martin, Robin E; Anderson, Christopher B; Knapp, David E; Kellner, James R; Wright, S Joseph

2015-01-01

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods--binary support vector machine (SVM) and biased SVM--for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer's accuracies of 94-97% for the three focal species, and field validation of the predicted crown objects indicated that these had user's accuracies of 94-100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems.

Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy

PubMed Central

Baldeck, Claire A.; Asner, Gregory P.; Martin, Robin E.; Anderson, Christopher B.; Knapp, David E.; Kellner, James R.; Wright, S. Joseph

2015-01-01

Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods—binary support vector machine (SVM) and biased SVM—for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer’s accuracies of 94–97% for the three focal species, and field validation of the predicted crown objects indicated that these had user’s accuracies of 94–100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems. PMID:26153693

Utility of BRDF Models for Estimating Optimal View Angles in Classification of Remotely Sensed Images

NASA Technical Reports Server (NTRS)

Valdez, P. F.; Donohoe, G. W.

1997-01-01

Statistical classification of remotely sensed images attempts to discriminate between surface cover types on the basis of the spectral response recorded by a sensor. It is well known that surfaces reflect incident radiation as a function of wavelength producing a spectral signature specific to the material under investigation. Multispectral and hyperspectral sensors sample the spectral response over tens and even hundreds of wavelength bands to capture the variation of spectral response with wavelength. Classification algorithms then exploit these differences in spectral response to distinguish between materials of interest. Sensors of this type, however, collect detailed spectral information from one direction (usually nadir); consequently, do not consider the directional nature of reflectance potentially detectable at different sensor view angles. Improvements in sensor technology have resulted in remote sensing platforms capable of detecting reflected energy across wavelengths (spectral signatures) and from multiple view angles (angular signatures) in the fore and aft directions. Sensors of this type include: the moderate resolution imaging spectroradiometer (MODIS), the multiangle imaging spectroradiometer (MISR), and the airborne solid-state array spectroradiometer (ASAS). A goal of this paper, then, is to explore the utility of Bidirectional Reflectance Distribution Function (BRDF) models in the selection of optimal view angles for the classification of remotely sensed images by employing a strategy of searching for the maximum difference between surface BRDFs. After a brief discussion of directional reflect ante in Section 2, attention is directed to the Beard-Maxwell BRDF model and its use in predicting the bidirectional reflectance of a surface. The selection of optimal viewing angles is addressed in Section 3, followed by conclusions and future work in Section 4.

a Data Field Method for Urban Remotely Sensed Imagery Classification Considering Spatial Correlation

NASA Astrophysics Data System (ADS)

Zhang, Y.; Qin, K.; Zeng, C.; Zhang, E. B.; Yue, M. X.; Tong, X.

2016-06-01

Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary's C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM) for the classification of multi-features (e.g. the spectral feature and spatial correlation feature). In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies.

Methodology for classification of geographical features with remote sensing images: Application to tidal flats

NASA Astrophysics Data System (ADS)

Revollo Sarmiento, G. N.; Cipolletti, M. P.; Perillo, M. M.; Delrieux, C. A.; Perillo, Gerardo M. E.

2016-03-01

Tidal flats generally exhibit ponds of diverse size, shape, orientation and origin. Studying the genesis, evolution, stability and erosive mechanisms of these geographic features is critical to understand the dynamics of coastal wetlands. However, monitoring these locations through direct access is hard and expensive, not always feasible, and environmentally damaging. Processing remote sensing images is a natural alternative for the extraction of qualitative and quantitative data due to their non-invasive nature. In this work, a robust methodology for automatic classification of ponds and tidal creeks in tidal flats using Google Earth images is proposed. The applicability of our method is tested in nine zones with different morphological settings. Each zone is processed by a segmentation stage, where ponds and tidal creeks are identified. Next, each geographical feature is measured and a set of shape descriptors is calculated. This dataset, together with a-priori classification of each geographical feature, is used to define a regression model, which allows an extensive automatic classification of large volumes of data discriminating ponds and tidal creeks against other various geographical features. In all cases, we identified and automatically classified different geographic features with an average accuracy over 90% (89.7% in the worst case, and 99.4% in the best case). These results show the feasibility of using freely available Google Earth imagery for the automatic identification and classification of complex geographical features. Also, the presented methodology may be easily applied in other wetlands of the world and perhaps employing other remote sensing imagery.

Satellite remote sensing of isolated wetlands using object-oriented classification of LANDSAT-7 data

EPA Science Inventory

There has been an increasing interest in characterizing and mapping isolated depressional wetlands due to a 2001 U.S. Supreme Court decision that effectively removed their protected status. Our objective was to determine the utility of satellite remote sensing to accurately map ...

Linear- and Repetitive Feature Detection Within Remotely Sensed Imagery

DTIC Science & Technology

2017-04-01

applicable to Python or other pro- gramming languages with image- processing capabilities. 4.1 Classification machine learning The first methodology uses...remotely sensed images that are in panchromatic or true-color formats. Image- processing techniques, in- cluding Hough transforms, machine learning, and...data fusion .................................................................................................... 44 6.3 Context-based processing

Research in remote sensing of agriculture, earth resources, and man's environment

NASA Technical Reports Server (NTRS)

Landgrebe, D. A.

1975-01-01

Progress is reported for several projects involving the utilization of LANDSAT remote sensing capabilities. Areas under study include crop inventory, crop identification, crop yield prediction, forest resources evaluation, land resources evaluation and soil classification. Numerical methods for image processing are discussed, particularly those for image enhancement and analysis.

Priority data on marine and estuarine resources within northeastern National Parks: Inventory and acquisition needs

USGS Publications Warehouse

Hart, Tracy E.; Neckles, Hilary A.; Kopp, Blaine S.

2013-01-01

The purpose of this project was to guide development of a strategy for the inventory and mapping of submerged natural resources associated within 10 coastal parks of the National Park Service (NPS) Northeast Region (NER; see Table 1). Priority data needs were identified by the NER Ocean Stewardship Task Force. The majority of the NER priority data needs involve the biotic, chemical, and geological characterization of the seabed. Taken collectively, this demands a consistent and unified approach to habitat classification. The Coastal and Marine Ecological Classification Standard (CMECS) is endorsed by the Federal Geographic Data Committee (FGDC-STD-018) for classifying ecological units in coastal and marine environments, and is recommended as a framework for acquiring and organizing NER data. We prepared an inventory of existing data on priority marine and estuarine natural resources within the ten NER coastal parks. This report describes the data and information sources relevant to each park and identifies gaps in available data. Overwhelmingly and uniformly across all parks, the most pressing needs are consistent, high-resolution bathymetry and seafloor characterization data. Approaches for acquiring these data using an integrated, multi-resolution sampling framework are recommended.

Evolving land cover classification algorithms for multispectral and multitemporal imagery

NASA Astrophysics Data System (ADS)

Brumby, Steven P.; Theiler, James P.; Bloch, Jeffrey J.; Harvey, Neal R.; Perkins, Simon J.; Szymanski, John J.; Young, Aaron C.

2002-01-01

The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multi-spectral and multi-temporal imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from before, during, and after the wildfire. Using an existing land cover classification based on a 1992 Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, and an algorithm to mask out clouds and cloud shadows. We report preliminary results of combining individual classification results using a K-means clustering approach. The details of our evolved classification are compared to the manually produced land-cover classification.

A vegetational and ecological resource analysis from space and high flight photography

NASA Technical Reports Server (NTRS)

Poulton, C. E.; Faulkner, D. P.; Schrumpf, B. J.

1970-01-01

A hierarchial classification of vegetation and related resources is considered that is applicable to convert remote sensing data in space and aerial synoptic photography. The numerical symbolization provides for three levels of vegetational classification and three levels of classification of environmental features associated with each vegetational class. It is shown that synoptic space photography accurately projects how urban sprawl affects agricultural land use areas and ecological resources.

A classification model of Hyperion image base on SAM combined decision tree

NASA Astrophysics Data System (ADS)

Wang, Zhenghai; Hu, Guangdao; Zhou, YongZhang; Liu, Xin

2009-10-01

Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. On the other hand, with increase in the input dimensionality the hypothesis space grows exponentially, which makes the classification performance highly unreliable. Traditional classification algorithms Classification of hyperspectral images is challenging. New algorithms have to be developed for hyperspectral data classification. The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. The key and difficulty is that we should artificial defining the threshold of SAM. The classification precision depends on the rationality of the threshold of SAM. In order to resolve this problem, this paper proposes a new automatic classification model of remote sensing image using SAM combined with decision tree. It can automatic choose the appropriate threshold of SAM and improve the classify precision of SAM base on the analyze of field spectrum. The test area located in Heqing Yunnan was imaged by EO_1 Hyperion imaging spectrometer using 224 bands in visual and near infrared. The area included limestone areas, rock fields, soil and forests. The area was classified into four different vegetation and soil types. The results show that this method choose the appropriate threshold of SAM and eliminates the disturbance and influence of unwanted objects effectively, so as to improve the classification precision. Compared with the likelihood classification by field survey data, the classification precision of this model heightens 9.9%.

Spatial information technologies for remote sensing today and tomorrow; Proceedings of the Ninth Pecora Symposium, Sioux Falls, SD, October 2-4, 1984

NASA Technical Reports Server (NTRS)

1984-01-01

Topics discussed at the symposium include hardware, geographic information system (GIS) implementation, processing remotely sensed data, spatial data structures, and NASA programs in remote sensing information systems. Attention is also given GIS applications, advanced techniques, artificial intelligence, graphics, spatial navigation, and classification. Papers are included on the design of computer software for geographic image processing, concepts for a global resource information system, algorithm development for spatial operators, and an application of expert systems technology to remotely sensed image analysis.

Measurement of Hydrologic Resource Parameters Through Remote Sensing in the Feather River Headwaters Area

NASA Technical Reports Server (NTRS)

Thorley, G. A.; Draeger, W. C.; Lauer, D. T.; Lent, J.; Roberts, E.

1971-01-01

The four problem are as being investigated are: (1) determination of the feasibility of providing the resource manager with operationally useful information through the use of remote sensing techniques; (2) definition of the spectral characteristics of earth resources and the optimum procedures for calibrating tone and color characteristics of multispectral imagery (3) determination of the extent to which humans can extract useful earth resource information through remote sensing imagery; (4) determination of the extent to which automatic classification and data processing can extract useful information from remote sensing data.

Multiresolution 3-D reconstruction from side-scan sonar images.

PubMed

Coiras, Enrique; Petillot, Yvan; Lane, David M

2007-02-01

In this paper, a new method for the estimation of seabed elevation maps from side-scan sonar images is presented. The side-scan image formation process is represented by a Lambertian diffuse model, which is then inverted by a multiresolution optimization procedure inspired by expectation-maximization to account for the characteristics of the imaged seafloor region. On convergence of the model, approximations for seabed reflectivity, side-scan beam pattern, and seabed altitude are obtained. The performance of the system is evaluated against a real structure of known dimensions. Reconstruction results for images acquired by different sonar sensors are presented. Applications to augmented reality for the simulation of targets in sonar imagery are also discussed.

Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

EPA Science Inventory

Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree...

Extraction of Shrimp Ponds Using Object Oriented Classification vis-a-vis Pixel Based Classification

DTIC Science & Technology

2004-11-01

302 25th ACRS 2004 Chiang Mai , Thailand B-3.6 Data Processing...Proceedings of the 25th Asian Conference on Remote Sensing, Held in Chiang Mai , Thailand on 22-26 November 2004. Copyrighted; Government Purpose Rights... Chiang Mai , Thailand B-3.6 Data Processing

Optimal land use/land cover classification using remote sensing imagery for hydrological modeling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2009-10-01

Land use/land cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/land cover. This paper presents different approaches to attain an optimal land use/land cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/land cover map was not sufficient for the delineation of HRUs, since the agricultural land use/land cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Subsequently the digital classification on fused data (ASAR and ASTER) were attempted to map land use/land cover classes with emphasis to delineate the paddy and maize crops but the supervised classification over fused datasets did not provide the desired accuracy and proper delineation of paddy and maize crops. Eventually, we adopted a visual classification approach on fused data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modeling.

Mapping ecological states in a complex environment

NASA Astrophysics Data System (ADS)

Steele, C. M.; Bestelmeyer, B.; Burkett, L. M.; Ayers, E.; Romig, K.; Slaughter, A.

2013-12-01

The vegetation of northern Chihuahuan Desert rangelands is sparse, heterogeneous and for most of the year, consists of a large proportion of non-photosynthetic material. The soils in this area are spectrally bright and variable in their reflectance properties. Both factors provide challenges to the application of remote sensing for estimating canopy variables (e.g., leaf area index, biomass, percentage canopy cover, primary production). Additionally, with reference to current paradigms of rangeland health assessment, remotely-sensed estimates of canopy variables have limited practical use to the rangeland manager if they are not placed in the context of ecological site and ecological state. To address these challenges, we created a multifactor classification system based on the USDA-NRCS ecological site schema and associated state-and-transition models to map ecological states on desert rangelands in southern New Mexico. Applying this system using per-pixel image processing techniques and multispectral, remotely sensed imagery raised other challenges. Per-pixel image classification relies upon the spectral information in each pixel alone, there is no reference to the spatial context of the pixel and its relationship with its neighbors. Ecological state classes may have direct relevance to managers but the non-unique spectral properties of different ecological state classes in our study area means that per-pixel classification of multispectral data performs poorly in discriminating between different ecological states. We found that image interpreters who are familiar with the landscape and its associated ecological site descriptions perform better than per-pixel classification techniques in assigning ecological states. However, two important issues affect manual classification methods: subjectivity of interpretation and reproducibility of results. An alternative to per-pixel classification and manual interpretation is object-based image analysis. Object-based image analysis provides a platform for classification that more closely resembles human recognition of objects within a remotely sensed image. The analysis presented here compares multiple thematic maps created for test locations on the USDA-ARS Jornada Experimental Range ranch. Three study sites in different pastures, each 300 ha in size, were selected for comparison on the basis of their ecological site type (';Clayey', ';Sandy' and a combination of both) and the degree of complexity of vegetation cover. Thematic maps were produced for each study site using (i) manual interpretation of digital aerial photography (by five independent interpreters); (ii) object-oriented, decision-tree classification of fine and moderate spatial resolution imagery (Quickbird; Landsat Thematic Mapper) and (iii) ground survey. To identify areas of uncertainty, we compared agreement in location, areal extent and class assignation between 5 independently produced, manually-digitized ecological state maps and with the map created from ground survey. Location, areal extent and class assignation of the map produced by object-oriented classification was also assessed with reference to the ground survey map.

Improved image classification with neural networks by fusing multispectral signatures with topological data

NASA Technical Reports Server (NTRS)

Harston, Craig; Schumacher, Chris

1992-01-01

Automated schemes are needed to classify multispectral remotely sensed data. Human intelligence is often required to correctly interpret images from satellites and aircraft. Humans suceed because they use various types of cues about a scene to accurately define the contents of the image. Consequently, it follows that computer techniques that integrate and use different types of information would perform better than single source approaches. This research illustrated that multispectral signatures and topographical information could be used in concert. Significantly, this dual source tactic classified a remotely sensed image better than the multispectral classification alone. These classifications were accomplished by fusing spectral signatures with topographical information using neural network technology. A neural network was trained to classify Landsat mulitspectral signatures. A file of georeferenced ground truth classifications were used as the training criterion. The network was trained to classify urban, agriculture, range, and forest with an accuracy of 65.7 percent. Another neural network was programmed and trained to fuse these multispectral signature results with a file of georeferenced altitude data. This topological file contained 10 levels of elevations. When this nonspectral elevation information was fused with the spectral signatures, the classifications were improved to 73.7 and 75.7 percent.

Object-based classification of earthquake damage from high-resolution optical imagery using machine learning

NASA Astrophysics Data System (ADS)

Bialas, James; Oommen, Thomas; Rebbapragada, Umaa; Levin, Eugene

2016-07-01

Object-based approaches in the segmentation and classification of remotely sensed images yield more promising results compared to pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods are often used, but yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time-sensitive applications, such as earthquake damage assessment. Herein, we used a systematic approach in evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery. We tested a variety of algorithms and parameters on post-event aerial imagery for the 2011 earthquake in Christchurch, New Zealand. Results were compared against manually selected test cases representing different classes. In doing so, we can evaluate the effectiveness of the segmentation and classification of different classes and compare different levels of multistep image segmentations. Our classifier is compared against recent pixel-based and object-based classification studies for postevent imagery of earthquake damage. Our results show an improvement against both pixel-based and object-based methods for classifying earthquake damage in high resolution, post-event imagery.

Nuclear waste disposal in subseabed geologic formatons: the Seabed Disposal Program

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

Anderson, D.R.

1979-05-01

The goal of the Seabed Disposal Program is to assess the technical and environmental feasibility of using geologic formations under the sea floor for the disposal of processed high-level radioactive wastes or repackaged spent reactor fuel. Studies are focused on the abyssal hill regions of the sea floors in the middle of tectonic plates and under massive surface current gyres. The red-clay sediments here are from 50 to 100 meters thick, are continuously depositional (without periods of erosion), and have been geologically and climatologically stable for millions of years. Mineral deposits and biological activity are minimal, and bottom currents aremore » weak and variable. Five years of research have revealed no technological reason why nuclear waste disposal in these areas would be impractical. However, scientific assessment is not complete. Also, legal political, and sociological factors may well become the governing elements in such use of international waters. These factors are being examined as part of the work of the Seabed Working Group, an international adjunct of the Seabed Program, with members from France, England, Japan, Canada, and the United States.« less

Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains.

PubMed

Dettmer, Jan; Dosso, Stan E

2012-10-01

This paper develops a trans-dimensional approach to matched-field geoacoustic inversion, including interacting Markov chains to improve efficiency and an autoregressive model to account for correlated errors. The trans-dimensional approach and hierarchical seabed model allows inversion without assuming any particular parametrization by relaxing model specification to a range of plausible seabed models (e.g., in this case, the number of sediment layers is an unknown parameter). Data errors are addressed by sampling statistical error-distribution parameters, including correlated errors (covariance), by applying a hierarchical autoregressive error model. The well-known difficulty of low acceptance rates for trans-dimensional jumps is addressed with interacting Markov chains, resulting in a substantial increase in efficiency. The trans-dimensional seabed model and the hierarchical error model relax the degree of prior assumptions required in the inversion, resulting in substantially improved (more realistic) uncertainty estimates and a more automated algorithm. In particular, the approach gives seabed parameter uncertainty estimates that account for uncertainty due to prior model choice (layering and data error statistics). The approach is applied to data measured on a vertical array in the Mediterranean Sea.

Comparison of cosmology and seabed acoustics measurements using statistical inference from maximum entropy

NASA Astrophysics Data System (ADS)

Knobles, David; Stotts, Steven; Sagers, Jason

2012-03-01

Why can one obtain from similar measurements a greater amount of information about cosmological parameters than seabed parameters in ocean waveguides? The cosmological measurements are in the form of a power spectrum constructed from spatial correlations of temperature fluctuations within the microwave background radiation. The seabed acoustic measurements are in the form of spatial correlations along the length of a spatial aperture. This study explores the above question from the perspective of posterior probability distributions obtained from maximizing a relative entropy functional. An answer is in part that the seabed in shallow ocean environments generally has large temporal and spatial inhomogeneities, whereas the early universe was a nearly homogeneous cosmological soup with small but important fluctuations. Acoustic propagation models used in shallow water acoustics generally do not capture spatial and temporal variability sufficiently well, which leads to model error dominating the statistical inference problem. This is not the case in cosmology. Further, the physics of the acoustic modes in cosmology is that of a standing wave with simple initial conditions, whereas for underwater acoustics it is a traveling wave in a strongly inhomogeneous bounded medium.

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

USGS Publications Warehouse

Ramsey, Elijah W.; Nelson, G.A.; Echols, D.; Sapkota, S.K.

2002-01-01

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LM-NRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments.

PubMed

Ramsey, Elijah W; Nelson, Gene A; Echols, Darrell; Sapkota, Sijan K

2002-05-01

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LMNRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.

Use of remote sensing in agriculture

NASA Technical Reports Server (NTRS)

Pettry, D. E.; Powell, N. L.

1975-01-01

The remote sensing studies of (a) cultivated peanut areas in Southeastern Virginia; (b) studies at the Virginia Truck and Ornamentals Research Station near Painter, Virginia, the Eastern Virginia Research Station near Warsaw, Virginia, the Tidewater Research and Continuing Education Center near Suffolk, Virginia, and the Southern Piedmont Research and Continuing Education Center Blackstone, Virginia; and (c) land use classification studies at Virginia Beach, Virginia are presented. The practical feasibility of using false color infrared imagery to detect and determine the areal extent of peanut disease infestation of Cylindrocladium black rot and Sclerotinia blight is demonstrated. These diseases pose a severe hazard to this major agricultural food commodity. The value of remote sensing technology in terrain analyses and land use classification of diverse land areas is also investigated. Continued refinement of spectral signatures of major agronomic crops and documentation of pertinent environmental variables have provided a data base for the generation of an agricultural-environmental prediction model.

Contribution of wave-induced liquefaction in triggering hyperpycnal flows in Yellow River Estuary

NASA Astrophysics Data System (ADS)

Liu, X.; Jia, Y.

2017-12-01

Hyperpycnal flows, driven mainly by the gravity of near-bed negatively buoyant layers, are one of the most important processes for moving marine sediment across the earth. The issue of hyperpycnal flows existing in marine environment has drawn increasing scholars' attention since that was observed in situ off the Yellow River estuary in the 1980s. Most researches maintain that hyperpycnal flows in the Yellow River estuary are caused by the high-concentration sediments discharged from the Yellow River into sea, however, other mechanisms have been discounted since the sediment input from the river has been significantly changed due to climate and anthropogenic change. Here we demonstrate that wave-seabed interactions can generate hyperpycnal flows, without river input, by sediment flux convergence above an originally consolidated seabed. Using physical model experiments and multi-sensor field measurements, we characterize the composition-dependent liquefaction properties of the sediment due to wave-induced pore water pressure accumulation. This allows quantification of attenuation of sediment threshold velocity and critical shear stress (predominant variables in transport mechanics) during the liquefaction under waves. Parameterising the wave-seabed interactions in a new concept model shows that high waves propagating over the seabed sediment can act as a scarifier plough remoulding the seabed sediment. This contributes to marine hyperpycnal flows as the sediment is quickly resuspended under accumulating attenuation in strength. Therefore, the development of more integrative numerical models could supply realistic predictions of marine record in response to rising magnitude and frequency of storms.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1992-01-01

Research findings are summarized for projects dealing with the following: application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated Mie scatterers with size distribution and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; theoretical modeling for passive microwave remote sensing of earth terrain; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Recent Advances in Tsunami-Seabed-Structure Interaction from Geotechnical and Hydrodynamic Perspectives

NASA Astrophysics Data System (ADS)

Sassa, S.

2017-12-01

This presentation shows some recent research advances on tsunami-seabed-structure interaction following the 2011 Tohoku Earthquake Tsunami, Japan. It presents a concise summary and discussion of utilizing a geotechnical centrifuge and a large-scale hydro flume for the modelling of tsunami-seabed-structure interaction. I highlight here the role of tsunami-induced seepage in piping/boiling, erosion and bearing capacity decrease and failure of the rubble/seabed foundation. A comparison and discussion are made on the stability assessment for the design of tsunami-resistant structures on the basis of the results from both geo-centrifuge and large-scale hydrodynamic experiments. The concurrent processes of the instability involving the scour of the mound/sandy seabed, bearing capacity failure and flow of the foundation and the failure of caisson breakwaters under tsunami overflow and seepage coupling are made clear in this presentation. Three series of experiments were conducted under fifty gravities. The first series of experiments targeted the instability of the mounds themselves, and the second series of experiments clarified how the mound scour would affect the overall stability of the caissons. The third series of experiments examined the effect of a countermeasure on the basis of the results from the two series of experiments. The experimental results first demonstrated that the coupled overflow-seepage actions promoted the development of the mound scour significantly, and caused bearing capacity failure of the mound, resulting in the total failure of the caisson breakwater, which otherwise remained stable without the coupling effect. The velocity vectors obtained from the high-resolution image analysis illustrated the series of such concurrent scour/bearing-capacity-failure/flow processes leading to the instability of the breakwater. The stability of the breakwaters was significantly improved with decreasing hydraulic gradient underneath the caissons due to an embankment effect. These findings elucidate the crucial role of overflow/seepage coupling in tsunami-seabed-structure interaction from both geotechnical and hydrodynamic perspectives, as an interdisciplinary tsunami science, warranting an enhanced disaster resilience.

System to provide 3D information on geological anomaly zone in deep subsea

NASA Astrophysics Data System (ADS)

Kim, W.; Kwon, O.; Kim, D.

2017-12-01

The study on building the ultra long and deep subsea tunnel of which length is 50km and depth is 200m at least, respectively, is underway in Korea. To analyze the geotechnical information required for designing and building subsea tunnel, topographic/geologiccal information analysis using 2D seabed geophysical prospecting and topographic, geologic, exploration and boring data were analyzed comprehensively and as a result, automation method to identify the geological structure zone under seabed which is needed to design the deep and long seabed tunnel was developed using geostatistical analysis. In addition, software using 3D visualized ground information to provide the information includes Gocad, MVS, Vulcan and DIMINE. This study is intended to analyze the geological anomaly zone for ultra deep seabed l and visualize the geological investigation result so as to develop the exclusive system for processing the ground investigation information which is convenient for the users. Particularly it's compatible depending on file of geophysical prospecting result and is realizable in Layer form and for 3D view as well. The data to be processed by 3D seabed information system includes (1) deep seabed topographic information, (2) geological anomaly zone, (3) geophysical prospecting, (4) boring investigation result and (5) 3D visualization of the section on seabed tunnel route. Each data has own characteristics depending on data and interface to allow interlocking with other data is granted. In each detail function, input data is displayed in a single space and each element is selectable to identify the further information as a project. Program creates the project when initially implemented and all output from detail information is stored by project unit. Each element representing detail information is stored in image file and is supported to store in text file as well. It also has the function to transfer, expand/reduce and rotate the model. To represent the all elements in 3D visualized platform, coordinate and time information are added to the data or data group to establish the conceptual model as a whole. This research was supported by the Korea Agency for Infrastructure Technology Advancement under the Ministry of Land, Infrastructure and Transport of the Korean government(Project Number: 13 Construction Research T01).

Thermal Remote Sensing and the Thermodynamics of Ecosystem Development

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Rickman, Doug; Fraser, Roydon F.

2011-01-01

Ecosystems develop structure and function that degrades the quality of the incoming energy more effectively. The ecosystem T and Rn/K* and TRN are excellent candidates for indicators of ecological integrity. The potential for these methods to be used for remote sensed ecosystem classification and ecosystem health/integrity evaluation is apparent

a Novel Deep Convolutional Neural Network for Spectral-Spatial Classification of Hyperspectral Data

NASA Astrophysics Data System (ADS)

Li, N.; Wang, C.; Zhao, H.; Gong, X.; Wang, D.

2018-04-01

Spatial and spectral information are obtained simultaneously by hyperspectral remote sensing. Joint extraction of these information of hyperspectral image is one of most import methods for hyperspectral image classification. In this paper, a novel deep convolutional neural network (CNN) is proposed, which extracts spectral-spatial information of hyperspectral images correctly. The proposed model not only learns sufficient knowledge from the limited number of samples, but also has powerful generalization ability. The proposed framework based on three-dimensional convolution can extract spectral-spatial features of labeled samples effectively. Though CNN has shown its robustness to distortion, it cannot extract features of different scales through the traditional pooling layer that only have one size of pooling window. Hence, spatial pyramid pooling (SPP) is introduced into three-dimensional local convolutional filters for hyperspectral classification. Experimental results with a widely used hyperspectral remote sensing dataset show that the proposed model provides competitive performance.

[Application of small remote sensing satellite constellations for environmental hazards in wetland landscape mapping: taking Liaohe Delta, Liaoning Province of Northeast China as a case].

PubMed

Yang, Yuan-Zheng; Chang, Yu; Hu, Yuan-Man; Liu, Miao; Li, Yue-Hui

2011-06-01

To timely and accurately acquire the spatial distribution pattern of wetlands is of significance for the dynamic monitoring, conservation, and sustainable utilization of wetlands. The small remote sensing satellite constellations A/B stars (HJ-1A/1B stars) for environmental hazards were launched by China for monitoring terrestrial resources, which could provide a new data source of remote sensing image acquisition for retrieving wetland types. Taking Liaohe Delta as a case, this paper compared the accuracy of wetland classification map and the area of each wetland type retrieved from CCD data (HJ CCD data) and TM5 data, and validated and explored the applicability and the applied potential of HJ CCD data in wetland resources dynamic monitoring. The results showed that HJ CCD data could completely replace Landsat TM5 data in feature extraction and remote sensing classification. In real-time monitoring, due to its 2 days of data acquisition cycle, HJ CCD data had the priority to Landsat TM5 data (16 days of data acquisition cycle).

Unsupervised classification of earth resources data.

NASA Technical Reports Server (NTRS)

Su, M. Y.; Jayroe, R. R., Jr.; Cummings, R. E.

1972-01-01

A new clustering technique is presented. It consists of two parts: (a) a sequential statistical clustering which is essentially a sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by existing supervised maximum liklihood classification technique.

Remote-Controlled Rotorcraft Blade Vibration and Modal Analysis at Low Frequencies

DTIC Science & Technology

2016-02-01

modal analysis, remote-controlled helicopter , remote-controlled rotorcraft, HUMS for rotorcraft 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...Experimental Setup 1 4. Results 4 4.1 Rotor Blade Acceleration 4 4.2 Modal Analysis: Using an Impact Hammer 7 4.3 Dynamic Response Revisited 8 5... Rotor blade response to shaker outputting 1-V sine wave at 100 Hz ....5 Fig. 6 Rotor blade response to shaker outputting 1-V sine sweep from 20- to 100

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

NASA Astrophysics Data System (ADS)

Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks

PubMed Central

Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping

2018-01-01

Simple Summary The understanding of the spatio-temporal distribution of the species habitats would facilitate wildlife resource management and conservation efforts. Existing methods have poor performance due to the limited availability of training samples. More recently, location-aware sensors have been widely used to track animal movements. The aim of the study was to generate suitability maps of bar-head geese using movement data coupled with environmental parameters, such as remote sensing images and temperature data. Therefore, we modified a deep convolutional neural network for the multi-scale inputs. The results indicate that the proposed method can identify the areas with the dense goose species around Qinghai Lake. In addition, this approach might also be interesting for implementation in other species with different niche factors or in areas where biological survey data are scarce. Abstract With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction. PMID:29701686

Environmental mapping and monitoring of Iceland by remote sensing (EMMIRS)

NASA Astrophysics Data System (ADS)

Pedersen, Gro B. M.; Vilmundardóttir, Olga K.; Falco, Nicola; Sigurmundsson, Friðþór S.; Rustowicz, Rose; Belart, Joaquin M.-C.; Gísladóttir, Gudrun; Benediktsson, Jón A.

2016-04-01

Iceland is exposed to rapid and dynamic landscape changes caused by natural processes and man-made activities, which impact and challenge the country. Fast and reliable mapping and monitoring techniques are needed on a big spatial scale. However, currently there is lack of operational advanced information processing techniques, which are needed for end-users to incorporate remote sensing (RS) data from multiple data sources. Hence, the full potential of the recent RS data explosion is not being fully exploited. The project Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS) bridges the gap between advanced information processing capabilities and end-user mapping of the Icelandic environment. This is done by a multidisciplinary assessment of two selected remote sensing super sites, Hekla and Öræfajökull, which encompass many of the rapid natural and man-made landscape changes that Iceland is exposed to. An open-access benchmark repository of the two remote sensing supersites is under construction, providing high-resolution LIDAR topography and hyperspectral data for land-cover and landform classification. Furthermore, a multi-temporal and multi-source archive stretching back to 1945 allows a decadal evaluation of landscape and ecological changes for the two remote sensing super sites by the development of automated change detection techniques. The development of innovative pattern recognition and machine learning-based approaches to image classification and change detection is one of the main tasks of the EMMIRS project, aiming to extract and compute earth observation variables as automatically as possible. Ground reference data collected through a field campaign will be used to validate the implemented methods, which outputs are then inferred with geological and vegetation models. Here, preliminary results of an automatic land-cover classification based on hyperspectral image analysis are reported. Furthermore, the EMMIRS project investigates the complex landscape dynamics between geological and ecological processes. This is done through cross-correlation of mapping results and implementation of modelling techniques that simulate geological and ecological processes in order to extrapolate the landscape evolution

Assessing urban forest canopy cover using airborne or satellite imagery

Treesearch

Jeffrey T. Walton; David J. Nowak; Eric J. Greenfield

2008-01-01

With the availability of many sources of imagery and various digital classification techniques, assessing urban forest canopy cover is readily accessible to most urban forest managers. Understanding the capability and limitations of various types of imagery and classification methods is essential to interpreting canopy cover values. An overview of several remote...

Evolving forest fire burn severity classification algorithms for multispectral imagery

NASA Astrophysics Data System (ADS)

Brumby, Steven P.; Harvey, Neal R.; Bloch, Jeffrey J.; Theiler, James P.; Perkins, Simon J.; Young, Aaron C.; Szymanski, John J.

2001-08-01

Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100% tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial color/infrared photography.

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

NASA Astrophysics Data System (ADS)

Su, Lihong

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

Research on remote sensing identification of rural abandoned homesteads using multiparameter characteristics method

NASA Astrophysics Data System (ADS)

Xu, Saiping; Zhao, Qianjun; Yin, Kai; Cui, Bei; Zhang, Xiupeng

2016-10-01

Hollow village is a special phenomenon in the process of urbanization in China, which causes the waste of land resources. Therefore, it's imminent to carry out the hollow village recognition and renovation. However, there are few researches on the remote sensing identification of hollow village. In this context, in order to recognize the abandoned homesteads by remote sensing technique, the experiment was carried out as follows. Firstly, Gram-Schmidt transform method was utilized to complete the image fusion between multi-spectral images and panchromatic image of WorldView-2. Then the fusion images were made edge enhanced by high pass filtering. The multi-resolution segmentation and spectral difference segmentation were carried out to obtain the image objects. Secondly, spectral characteristic parameters were calculated, such as the normalized difference vegetation index (NDVI), the normalized difference water index (NDWI), the normalized difference Soil index (NDSI) etc. The shape feature parameters were extracted, such as Area, Length/Width Ratio and Rectangular Fit etc.. Thirdly, the SEaTH algorithm was used to determine the thresholds and optimize the feature space. Furthermore, the threshold classification method and the random forest classifier were combined, and the appropriate amount of samples were selected to train the classifier in order to determine the important feature parameters and the best classifier parameters involved in classification. Finally, the classification results was verified by computing the confusion matrix. The classification results were continuous and the phenomenon of salt and pepper using pixel classification was avoided effectively. In addition, the results showed that the extracted Abandoned Homesteads were in complete shapes, which could be distinguished from those confusing classes such as Homestead in Use and Roads.

The Analysis of Object-Based Change Detection in Mining Area: a Case Study with Pingshuo Coal Mine

NASA Astrophysics Data System (ADS)

Zhang, M.; Zhou, W.; Li, Y.

2017-09-01

Accurate information on mining land use and land cover change are crucial for monitoring and environmental change studies. In this paper, RapidEye Remote Sensing Image (Map 2012) and SPOT7 Remote Sensing Image (Map 2015) in Pingshuo Mining Area are selected to monitor changes combined with object-based classification and change vector analysis method, we also used R in highresolution remote sensing image for mining land classification, and found the feasibility and the flexibility of open source software. The results show that (1) the classification of reclaimed mining land has higher precision, the overall accuracy and kappa coefficient of the classification of the change region map were 86.67 % and 89.44 %. It's obvious that object-based classification and change vector analysis which has a great significance to improve the monitoring accuracy can be used to monitor mining land, especially reclaiming mining land; (2) the vegetation area changed from 46 % to 40 % accounted for the proportion of the total area from 2012 to 2015, and most of them were transformed into the arable land. The sum of arable land and vegetation area increased from 51 % to 70 %; meanwhile, build-up land has a certain degree of increase, part of the water area was transformed into arable land, but the extent of the two changes is not obvious. The result illustrated the transformation of reclaimed mining area, at the same time, there is still some land convert to mining land, and it shows the mine is still operating, mining land use and land cover are the dynamic procedure.

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

PubMed

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

2017-12-14

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

Comparison of Different Machine Learning Algorithms for Lithological Mapping Using Remote Sensing Data and Morphological Features: A Case Study in Kurdistan Region, NE Iraq

NASA Astrophysics Data System (ADS)

Othman, Arsalan; Gloaguen, Richard

2015-04-01

Topographic effects and complex vegetation cover hinder lithology classification in mountain regions based not only in field, but also in reflectance remote sensing data. The area of interest "Bardi-Zard" is located in the NE of Iraq. It is part of the Zagros orogenic belt, where seven lithological units outcrop and is known for its chromite deposit. The aim of this study is to compare three machine learning algorithms (MLAs): Maximum Likelihood (ML), Support Vector Machines (SVM), and Random Forest (RF) in the context of a supervised lithology classification task using Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite, its derived, spatial information (spatial coordinates) and geomorphic data. We emphasize the enhancement in remote sensing lithological mapping accuracy that arises from the integration of geomorphic features and spatial information (spatial coordinates) in classifications. This study identifies that RF is better than ML and SVM algorithms in almost the sixteen combination datasets, which were tested. The overall accuracy of the best dataset combination with the RF map for the all seven classes reach ~80% and the producer and user's accuracies are ~73.91% and 76.09% respectively while the kappa coefficient is ~0.76. TPI is more effective with SVM algorithm than an RF algorithm. This paper demonstrates that adding geomorphic indices such as TPI and spatial information in the dataset increases the lithological classification accuracy.

A spectral-structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhao, Bei; Zhong, Yanfei; Zhang, Liangpei

2016-06-01

Land-use classification of very high spatial resolution remote sensing (VHSR) imagery is one of the most challenging tasks in the field of remote sensing image processing. However, the land-use classification is hard to be addressed by the land-cover classification techniques, due to the complexity of the land-use scenes. Scene classification is considered to be one of the expected ways to address the land-use classification issue. The commonly used scene classification methods of VHSR imagery are all derived from the computer vision community that mainly deal with terrestrial image recognition. Differing from terrestrial images, VHSR images are taken by looking down with airborne and spaceborne sensors, which leads to the distinct light conditions and spatial configuration of land cover in VHSR imagery. Considering the distinct characteristics, two questions should be answered: (1) Which type or combination of information is suitable for the VHSR imagery scene classification? (2) Which scene classification algorithm is best for VHSR imagery? In this paper, an efficient spectral-structural bag-of-features scene classifier (SSBFC) is proposed to combine the spectral and structural information of VHSR imagery. SSBFC utilizes the first- and second-order statistics (the mean and standard deviation values, MeanStd) as the statistical spectral descriptor for the spectral information of the VHSR imagery, and uses dense scale-invariant feature transform (SIFT) as the structural feature descriptor. From the experimental results, the spectral information works better than the structural information, while the combination of the spectral and structural information is better than any single type of information. Taking the characteristic of the spatial configuration into consideration, SSBFC uses the whole image scene as the scope of the pooling operator, instead of the scope generated by a spatial pyramid (SP) commonly used in terrestrial image classification. The experimental results show that the whole image as the scope of the pooling operator performs better than the scope generated by SP. In addition, SSBFC codes and pools the spectral and structural features separately to avoid mutual interruption between the spectral and structural features. The coding vectors of spectral and structural features are then concatenated into a final coding vector. Finally, SSBFC classifies the final coding vector by support vector machine (SVM) with a histogram intersection kernel (HIK). Compared with the latest scene classification methods, the experimental results with three VHSR datasets demonstrate that the proposed SSBFC performs better than the other classification methods for VHSR image scenes.

Evaluation of results of US corn and soybeans exploratory experiment: Classification procedures verification test. [Missouri, Iowa, Indiana, and Illinois

NASA Technical Reports Server (NTRS)

Carnes, J. G.; Baird, J. E. (Principal Investigator)

1980-01-01

The classification procedure utilized in making crop proportion estimates for corn and soybeans using remotely sensed data was evaluated. The procedure was derived during the transition year of the Large Area Crop Inventory Experiment. Analysis of variance techniques were applied to classifications performed by 3 groups of analysts who processed 25 segments selected from 4 agrophysical units (APU's). Group and APU effects were assessed to determine factors which affected the quality of the classifications. The classification results were studied to determine the effectiveness of the procedure in producing corn and soybeans proportion estimates.

Megafauna of the UKSRL exploration contract area and eastern Clarion-Clipperton Zone in the Pacific Ocean: Annelida, Arthropoda, Bryozoa, Chordata, Ctenophora, Mollusca.

PubMed

Amon, Diva J; Ziegler, Amanda F; Drazen, Jeffrey C; Grischenko, Andrei V; Leitner, Astrid B; Lindsay, Dhugal J; Voight, Janet R; Wicksten, Mary K; Young, Craig M; Smith, Craig R

2017-01-01

There is growing interest in mining polymetallic nodules from the abyssal Clarion-Clipperton Zone (CCZ) in the tropical Pacific Ocean. Despite having been the focus of environmental studies for decades, the benthic megafauna of the CCZ remain poorly known. To predict and manage the environmental impacts of mining in the CCZ, baseline knowledge of the megafauna is essential. The ABYSSLINE Project has conducted benthic biological baseline surveys in the UK Seabed Resources Ltd polymetallic-nodule exploration contract area (UK-1). Prior to ABYSSLINE research cruises in 2013 and 2015, no biological studies had been done in this area of the eastern CCZ. Using a Remotely Operated Vehicle and Autonomous Underwater Vehicle (as well as several other pieces of equipment), the megafauna within the UK Seabed Resources Ltd exploration contract area (UK-1) and at a site ~250 km east of the UK-1 area were surveyed, allowing us to make the first estimates of megafaunal morphospecies richness from the imagery collected. Here, we present an atlas of the abyssal annelid, arthropod, bryozoan, chordate, ctenophore and molluscan megafauna observed and collected during the ABYSSLINE cruises to the UK-1 polymetallic-nodule exploration contract area in the CCZ. There appear to be at least 55 distinct morphospecies (8 Annelida, 12 Arthropoda, 4 Bryozoa, 22 Chordata, 5 Ctenophora, and 4 Mollusca) identified mostly by morphology but also using molecular barcoding for a limited number of animals that were collected. This atlas will aid the synthesis of megafaunal presence/absence data collected by contractors, scientists and other stakeholders undertaking work in the CCZ, ultimately helping to decipher the biogeography of the megafauna in this threatened habitat.

Megafauna of the UKSRL exploration contract area and eastern Clarion-Clipperton Zone in the Pacific Ocean: Annelida, Arthropoda, Bryozoa, Chordata, Ctenophora, Mollusca

PubMed Central

Ziegler, Amanda F; Drazen, Jeffrey C; Grischenko, Andrei V; Leitner, Astrid B; Lindsay, Dhugal J; Voight, Janet R; Wicksten, Mary K; Young, Craig M; Smith, Craig R

2017-01-01

Abstract Background There is growing interest in mining polymetallic nodules from the abyssal Clarion-Clipperton Zone (CCZ) in the tropical Pacific Ocean. Despite having been the focus of environmental studies for decades, the benthic megafauna of the CCZ remain poorly known. To predict and manage the environmental impacts of mining in the CCZ, baseline knowledge of the megafauna is essential. The ABYSSLINE Project has conducted benthic biological baseline surveys in the UK Seabed Resources Ltd polymetallic-nodule exploration contract area (UK-1). Prior to ABYSSLINE research cruises in 2013 and 2015, no biological studies had been done in this area of the eastern CCZ. New information Using a Remotely Operated Vehicle and Autonomous Underwater Vehicle (as well as several other pieces of equipment), the megafauna within the UK Seabed Resources Ltd exploration contract area (UK-1) and at a site ~250 km east of the UK-1 area were surveyed, allowing us to make the first estimates of megafaunal morphospecies richness from the imagery collected. Here, we present an atlas of the abyssal annelid, arthropod, bryozoan, chordate, ctenophore and molluscan megafauna observed and collected during the ABYSSLINE cruises to the UK-1 polymetallic-nodule exploration contract area in the CCZ. There appear to be at least 55 distinct morphospecies (8 Annelida, 12 Arthropoda, 4 Bryozoa, 22 Chordata, 5 Ctenophora, and 4 Mollusca) identified mostly by morphology but also using molecular barcoding for a limited number of animals that were collected. This atlas will aid the synthesis of megafaunal presence/absence data collected by contractors, scientists and other stakeholders undertaking work in the CCZ, ultimately helping to decipher the biogeography of the megafauna in this threatened habitat. PMID:28874906

Testing animal-assisted cleaning prior to transplantation in coral reef restoration.

PubMed

Frias-Torres, Sarah; van de Geer, Casper

2015-01-01

Rearing coral fragments in nurseries and subsequent transplantation onto a degraded reef is a common approach for coral reef restoration. However, if barnacles and other biofouling organisms are not removed prior to transplantation, fish will dislodge newly cemented corals when feeding on biofouling organisms. This behavior can lead to an increase in diver time due to the need to reattach the corals. Thus, cleaning nurseries to remove biofouling organisms such as algae and invertebrates is necessary prior to transplantation, and this cleaning constitutes a significant time investment in a restoration project. We tested a novel biomimicry technique of animal-assisted cleaning on nursery corals prior to transplantation at a coral reef restoration site in Seychelles, Indian Ocean. To determine whether animal-assisted cleaning was possible, preliminary visual underwater surveys were performed to quantify the fish community at the study site. Then, cleaning stations consisting of nursery ropes carrying corals and biofouling organisms, set at 0.3 m, 2 m, 4 m, 6 m and 8 m from the seabed, were placed at both the transplantation (treatment) site and the nursery (control) site. Remote GoPro video cameras recorded fish feeding at the nursery ropes without human disturbance. A reef fish assemblage of 32 species from 4 trophic levels (18.8% herbivores, 18.8% omnivores, 59.3% secondary consumers and 3.1% carnivores) consumed 95% of the barnacles on the coral nursery ropes placed 0.3 m above the seabed. Using this cleaning station, we reduced coral dislodgement from 16% to zero. This cleaning station technique could be included as a step prior to coral transplantation worldwide on the basis of location-specific fish assemblages and during the early nursery phase of sexually produced juvenile corals.

Testing animal-assisted cleaning prior to transplantation in coral reef restoration

PubMed Central

van de Geer, Casper

2015-01-01

Rearing coral fragments in nurseries and subsequent transplantation onto a degraded reef is a common approach for coral reef restoration. However, if barnacles and other biofouling organisms are not removed prior to transplantation, fish will dislodge newly cemented corals when feeding on biofouling organisms. This behavior can lead to an increase in diver time due to the need to reattach the corals. Thus, cleaning nurseries to remove biofouling organisms such as algae and invertebrates is necessary prior to transplantation, and this cleaning constitutes a significant time investment in a restoration project. We tested a novel biomimicry technique of animal-assisted cleaning on nursery corals prior to transplantation at a coral reef restoration site in Seychelles, Indian Ocean. To determine whether animal-assisted cleaning was possible, preliminary visual underwater surveys were performed to quantify the fish community at the study site. Then, cleaning stations consisting of nursery ropes carrying corals and biofouling organisms, set at 0.3 m, 2 m, 4 m, 6 m and 8 m from the seabed, were placed at both the transplantation (treatment) site and the nursery (control) site. Remote GoPro video cameras recorded fish feeding at the nursery ropes without human disturbance. A reef fish assemblage of 32 species from 4 trophic levels (18.8% herbivores, 18.8% omnivores, 59.3% secondary consumers and 3.1% carnivores) consumed 95% of the barnacles on the coral nursery ropes placed 0.3 m above the seabed. Using this cleaning station, we reduced coral dislodgement from 16% to zero. This cleaning station technique could be included as a step prior to coral transplantation worldwide on the basis of location-specific fish assemblages and during the early nursery phase of sexually produced juvenile corals. PMID:26468440

Rhodolith Beds Are Major CaCO3 Bio-Factories in the Tropical South West Atlantic

PubMed Central

Amado-Filho, Gilberto M.; Moura, Rodrigo L.; Bastos, Alex C.; Salgado, Leonardo T.; Sumida, Paulo Y.; Guth, Arthur Z.; Francini-Filho, Ronaldo B.; Pereira-Filho, Guilherme H.; Abrantes, Douglas P.; Brasileiro, Poliana S.; Bahia, Ricardo G.; Leal, Rachel N.; Kaufman, Les; Kleypas, Joanie A.; Farina, Marcos; Thompson, Fabiano L.

2012-01-01

Rhodoliths are nodules of non-geniculate coralline algae that occur in shallow waters (<150 m depth) subjected to episodic disturbance. Rhodolith beds stand with kelp beds, seagrass meadows, and coralline algal reefs as one of the world's four largest macrophyte-dominated benthic communities. Geographic distribution of rhodolith beds is discontinuous, with large concentrations off Japan, Australia and the Gulf of California, as well as in the Mediterranean, North Atlantic, eastern Caribbean and Brazil. Although there are major gaps in terms of seabed habitat mapping, the largest rhodolith beds are purported to occur off Brazil, where these communities are recorded across a wide latitudinal range (2°N - 27°S). To quantify their extent, we carried out an inter-reefal seabed habitat survey on the Abrolhos Shelf (16°50′ - 19°45′S) off eastern Brazil, and confirmed the most expansive and contiguous rhodolith bed in the world, covering about 20,900 km2. Distribution, extent, composition and structure of this bed were assessed with side scan sonar, remotely operated vehicles, and SCUBA. The mean rate of CaCO3 production was estimated from in situ growth assays at 1.07 kg m−2 yr−1, with a total production rate of 0.025 Gt yr−1, comparable to those of the world's largest biogenic CaCO3 deposits. These gigantic rhodolith beds, of areal extent equivalent to the Great Barrier Reef, Australia, are a critical, yet poorly understood component of the tropical South Atlantic Ocean. Based on the relatively high vulnerability of coralline algae to ocean acidification, these beds are likely to experience a profound restructuring in the coming decades. PMID:22536356

NeMO-Net & Fluid Lensing: The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Using Fluid Lensing Augmentation of NASA EOS Data

NASA Technical Reports Server (NTRS)

Chirayath, Ved

2018-01-01

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low-resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.

Improving Oil Palm Classification in the Peruvian Amazon by Combining Active and Passive Remote Sensing Data

NASA Astrophysics Data System (ADS)

Gutierrez-Velez, V. H.; DeFries, R. S.

2011-12-01

Oil palm expansion has led to clearing of extensive forest areas in the tropics. However quantitative assessments of the magnitude of oil palm expansion to deforestation have been challenging due in large part to the limitations presented by conventional optical data sets for discriminating plantations from forests and other tree cover vegetations. Recently available information from active remote sensors has opened the possibility of using these data sources to overcome these limitations. The purpose of this analysis is to evaluate the accuracy of oil palm classification when using ALOS/PALSAR active satellite data in conjunction with Landsat information, compared to the use of Landsat data only. The analysis takes place in a focused region around the city of Pucallpa in the Ucayali province of the Peruvian Amazon for the year 2010. Oil palm plantations were separated in five categories consisting of four age classes (0-3, 3-5, 5-10 and > 10 yrs) and an additional class accounting for degraded plantations older than 15 yr. Other land covers were water bodies, unvegetated land, short and tall grass, fallow, secondary vegetation, and forest. Classifications were performed using random forests. Training points for calibration and validation consisted of 411 polygons measured in areas representative of the land covers of interest and totaled 6,367 ha. Overall classification accuracy increased from 89.9% using only Landsat data sets to 94.3% using both Landast and ALOS/PALSAR. Both user's and producer's accuracy increased in all classes when using both data sets except for producer's accuracy in short grass which decreased by 1%. The largest increase in user's accuracy was obtained in oil palm plantations older than 10 years from 62 to 80% while producer's accuracy improved the most in plantations in age class 3-5 from 63 to 80%. Results demonstrate the suitability of data from ALOS/PALSAR and other active remote sensors to improve classification of oil palm plantations in age classes and discriminate them from other land covers. Results suggest a potential for improving discrimination of other tree cover types using a combination of active and conventional optical remote sensors.

The use of the modified Cholesky decomposition in divergence and classification calculations

NASA Technical Reports Server (NTRS)

Vanroony, D. L.; Lynn, M. S.; Snyder, C. H.

1973-01-01

The use of the Cholesky decomposition technique is analyzed as applied to the feature selection and classification algorithms used in the analysis of remote sensing data (e.g. as in LARSYS). This technique is approximately 30% faster in classification and a factor of 2-3 faster in divergence, as compared with LARSYS. Also numerical stability and accuracy are slightly improved. Other methods necessary to deal with numerical stablity problems are briefly discussed.

The use of the modified Cholesky decomposition in divergence and classification calculations

NASA Technical Reports Server (NTRS)

Van Rooy, D. L.; Lynn, M. S.; Snyder, C. H.

1973-01-01

This report analyzes the use of the modified Cholesky decomposition technique as applied to the feature selection and classification algorithms used in the analysis of remote sensing data (e.g., as in LARSYS). This technique is approximately 30% faster in classification and a factor of 2-3 faster in divergence, as compared with LARSYS. Also numerical stability and accuracy are slightly improved. Other methods necessary to deal with numerical stability problems are briefly discussed.

The information extraction of Gannan citrus orchard based on the GF-1 remote sensing image

NASA Astrophysics Data System (ADS)

Wang, S.; Chen, Y. L.

2017-02-01

The production of Gannan oranges is the largest in China, which occupied an important part in the world. The extraction of citrus orchard quickly and effectively has important significance for fruit pathogen defense, fruit production and industrial planning. The traditional spectra extraction method of citrus orchard based on pixel has a lower classification accuracy, difficult to avoid the “pepper phenomenon”. In the influence of noise, the phenomenon that different spectrums of objects have the same spectrum is graveness. Taking Xunwu County citrus fruit planting area of Ganzhou as the research object, aiming at the disadvantage of the lower accuracy of the traditional method based on image element classification method, a decision tree classification method based on object-oriented rule set is proposed. Firstly, multi-scale segmentation is performed on the GF-1 remote sensing image data of the study area. Subsequently the sample objects are selected for statistical analysis of spectral features and geometric features. Finally, combined with the concept of decision tree classification, a variety of empirical values of single band threshold, NDVI, band combination and object geometry characteristics are used hierarchically to execute the information extraction of the research area, and multi-scale segmentation and hierarchical decision tree classification is implemented. The classification results are verified with the confusion matrix, and the overall Kappa index is 87.91%.

Applying aerial digital photography as a spectral remote sensing technique for macrophytic cover assessment in small rural streams

NASA Astrophysics Data System (ADS)

Anker, Y.; Hershkovitz, Y.; Gasith, A.; Ben-Dor, E.

2011-12-01

Although remote sensing of fluvial ecosystems is well developed, the tradeoff between spectral and spatial resolutions prevents its application in small streams (<3m width). In the current study, a remote sensing approach for monitoring and research of small ecosystem was developed. The method is based on differentiation between two indicative vegetation species out of the ecosystem flora. Since when studied, the channel was covered mostly by a filamentous green alga (Cladophora glomerata) and watercress (Nasturtium officinale), these species were chosen as indicative; nonetheless, common reed (Phragmites australis) was also classified in order to exclude it from the stream ROI. The procedure included: A. For both section and habitat scales classifications, acquisition of aerial digital RGB datasets. B. For section scale classification, hyperspectral (HSR) dataset acquisition. C. For calibration, HSR reflectance measurements of specific ground targets, in close proximity to each dataset acquisition swath. D. For habitat scale classification, manual, in-stream flora grid transects classification. The digital RGB datasets were converted to reflectance units by spectral calibration against colored reference plates. These red, green, blue, white, and black EVA foam reference plates were measured by an ASD field spectrometer and each was given a spectral value. Each spectral value was later applied to the spectral calibration and radiometric correction of spectral RGB (SRGB) cube. Spectral calibration of the HSR dataset was done using the empirical line method, based on reference values of progressive grey scale targets. Differentiation between the vegetation species was done by supervised classification both for the HSR and for the SRGB datasets. This procedure was done using the Spectral Angle Mapper function with the spectral pattern of each vegetation species as a spectral end member. Comparison between the two remote sensing techniques and between the SRGB classification and the in-situ transects indicates that: A. Stream vegetation classification resolution is about 4 cm by the SRGB method compared to about 1 m by HSR. Moreover, this resolution is also higher than of the manual grid transect classification. B. The SRGB method is by far the most cost-efficient. The combination of spectral information (rather than the cognitive color) and high spatial resolution of aerial photography provides noise filtration and better sub-water detection capabilities than the HSR technique. C. Only the SRGB method applies for habitat and section scales; hence, its application together with in-situ grid transects for validation, may be optimal for use in similar scenarios.
The HSR dataset was first degraded to 17 bands with the same spectral range as the RGB dataset and also to a dataset with 3 equivalent bands

Supervised Semantic Classification for Nuclear Proliferation Monitoring

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

Vatsavai, Raju; Cheriyadat, Anil M; Gleason, Shaun Scott

2010-01-01

Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remote sensing imagery. In this paper we present a supervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 120 images collected under different spatial and temporal settings over the globe representing three major semantic categories: airports, nuclear, and coal power plants. Initial experimental results show a reasonable discrimination of these three categories even though coal and nuclear images share highly common and overlapping objects. This research also identified several research challenges associated with nuclear proliferationmore » monitoring using high resolution remote sensing images.« less

A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites

PubMed Central

Karl, Jason W.

2017-01-01

Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral ‘fingerprint’ of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches. PMID:28414731

A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites.

PubMed

Maynard, Jonathan J; Karl, Jason W

2017-01-01

Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral 'fingerprint' of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches.

Key Issues in the Analysis of Remote Sensing Data: A report on the workshop

NASA Technical Reports Server (NTRS)

Swain, P. H. (Principal Investigator)

1981-01-01

The procedures of a workshop assessing the state of the art of machine analysis of remotely sensed data are summarized. Areas discussed were: data bases, image registration, image preprocessing operations, map oriented considerations, advanced digital systems, artificial intelligence methods, image classification, and improved classifier training. Recommendations of areas for further research are presented.

Vertical migration of fine-grained sediments from interior to surface of seabed driven by seepage flows-`sub-bottom sediment pump action'

NASA Astrophysics Data System (ADS)

Zhang, Shaotong; Jia, Yonggang; Wen, Mingzheng; Wang, Zhenhao; Zhang, Yaqi; Zhu, Chaoqi; Li, Bowen; Liu, Xiaolei

2017-02-01

A scientific hypothesis is proposed and preliminarily verified in this paper: under the driving of seepage flows, there might be a vertical migration of fine-grained soil particles from interior to surface of seabed, which is defined as `sub-bottom sediment pump action' in this paper. Field experiments were performed twice on the intertidal flat of the Yellow River delta to study this process via both trapping the pumped materials and recording the pore pressures in the substrate. Experimental results are quite interesting as we did observe yellow slurry which is mainly composed of fine-grained soil particles appearing on the seabed surface; seepage gradients were also detected in the intertidal flat, under the action of tides and small wind waves. Preliminary conclusions are that `sediment pump' occurs when seepage force exceeds a certain threshold: firstly, it is big enough to disconnect the soil particles from the soil skeleton; secondly, the degree of seabed fluidization or bioturbation is big enough to provide preferred paths for the detached materials to migrate upwards. Then they would be firstly pumped from interior to the surface of seabed and then easily re-suspended into overlying water column. Influential factors of `sediment pump' are determined as hydrodynamics (wave energy), degree of consolidation, index of bioturbation (permeability) and content of fine-grained materials (sedimentary age). This new perspective of `sediment pump' may provide some implications for the mechanism interpretation of several unclear geological phenomena in the Yellow River delta area.

Assessment of the role of remote sensing in the study of inland and coastal waters

NASA Technical Reports Server (NTRS)

Curfman, H. J.; Oberholtzer, J. D.; Schertler, R. J.

1980-01-01

Several problems within Great Lakes, coastal, and continental shelf water were selected and organized under the topical headings of Productivity, Sedimentation, Water Dynamics, Eutrophication, and Hazardous Substances. The measurements required in the study of each of the problems were identified. An assessment was made of the present capability and the potential of remote sensing to make these measurements. The relevant remote-sensing technology for each of these classifications was discussed and needed advancements indicated.

Image Classification Workflow Using Machine Learning Methods

NASA Astrophysics Data System (ADS)

Christoffersen, M. S.; Roser, M.; Valadez-Vergara, R.; Fernández-Vega, J. A.; Pierce, S. A.; Arora, R.

2016-12-01

Recent increases in the availability and quality of remote sensing datasets have fueled an increasing number of scientifically significant discoveries based on land use classification and land use change analysis. However, much of the software made to work with remote sensing data products, specifically multispectral images, is commercial and often prohibitively expensive. The free to use solutions that are currently available come bundled up as small parts of much larger programs that are very susceptible to bugs and difficult to install and configure. What is needed is a compact, easy to use set of tools to perform land use analysis on multispectral images. To address this need, we have developed software using the Python programming language with the sole function of land use classification and land use change analysis. We chose Python to develop our software because it is relatively readable, has a large body of relevant third party libraries such as GDAL and Spectral Python, and is free to install and use on Windows, Linux, and Macintosh operating systems. In order to test our classification software, we performed a K-means unsupervised classification, Gaussian Maximum Likelihood supervised classification, and a Mahalanobis Distance based supervised classification. The images used for testing were three Landsat rasters of Austin, Texas with a spatial resolution of 60 meters for the years of 1984 and 1999, and 30 meters for the year 2015. The testing dataset was easily downloaded using the Earth Explorer application produced by the USGS. The software should be able to perform classification based on any set of multispectral rasters with little to no modification. Our software makes the ease of land use classification using commercial software available without an expensive license.

A field investigation on the effects of background erosion on the free span development of a submarine pipeline

NASA Astrophysics Data System (ADS)

Wen, Shipeng; Xu, Jishang; Hu, Guanghai; Dong, Ping; Shen, Hong

2015-08-01

The safety of submarine pipelines is largely influenced by free spans and corrosions. Previous studies on free spans caused by seabed scours are mainly based on the stable environment, where the background seabed scour is in equilibrium and the soil is homogeneous. To study the effects of background erosion on the free span development of subsea pipelines, a submarine pipeline located at the abandoned Yellow River subaqueous delta lobe was investigated with an integrated surveying system which included a Multibeam bathymetric system, a dual-frequency side-scan sonar, a high resolution sub-bottom profiler, and a Magnetic Flux Leakage (MFL) sensor. We found that seabed homogeneity has a great influence on the free span development of the pipeline. More specifically, for homogeneous background scours, the morphology of scour hole below the pipeline is quite similar to that without the background scour, whereas for inhomogeneous background scour, the nature of spanning is mainly dependent on the evolution of seabed morphology near the pipeline. Magnetic Flux Leakage (MFL) detection results also reveal the possible connection between long free spans and accelerated corrosion of the pipeline.

Assessment of pollution impact on biological activity and structure of seabed bacterial communities in the Port of Livorno (Italy).

PubMed

Iannelli, Renato; Bianchi, Veronica; Macci, Cristina; Peruzzi, Eleonora; Chiellini, Carolina; Petroni, Giulio; Masciandaro, Grazia

2012-06-01

The main objective of this study was to assess the impact of pollution on seabed bacterial diversity, structure and activity in the Port of Livorno. Samples of seabed sediments taken from five selected sites within the port were subjected to chemical analyses, enzymatic activity detection, bacterial count and biomolecular analysis. Five different statistics were used to correlate the level of contamination with the detected biological indicators. The results showed that the port is mainly contaminated by variable levels of petroleum hydrocarbons and heavy metals, which affect the structure and activity of the bacterial population. Irrespective of pollution levels, the bacterial diversity did not diverge significantly among the assessed sites and samples, and no dominance was observed. The type of impact of hydrocarbons and heavy metals was controversial, thus enforcing the supposition that the structure of the bacterial community is mainly driven by the levels of nutrients. The combined use of chemical and biological essays resulted in an in-depth observation and analysis of the existing links between pollution macro-indicators and biological response of seabed bacterial communities. Copyright © 2012 Elsevier B.V. All rights reserved.

Integrating remote sensing and terrain data in forest fire modeling

NASA Astrophysics Data System (ADS)

Medler, Michael Johns

Forest fire policies are changing. Managers now face conflicting imperatives to re-establish pre-suppression fire regimes, while simultaneously preventing resource destruction. They must, therefore, understand the spatial patterns of fires. Geographers can facilitate this understanding by developing new techniques for mapping fire behavior. This dissertation develops such techniques for mapping recent fires and using these maps to calibrate models of potential fire hazards. In so doing, it features techniques that strive to address the inherent complexity of modeling the combinations of variables found in most ecological systems. Image processing techniques were used to stratify the elements of terrain, slope, elevation, and aspect. These stratification images were used to assure sample placement considered the role of terrain in fire behavior. Examination of multiple stratification images indicated samples were placed representatively across a controlled range of scales. The incorporation of terrain data also improved preliminary fire hazard classification accuracy by 40%, compared with remotely sensed data alone. A Kauth-Thomas transformation (KT) of pre-fire and post-fire Thematic Mapper (TM) remotely sensed data produced brightness, greenness, and wetness images. Image subtraction indicated fire induced change in brightness, greenness, and wetness. Field data guided a fuzzy classification of these change images. Because fuzzy classification can characterize a continuum of a phenomena where discrete classification may produce artificial borders, fuzzy classification was found to offer a range of fire severity information unavailable with discrete classification. These mapped fire patterns were used to calibrate a model of fire hazards for the entire mountain range. Pre-fire TM, and a digital elevation model produced a set of co-registered images. Training statistics were developed from 30 polygons associated with the previously mapped fire severity. Fuzzy classifications of potential burn patterns were produced from these images. Observed field data values were displayed over the hazard imagery to indicate the effectiveness of the model. Areas that burned without suppression during maximum fire severity are predicted best. Areas with widely spaced trees and grassy understory appear to be misrepresented, perhaps as a consequence of inaccuracies in the initial fire mapping.

Assessing Hurricane Katrina Damage to the Mississippi Gulf Coast Using IKONOS Imagery

NASA Technical Reports Server (NTRS)

Spruce, Joseph; McKellip, Rodney

2006-01-01

Hurricane Katrina hit southeastern Louisiana and the Mississippi Gulf Coast as a Category 3 hurricane with storm surges as high as 9 m. Katrina devastated several coastal towns by destroying or severely damaging hundreds of homes. Several Federal agencies are assessing storm impacts and assisting recovery using high-spatial-resolution remotely sensed data from satellite and airborne platforms. High-quality IKONOS satellite imagery was collected on September 2, 2005, over southwestern Mississippi. Pan-sharpened IKONOS multispectral data and ERDAS IMAGINE software were used to classify post-storm land cover for coastal Hancock and Harrison Counties. This classification included a storm debris category of interest to FEMA for disaster mitigation. The classification resulted from combining traditional unsupervised and supervised classification techniques. Higher spatial resolution aerial and handheld photography were used as reference data. Results suggest that traditional classification techniques and IKONOS data can map wood-dominated storm debris in open areas if relevant training areas are used to develop the unsupervised classification signatures. IKONOS data also enabled other hurricane damage assessment, such as flood-deposited mud on lawns and vegetation foliage loss from the storm. IKONOS data has also aided regional Katrina vegetation damage surveys from multidate Land Remote Sensing Satellite and Moderate Resolution Imaging Spectroradiometer data.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1993-01-01

Progress report on remote sensing of Earth terrain covering the period from Jan. to June 1993 is presented. Areas of research include: radiative transfer model for active and passive remote sensing of vegetation canopy; polarimetric thermal emission from rough ocean surfaces; polarimetric passive remote sensing of ocean wind vectors; polarimetric thermal emission from periodic water surfaces; layer model with tandom spheriodal scatterers for remote sensing of vegetation canopy; application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated mie scatterers with size distributions and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Field validation of Burned Area Reflectance Classification (BARC) products for post fire assessment

Treesearch

Andrew T. Hudak; Peter R. Robichaud; Jeffery B. Evans; Jess Clark; Keith Lannom; Penelope Morgan; Carter Stone

2004-01-01

The USFS Remote Sensing Applications Center (RSAC) and the USGS EROS Data Center (EDC) produce Burned Area Reflectance Classification (BARC) maps for use by Burned Area Emergency Rehabilitation (BAER) teams in rapid response to wildfires. BAER teams desire maps indicative of soil burn severity, but photosynthetic and nonphotosynthetic vegetation also influences the...

Mapping fuels at multiple scales: landscape application of the fuel characteristic classification system.

Treesearch

D. McKenzie; C.L. Raymond; L.-K.B. Kellogg; R.A. Norheim; A.G. Andreu; A.C. Bayard; K.E. Kopper; E. Elman

2007-01-01

Fuel mapping is a complex and often multidisciplinary process, involving remote sensing, ground-based validation, statistical modeling, and knowledge-based systems. The scale and resolution of fuel mapping depend both on objectives and availability of spatial data layers. We demonstrate use of the Fuel Characteristic Classification System (FCCS) for fuel mapping at two...

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

NASA Astrophysics Data System (ADS)

Cheng, Gong; Han, Junwei; Zhou, Peicheng; Guo, Lei

2014-12-01

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.

Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

PubMed Central

Wang, Kai; Franklin, Steven E.; Guo, Xulin; Cattet, Marc

2010-01-01

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS). PMID:22163432

Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists.

PubMed

Wang, Kai; Franklin, Steven E; Guo, Xulin; Cattet, Marc

2010-01-01

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).

Remote Sensing and Reflectance Profiling in Entomology.

PubMed

Nansen, Christian; Elliott, Norman

2016-01-01

Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

Applications of remote sensing, volume 3

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1977-01-01

The author has identified the following significant results. Of the four change detection techniques (post classification comparison, delta data, spectral/temporal, and layered spectral temporal), the post classification comparison was selected for further development. This was based upon test performances of the four change detection method, straightforwardness of the procedures, and the output products desired. A standardized modified, supervised classification procedure for analyzing the Texas coastal zone data was compiled. This procedure was developed in order that all quadrangles in the study are would be classified using similar analysis techniques to allow for meaningful comparisons and evaluations of the classifications.

Design of neural networks for classification of remotely sensed imagery

NASA Technical Reports Server (NTRS)

Chettri, Samir R.; Cromp, Robert F.; Birmingham, Mark

1992-01-01

Classification accuracies of a backpropagation neural network are discussed and compared with a maximum likelihood classifier (MLC) with multivariate normal class models. We have found that, because of its nonparametric nature, the neural network outperforms the MLC in this area. In addition, we discuss techniques for constructing optimal neural nets on parallel hardware like the MasPar MP-1 currently at GSFC. Other important discussions are centered around training and classification times of the two methods, and sensitivity to the training data. Finally, we discuss future work in the area of classification and neural nets.

Computer classification of remotely sensed multispectral image data by extraction and classification of homogeneous objects

NASA Technical Reports Server (NTRS)

Kettig, R. L.

1975-01-01

A method of classification of digitized multispectral images is developed and experimentally evaluated on actual earth resources data collected by aircraft and satellite. The method is designed to exploit the characteristic dependence between adjacent states of nature that is neglected by the more conventional simple-symmetric decision rule. Thus contextual information is incorporated into the classification scheme. The principle reason for doing this is to improve the accuracy of the classification. For general types of dependence this would generally require more computation per resolution element than the simple-symmetric classifier. But when the dependence occurs in the form of redundance, the elements can be classified collectively, in groups, therby reducing the number of classifications required.

Automated estimation of seabed properties from acoustic recordings by an autonomous moving system

NASA Astrophysics Data System (ADS)

Dosso, Stan; Dettmer, Jan; Holland, Charles; Mandolesi, Eric

2016-04-01

This work develops an automated Bayesian method to infer fluid seabed properties as a function of depth along tracks that are surveyed by an autonomous underwater vehicle (AUV). The AUV tows an acoustic source and a 32-element array. The source bandwidth is from 950 to 3000 Hz and frequency-modulated signals are emitted at regular intervals ('pings') as the AUV moves along the track. The recordings of each ping are processed to account for source directionality and reflection coefficients as a function of frequency and grazing angle are extracted by taking the ratio of time-windowed direct and bottom-interacted paths. Each ping provides one data set. This process results in large data volumes with an information content that is much higher than for traditional seismic profiling. However, extracting interpretable results about the lateral and vertical spatial variability of the seabed requires sophisticated and efficient inversion methods. The seabed is approximated as a horizontally stratified, lossy fluid for each ping. Each layer is homogeneous and parametrized by a thickness, velocity, density and attenuation. Since both source and array are towed close to the seabed, a plane-wave approximation is not sufficient to model these data and spherical reflection coefficients must be computed to predict data. Therefore, for each specular angle at each frequency, the Sommerfeld integral is solved efficiently by massively parallel implementation of Levin integration on a graphics processing unit (GPU). The inverse problem is strongly non-linear and requires application of Bayesian sampling to quantify parameter uncertainties. To account for the unknown number of layers in the seabed at each ping, the seabed is parametrized by a trans-dimensional (trans-D) model which treats the number of layers as unknown. To constrain model complexity and improve efficiency, we apply a Poisson prior with even-numbered order statistics to the number of layers. The trans-D model is sampled with a reversible-jump algorithm and efficiency is addressed by parallel tempering. The method is applied to data acquired along a 14-km track on the Malta Plateau with water depths from 144 to 152 m. The reflection coefficient data are sensitive to the upper 7 m of the seabed. Data sets are available at 4-m spacing along this track which is currently still intractable. Therefore, we apply ping averaging and consider data at 40-m spacing. A total of 340 inversions were carried out employing 8 K80 GPUs for approximately 2 weeks of computing time. The results resolve layering along the track with previously unreported complexity and detail. An erosional boundary with rough topography is clearly resolved as a high-velocity, high-density layer. This boundary appears rougher and is buried deeper in more shallow water. Depressions along this boundary are filled in with lower velocity material along the shallow parts of the track. In addition, attenuation is well constrained in a thick low-velocity wedge. [Work supported by ONR and SERDP.

Exploring Models and Data for Remote Sensing Image Caption Generation

NASA Astrophysics Data System (ADS)

Lu, Xiaoqiang; Wang, Binqiang; Zheng, Xiangtao; Li, Xuelong

2018-04-01

Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, noticeable progress has been made in scene classification and target detection.However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal

Landslides and mass wasting offshore Sumatra - results from the Sumatra Earthquake HMS Scott survey January-February 2005

NASA Astrophysics Data System (ADS)

Tappin, D. R.; Henstock, T.; McNeill, L.; Grilli, S.; Biscontin, G.; Watts, P.

2005-12-01

Earthquakes are a commonly cited mechanism for triggering submarine landslides that have the potential to generate damaging tsunamis (e.g. Papua New Guinea 1998). Notwithstanding, the Indian Ocean earthquake of December 26th 2005 has been cited as the cause of both far field and local tsunami runups that have been measured at over 35 metres on the west coast of Sumatra. On the basis of present modelling this seems to be the case. However, if earthquakes are such a common trigger for landslides then the magnitude 9.3 earthquake of December 26th might be expected to have caused numerous seabed failures within the area of rupture that may have contributed to local tsunami runup. This contribution discusses the seabed morphology offshore of Sumatra acquired during the survey carried out by HMS Scott in January and February 2005. Utilising a unique high resolution 12 kHz, 361-beam hull-mounted Sass IV sonar, over 40,000 square kilometres of seabed were mapped. The objective was to identify seabed movements that were the result of the earthquake and to identify submarine slope failures that may have contributed to the tsunami. This paper reports on the results of the survey using Fledermaus imaging software. The area mapped is an accretionary complex formed as the two plates have converged over the past 40 million years. From the data several seabed failure mechanisms of different ages have been identified. Along the plate margin in the west of the survey area the deformation front comprises a series of young thrust folds up to 1000m in elevation and tens of kilometres in length. In places the seaward faces of these folds have failed cohesively and slumped blocks 100's of metres high and up to several kilometres long have been displaced up to 13 kilometres onto the inner trench floor. At other locations older episodes of failure are identified by the presence of displaced slumped blocks located on the crests of the folds; the slumps thus predating uplift. Where young thrust folds are absent, the outer margin of the accretionary prism is deeply dissected and comprises a steeply sloping seabed incised by numerous gullies and slide scars. Here, mechanisms of failure are incremental, and take place mainly through headwall erosion. There are small cohesive failures, although most sediment appears to be shed from the gullies onto the inner trench through channels incised into the seabed. Sediment overflow from the channels has resulted in the construction of sediment fans upon which are located giant sediment waves.

Application of a new genetic classification and semi-automated geomorphic mapping approach in the Perth submarine canyon, Australia

NASA Astrophysics Data System (ADS)

Picard, K.; Nanson, R.; Huang, Z.; Nichol, S.; McCulloch, M.

2017-12-01

The acquisition of high resolution marine geophysical data has intensified in recent years (e.g. multibeam echo-sounding, sub-bottom profiling). This progress provides the opportunity to classify and map the seafloor in greater detail, using new methods that preserve the links between processes and morphology. Geoscience Australia has developed a new genetic classification approach, nested within the Harris et al (2014) global seafloor mapping framework. The approach divides parent units into sub-features based on established classification schemes and feature descriptors defined by Bradwell et al. (2016: http://nora.nerc.ac.uk/), the International Hydrographic Organization (https://www.iho.int) and the Coastal Marine and Ecological Classification Standard (https://www.cmecscatalog.org). Owing to the ecological significance of submarine canyon systems in particular, much recent attention has focused on defining their variation in form and process, whereby they can be classified using a range of topographic metrics, fluvial dis/connection and shelf-incising status. The Perth Canyon is incised into the continental slope and shelf of southwest Australia, covering an area of >1500 km2 and extending from 4700 m water depth to the shelf break in 170 m. The canyon sits within a Marine Protected Area, incorporating a Marine National Park and Habitat Protection Zone in recognition of its benthic and pelagic biodiversity values. However, detailed information of the spatial patterns of the seabed habitats that influence this biodiversity is lacking. Here we use 20 m resolution bathymetry and acoustic backscatter data acquired in 2015 by the Schmidt Ocean Institute plus sub-bottom datasets and sediment samples collected Geoscience Australia in 2005 to apply the new geomorphic classification system to the Perth Canyon. This presentation will show the results of the geomorphic feature mapping of the canyon and its application to better defining potential benthic habitats.

American Thyroid Association Statement on Remote-Access Thyroid Surgery

PubMed Central

Bernet, Victor; Fahey, Thomas J.; Kebebew, Electron; Shaha, Ashok; Stack, Brendan C.; Stang, Michael; Steward, David L.; Terris, David J.

2016-01-01

Background: Remote-access techniques have been described over the recent years as a method of removing the thyroid gland without an incision in the neck. However, there is confusion related to the number of techniques available and the ideal patient selection criteria for a given technique. The aims of this review were to develop a simple classification of these approaches, describe the optimal patient selection criteria, evaluate the outcomes objectively, and define the barriers to adoption. Methods: A review of the literature was performed to identify the described techniques. A simple classification was developed. Technical details, outcomes, and the learning curve were described. Expert opinion consensus was formulated regarding recommendations for patient selection and performance of remote-access thyroid surgery. Results: Remote-access thyroid procedures can be categorized into endoscopic or robotic breast, bilateral axillo-breast, axillary, and facelift approaches. The experience in the United States involves the latter two techniques. The limited data in the literature suggest long operative times, a steep learning curve, and higher costs with remote-access thyroid surgery compared with conventional thyroidectomy. Nevertheless, a consensus was reached that, in appropriate hands, it can be a viable option for patients with unilateral small nodules who wish to avoid a neck incision. Conclusions: Remote-access thyroidectomy has a role in a small group of patients who fit strict selection criteria. These approaches require an additional level of expertise, and therefore should be done by surgeons performing a high volume of thyroid and robotic surgery. PMID:26858014

American Thyroid Association Statement on Remote-Access Thyroid Surgery.

PubMed

Berber, Eren; Bernet, Victor; Fahey, Thomas J; Kebebew, Electron; Shaha, Ashok; Stack, Brendan C; Stang, Michael; Steward, David L; Terris, David J

2016-03-01

Remote-access techniques have been described over the recent years as a method of removing the thyroid gland without an incision in the neck. However, there is confusion related to the number of techniques available and the ideal patient selection criteria for a given technique. The aims of this review were to develop a simple classification of these approaches, describe the optimal patient selection criteria, evaluate the outcomes objectively, and define the barriers to adoption. A review of the literature was performed to identify the described techniques. A simple classification was developed. Technical details, outcomes, and the learning curve were described. Expert opinion consensus was formulated regarding recommendations for patient selection and performance of remote-access thyroid surgery. Remote-access thyroid procedures can be categorized into endoscopic or robotic breast, bilateral axillo-breast, axillary, and facelift approaches. The experience in the United States involves the latter two techniques. The limited data in the literature suggest long operative times, a steep learning curve, and higher costs with remote-access thyroid surgery compared with conventional thyroidectomy. Nevertheless, a consensus was reached that, in appropriate hands, it can be a viable option for patients with unilateral small nodules who wish to avoid a neck incision. Remote-access thyroidectomy has a role in a small group of patients who fit strict selection criteria. These approaches require an additional level of expertise, and therefore should be done by surgeons performing a high volume of thyroid and robotic surgery.

75 FR 11169 - Reedsport OPT Wave Park Project; Reedsport OPT Wave Park; LLC Notice of Scoping Meetings and...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-03-10

... seabed. The 10 PowerBuoy units would be connected to a single Underwater Substation Pod (USP) via power... transmission cable, buried in the seabed to a depth of 3 to 6 feet, would extend from the USP to an existing... continue within the effluent pipe eastward for approximately 3 miles, where it would connect to the Douglas...

75 FR 32451 - Reedsport OPT Wave Park, LLC; Notice of Application Accepted for Filing, Ready for Environmental...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-08

... seabed. The 10 PowerBuoy units would be connected to a single Underwater Substation Pod (USP) via power... transmission cable, buried in the seabed to a depth of 3 to 6 feet, would extend from the USP to an existing... continue within the effluent pipe eastward for approximately 3 miles, where it would connect to the Douglas...

Development of a Spectropolarimetric Remote Sensing Capability

DTIC Science & Technology

2013-03-01

34Review of passive imaging polarimetry for remote sensing applications," Appl. Opt. 45, 5453-5469 (2006). [8] D. B. Chenault, "Infrared...Annen, “Hyperspectral IR polarimetry with application in demining and unexploded ordnance detection,” SPIE Vol. 3534 (1998). [30] Pesses, M... Polarimetry , Fourier Transform Spectrometer, DOLP, Spectropolarimetry, Stokes 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT UU 18

Proceedings of the Third Annual Symposium on Mathematical Pattern Recognition and Image Analysis

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.

1985-01-01

Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters.

Dynamic stratification of the landscape of Mexico: analysis of vegetation patterns observed with multitemporal remotely sensed images

Treesearch

Franz Mora; Louis R. Iverson; Louis R. Iverson

1997-01-01

Rapid deforestation in Mexico, when coupled with poor access to current and consistent ecological information across the country underscores the need for an ecological classification system that can be readily updated as new data become available. In this study, regional vegetation resources in Mexico were evaluated using remotely sensed information. Multitemporal...

Multivariate Density Estimation and Remote Sensing

NASA Technical Reports Server (NTRS)

Scott, D. W.

1983-01-01

Current efforts to develop methods and computer algorithms to effectively represent multivariate data commonly encountered in remote sensing applications are described. While this may involve scatter diagrams, multivariate representations of nonparametric probability density estimates are emphasized. The density function provides a useful graphical tool for looking at data and a useful theoretical tool for classification. This approach is called a thunderstorm data analysis.

An analysis of metropolitan land-use by machine processing of earth resources technology satellite data

NASA Technical Reports Server (NTRS)

Mausel, P. W.; Todd, W. J.; Baumgardner, M. F.

1976-01-01

A successful application of state-of-the-art remote sensing technology in classifying an urban area into its broad land use classes is reported. This research proves that numerous urban features are amenable to classification using ERTS multispectral data automatically processed by computer. Furthermore, such automatic data processing (ADP) techniques permit areal analysis on an unprecedented scale with a minimum expenditure of time. Also, classification results obtained using ADP procedures are consistent, comparable, and replicable. The results of classification are compared with the proposed U. S. G. S. land use classification system in order to determine the level of classification that is feasible to obtain through ERTS analysis of metropolitan areas.

Couple Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification

NASA Astrophysics Data System (ADS)

Wang, X. P.; Hu, Y.; Chen, J.

2018-04-01

Graph based semi-supervised classification method are widely used for hyperspectral image classification. We present a couple graph based label propagation method, which contains both the adjacency graph and the similar graph. We propose to construct the similar graph by using the similar probability, which utilize the label similarity among examples probably. The adjacency graph was utilized by a common manifold learning method, which has effective improve the classification accuracy of hyperspectral data. The experiments indicate that the couple graph Laplacian which unite both the adjacency graph and the similar graph, produce superior classification results than other manifold Learning based graph Laplacian and Sparse representation based graph Laplacian in label propagation framework.

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.

Classification with spatio-temporal interpixel class dependency contexts

NASA Technical Reports Server (NTRS)

Jeon, Byeungwoo; Landgrebe, David A.

1992-01-01

A contextual classifier which can utilize both spatial and temporal interpixel dependency contexts is investigated. After spatial and temporal neighbors are defined, a general form of maximum a posterior spatiotemporal contextual classifier is derived. This contextual classifier is simplified under several assumptions. Joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Gibbs random field. The classification is performed in a recursive manner to allow a computationally efficient contextual classification. Experimental results with bitemporal TM data show significant improvement of classification accuracy over noncontextual pixelwise classifiers. This spatiotemporal contextual classifier should find use in many applications of remote sensing, especially when the classification accuracy is important.

Polarimetric SAR image classification based on discriminative dictionary learning model

NASA Astrophysics Data System (ADS)

Sang, Cheng Wei; Sun, Hong

2018-03-01

Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

Investigation of forestry resources and other remote sensing data. 1: LANDSAT. 2: Remote sensing of volcanic emissions

NASA Technical Reports Server (NTRS)

Birnie, R. W.; Stoiber, R. E. (Principal Investigator)

1983-01-01

Computer classification of LANDSAT data was used for forest type mapping in New England. The ability to classify areas of hardwood, softwood, and mixed tree types was assessed along with determining clearcut regions and gypsy moth defoliation. Applications of the information to forest management and locating potential deer yards were investigated. The principal activities concerned with remote sensing of volcanic emissions centered around the development of remote sensors for SO2 and HCl gas, and their use at appropriate volcanic sites. Two major areas were investigated (Masaya, Nicaragua, and St. Helens, Washington) along with several minor ones.

Fusion of shallow and deep features for classification of high-resolution remote sensing images

NASA Astrophysics Data System (ADS)

Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

2018-02-01

Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

Land Use and Land Cover (LULC) Change Detection in Islamabad and its Comparison with Capital Development Authority (CDA) 2006 Master Plan

NASA Astrophysics Data System (ADS)

Hasaan, Zahra

2016-07-01

Remote sensing is very useful for the production of land use and land cover statistics which can be beneficial to determine the distribution of land uses. Using remote sensing techniques to develop land use classification mapping is a convenient and detailed way to improve the selection of areas designed to agricultural, urban and/or industrial areas of a region. In Islamabad city and surrounding the land use has been changing, every day new developments (urban, industrial, commercial and agricultural) are emerging leading to decrease in vegetation cover. The purpose of this work was to develop the land use of Islamabad and its surrounding area that is an important natural resource. For this work the eCognition Developer 64 computer software was used to develop a land use classification using SPOT 5 image of year 2012. For image processing object-based classification technique was used and important land use features i.e. Vegetation cover, barren land, impervious surface, built up area and water bodies were extracted on the basis of object variation and compared the results with the CDA Master Plan. The great increase was found in built-up area and impervious surface area. On the other hand vegetation cover and barren area followed a declining trend. Accuracy assessment of classification yielded 92% accuracies of the final land cover land use maps. In addition these improved land cover/land use maps which are produced by remote sensing technique of class definition, meet the growing need of legend standardization.

An AdaBoost Based Approach to Automatic Classification and Detection of Buildings Footprints, Vegetation Areas and Roads from Satellite Images

NASA Astrophysics Data System (ADS)

Gonulalan, Cansu

In recent years, there has been an increasing demand for applications to monitor the targets related to land-use, using remote sensing images. Advances in remote sensing satellites give rise to the research in this area. Many applications ranging from urban growth planning to homeland security have already used the algorithms for automated object recognition from remote sensing imagery. However, they have still problems such as low accuracy on detection of targets, specific algorithms for a specific area etc. In this thesis, we focus on an automatic approach to classify and detect building foot-prints, road networks and vegetation areas. The automatic interpretation of visual data is a comprehensive task in computer vision field. The machine learning approaches improve the capability of classification in an intelligent way. We propose a method, which has high accuracy on detection and classification. The multi class classification is developed for detecting multiple objects. We present an AdaBoost-based approach along with the supervised learning algorithm. The combi- nation of AdaBoost with "Attentional Cascade" is adopted from Viola and Jones [1]. This combination decreases the computation time and gives opportunity to real time applications. For the feature extraction step, our contribution is to combine Haar-like features that include corner, rectangle and Gabor. Among all features, AdaBoost selects only critical features and generates in extremely efficient cascade structured classifier. Finally, we present and evaluate our experimental results. The overall system is tested and high performance of detection is achieved. The precision rate of the final multi-class classifier is over 98%.

Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image

NASA Astrophysics Data System (ADS)

Li, Long; Solana, Carmen; Canters, Frank; Kervyn, Matthieu

2017-10-01

Mapping lava flows using satellite images is an important application of remote sensing in volcanology. Several volcanoes have been mapped through remote sensing using a wide range of data, from optical to thermal infrared and radar images, using techniques such as manual mapping, supervised/unsupervised classification, and elevation subtraction. So far, spectral-based mapping applications mainly focus on the use of traditional pixel-based classifiers, without much investigation into the added value of object-based approaches and into advantages of using machine learning algorithms. In this study, Nyamuragira, characterized by a series of > 20 overlapping lava flows erupted over the last century, was used as a case study. The random forest classifier was tested to map lava flows based on pixels and objects. Image classification was conducted for the 20 individual flows and for 8 groups of flows of similar age using a Landsat 8 image and a DEM of the volcano, both at 30-meter spatial resolution. Results show that object-based classification produces maps with continuous and homogeneous lava surfaces, in agreement with the physical characteristics of lava flows, while lava flows mapped through the pixel-based classification are heterogeneous and fragmented including much "salt and pepper noise". In terms of accuracy, both pixel-based and object-based classification performs well but the former results in higher accuracies than the latter except for mapping lava flow age groups without using topographic features. It is concluded that despite spectral similarity, lava flows of contrasting age can be well discriminated and mapped by means of image classification. The classification approach demonstrated in this study only requires easily accessible image data and can be applied to other volcanoes as well if there is sufficient information to calibrate the mapping.

Land cover's refined classification based on multi source of remote sensing information fusion: a case study of national geographic conditions census in China

NASA Astrophysics Data System (ADS)

Cheng, Tao; Zhang, Jialong; Zheng, Xinyan; Yuan, Rujin

2018-03-01

The project of The First National Geographic Conditions Census developed by Chinese government has designed the data acquisition content and indexes, and has built corresponding classification system mainly based on the natural property of material. However, the unified standard for land cover classification system has not been formed; the production always needs converting to meet the actual needs. Therefore, it proposed a refined classification method based on multi source of remote sensing information fusion. It takes the third-level classes of forest land and grassland for example, and has collected the thematic data of Vegetation Map of China (1:1,000,000), attempts to develop refined classification utilizing raster spatial analysis model. Study area is selected, and refined classification is achieved by using the proposed method. The results show that land cover within study area is divided principally among 20 classes, from subtropical broad-leaved forest (31131) to grass-forb community type of low coverage grassland (41192); what's more, after 30 years in the study area, climatic factors, developmental rhythm characteristics and vegetation ecological geographical characteristics have not changed fundamentally, only part of the original vegetation types have changed in spatial distribution range or land cover types. Research shows that refined classification for the third-level classes of forest land and grassland could make the results take on both the natural attributes of the original and plant community ecology characteristics, which could meet the needs of some industry application, and has certain practical significance for promoting the product of The First National Geographic Conditions Census.

Improving LUC estimation accuracy with multiple classification system for studying impact of urbanization on watershed flood

NASA Astrophysics Data System (ADS)

Dou, P.

2017-12-01

Guangzhou has experienced a rapid urbanization period called "small change in three years and big change in five years" since the reform of China, resulting in significant land use/cover changes(LUC). To overcome the disadvantages of single classifier for remote sensing image classification accuracy, a multiple classifier system (MCS) is proposed to improve the quality of remote sensing image classification. The new method combines advantages of different learning algorithms, and achieves higher accuracy (88.12%) than any single classifier did. With the proposed MCS, land use/cover (LUC) on Landsat images from 1987 to 2015 was obtained, and the LUCs were used on three watersheds (Shijing river, Chebei stream, and Shahe stream) to estimate the impact of urbanization on water flood. The results show that with the high accuracy LUC, the uncertainty in flood simulations are reduced effectively (for Shijing river, Chebei stream, and Shahe stream, the uncertainty reduced 15.5%, 17.3% and 19.8% respectively).

Hyperspectral landcover classification for the Yakima Training Center, Yakima, Washington

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

Steinmaus, K.L.; Perry, E.M.; Petrie, G.M.

1998-04-01

The US Department of Energy`s (DOE`s) Pacific Northwest National Laboratory (PNNL) was tasked in FY97-98 to conduct a multisensor feature extraction project for the Terrain Modeling Project Office (TMPO) of the National Imagery and Mapping Agency (NIMA). The goal of this research is the development of near-autonomous methods to remotely classify and characterize regions of military interest, in support of the TMPO of NIMA. These methods exploit remotely sensed datasets including hyperspectral (HYDICE) imagery, near-infrared and thermal infrared (Daedalus 3600), radar, and terrain datasets. The study site for this project is the US Army`s Yakima Training Center (YTC), a 326,741-acremore » training area located near Yakima, Washington. Two study areas at the YTC were selected to conduct and demonstrate multisensor feature extraction, the 2-km x 2-km Cantonment Area and the 3-km x 3-km Choke Point area. Classification of the Cantonment area afforded a comparison of classification results at different scales.« less

Urban Shanty Town Recognition Based on High-Resolution Remote Sensing Images and National Geographical Monitoring Features - a Case Study of Nanning City

NASA Astrophysics Data System (ADS)

He, Y.; He, Y.

2018-04-01

Urban shanty towns are communities that has contiguous old and dilapidated houses with more than 2000 square meters built-up area or more than 50 households. This study makes attempts to extract shanty towns in Nanning City using the product of Census and TripleSat satellite images. With 0.8-meter high-resolution remote sensing images, five texture characteristics (energy, contrast, maximum probability, and inverse difference moment) of shanty towns are trained and analyzed through GLCM. In this study, samples of shanty town are well classified with 98.2 % producer accuracy of unsupervised classification and 73.2 % supervised classification correctness. Low-rise and mid-rise residential blocks in Nanning City are classified into 4 different types by using k-means clustering and nearest neighbour classification respectively. This study initially establish texture feature descriptions of different types of residential areas, especially low-rise and mid-rise buildings, which would help city administrator evaluate residential blocks and reconstruction shanty towns.

Image processing developments and applications for water quality monitoring and trophic state determination

NASA Technical Reports Server (NTRS)

Blackwell, R. J.

1982-01-01

Remote sensing data analysis of water quality monitoring is evaluated. Data anaysis and image processing techniques are applied to LANDSAT remote sensing data to produce an effective operational tool for lake water quality surveying and monitoring. Digital image processing and analysis techniques were designed, developed, tested, and applied to LANDSAT multispectral scanner (MSS) data and conventional surface acquired data. Utilization of these techniques facilitates the surveying and monitoring of large numbers of lakes in an operational manner. Supervised multispectral classification, when used in conjunction with surface acquired water quality indicators, is used to characterize water body trophic status. Unsupervised multispectral classification, when interpreted by lake scientists familiar with a specific water body, yields classifications of equal validity with supervised methods and in a more cost effective manner. Image data base technology is used to great advantage in characterizing other contributing effects to water quality. These effects include drainage basin configuration, terrain slope, soil, precipitation and land cover characteristics.

The Abnormal vs. Normal ECG Classification Based on Key Features and Statistical Learning

NASA Astrophysics Data System (ADS)

Dong, Jun; Tong, Jia-Fei; Liu, Xia

As cardiovascular diseases appear frequently in modern society, the medicine and health system should be adjusted to meet the new requirements. Chinese government has planned to establish basic community medical insurance system (BCMIS) before 2020, where remote medical service is one of core issues. Therefore, we have developed the "remote network hospital system" which includes data server and diagnosis terminal by the aid of wireless detector to sample ECG. To improve the efficiency of ECG processing, in this paper, abnormal vs. normal ECG classification approach based on key features and statistical learning is presented, and the results are analyzed. Large amount of normal ECG could be filtered by computer automatically and abnormal ECG is left to be diagnosed specially by physicians.

Quantification of urban structure on building block level utilizing multisensoral remote sensing data

NASA Astrophysics Data System (ADS)

Wurm, Michael; Taubenböck, Hannes; Dech, Stefan

2010-10-01

Dynamics of urban environments are a challenge to a sustainable development. Urban areas promise wealth, realization of individual dreams and power. Hence, many cities are characterized by a population growth as well as physical development. Traditional, visual mapping and updating of urban structure information of cities is a very laborious and cost-intensive task, especially for large urban areas. For this purpose, we developed a workflow for the extraction of the relevant information by means of object-based image classification. In this manner, multisensoral remote sensing data has been analyzed in terms of very high resolution optical satellite imagery together with height information by a digital surface model to retrieve a detailed 3D city model with the relevant land-use / land-cover information. This information has been aggregated on the level of the building block to describe the urban structure by physical indicators. A comparison between the indicators derived by the classification and a reference classification has been accomplished to show the correlation between the individual indicators and a reference classification of urban structure types. The indicators have been used to apply a cluster analysis to group the individual blocks into similar clusters.

Backscattering from a sandy seabed measured by a calibrated multibeam echosounder in the 190–400 kHz frequency range

NASA Astrophysics Data System (ADS)

Wendelboe, Gorm

2018-06-01

A SeaBat T50 calibration that combines measurements in a test tank with data from numerical models is presented. The calibration is assessed with data obtained from a series of tests conducted over a sandy seabed outside the harbor of Santa Barbara, California (April 2016). The tests include different tone-burst durations, sound pressure levels, and receive gains in order to verify that the estimated seabed backscattering strength (S_b) is invariant to sonar settings. Finally, S_b-estimates obtained in the frequency range from 190 kHz in steps of 10 kHz up to 400 kHz, and for grazing angles from 20° up to 90° in bins of width 5°, are presented. The results are compared with results found in the literature.

The edge-preservation multi-classifier relearning framework for the classification of high-resolution remotely sensed imagery

NASA Astrophysics Data System (ADS)

Han, Xiaopeng; Huang, Xin; Li, Jiayi; Li, Yansheng; Yang, Michael Ying; Gong, Jianya

2018-04-01

In recent years, the availability of high-resolution imagery has enabled more detailed observation of the Earth. However, it is imperative to simultaneously achieve accurate interpretation and preserve the spatial details for the classification of such high-resolution data. To this aim, we propose the edge-preservation multi-classifier relearning framework (EMRF). This multi-classifier framework is made up of support vector machine (SVM), random forest (RF), and sparse multinomial logistic regression via variable splitting and augmented Lagrangian (LORSAL) classifiers, considering their complementary characteristics. To better characterize complex scenes of remote sensing images, relearning based on landscape metrics is proposed, which iteratively quantizes both the landscape composition and spatial configuration by the use of the initial classification results. In addition, a novel tri-training strategy is proposed to solve the over-smoothing effect of relearning by means of automatic selection of training samples with low classification certainties, which always distribute in or near the edge areas. Finally, EMRF flexibly combines the strengths of relearning and tri-training via the classification certainties calculated by the probabilistic output of the respective classifiers. It should be noted that, in order to achieve an unbiased evaluation, we assessed the classification accuracy of the proposed framework using both edge and non-edge test samples. The experimental results obtained with four multispectral high-resolution images confirm the efficacy of the proposed framework, in terms of both edge and non-edge accuracy.

Microwave remote sensing from space for earth resource surveys

NASA Technical Reports Server (NTRS)

1977-01-01

The concepts of radar remote sensing and microwave radiometry are discussed and their utility in earth resource sensing is examined. The direct relationship between the character of the remotely sensed data and the level of decision making for which the data are appropriate is considered. Applications of active and a passive microwave sensing covered include hydrology, land use, mapping, vegetation classification, environmental monitoring, coastal features and processes, geology, and ice and snow. Approved and proposed microwave sensors are described and the use of space shuttle as a development platform is evaluated.

Processing and Analysis of Multibeam Sonar Data and Images near the Yellow River Estuary

NASA Astrophysics Data System (ADS)

Tang, Q.

2017-12-01

Yellow River Estuary is a typical high-suspended particulate matter estuary in the world. A lot of sediments from Yellow River and other substances produced by human activity cause high-concentration suspended matter and depositional system in the estuary and adjacent water area. Multibeam echo sounder (MBES) was developed in the 1970s, and it not only provided high-precision bathymetric data, but also provided seabed backscatter strength data and water column data with high temporal and spatial resolution. Here, based on high-precision sonar data of the seabed and water column collected by SeaBat7125 MBES system near the Yellow River Estuary, we use advanced data and image processing methods to generate seabed sonar images and water suspended particulate matter acoustic images. By analyzing these data and images, we get a lot of details of the seabed and whole water column features, and we also acquire their shape, size and basic physical characteristics of suspended particulate matters in the experiment area near the Yellow River Estuary. This study shows great potential for monitoring suspended particulate matter use MBES, and the research results will contribute to a comprehensive understanding of sediment transportation, evolution of river trough and shoal in Yellow River Estuary.

Foraging habits in a generalist predator: sex and age influence habitat selection and resource use among bottlenose dolphins (Tursiops truncatus)

USGS Publications Warehouse

Sam Rossman,; McCabe, Elizabeth Berens; Nelio B. Barros,; Hasand Gandhi,; Peggy H. Ostrom,; Stricker, Craig A.; Randall S. Wells,

2015-01-01

This study examines resource use (diet, habitat use, and trophic level) within and among demographic groups (males, females, and juveniles) of bottlenose dolphins (Tursiops truncatus). We analyzed the δ13C and δ15N values of 15 prey species constituting 84% of the species found in stomach contents. We used these data to establish a trophic enrichment factor (TEF) to inform dietary analysis using a Bayesian isotope mixing model. We document a TEF of 0‰ and 2.0‰ for δ13C and δ15N, respectively. The dietary results showed that all demographic groups relied heavily on low trophic level seagrass-associated prey. Bayesian standard ellipse areas (SEAb) were calculated to assess diversity in resource use. The SEAb of females was nearly four times larger than that of males indicating varied resource use, likely a consequence of small home ranges and habitat specialization. Juveniles possessed an intermediate SEAb, generally feeding at a lower trophic level compared to females, potentially an effect of natal philopatry and immature foraging skills. The small SEAb of males reflects a high degree of specialization on seagrass associated prey. Patterns in resource use by the demographic groups are likely linked to differences in the relative importance of social and ecological factors.

Enhancement of the surface methane hydrate-bearing layer based on the specific microorganisms form deep seabed sediment in Japan Sea.

NASA Astrophysics Data System (ADS)

Hata, T.; Yoneda, J.; Yamamoto, K.

2017-12-01

A methane hydrate-bearing layer located near the Japan Sea has been investigated as a new potential energy resource. In this study examined the feasibility of the seabed surface sediment strength located in the Japan Sea improvement technologies for enhancing microbial induced carbonate precipitation (MICP) process. First, the authors cultivated the specific urease production bacterium culture medium from this surface methane hydrate-bearing layer in the seabed (-600m depth) of Japan Sea. After that, two types of the laboratory test (consolidated-drained triaxial tests) were conducted using this specific culture medium from the seabed in the Japan Sea near the Toyama Prefecture and high urease activities bacterium named Bacillus pasteurii. The main outcomes of this research are as follows. 1) Specific culture medium focused on the urease production bacterium can enhancement of the urease activities from the methane hydrate-bearing layer near the Japan Sea side, 2) This specific culture medium can be enhancement of the surface layer strength, 3) The microbial induced carbonate precipitation process can increase the particle size compared to that of the original particles coating the calcite layer surface, 4) The mechanism for increasing the soil strength is based on the addition of cohesion like a cement stabilized soil.

The effects of environmental factors on daytime sandeel distribution and abundance on the Dogger Bank

NASA Astrophysics Data System (ADS)

van der Kooij, Jeroen; Scott, Beth E.; Mackinson, Steven

2008-10-01

Spring distribution and abundance of lesser sandeels during the day were linked to zooplankton densities, seabed substrate and various hydrographic factors using small scale empirical data collected in two areas on the Dogger Bank in 2004, 2005 and 2006. The results of a two-step generalized additive model (GAM) suggested that suitable seabed substrate and temperature best explain sandeel distribution (presence/absence) and that sandeel abundance (given presence) was best described by a model that included bottom temperature, difference between surface and bottom temperature and surface salinity. The current study suggests that suitable seabed substrate explains sandeel distribution in the water column. Bottom temperature and surface salinity also played an important role in explaining distribution and abundance, and we speculate that sandeels favour hydrographically dynamic areas. Contrary to our hypothesis sandeels were not strongly associated with areas of high zooplankton density. We speculate that in early spring on the western Dogger Bank plankton is still patchily distributed and that sandeels only emerge from the seabed when feeding conditions near their night-time burrowing habitat are optimal. The results also suggested that when abundance is over a threshold level, the number of sandeel schools increased rather than the schools becoming bigger. This relationship between patchiness and abundance has implications for mortality rates and hence fisheries management.

Deformation patterns in the southwestern part of the Mediterranean Ridge (South Matapan Trench, Western Greece)

NASA Astrophysics Data System (ADS)

Andronikidis, Nikolaos; Kokinou, Eleni; Vafidis, Antonios; Kamberis, Evangelos; Manoutsoglou, Emmanouil

2017-12-01

Seismic reflection data and bathymetry analyses, together with geological information, are combined in the present work to identify seabed structural deformation and crustal structure in the Western Mediterranean Ridge (the backstop and the South Matapan Trench). As a first step, we apply bathymetric data and state of art methods of pattern recognition to automatically detect seabed lineaments, which are possibly related to the presence of tectonic structures (faults). The resulting pattern is tied to seismic reflection data, further assisting in the construction of a stratigraphic and structural model for this part of the Mediterranean Ridge. Structural elements and stratigraphic units in the final model are estimated based on: (a) the detected lineaments on the seabed, (b) the distribution of the interval velocities and the presence of velocity inversions, (c) the continuity and the amplitudes of the seismic reflections, the seismic structure of the units and (d) well and stratigraphic data as well as the main tectonic structures from the nearest onshore areas. Seabed morphology in the study area is probably related with the past and recent tectonics movements that result from African and European plates' convergence. Backthrusts and reverse faults, flower structures and deep normal faults are among the most important extensional/compressional structures interpreted in the study area.

A Macroecological Analysis of SERA Derived Forest Heights and Implications for Forest Volume Remote Sensing

PubMed Central

Brolly, Matthew; Woodhouse, Iain H.; Niklas, Karl J.; Hammond, Sean T.

2012-01-01

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H100, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H100 and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 102–106 plants/hectare and heights 6–49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H100. PMID:22457800

A macroecological analysis of SERA derived forest heights and implications for forest volume remote sensing.

PubMed

Brolly, Matthew; Woodhouse, Iain H; Niklas, Karl J; Hammond, Sean T

2012-01-01

Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H₁₀₀, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H₁₀₀ and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 10²-10⁶ plants/hectare and heights 6-49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H₁₀₀.

Land Cover Changes between 1974 and 2008 in Ulaanbaatar, Mongolia

NASA Astrophysics Data System (ADS)

Bagan, H.; Kinoshita, T.; Yamagata, Y.

2009-12-01

In the past 35 years, a combination of human actions and natural causes has led to a significant decline in land quality in Ulaanbaatar, the capital city of Mongolia. Human causes include changes in conventional livestock husbandry, overgrazing, and exploitation for traditional uses. Natural causes include a harsh, dry climate, short growing seasons, and thin soils. Since 1995, many herders left the countryside to come to the city in search of new opportunities, the Ger areas (wooden houses and Ger) have expended, resulting in urban sprawl. Since urbanization usually advance in an uncontrolled or unorganized way in Mongolia, they have destructive effects on the environment, particularly on basic ecosystems, wildlife habitat, and pollution of natural resources (e.g. air and water). Land use and land cover changes occurred in the region are investigated using satellite images acquired in 1974 (Landsat MSS), 1990 (Landsat TM), 2000 (ASTER), 2006 (IKONOS), and 2008 (ALOS). Pre-processing of all data included orthorectification and registration to precisely geolocated imagery. In the detection of changes, classification approaches were employed using a self-organizing map (SOM) neural network classifier (Fig. 1a) and new developed subspace classification method (Fig. 1b). From the time-series classified remote sensing images, we extract the land cover and land cover temporal changes from 1974 to 2008. The results show some important findings regarding the size and nature of the change occurred in the study area. A significant amount of steppe and forest lands have been destroyed or replaced by residential areas; as a result, the total area of urban region doubled in the 35-year period with a higher urbanization rate between 2000 and 2008. Key words: Environment; Land Cover; Urban; Change detection; Classification. References Chinbat,B., Bayantur,M., & Amarsaikhan.D. (2006). Investigation of the internal structure changes of ulaanbaatar city using RS and GIS. ISPRS Commission VII Mid-term Symposium “Remote Sensing: From Pixels to Processes”, Enschede, the Netherlands, 8-11 May 2006. 511-516. Bagan, H., Wang, Q., Watanabe, M., Karneyarna, S., & Bao, Y. (2008). Land-cover classification using ASTER multi-band combinations based on wavelet fusion and SOM neural network. Photogrammetric Engineering and Remote Sensing, 74, 333-342. Bagan, H., Yasuoka, Y., Endo, T., Wang, X., & Feng, Z. (2008). Classification of airborne hyperspectral data based on the average learning subspace method. IEEE Geoscience and Remote Sensing Letters, 5, 368-372. Figure 1. The self-organizing map (SOM) neural network classifier (a) and the subspace classification method (b).

ADP of multispectral scanner data for land use mapping

NASA Technical Reports Server (NTRS)

Hoffer, R. M.

1971-01-01

The advantages and disadvantages of various remote sensing instrumentation and analysis techniques are reviewed. The use of multispectral scanner data and the automatic data processing techniques are considered. A computer-aided analysis system for remote sensor data is described with emphasis on the image display, statistics processor, wavelength band selection, classification processor, and results display. Advanced techniques in using spectral and temporal data are also considered.

Detection, identification, and classification of mosquito larval habitats using remote sensing scanners in earth-orbiting satellites.

PubMed

Hayes, R O; Maxwell, E L; Mitchell, C J; Woodzick, T L

1985-01-01

A method of identifying mosquito larval habitats associated with fresh-water plant communities, wetlands, and other aquatic locations at Lewis and Clark Lake in the states of Nebraska and South Dakota, USA, using remote sensing imagery obtained by multispectral scanners aboard earth-orbiting satellites (Landsat 1 and 2) is described. The advantages and limitations of this method are discussed.

Potential for boom-mounted remote sensing applications in seedling quality monitoring

Treesearch

Robert F. Keefe; Jan U. H. Eitel; Daniel S. Long; Anthony S. Davis; Paul Gessler; Alistair M. S. Smith

2009-01-01

Remotely sensed aerial and satellite sensor imagery is widely used for classification of vegetation structure and health on industrial and public lands. More intensively than at any other time in the life of a planted tree, its health and status will be maintained and monitored while under culture in a bareroot or container nursery. As a case in point, inventories to...

A Hybrid Semi-supervised Classification Scheme for Mining Multisource Geospatial Data

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

Vatsavai, Raju; Bhaduri, Budhendra L

2011-01-01

Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of large number of accurate training samples (10 to 30 |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, itmore » is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately there is no convenient multivariate statistical model that can be employed for mulitsource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on real datasets, and our new hybrid approach shows over 25 to 35% improvement in overall classification accuracy over conventional classification schemes.« less

Object Manifold Alignment for Multi-Temporal High Resolution Remote Sensing Images Classification

NASA Astrophysics Data System (ADS)

Gao, G.; Zhang, M.; Gu, Y.

2017-05-01

Multi-temporal remote sensing images classification is very useful for monitoring the land cover changes. Traditional approaches in this field mainly face to limited labelled samples and spectral drift of image information. With spatial resolution improvement, "pepper and salt" appears and classification results will be effected when the pixelwise classification algorithms are applied to high-resolution satellite images, in which the spatial relationship among the pixels is ignored. For classifying the multi-temporal high resolution images with limited labelled samples, spectral drift and "pepper and salt" problem, an object-based manifold alignment method is proposed. Firstly, multi-temporal multispectral images are cut to superpixels by simple linear iterative clustering (SLIC) respectively. Secondly, some features obtained from superpixels are formed as vector. Thirdly, a majority voting manifold alignment method aiming at solving high resolution problem is proposed and mapping the vector data to alignment space. At last, all the data in the alignment space are classified by using KNN method. Multi-temporal images from different areas or the same area are both considered in this paper. In the experiments, 2 groups of multi-temporal HR images collected by China GF1 and GF2 satellites are used for performance evaluation. Experimental results indicate that the proposed method not only has significantly outperforms than traditional domain adaptation methods in classification accuracy, but also effectively overcome the problem of "pepper and salt".

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

NASA Astrophysics Data System (ADS)

Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

2018-06-01

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

Spectral-Spatial Classification of Hyperspectral Images Using Hierarchical Optimization

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.

2011-01-01

A new spectral-spatial method for hyperspectral data classification is proposed. For a given hyperspectral image, probabilistic pixelwise classification is first applied. Then, hierarchical step-wise optimization algorithm is performed, by iteratively merging neighboring regions with the smallest Dissimilarity Criterion (DC) and recomputing class labels for new regions. The DC is computed by comparing region mean vectors, class labels and a number of pixels in the two regions under consideration. The algorithm is converged when all the pixels get involved in the region merging procedure. Experimental results are presented on two remote sensing hyperspectral images acquired by the AVIRIS and ROSIS sensors. The proposed approach improves classification accuracies and provides maps with more homogeneous regions, when compared to previously proposed classification techniques.

BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the NSA

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Knapp, David

2000-01-01

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification. A standard supervised maximum likelihood classification approach was used to produce this classification. The data are provided in a binary image format file. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

A demonstration of wetland vegetation mapping in Florida from computer-processed satellite and aircraft multispectral scanner data

NASA Technical Reports Server (NTRS)

Butera, M. K. (Principal Investigator)

1978-01-01

The author has identified the following significant results. Major vegetative classes identified by the remote sensing technique were cypress swamp, pine, wetland grasses, salt grass, mixed mangrove, black mangrove, Brazilian pepper. Australian pine and melaleuca were not satisfactorily classified from LANDSAT. Aircraft scanners provided better resolution resulting in a classification of finer surface detail. An edge effect, created by the integration of diverse spectral responses within boundary elements of digital data, affected the wetlands classification. Accuracy classification for aircraft was 68% and for LANDSAT was 74%.

Mid Infrared Polarized Light Scattering; Applications for the Remote Detection of Chemical and Biological Contaminations

DTIC Science & Technology

1992-01-01

CLASSIFICATION 11. SECURITY CLASSIFICATION 19. SECURITY CLASSIFICATION 20. LIMITATION OF ABSTRACTI rnEHUC AGE OF ABSTRACTUNCLSIFIED UNCLASSIFIED UL NSN... ag . ;nst liquid chemical agent simulants SF96, DIMP, and DMMP on a soil surface. The crosshatched wavelengthi-angle domains are areas where the...WHITE ag WRIUT pE/pmv" I MAKE LINE DASHED "fl WRlE,(*WML1r LINE COLOR WHITE al2 ELSE 3 WIUrTE(*,jEJ/MVO MAKE LINE SOUD 244 CALL INThPr(ICOL.COL) I NEER

An Object-Oriented Classification Method on High Resolution Satellite Data

DTIC Science & Technology

2004-11-01

25th ACRS 2004 Chiang Mai , Thailand 347 Data Processing B-4.6 AN OBJECT-ORIENTED CLASSIFICATION METHOD ON...unlimited 13. SUPPLEMENTARY NOTES Proceedings of the 25th Asian Conference on Remote Sensing, Held in Chiang Mai , Thailand on 22-26 November 2004...panchromatic (left) and multispectral (right) 25th ACRS 2004 Chiang Mai , Thailand 349 Data Processing B-4.6 First of all, the

A clinical decision-making mechanism for context-aware and patient-specific remote monitoring systems using the correlations of multiple vital signs.

PubMed

Forkan, Abdur Rahim Mohammad; Khalil, Ibrahim

2017-02-01

In home-based context-aware monitoring patient's real-time data of multiple vital signs (e.g. heart rate, blood pressure) are continuously generated from wearable sensors. The changes in such vital parameters are highly correlated. They are also patient-centric and can be either recurrent or can fluctuate. The objective of this study is to develop an intelligent method for personalized monitoring and clinical decision support through early estimation of patient-specific vital sign values, and prediction of anomalies using the interrelation among multiple vital signs. In this paper, multi-label classification algorithms are applied in classifier design to forecast these values and related abnormalities. We proposed a completely new approach of patient-specific vital sign prediction system using their correlations. The developed technique can guide healthcare professionals to make accurate clinical decisions. Moreover, our model can support many patients with various clinical conditions concurrently by utilizing the power of cloud computing technology. The developed method also reduces the rate of false predictions in remote monitoring centres. In the experimental settings, the statistical features and correlations of six vital signs are formulated as multi-label classification problem. Eight multi-label classification algorithms along with three fundamental machine learning algorithms are used and tested on a public dataset of 85 patients. Different multi-label classification evaluation measures such as Hamming score, F1-micro average, and accuracy are used for interpreting the prediction performance of patient-specific situation classifications. We achieved 90-95% Hamming score values across 24 classifier combinations for 85 different patients used in our experiment. The results are compared with single-label classifiers and without considering the correlations among the vitals. The comparisons show that multi-label method is the best technique for this problem domain. The evaluation results reveal that multi-label classification techniques using the correlations among multiple vitals are effective ways for early estimation of future values of those vitals. In context-aware remote monitoring this process can greatly help the doctors in quick diagnostic decision making. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

European Science Notes Information Bulletin Reports on Current European/ Middle Eastern Science

DTIC Science & Technology

1991-12-01

50 m); innovative acoustic, substances laser, and biosensors; fluxes through the seabed, and . Biological processes real - time measurement of seabed...Woodhouse, Lowestoft, U.K. 4 r ESNIB 91-07 Title Coordinator and Partners FAX Number European River Ocean System (EROS 2000): J.M. Martin, tcole Normale...Athens, Greece; F. Voutsinou, Athens, Greece European River Ocean System (EROS 2000) - J.-M. Martin, Icole Normale Superierre, Montrouge, 33 1 46570497

Scientific Literature Review on the Topic of Monitoring and Modeling Seabed Evolution Rates

DTIC Science & Technology

2014-11-01

measurement techniques Benthic sea-floor characterisation Coastal Mapping/LIDAR Biomass /benthic habitat Climatology XBeach Policy 1.2.2...http://ed.gdr.nrcan.gc.ca/index_e.php On online data base of “Measurements of biomass and productivity of seabed macrobenthic and megabenthic...unknown origin. At one site, long wavelength ripples are present in what is presumed to be sediment composed of broken shells , tidal velocities exceed

Acoustic tracking of woodhead seabed drifters

NASA Technical Reports Server (NTRS)

Mayhue, R. J.; Lovelady, R. W.

1977-01-01

An investigation was conducted to determine the feasibility of tracking Woodhead seabed drifters that were instrumented with miniature acoustic transmitters having a range in water in excess of 1.0 n.mi. A trial cruise at the entrance of Delaware Bay, with the R.V. Annandale as the sonar-tracking vessel, verified acoustic communications and positioning of the bottom drifters. A demonstration cruise with the R.V. Annandale was also performed in the New York Bight to attempt to collect information on bottom water movement near the sewage-sluge dump site. Results from the tracking mission in the New York Bight suggested that bottom water currents were negligible near the dump site during the time interval from November 7-12, 1975, and that shipboard sonar tracking of acoustic Woodhead seabed drifters could provide useful Lagragian information on bottom water movement caused by tidal and other nonstorm effects.

Modification of the random forest algorithm to avoid statistical dependence problems when classifying remote sensing imagery

NASA Astrophysics Data System (ADS)

Cánovas-García, Fulgencio; Alonso-Sarría, Francisco; Gomariz-Castillo, Francisco; Oñate-Valdivieso, Fernando

2017-06-01

Random forest is a classification technique widely used in remote sensing. One of its advantages is that it produces an estimation of classification accuracy based on the so called out-of-bag cross-validation method. It is usually assumed that such estimation is not biased and may be used instead of validation based on an external data-set or a cross-validation external to the algorithm. In this paper we show that this is not necessarily the case when classifying remote sensing imagery using training areas with several pixels or objects. According to our results, out-of-bag cross-validation clearly overestimates accuracy, both overall and per class. The reason is that, in a training patch, pixels or objects are not independent (from a statistical point of view) of each other; however, they are split by bootstrapping into in-bag and out-of-bag as if they were really independent. We believe that putting whole patch, rather than pixels/objects, in one or the other set would produce a less biased out-of-bag cross-validation. To deal with the problem, we propose a modification of the random forest algorithm to split training patches instead of the pixels (or objects) that compose them. This modified algorithm does not overestimate accuracy and has no lower predictive capability than the original. When its results are validated with an external data-set, the accuracy is not different from that obtained with the original algorithm. We analysed three remote sensing images with different classification approaches (pixel and object based); in the three cases reported, the modification we propose produces a less biased accuracy estimation.

Using remote sensing in support of environmental management: A framework for selecting products, algorithms and methods.

PubMed

de Klerk, Helen M; Gilbertson, Jason; Lück-Vogel, Melanie; Kemp, Jaco; Munch, Zahn

2016-11-01

Traditionally, to map environmental features using remote sensing, practitioners will use training data to develop models on various satellite data sets using a number of classification approaches and use test data to select a single 'best performer' from which the final map is made. We use a combination of an omission/commission plot to evaluate various results and compile a probability map based on consistently strong performing models across a range of standard accuracy measures. We suggest that this easy-to-use approach can be applied in any study using remote sensing to map natural features for management action. We demonstrate this approach using optical remote sensing products of different spatial and spectral resolution to map the endemic and threatened flora of quartz patches in the Knersvlakte, South Africa. Quartz patches can be mapped using either SPOT 5 (used due to its relatively fine spatial resolution) or Landsat8 imagery (used because it is freely accessible and has higher spectral resolution). Of the variety of classification algorithms available, we tested maximum likelihood and support vector machine, and applied these to raw spectral data, the first three PCA summaries of the data, and the standard normalised difference vegetation index. We found that there is no 'one size fits all' solution to the choice of a 'best fit' model (i.e. combination of classification algorithm or data sets), which is in agreement with the literature that classifier performance will vary with data properties. We feel this lends support to our suggestion that rather than the identification of a 'single best' model and a map based on this result alone, a probability map based on the range of consistently top performing models provides a rigorous solution to environmental mapping. Copyright © 2016 Elsevier Ltd. All rights reserved.

Continental shelf sediment dynamics in the Anthropocene: A global shift

NASA Astrophysics Data System (ADS)

Oberle, Ferdinand K. J.; Puig, Pere; Martin, Jacobo

2017-04-01

Recent technological advances in remote sensing and deep marine sampling have revealed the extent and magnitude of the anthropogenic impacts to the seafloor. In particular, bottom trawling, a fishing technique consisting of dragging a net and fishing gear over the seafloor to capture bottom-dwelling living resources has gained attention due to its destructive effects on the seabed. Trawling gear produces acute impacts on biota and the physical substratum of the seafloor by disrupting the sediment column structure, overturning boulders, resuspending sediments and imprinting deep scars on muddy bottoms. Also, the repetitive passage of trawling gear over the same areas creates long-lasting, cumulative impacts that modify the cohesiveness and texture of sediments. It can be asserted nowadays that due to its recurrence, mobility and wide geographical extent, industrial trawling has become a major force driving seafloor change and affecting not only its physical integrity on short spatial scales but also imprinting measurable modifications to the geomorphology of entire continental margins.

A Satellite-Based Lagrangian View on Phytoplankton Dynamics

NASA Astrophysics Data System (ADS)

Lehahn, Yoav; d'Ovidio, Francesco; Koren, Ilan

2018-01-01

The well-lit upper layer of the open ocean is a dynamical environment that hosts approximately half of global primary production. In the remote parts of this environment, distant from the coast and from the seabed, there is no obvious spatially fixed reference frame for describing the dynamics of the microscopic drifting organisms responsible for this immense production of organic matter—the phytoplankton. Thus, a natural perspective for studying phytoplankton dynamics is to follow the trajectories of water parcels in which the organisms are embedded. With the advent of satellite oceanography, this Lagrangian perspective has provided valuable information on different aspects of phytoplankton dynamics, including bloom initiation and termination, spatial distribution patterns, biodiversity, export of carbon to the deep ocean, and, more recently, bottom-up mechanisms that affect the distribution and behavior of higher-trophic-level organisms. Upcoming submesoscale-resolving satellite observations and swarms of autonomous platforms open the way to the integration of vertical dynamics into the Lagrangian view of phytoplankton dynamics.

A Satellite-Based Lagrangian View on Phytoplankton Dynamics.

PubMed

Lehahn, Yoav; d'Ovidio, Francesco; Koren, Ilan

2018-01-03

The well-lit upper layer of the open ocean is a dynamical environment that hosts approximately half of global primary production. In the remote parts of this environment, distant from the coast and from the seabed, there is no obvious spatially fixed reference frame for describing the dynamics of the microscopic drifting organisms responsible for this immense production of organic matter-the phytoplankton. Thus, a natural perspective for studying phytoplankton dynamics is to follow the trajectories of water parcels in which the organisms are embedded. With the advent of satellite oceanography, this Lagrangian perspective has provided valuable information on different aspects of phytoplankton dynamics, including bloom initiation and termination, spatial distribution patterns, biodiversity, export of carbon to the deep ocean, and, more recently, bottom-up mechanisms that affect the distribution and behavior of higher-trophic-level organisms. Upcoming submesoscale-resolving satellite observations and swarms of autonomous platforms open the way to the integration of vertical dynamics into the Lagrangian view of phytoplankton dynamics.

Climate change, future Arctic Sea ice, and the competitiveness of European Arctic offshore oil and gas production on world markets.

PubMed

Petrick, Sebastian; Riemann-Campe, Kathrin; Hoog, Sven; Growitsch, Christian; Schwind, Hannah; Gerdes, Rüdiger; Rehdanz, Katrin

2017-12-01

A significant share of the world's undiscovered oil and natural gas resources are assumed to lie under the seabed of the Arctic Ocean. Up until now, the exploitation of the resources especially under the European Arctic has largely been prevented by the challenges posed by sea ice coverage, harsh weather conditions, darkness, remoteness of the fields, and lack of infrastructure. Gradual warming has, however, improved the accessibility of the Arctic Ocean. We show for the most resource-abundant European Arctic Seas whether and how a climate induced reduction in sea ice might impact future accessibility of offshore natural gas and crude oil resources. Based on this analysis we show for a number of illustrative but representative locations which technology options exist based on a cost-minimization assessment. We find that under current hydrocarbon prices, oil and gas from the European offshore Arctic is not competitive on world markets.

Seamount egg-laying grounds of the deep-water skate Bathyraja richardsoni.

PubMed

Henry, L-A; Stehmann, M F W; De Clippele, L; Findlay, H S; Golding, N; Roberts, J M

2016-08-01

Highly localized concentrations of elasmobranch egg capsules of the deep-water skate Bathyraja richardsoni were discovered during the first remotely operated vehicle (ROV) survey of the Hebrides Terrace Seamount in the Rockall Trough, north-east Atlantic Ocean. Conductivity-temperature-depth profiling indicated that the eggs were bathed in a specific environmental niche of well-oxygenated waters between 4·20 and 4·55° C, and salinity 34·95-35·06, on a coarse to fine-grained sandy seabed on the seamount's eastern flank, whereas a second type of egg capsule (possibly belonging to the skate Dipturus sp.) was recorded exclusively amongst the reef-building stony coral Solenosmilia variabilis. The depths of both egg-laying habitats (1489-1580 m) provide a de facto refuge from fisheries mortality for younger life stages of these skates. © 2016 The Authors. Journal of Fish Biology published by John Wiley & Sons Ltd on behalf of The Fisheries Society of the British Isles.

Bush Encroachment Mapping for Africa - Multi-Scale Analysis with Remote Sensing and GIS

NASA Astrophysics Data System (ADS)

Graw, V. A. M.; Oldenburg, C.; Dubovyk, O.

2015-12-01

Bush encroachment describes a global problem which is especially facing the savanna ecosystem in Africa. Livestock is directly affected by decreasing grasslands and inedible invasive species which defines the process of bush encroachment. For many small scale farmers in developing countries livestock represents a type of insurance in times of crop failure or drought. Among that bush encroachment is also a problem for crop production. Studies on the mapping of bush encroachment so far focus on small scales using high-resolution data and rarely provide information beyond the national level. Therefore a process chain was developed using a multi-scale approach to detect bush encroachment for whole Africa. The bush encroachment map is calibrated with ground truth data provided by experts in Southern, Eastern and Western Africa. By up-scaling location specific information on different levels of remote sensing imagery - 30m with Landsat images and 250m with MODIS data - a map is created showing potential and actual areas of bush encroachment on the African continent and thereby provides an innovative approach to map bush encroachment on the regional scale. A classification approach links location data based on GPS information from experts to the respective pixel in the remote sensing imagery. Supervised classification is used while actual bush encroachment information represents the training samples for the up-scaling. The classification technique is based on Random Forests and regression trees, a machine learning classification approach. Working on multiple scales and with the help of field data an innovative approach can be presented showing areas affected by bush encroachment on the African continent. This information can help to prevent further grassland decrease and identify those regions where land management strategies are of high importance to sustain livestock keeping and thereby also secure livelihoods in rural areas.

Atmospheric effects in multispectral remote sensor data

NASA Technical Reports Server (NTRS)

Turner, R. E.

1975-01-01

The problem of radiometric variations in multispectral remote sensing data which occur as a result of a change in geometric and environmental factors is studied. The case of spatially varying atmospheres is considered and the effect of atmospheric scattering is analyzed for realistic conditions. Emphasis is placed upon a simulation of LANDSAT spectral data for agricultural investigations over the United States. The effect of the target-background interaction is thoroughly analyzed in terms of various atmospheric states, geometric parameters, and target-background materials. Results clearly demonstrate that variable atmospheres can alter the classification accuracy and that the presence of various backgrounds can change the effective target radiance by a significant amount. A failure to include these effects in multispectral data analysis will result in a decrease in the classification accuracy.

A coastal and marine digital library at USGS

USGS Publications Warehouse

Lightsom, Fran

2003-01-01

The Marine Realms Information Bank (MRIB) is a distributed geolibrary [NRC, 1999] from the U.S. Geological Survey (USGS) and the Woods Hole Oceanographic Institution (WHOI), whose purpose is to classify, integrate, and facilitate access to Earth systems science information about ocean, lake, and coastal environments. Core MRIB services are: (1) the search and display of information holdings by place and subject, and (2) linking of information assets that exist in remote physical locations. The design of the MRIB features a classification system to integrate information from remotely maintained sources. This centralized catalogue organizes information using 12 criteria: locations, geologic time, physiographic features, biota, disciplines, research methods, hot topics, project names, agency names, authors, content type, and file type. For many of these fields, MRIB has developed classification hierarchies.

Remote sensing applications in agriculture and forestry. Applications of aerial photography and ERTS data to agricultural, forest and water resources management

NASA Technical Reports Server (NTRS)

1973-01-01

Remote sensing techniques are being used in Minnesota to study: (1) forest disease detection and control; (2) water quality indicators; (3) forest vegetation classification and management; (4) detection of saline soils in the Red River Valley; (5) corn defoliation; and (6) alfalfa crop productivity. Results of progress, and plans for future work in these areas, are discussed.

Earth Resources. A Continuing Bibliography with Indexes

DTIC Science & Technology

1987-11-01

Airborne microwave Doppler measurements of ocean of Guinea according to ground-based and satellite Coral reef remote sensing applications wave directional...understanding of internal Coral reef remote sensing applications an earth-to-satellite Hadamard transform laser long-path waves in the ocean p 20 A87-32951...classifications of coral reefs , and an are provided and new topographic features that are revealed are autocorrelation technique is being developed to

Remote Sensing Monitoring of Changes in Soil Salinity: A Case Study in Inner Mongolia, China.

PubMed

Wu, Jingwei; Vincent, Bernard; Yang, Jinzhong; Bouarfa, Sami; Vidal, Alain

2008-11-07

This study used archived remote sensing images to depict the history of changes in soil salinity in the Hetao Irrigation District in Inner Mongolia, China, with the purpose of linking these changes with land and water management practices and to draw lessons for salinity control. Most data came from LANDSAT satellite images taken in 1973, 1977, 1988, 1991, 1996, 2001, and 2006. In these years salt-affected areas were detected using a normal supervised classification method. Corresponding cropped areas were detected from NVDI (Normalized Difference Vegetation Index) values using an unsupervised method. Field samples and agricultural statistics were used to estimate the accuracy of the classification. Historical data concerning irrigation/drainage and the groundwater table were used to analyze the relation between changes in soil salinity and land and water management practices. Results showed that: (1) the overall accuracy of remote sensing in detecting soil salinity was 90.2%, and in detecting cropped area, 98%; (2) the installation/innovation of the drainage system did help to control salinity; and (3) a low ratio of cropped land helped control salinity in the Hetao Irrigation District. These findings suggest that remote sensing is a useful tool to detect soil salinity and has potential in evaluating and improving land and water management practices.

Application of airborne hyperspectral remote sensing for the retrieval of forest inventory parameters

NASA Astrophysics Data System (ADS)

Dmitriev, Yegor V.; Kozoderov, Vladimir V.; Sokolov, Anton A.

2016-04-01

Collecting and updating forest inventory data play an important part in the forest management. The data can be obtained directly by using exact enough but low efficient ground based methods as well as from the remote sensing measurements. We present applications of airborne hyperspectral remote sensing for the retrieval of such important inventory parameters as the forest species and age composition. The hyperspectral images of the test region were obtained from the airplane equipped by the produced in Russia light-weight airborne video-spectrometer of visible and near infrared spectral range and high resolution photo-camera on the same gyro-stabilized platform. The quality of the thematic processing depends on many factors such as the atmospheric conditions, characteristics of measuring instruments, corrections and preprocessing methods, etc. An important role plays the construction of the classifier together with methods of the reduction of the feature space. The performance of different spectral classification methods is analyzed for the problem of hyperspectral remote sensing of soil and vegetation. For the reduction of the feature space we used the earlier proposed stable feature selection method. The results of the classification of hyperspectral airborne images by using the Multiclass Support Vector Machine method with Gaussian kernel and the parametric Bayesian classifier based on the Gaussian mixture model and their comparative analysis are demonstrated.

Sediment Transport and Infilling of a Borrow Pit on an Energetic Sandy Ebb Tidal Delta Offshore of Hilton Head Island, South Carolina

NASA Astrophysics Data System (ADS)

Wren, A.; Xu, K.; Ma, Y.; Sanger, D.; Van Dolah, R.

2014-12-01

Bottom-mounted instrumentation was deployed at two sites on an ebb tidal delta to measure hydrodynamics, sediment transport, and seabed elevation. One site ('borrow site') was 2 km offshore and used as a dredging site for beach nourishment of nearby Hilton Head Island in South Carolina, and the other site ('reference site') was 10 km offshore and not directly impacted by the dredging. In-situ time-series data were collected during two periods after the dredging: March 15 - June 12, 2012('spring') and August 18 - November 18, 2012 ('fall'). At the reference site directional wave spectra and upper water column current velocities were measured, as well as high-resolution current velocity profiles and suspended sediment concentration profiles in the Bottom Boundary Layer (BBL). Seabed elevation and small-scale seabed changes were also measured. At the borrow site seabed elevation and near-bed wave and current velocities were collected using an Acoustic Doppler Velocimeter. Throughout both deployments bottom wave orbital velocities ranged from 0 - 110 m/s at the reference site. Wave orbital velocities were much lower at the borrow site ranging from 10-20 cm/s, as wave energy was dissipated on the extensive and rough sand banks before reaching the borrow site. Suspended sediment concentrations increased throughout the BBL when orbital velocities increased to approximately 20 cm/s. Sediment grain size and critical shear stresses were similar at both sites, therefore, re-suspension due to waves was less frequent at the borrow site. However, sediment concentrations were highly correlated with the tidal cycle at both sites. Semidiurnal tidal currents were similar at the two sites, typically ranging from 0 - 50 cm/s in the BBL. Maximum currents exceeded the critical shear stress and measured suspended sediment concentrations increased during the first hours of the tidal cycle when the tide switched to flood tide. Results indicate waves contributed more to sediment mobility at the reference site, while tidal forcing was the dominant factor at the borrow site. The seabed elevation data corraborates these results as active migrating ripples of 10 cm were measured at the reference site, while changes in seabed elevation at the borrow site were more gradual with approximately 30 cm of net accretion throughout the study.

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.

A new method for weakening the combined effect of residual errors on multibeam bathymetric data

NASA Astrophysics Data System (ADS)

Zhao, Jianhu; Yan, Jun; Zhang, Hongmei; Zhang, Yuqing; Wang, Aixue

2014-12-01

Multibeam bathymetric system (MBS) has been widely applied in the marine surveying for providing high-resolution seabed topography. However, some factors degrade the precision of bathymetry, including the sound velocity, the vessel attitude, the misalignment angle of the transducer and so on. Although these factors have been corrected strictly in bathymetric data processing, the final bathymetric result is still affected by their residual errors. In deep water, the result usually cannot meet the requirements of high-precision seabed topography. The combined effect of these residual errors is systematic, and it's difficult to separate and weaken the effect using traditional single-error correction methods. Therefore, the paper puts forward a new method for weakening the effect of residual errors based on the frequency-spectrum characteristics of seabed topography and multibeam bathymetric data. Four steps, namely the separation of the low-frequency and the high-frequency part of bathymetric data, the reconstruction of the trend of actual seabed topography, the merging of the actual trend and the extracted microtopography, and the accuracy evaluation, are involved in the method. Experiment results prove that the proposed method could weaken the combined effect of residual errors on multibeam bathymetric data and efficiently improve the accuracy of the final post-processing results. We suggest that the method should be widely applied to MBS data processing in deep water.

Acoustic Seabed Characterization of the Porcupine Bank, Irish Margin

NASA Astrophysics Data System (ADS)

O'Toole, Ronan; Monteys, Xavier

2010-05-01

The Porcupine Bank represents a large section of continental shelf situated west of the Irish landmass, located in water depths ranging between 150 and 500m. Under the Irish National Seabed Survey (INSS 1999-2006) this area was comprehensively mapped, generating multiple acoustic datasets including high resolution multibeam echosounder data. The unique nature of the area's datasets in terms of data density, consistency and geographic extent has allowed the development of a large-scale integrated physical characterization of the Porcupine Bank for multidisciplinary applications. Integrated analysis of backscatter and bathymetry data has resulted in a baseline delineation of sediment distribution, seabed geology and geomorphological features on the bank, along with an inclusive set of related database information. The methodology used incorporates a variety of statistical techniques which are necessary in isolating sonar system artefacts and addressing sonar geometry related issues. A number of acoustic backscatter parameters at several angles of incidence have been analysed in order to complement the characterization for both surface and subsurface sediments. Acoustic sub bottom records have also been incorporated in order to investigate the physical characteristics of certain features on the Porcupine Bank. Where available, groundtruthing information in terms of sediment samples, video footage and cores has been applied to add physical descriptors and validation to the characterization. Extensive mapping of different rock outcrops, sediment drifts, seabed features and other geological classes has been achieved using this methodology.

Effects of wave energy converters on the surrounding soft-bottom macrofauna (west coast of Sweden).

PubMed

Langhamer, O

2010-06-01

Offshore wave energy conversion is expected to develop, thus contributing to an increase in submerged constructions on the seabed. An essential concern related to the deployment of wave energy converters (WECs) is their possible impact on the surrounding soft-bottom habitats. In this study, the macrofaunal assemblages in the seabed around the wave energy converters in the Lysekil research site on the Swedish west coast and a neighbouring reference site were examined yearly during a period of 5 years (2004-2008). Macrobenthic communities living in the WECs' surrounding seabed were mainly composed by organisms typical for the area and depth off the Swedish west coast. At both sites the number of individuals, number of species and biodiversity were low, and were mostly small, juvenile organisms. The species assemblages during the first years of sampling were significantly different between the Lysekil research site and the nearby reference site with higher species abundance in the research site. The high contribution to dissimilarities was mostly due to polychaetes. Sparse macrofaunal densities can be explained by strong hydrodynamic forces and/or earlier trawling. WECs may alter the surrounding seabed with an accumulation of organic matter inside the research area. This indicates that the deployment of WECs in the Lysekil research site tends to have rather minor direct ecological impacts on the surrounding benthic community relative to the natural high variances.

Remote sensing based approach for monitoring urban growth in Mexico city, Mexico: A case study

NASA Astrophysics Data System (ADS)

Obade, Vincent

The world is experiencing a rapid rate of urban expansion, largely contributed by the population growth. Other factors supporting urban growth include the improved efficiency in the transportation sector and increasing dependence on cars as a means of transport. The problems attributed to the urban growth include: depletion of energy resources, water and air pollution; loss of landscapes and wildlife, loss of agricultural land, inadequate social security and lack of employment or underemployment. Aerial photography is one of the popular techniques for analyzing, planning and minimizing urbanization related problems. However, with the advances in space technology, satellite remote sensing is increasingly being utilized in the analysis and planning of the urban environment. This article outlines the strengths and limitations of potential remote sensing techniques for monitoring urban growth. The selected methods include: Principal component analysis, Maximum likelihood classification and "decision tree". The results indicate that the "classification tree" approach is the most promising for monitoring urban change, given the improved accuracy and smooth transition between the various land cover classes

Measuring and Monitoring Long Term Disaster Recovery Using Remote Sensing: A Case Study of Post Katrina New Orleans

NASA Astrophysics Data System (ADS)

Archer, Reginald S.

This research focuses on measuring and monitoring long term recovery progress from the impacts of Hurricane Katrina on New Orleans, LA. Remote sensing has frequently been used for emergency response and damage assessment after natural disasters. However, techniques for analysis of long term disaster recovery using remote sensing have not been widely explored. With increased availability and lower costs, remote sensing offers an objective perspective, systematic and repeatable analysis, and provides a substitute to multiple site visits. In addition, remote sensing allows access to large geographical areas and areas where ground access may be disrupted, restricted or denied. This dissertation addressed the primary difficulties involved in the development of change detection methods capable of detecting changes experienced by disaster recovery indicators. Maximum likelihood classification and post-classification change detection were applied to multi-temporal high resolution aerial images to quantitatively measure the progress of recovery. Images were classified to automatically identify disaster recovery indicators and exploit the indicators that are visible within each image. The spectral analysis demonstrated that employing maximum likelihood classification to high resolution true color aerial images performed adequately and provided a good indication of spectral pattern recognition, despite the limited spectral information. Applying the change detection to the classified images was effective for determining the temporal trajectory of indicators categorized as blue tarps, FEMA trailers, houses, vegetation, bare earth and pavement. The results of the post classification change detection revealed a dominant change trajectory from bluetarp to house, as damaged houses became permanently repaired. Specifically, the level of activity of blue tarps, housing, vegetation, FEMA trailers (temporary housing) pavement and bare earth were derived from aerial image processing to measure and monitor the progress of recovery. Trajectories of recovery for each individual indicator were examined to provide a better understanding of activity during reconstruction. A collection of spatial metrics was explored in order to identify spatial patterns and characterize classes in terms of patches of pixels. One of the key findings of the spatial analysis is that patch shapes were more complex in the presence of debris and damaged or destroyed buildings. The combination of spectral, temporal, and spatial analysis provided a satisfactory, though limited, solution to the question of whether remote sensing alone, can be used to quantitatively assess and monitor the progress of long term recovery following a major disaster. The research described in this dissertation provided a detailed illustration of the level of activity experienced by different recovery indicators during the long term recovery process. It also addressed the primary difficulties involved in the development of change detection methods capable of detecting changes experienced by disaster recovery indicators identified from classified high resolution true color aerial imagery. The results produced in this research demonstrate that the observed trajectories for actual indicators of recovery indicate different levels of recovery activity even within the same community. The level of activity of the long term reconstruction phase observed in the Kates model is not consistent with the level of activity of key recovery indicators in the Lower 9th Ward during the same period. Used in the proper context, these methods and results provide decision making information for determining resources. KEYWORDS: Change detection, classification, Katrina, New Orleans, remote sensing, disaster recovery, spatial metrics

Classification and overview of research in real-time imaging

NASA Astrophysics Data System (ADS)

Sinha, Purnendu; Gorinsky, Sergey V.; Laplante, Phillip A.; Stoyenko, Alexander D.; Marlowe, Thomas J.

1996-10-01

Real-time imaging has application in areas such as multimedia, virtual reality, medical imaging, and remote sensing and control. Recently, the imaging community has witnessed a tremendous growth in research and new ideas in these areas. To lend structure to this growth, we outline a classification scheme and provide an overview of current research in real-time imaging. For convenience, we have categorized references by research area and application.

Integration of spectral, spatial and morphometric data into lithological mapping: A comparison of different Machine Learning Algorithms in the Kurdistan Region, NE Iraq

NASA Astrophysics Data System (ADS)

Othman, Arsalan A.; Gloaguen, Richard

2017-09-01

Lithological mapping in mountainous regions is often impeded by limited accessibility due to relief. This study aims to evaluate (1) the performance of different supervised classification approaches using remote sensing data and (2) the use of additional information such as geomorphology. We exemplify the methodology in the Bardi-Zard area in NE Iraq, a part of the Zagros Fold - Thrust Belt, known for its chromite deposits. We highlighted the improvement of remote sensing geological classification by integrating geomorphic features and spatial information in the classification scheme. We performed a Maximum Likelihood (ML) classification method besides two Machine Learning Algorithms (MLA): Support Vector Machine (SVM) and Random Forest (RF) to allow the joint use of geomorphic features, Band Ratio (BR), Principal Component Analysis (PCA), spatial information (spatial coordinates) and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. The RF algorithm showed reliable results and discriminated serpentinite, talus and terrace deposits, red argillites with conglomerates and limestone, limy conglomerates and limestone conglomerates, tuffites interbedded with basic lavas, limestone and Metamorphosed limestone and reddish green shales. The best overall accuracy (∼80%) was achieved by Random Forest (RF) algorithms in the majority of the sixteen tested combination datasets.

Floating Forests: Validation of a Citizen Science Effort to Answer Global Ecological Questions

NASA Astrophysics Data System (ADS)

Rosenthal, I.; Byrnes, J.; Cavanaugh, K. C.; Haupt, A. J.; Trouille, L.; Bell, T. W.; Rassweiler, A.; Pérez-Matus, A.; Assis, J.

2017-12-01

Researchers undertaking long term, large-scale ecological analyses face significant challenges for data collection and processing. Crowdsourcing via citizen science can provide an efficient method for analyzing large data sets. However, many scientists have raised questions about the quality of data collected by citizen scientists. Here we use Floating-Forests (http://floatingforests.org), a citizen science platform for creating a global time series of giant kelp abundance, to show that ensemble classifications of satellite data can ensure data quality. Citizen scientists view satellite images of coastlines and classify kelp forests by tracing all visible patches of kelp. Each image is classified by fifteen citizen scientists before being retired. To validate citizen science results, all fifteen classifications are converted to a raster and overlaid on a calibration dataset generated from previous studies. Results show that ensemble classifications from citizen scientists are consistently accurate when compared to calibration data. Given that all source images were acquired by Landsat satellites, we expect this consistency to hold across all regions. At present, we have over 6000 web-based citizen scientists' classifications of almost 2.5 million images of kelp forests in California and Tasmania. These results are not only useful for remote sensing of kelp forests, but also for a wide array of applications that combine citizen science with remote sensing.

Using mm-scale seafloor roughness to improve monitoring of macrobenthos by remote sensing

NASA Astrophysics Data System (ADS)

Feldens, Peter; Schönke, Mischa; Wilken, Dennis; Papenmeier, Svenja

2017-04-01

In this study, we determine seafloor roughness at mm-scales by laser line-scanning to improve the remote marine habitat monitoring of macrobenthic organisms. Towards this purpose, a new autonomous lander system has been developed. Remote sensing of the seafloor is required to obtain a comprehensive view of the marine environment. It allows for analyzing spatiotemporal dynamics, monitoring of natural seabed variations, and evaluating possible anthropogenic impacts, all being crucial in regard to marine spatial planning as well as the sustainable and economic use of the sea. One aspect of ongoing remote sensing research is the identification of marine life, including both fauna and flora. The monitoring of seafloor fauna - including benthic communities - is mainly done using optical imaging systems and sample retrieval. The identification of new remote sensing indicator variables characteristic for the physical nature of the respective habitat would allow an improved spatial monitoring. A poorly investigated indicator variable is mm-scale seafloor microtopography and -roughness, which can be measured by laser line scanning and in turn strongly affects acoustic scatter. Two field campaigns have been conducted offshore Sylt Island in 2015 and 2016 to measure the microtopography of seafloor covered by sand masons, blue mussels, and oysters and to collect multi-frequency acoustic data. The acoustic data and topography of the blue mussel and oyster fields are currently being analyzed. The mm-scale microtopography of sand mason covered seafloor were transformed into the frequency domain and the average of the magnitude at different spatial wavelengths was used as a measure of roughness. The presence of sand masons causes a measurable difference in roughness magnitude at spatial wavelengths between 0.02 m and 0.0036 m, with the magnitude depending on sand mason abundance. This effect was not detected by commonly used 1D roughness profiles but required consideration of the complete spectrum. The influenced spatial wavelengths correspond to acoustic frequencies of 75 kHz and 400 kHz that are common for acoustic monitoring purposes. The available results indicate that the development of habitat-specific indicator variables, e.g. related to the abundance of sand masons or mussels, is possible and that remote sensing may assist the monitoring of benthic habitats in the future.

Proportion estimation and classification of mixed pixels in multispectral data

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

Crouse, K.R.

1979-01-01

Remote sensing applications to crop productivity estimations are discussed with detailed instructions for developing classifier skills in multispectral data analysis for corn, soybeans, oats, and alfalfa crops. (PCS)

The Land Use and Land Cover Dichotomy: A Comparison of Two Land Classification Systems in Support of Urban Earth Science Applications

NASA Technical Reports Server (NTRS)

McAllister, William K.

2003-01-01

One is likely to read the terms 'land use' and 'land cover' in the same sentence, yet these concepts have different origins and different applications. Land cover is typically analyzed by earth scientists working with remotely sensed images. Land use is typically studied by urban planners who must prescribe solutions that could prevent future problems. This apparent dichotomy has led to different classification systems for land-based data. The works of earth scientists and urban planning practitioners are beginning to come together in the field of spatial analysis and in their common use of new spatial analysis technology. In this context, the technology can stimulate a common 'language' that allows a broader sharing of ideas. The increasing amount of land use and land cover change challenges the various efforts to classify in ways that are efficient, effective, and agreeable to all groups of users. If land cover and land uses can be identified by remote methods using aerial photography and satellites, then these ways are more efficient than field surveys of the same area. New technology, such as high-resolution satellite sensors, and new methods, such as more refined algorithms for image interpretation, are providing refined data to better identify the actual cover and apparent use of land, thus effectiveness is improved. However, the closer together and the more vertical the land uses are, the more difficult the task of identification is, and the greater is the need to supplement remotely sensed data with field study (in situ). Thus, a number of land classification methods were developed in order to organize the greatly expanding volume of data on land characteristics in ways useful to different groups. This paper distinguishes two land based classification systems, one developed primarily for remotely sensed data, and the other, a more comprehensive system requiring in situ collection methods. The intent is to look at how the two systems developed and how they can work together so that land based information can be shared among different users and compared over time.

Analyzing Red and Gray Stages of Bark Beetle Attack in the San Bernardino National Forest Using Remote Sensing

NASA Astrophysics Data System (ADS)

Morgan, Andy J.

The San Bernardino National Forest (SBNF) has experienced periods of high, concentrated bark beetle epidemics in the late 1990's and into the 2000's. This increased activity has caused huge amounts of forest loss, resulting from disease introduced by bark beetles. Using remote sensing techniques and Landsat Thematic Mapper 5 (TM5) imagery, the spread of bark beetle diseased trees is mapped over a period from 1998 to 2008. Acreage of two attack stages (red and gray) were calculated from a level sliced classification method developed on data training sites. In each image using Normalized Difference Vegetation Index (NDVI) is the driver of forest health classifications. The results of the analysis are classification maps for each year, red acreage estimated for each study year, and gray attack acreage estimated for each study year. Additionally, for the period of 2001-2004, acreage was compared to those reported by the USDA with a thirteen percent lower mortality total in comparison to USDA federal land and a thirty-two percent lower total mortality (federal and non-federal) land in the SBNF.

Land cover/use classification of Cairns, Queensland, Australia: A remote sensing study involving the conjunctive use of the airborne imaging spectrometer, the large format camera and the thematic mapper simulator

NASA Technical Reports Server (NTRS)

Heric, Matthew; Cox, William; Gordon, Daniel K.

1987-01-01

In an attempt to improve the land cover/use classification accuracy obtainable from remotely sensed multispectral imagery, Airborne Imaging Spectrometer-1 (AIS-1) images were analyzed in conjunction with Thematic Mapper Simulator (NS001) Large Format Camera color infrared photography and black and white aerial photography. Specific portions of the combined data set were registered and used for classification. Following this procedure, the resulting derived data was tested using an overall accuracy assessment method. Precise photogrammetric 2D-3D-2D geometric modeling techniques is not the basis for this study. Instead, the discussion exposes resultant spectral findings from the image-to-image registrations. Problems associated with the AIS-1 TMS integration are considered, and useful applications of the imagery combination are presented. More advanced methodologies for imagery integration are needed if multisystem data sets are to be utilized fully. Nevertheless, research, described herein, provides a formulation for future Earth Observation Station related multisensor studies.

Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data

USGS Publications Warehouse

Myint, S.W.; Yuan, M.; Cerveny, R.S.; Giri, C.P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and objectoriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. ?? 2008 by MDPI.

Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data

PubMed Central

Myint, Soe W.; Yuan, May; Cerveny, Randall S.; Giri, Chandra P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. PMID:27879757

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery.

PubMed

Li, Guiying; Lu, Dengsheng; Moran, Emilio; Hetrick, Scott

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery

PubMed Central

LI, GUIYING; LU, DENGSHENG; MORAN, EMILIO; HETRICK, SCOTT

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms – maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes. PMID:22368311

Sensitivity of Tsunami Waves and Coastal Inundation/Runup to Seabed Displacement Models: Application to the Cascadia Subduction zone

NASA Astrophysics Data System (ADS)

Jalali Farahani, R.; Fitzenz, D. D.; Nyst, M.

2015-12-01

Major components of tsunami hazard modeling include earthquake source characterization, seabed displacement, wave propagation, and coastal inundation/run-up. Accurate modeling of these components is essential to identify the disaster risk exposures effectively, which would be crucial for insurance industry as well as policy makers to have tsunami resistant design of structures and evacuation planning (FEMA, 2008). In this study, the sensitivity and variability of tsunami coastal inundation due to Cascadia megathrust subduction earthquake are studied by considering the different approaches for seabed displacement model. The first approach is the analytical expressions that were proposed by Okada (1985, 1992) for the surface displacements and strains of rectangular sources. The second approach was introduced by Meade (2006) who introduced analytical solutions for calculating displacements, strains, and stresses on triangular sources. In this study, the seabed displacement using triangular representation of geometrically complex fault surfaces is compared with the Okada rectangular representations for the Cascadia subduction zone. In the triangular dislocation algorithm, the displacement is calculated using superposition of two angular dislocations for each of the three triangle legs. The triangular elements could give a better and gap-free representation of the fault surfaces. In addition, the rectangular representation gives large unphysical vertical displacement along the shallow-depth fault edge that generates unrealistic short-wavelength waves. To study the impact of these two different algorithms on the final tsunami inundation, the initial tsunami wave as well as wave propagation and the coastal inundation are simulated. To model the propagation of tsunami waves and coastal inundation, 2D shallow water equations are modeled using the seabed displacement as the initial condition for the numerical model. Tsunami numerical simulation has been performed on high-resolution bathymetric/topographic computational grids to identify accurate tsunami impact and flooding limits for the west of USA.

Semidiurnal temperature changes caused by tidal front movements in the warm season in seabed habitats on the georges bank northern margin and their ecological implications.

PubMed

Guida, Vincent G; Valentine, Page C; Gallea, Leslie B

2013-01-01

Georges Bank is a large, shallow feature separating the Gulf of Maine from the Atlantic Ocean. Previous studies demonstrated a strong tidal-mixing front during the warm season on the northern bank margin between thermally stratified water in the Gulf of Maine and mixed water on the bank. Tides transport warm water off the bank during flood tide and cool gulf water onto the bank during ebb tide. During 10 days in August 2009, we mapped frontal temperatures in five study areas along ∼100 km of the bank margin. The seabed "frontal zone", where temperature changed with frontal movment, experienced semidiurnal temperature maxima and minima. The tidal excursion of the frontal boundary between stratified and mixed water ranged 6 to 10 km. This "frontal boundary zone" was narrower than the frontal zone. Along transects perpendicular to the bank margin, seabed temperature change at individual sites ranged from 7.0°C in the frontal zone to 0.0°C in mixed bank water. At time series in frontal zone stations, changes during tidal cycles ranged from 1.2 to 6.1°C. The greatest rate of change (-2.48°C hr(-1)) occurred at mid-ebb. Geographic plots of seabed temperature change allowed the mapping of up to 8 subareas in each study area. The magnitude of temperature change in a subarea depended on its location in the frontal zone. Frontal movement had the greatest effect on seabed temperature in the 40 to 80 m depth interval. Subareas experiencing maximum temperature change in the frontal zone were not in the frontal boundary zone, but rather several km gulfward (off-bank) of the frontal boundary zone. These results provide a new ecological framework for examining the effect of tidally-driven temperature variability on the distribution, food resources, and reproductive success of benthic invertebrate and demersal fish species living in tidal front habitats.

Semidiurnal temperature changes caused by tidal front movements in the warm season in seabed habitats on the Georges Bank northern margin and their ecological implications

USGS Publications Warehouse

Guida, Vincent G.; Valentine, Page C.; Gallea, Leslie B.

2013-01-01

Georges Bank is a large, shallow feature separating the Gulf of Maine from the Atlantic Ocean. Previous studies demonstrated a strong tidal-mixing front during the warm season on the northern bank margin between thermally stratified water in the Gulf of Maine and mixed water on the bank. Tides transport warm water off the bank during flood tide and cool gulf water onto the bank during ebb tide. During 10 days in August 2009, we mapped frontal temperatures in five study areas along ∼100 km of the bank margin. The seabed “frontal zone”, where temperature changed with frontal movment, experienced semidiurnal temperature maxima and minima. The tidal excursion of the frontal boundary between stratified and mixed water ranged 6 to 10 km. This “frontal boundary zone” was narrower than the frontal zone. Along transects perpendicular to the bank margin, seabed temperature change at individual sites ranged from 7.0°C in the frontal zone to 0.0°C in mixed bank water. At time series in frontal zone stations, changes during tidal cycles ranged from 1.2 to 6.1°C. The greatest rate of change (-2.48°C hr-1) occurred at mid-ebb. Geographic plots of seabed temperature change allowed the mapping of up to 8 subareas in each study area. The magnitude of temperature change in a subarea depended on its location in the frontal zone. Frontal movement had the greatest effect on seabed temperature in the 40 to 80 m depth interval. Subareas experiencing maximum temperature change in the frontal zone were not in the frontal boundary zone, but rather several km gulfward (off-bank) of the frontal boundary zone. These results provide a new ecological framework for examining the effect of tidally-driven temperature variability on the distribution, food resources, and reproductive success of benthic invertebrate and demersal fish species living in tidal front habitats.

Predicting Tillage Patterns in the Tiffin River Watershed Using Remote Sensing Methods

NASA Astrophysics Data System (ADS)

Brooks, C.; McCarty, J. L.; Dean, D. B.; Mann, B. F.

2012-12-01

Previous research in tillage mapping has focused primarily on utilizing low to no-cost, moderate (30 m to 15 m) resolution satellite data. Successful data processing techniques published in the scientific literature have focused on extracting and/or classifying tillage patterns through manipulation of spectral bands. For instance, Daughtry et al. (2005) evaluated several spectral indices for crop residue cover using satellite multispectral and hyperspectral data and to categorize soil tillage intensity in agricultural fields. A weak to moderate relationship between Landsat Thematic Mapper (TM) indices and crop residue cover was found; similar results were reported in Minnesota. Building on the findings from the scientific literature and previous work done by MTRI in the heavily agricultural Tiffin watershed of northwest Ohio and southeast Michigan, a decision tree classifier approach (also referred to as a classification tree) was used, linking several satellite data to on-the-ground tillage information in order to boost classification results. This approach included five tillage indices and derived products. A decision tree methodology enabled the development of statistically optimized (i.e., minimizing misclassification rates) classification algorithms at various desired time steps: monthly, seasonally, and annual over the 2006-2010 time period. Due to their flexibility, processing speed, and availability within all major remote sensing and statistical software packages, decision trees can ingest several data inputs from multiple sensors and satellite products, selecting only the bands, band ratios, indices, and products that further reduce misclassification errors. The project team created crop-specific tillage pattern classification trees whereby a training data set (~ 50% of available ground data) was created for production of the actual decision tree and a validation data set was set aside (~ 50% of available ground data) in order to assess the accuracy of the classification. A seasonal time step was used, optimizing a decision tree based on seasonal ground data for tillage patterns and satellite data and products for years 2006 through 2010. Annual crop type maps derived by the project team and the USDA Cropland Data Layer project was used an input to understand locations of corn, soybeans, wheat, etc. on a yearly basis. As previously stated, the robustness of the decision tree approach is the ability to implement various satellite data and products across temporal, spectral, and spatial resolutions, thereby improving the resulting classification and providing a reliable method that is not sensor-dependent. Tillage pattern classification from satellite imagery is not a simple task and has proven a challenge to previous researchers investigating this remote sensing topic. The team's decision tree method produced a practical, usable output within a focused project time period. Daughtry, C.S.T., Hunt Jr., E.R., Doraiswamy, P.C., McMurtrey III, J.E. 2005. Remote sensing the spatial distribution of crop residues. Agron. J. 97, 864-871.

Object-based methods for individual tree identification and tree species classification from high-spatial resolution imagery

NASA Astrophysics Data System (ADS)

Wang, Le

2003-10-01

Modern forest management poses an increasing need for detailed knowledge of forest information at different spatial scales. At the forest level, the information for tree species assemblage is desired whereas at or below the stand level, individual tree related information is preferred. Remote Sensing provides an effective tool to extract the above information at multiple spatial scales in the continuous time domain. To date, the increasing volume and readily availability of high-spatial-resolution data have lead to a much wider application of remotely sensed products. Nevertheless, to make effective use of the improving spatial resolution, conventional pixel-based classification methods are far from satisfactory. Correspondingly, developing object-based methods becomes a central challenge for researchers in the field of Remote Sensing. This thesis focuses on the development of methods for accurate individual tree identification and tree species classification. We develop a method in which individual tree crown boundaries and treetop locations are derived under a unified framework. We apply a two-stage approach with edge detection followed by marker-controlled watershed segmentation. Treetops are modeled from radiometry and geometry aspects. Specifically, treetops are assumed to be represented by local radiation maxima and to be located near the center of the tree-crown. As a result, a marker image was created from the derived treetop to guide a watershed segmentation to further differentiate overlapping trees and to produce a segmented image comprised of individual tree crowns. The image segmentation method developed achieves a promising result for a 256 x 256 CASI image. Then further effort is made to extend our methods to the multiscales which are constructed from a wavelet decomposition. A scale consistency and geometric consistency are designed to examine the gradients along the scale-space for the purpose of separating true crown boundary from unwanted textures occurring due to branches and twigs. As a result from the inverse wavelet transform, the tree crown boundary is enhanced while the unwanted textures are suppressed. Based on the enhanced image, an improvement is achieved when applying the two-stage methods to a high resolution aerial photograph. To improve tree species classification, we develop a new method to choose the optimal scale parameter with the aid of Bhattacharya Distance (BD), a well-known index of class separability in traditional pixel-based classification. The optimal scale parameter is then fed in the process of a region-growing-based segmentation as a break-off value. Our object classification achieves a better accuracy in separating tree species when compared to the conventional Maximum Likelihood Classification (MLC). In summary, we develop two object-based methods for identifying individual trees and classifying tree species from high-spatial resolution imagery. Both methods achieve promising results and will promote integration of Remote Sensing and GIS in forest applications.

Agricultural Land Use mapping by multi-sensor approach for hydrological water quality monitoring

NASA Astrophysics Data System (ADS)

Brodsky, Lukas; Kodesova, Radka; Kodes, Vit

2010-05-01

The main objective of this study is to demonstrate potential of operational use of the high and medium resolution remote sensing data for hydrological water quality monitoring by mapping agriculture intensity and crop structures. In particular use of remote sensing mapping for optimization of pesticide monitoring. The agricultural mapping task is tackled by means of medium spatial and high temporal resolution ESA Envisat MERIS FR images together with single high spatial resolution IRS AWiFS image covering the whole area of interest (the Czech Republic). High resolution data (e.g. SPOT, ALOS, Landsat) are often used for agricultural land use classification, but usually only at regional or local level due to data availability and financial constraints. AWiFS data (nominal spatial resolution 56 m) due to the wide satellite swath seems to be more suitable for use at national level. Nevertheless, one of the critical issues for such a classification is to have sufficient image acquisitions over the whole vegetation period to describe crop development in appropriate way. ESA MERIS middle-resolution data were used in several studies for crop classification. The high temporal and also spectral resolution of MERIS data has indisputable advantage for crop classification. However, spatial resolution of 300 m results in mixture signal in a single pixel. AWiFS-MERIS data synergy brings new perspectives in agricultural Land Use mapping. Also, the developed methodology procedure is fully compatible with future use of ESA (GMES) Sentinel satellite images. The applied methodology of hybrid multi-sensor approach consists of these main stages: a/ parcel segmentation and spectral pre-classification of high resolution image (AWiFS); b/ ingestion of middle resolution (MERIS) vegetation spectro-temporal features; c/ vegetation signatures unmixing; and d/ semantic object-oriented classification of vegetation classes into final classification scheme. These crop groups were selected to be classified: winter crops, spring crops, oilseed rape, legumes, summer and other crops. This study highlights operational potentials of high temporal full resolution MERIS images in agricultural land use monitoring. Practical application of this methodology is foreseen, among others, in the water quality monitoring. Effective pesticide monitoring relies also on spatial distribution of applied pesticides, which can be derived from crop - plant protection product relationship. Knowledge of areas with predominant occurrence of specific crop based on remote sensing data described above can be used for a forecast of probable plant protection product application, thus cost-effective pesticide monitoring. The remote sensing data used on a continuous basis can be used in other long-term water management issues and provide valuable data for decision makers. Acknowledgement: Authors acknowledge the financial support of the Ministry of Education, Youth and Sports of the Czech Republic (grants No. 2B06095 and No. MSM 6046070901). The study was also supported by ESA CAT-1 (ref. 4358) and SOSI projects (Spatial Observation Services and Infrastructure; ref. GSTP-RTDA-EOPG-SW-08-0004).

Data processing 1: Advancements in machine analysis of multispectral data

NASA Technical Reports Server (NTRS)

Swain, P. H.

1972-01-01

Multispectral data processing procedures are outlined beginning with the data display process used to accomplish data editing and proceeding through clustering, feature selection criterion for error probability estimation, and sample clustering and sample classification. The effective utilization of large quantities of remote sensing data by formulating a three stage sampling model for evaluation of crop acreage estimates represents an improvement in determining the cost benefit relationship associated with remote sensing technology.

Remotely sensing the German Wadden Sea-a new approach to address national and international environmental legislation.

PubMed

Müller, Gabriele; Stelzer, Kerstin; Smollich, Susan; Gade, Martin; Adolph, Winny; Melchionna, Sabrina; Kemme, Linnea; Geißler, Jasmin; Millat, Gerald; Reimers, Hans-Christian; Kohlus, Jörn; Eskildsen, Kai

2016-10-01

The Wadden Sea along the North Sea coasts of Denmark, Germany, and the Netherlands is the largest unbroken system of intertidal sand and mud flats in the world. Its habitats are highly productive and harbour high standing stocks and densities of benthic species, well adapted to the demanding environmental conditions. Therefore, the Wadden Sea is one of the most important areas for migratory birds in the world and thus protected by national and international legislation, which amongst others requires extensive monitoring. Due to the inaccessibility of major areas of the Wadden Sea, a classification approach based on optical and radar remote sensing has been developed to support environmental monitoring programmes. In this study, the general classification framework as well as two specific monitoring cases, mussel beds and seagrass meadows, are presented. The classification of mussel beds profits highly from inclusion of radar data due to their rough surface and achieves agreements of up to 79 % with areal data from the regular monitoring programme. Classification of seagrass meadows reaches even higher agreements with monitoring data (up to 100 %) and furthermore captures seagrass densities as low as 10 %. The main classification results are information on area and location of individual habitats. These are needed to fulfil environmental legislation requirements. One of the major advantages of this approach is the large areal coverage with individual satellite images, allowing simultaneous assessment of both accessible and inaccessible areas and thus providing a more complete overall picture.

Geological and Geochemical Analysis of Seabed Stability at the Norfolk Ocean Disposal Site. Part 1: Geological Analysis.

DTIC Science & Technology

1983-05-31

ANALYSIS OF SEABED STABILITY AT THE NORFOLK OCEAN DISPOSAL SITE PART 1: GEOLOGICAL ANALYSIS U LU D George F. Oertel, Principal Investigator i- Final Report...eC.it ,ie _. _ r. .. .... All e d !t-o i’~$ ~- - - • ° . .. • : " . o . . . , . . ... - . • , .. . . . . . kkN 4. 18. parameters, diver reconnaissance of...For the period ending September 30, 1982 Prepared for the Department of the Army Norfolk District, Corps of Engineers 803 Front Street D I *Norfolk

Artificial neural network classification using a minimal training set - Comparison to conventional supervised classification

NASA Technical Reports Server (NTRS)

Hepner, George F.; Logan, Thomas; Ritter, Niles; Bryant, Nevin

1990-01-01

Recent research has shown an artificial neural network (ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second objective is to provide a preliminary comparison and ANN classification. An artificial neural network can be trained to do land-cover classification of satellite imagery using selected sites representative of each class in a manner similar to conventional supervised classification. One of the major problems associated with recognition and classifications of pattern from remotely sensed data is the time and cost of developing a set of training sites. This reseach compares the use of an ANN back propagation classification procedure with a conventional supervised maximum likelihood classification procedure using a minimal training set. When using a minimal training set, the neural network is able to provide a land-cover classification superior to the classification derived from the conventional classification procedure. This research is the foundation for developing application parameters for further prototyping of software and hardware implementations for artificial neural networks in satellite image and geographic information processing.

AgRISTARS - Plans and first-year achievements. [Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing

NASA Technical Reports Server (NTRS)

Hall, F. G.; Hogg, R. C.; Caudill, C. E.

1981-01-01

The results of the agriculture and resources inventory surveys through aerospace remote sensing (AgRISTARS) program managed by the USDA for exploring the use of satellite data for domestic and global commodity information needs are discussed. The program was intended to gather early warning of changes affecting production and quality of commodities and renewable resources, for predicting commodity production, land use classification and quantification, for inventories and assessments of renewable resources, land productivity measurements, assessment of conservation practices, and for pollution detection and impact evaluation. Up to 20 crop/region combinations in 7 countries were covered by the experiments, which comprised NOAA 6 and Landsat data analyses. Attempts to reduce variances through improved machine classification techniques are reported, together with soil moisture profiling, and the use of airborne sensors for providing comparative data.

Potentially efficient forest and range applications of remote sensing using earth orbital space craft, circa 1980

NASA Technical Reports Server (NTRS)

Wilson, R. C.

1970-01-01

Sixteen remote sensing applications or groups of related applications judged to be most important of any in the forestry and range disciplines were evaluated. In one application, major land classification, large amounts of useful data are anticipated to be contributed by space sensors in 1980. In four applications moderate amounts are anticipated to be so contributed. These are timber inventory, range inventory, fire weather forecasting, and monitoring snowfields. In the following seven applications small but significant amounts of data are anticipated to be contributed by space sensors: (1) detailed land classification; (2) inventory of wildlife habitat; (3) recreation resource inventory; (4) detecting stresses on the vegetation (5) monitoring air pollution caused by wildfires and prescribed burning; (6) monitoring water cycle, (7) pollution and erosion; and (8) evaluating damage to forests and ranges.

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

Hansen, E.A.; Smed, P.F.; Bryndum, M.B.

The paper describes the numerical program, PIPESIN, that simulates the behavior of a pipeline placed on an erodible seabed. PIPEline Seabed INteraction from installation until a stable pipeline seabed configuration has occurred is simulated in the time domain including all important physical processes. The program is the result of the joint research project, ``Free Span Development and Self-lowering of Offshore Pipelines`` sponsored by EU and a group of companies and carried out by the Danish Hydraulic Institute and Delft Hydraulics. The basic modules of PIPESIN are described. The description of the scouring processes has been based on and verified throughmore » physical model tests carried out as part of the research project. The program simulates a section of the pipeline (typically 500 m) in the time domain, the main input being time series of the waves and current. The main results include predictions of the onset of free spans, their length distribution, their variation in time, and the lowering of the pipeline as function of time.« less

Submergible barge retrievable storage and permanent disposal system for radioactive waste

DOEpatents

Goldsberry, Fred L.; Cawley, William E.

1981-01-01

A submergible barge and process for submerging and storing radioactive waste material along a seabed. A submergible barge receives individual packages of radwaste within segregated cells. The cells are formed integrally within the barge, preferably surrounded by reinforced concrete. The cells are individually sealed by a concrete decking and by concrete hatch covers. Seawater may be vented into the cells for cooling, through an integral vent arrangement. The vent ducts may be attached to pumps when the barge is bouyant. The ducts are also arranged to promote passive ventilation of the cells when the barge is submerged. Packages of the radwaste are loaded into individual cells within the barge. The cells are then sealed and the barge is towed to the designated disposal-storage site. There, the individual cells are flooded and the barge will begin descent controlled by a powered submarine control device to the seabed storage site. The submerged barge will rest on the seabed permanently or until recovered by a submarine control device.

A new macrofaunal limit in the deep biosphere revealed by extreme burrow depths in ancient sediments.

PubMed

Cobain, S L; Hodgson, D M; Peakall, J; Wignall, P B; Cobain, M R D

2018-01-10

Macrofauna is known to inhabit the top few 10s cm of marine sediments, with rare burrows up to two metres below the seabed. Here, we provide evidence from deep-water Permian strata for a previously unrecognised habitat up to at least 8 metres below the sediment-water interface. Infaunal organisms exploited networks of forcibly injected sand below the seabed, forming living traces and reworking sediment. This is the first record that shows sediment injections are responsible for hosting macrofaunal life metres below the contemporaneous seabed. In addition, given the widespread occurrence of thick sandy successions that accumulate in deep-water settings, macrofauna living in the deep biosphere are likely much more prevalent than considered previously. These findings should influence future sampling strategies to better constrain the depth range of infaunal animals living in modern deep-sea sands. One Sentence Summary: The living depth of infaunal macrofauna is shown to reach at least 8 metres in new habitats associated with sand injections.

Lagrangian circulation study near Cape Henry, Virginia. [Chesapeake Bay

NASA Technical Reports Server (NTRS)

Johnson, R. E.

1981-01-01

A study of the circulation near Cape Henry, Virginia, was made using surface and seabed drifters and radar tracked surface buoys coupled to subsurface drag plates. Drifter releases were conducted on a line normal to the beach just south of Cape Henry. Surface drifter recoveries were few; wind effects were strongly noted. Seabed drifter recoveries all exhibited onshore motion into Chesapeake Bay. Strong winds also affected seabed recoveries, tending to move them farther before recovery. Buoy trajectories in the vicinity of Cape Henry appeared to be of an irrotational nature, showing a clockwise rotary tide motion. Nearest the cape, the buoy motion elongated to almost parallel depth contours around the cape. Buoy motion under the action of strong winds showed that currents to at least the depth of the drag plates substantially are altered from those of low wind conditions near the Bay mouth. Only partial evidence could be found to support the presence of a clockwise nontidal eddy at Virginia Beach, south of Cape Henry.

Seep Detection using E/V Nautilus Integrated Seafloor Mapping and Remotely Operated Vehicles on the United States West Coast

NASA Astrophysics Data System (ADS)

Gee, L. J.; Raineault, N.; Kane, R.; Saunders, M.; Heffron, E.; Embley, R. W.; Merle, S. G.

2017-12-01

Exploration Vessel (E/V) Nautilus has been mapping the seafloor off the west coast of the United States, from Washington to California, for the past three years with a Kongsberg EM302 multibeam sonar. This system simultaneously collects bathymetry, seafloor and water column backscatter data, allowing an integrated approach to mapping to more completely characterize a region, and has identified over 1,000 seafloor seeps. Hydrographic multibeam sonars like the EM302 were designed for mapping the bathymetry. It is only in the last decade that major mapping projects included an integrated approach that utilizes the seabed and water column backscatter information in addition to the bathymetry. Nautilus mapping in the Eastern Pacific over the past three years has included a number of seep-specific expeditions, and utilized and adapted the preliminary mapping guidelines that have emerged from research. The likelihood of seep detection is affected by many factors: the environment: seabed geomorphology, surficial sediment, seep location/depth, regional oceanography and biology, the nature of the seeps themselves: size variation, varying flux, depth, and transience, the detection system: design of hydrographic multibeam sonars limits use for water column detection, the platform: variations in the vessel and operations such as noise, speed, and swath overlap. Nautilus integrated seafloor mapping provided multiple indicators of seep locations, but it remains difficult to assess the probability of seep detection. Even when seeps were detected, they have not always been located during ROV dives. However, the presence of associated features (methane hydrate and bacterial mats) serve as evidence of potential seep activity and reinforce the transient nature of the seeps. Not detecting a seep in the water column data does not necessarily indicate that there is not a seep at a given location, but with multiple passes over an area and by the use of other contextual data, an area may be classified as likely or unlikely to host seeps.

Using novel acoustic and visual mapping tools to predict the small-scale spatial distribution of live biogenic reef framework in cold-water coral habitats

NASA Astrophysics Data System (ADS)

De Clippele, L. H.; Gafeira, J.; Robert, K.; Hennige, S.; Lavaleye, M. S.; Duineveld, G. C. A.; Huvenne, V. A. I.; Roberts, J. M.

2017-03-01

Cold-water corals form substantial biogenic habitats on continental shelves and in deep-sea areas with topographic highs, such as banks and seamounts. In the Atlantic, many reef and mound complexes are engineered by Lophelia pertusa, the dominant framework-forming coral. In this study, a variety of mapping approaches were used at a range of scales to map the distribution of both cold-water coral habitats and individual coral colonies at the Mingulay Reef Complex (west Scotland). The new ArcGIS-based British Geological Survey (BGS) seabed mapping toolbox semi-automatically delineated over 500 Lophelia reef `mini-mounds' from bathymetry data with 2-m resolution. The morphometric and acoustic characteristics of the mini-mounds were also automatically quantified and captured using this toolbox. Coral presence data were derived from high-definition remotely operated vehicle (ROV) records and high-resolution microbathymetry collected by a ROV-mounted multibeam echosounder. With a resolution of 0.35 × 0.35 m, the microbathymetry covers 0.6 km2 in the centre of the study area and allowed identification of individual live coral colonies in acoustic data for the first time. Maximum water depth, maximum rugosity, mean rugosity, bathymetric positioning index and maximum current speed were identified as the environmental variables that contributed most to the prediction of live coral presence. These variables were used to create a predictive map of the likelihood of presence of live cold-water coral colonies in the area of the Mingulay Reef Complex covered by the 2-m resolution data set. Predictive maps of live corals across the reef will be especially valuable for future long-term monitoring surveys, including those needed to understand the impacts of global climate change. This is the first study using the newly developed BGS seabed mapping toolbox and an ROV-based microbathymetric grid to explore the environmental variables that control coral growth on cold-water coral reefs.

Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal

USGS Publications Warehouse

Dieye, A.M.; Roy, David P.; Hanan, N.P.; Liu, S.; Hansen, M.; Toure, A.

2012-01-01

Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

Satellite Image Classification of Building Damages Using Airborne and Satellite Image Samples in a Deep Learning Approach

NASA Astrophysics Data System (ADS)

Duarte, D.; Nex, F.; Kerle, N.; Vosselman, G.

2018-05-01

The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of building damages. However, there is an overall interest in the use of automated methods to perform this task, regardless of the used platform. Owing to its synoptic coverage and predictable availability, satellite imagery is currently used as input for the identification of building damages by the International Charter, as well as the Copernicus Emergency Management Service for the production of damage grading and reference maps. Recently proposed methods to perform image classification of building damages rely on convolutional neural networks (CNN). These are usually trained with only satellite image samples in a binary classification problem, however the number of samples derived from these images is often limited, affecting the quality of the classification results. The use of up/down-sampling image samples during the training of a CNN, has demonstrated to improve several image recognition tasks in remote sensing. However, it is currently unclear if this multi resolution information can also be captured from images with different spatial resolutions like satellite and airborne imagery (from both manned and unmanned platforms). In this paper, a CNN framework using residual connections and dilated convolutions is used considering both manned and unmanned aerial image samples to perform the satellite image classification of building damages. Three network configurations, trained with multi-resolution image samples are compared against two benchmark networks where only satellite image samples are used. Combining feature maps generated from airborne and satellite image samples, and refining these using only the satellite image samples, improved nearly 4 % the overall satellite image classification of building damages.

A supervised learning rule for classification of spatiotemporal spike patterns.

PubMed

Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

2016-08-01

This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

100% of the World Ocean Floor Mapped by 2030 - Contribution of the South and West Pacific Regional Data Assembly and Coordination Centre to the Seabed 2030 Initiative

NASA Astrophysics Data System (ADS)

Lamarche, G.; Neil, H.; Stagpoole, V. M.; Greenland, A.; Mackay, K.; Black, J.; Griffin, E.

2017-12-01

The Seabed 2030 SaWPac Centre (South and West Pacific Ocean Regional Data Assembly and Coordination Centre) has been formed to generate new high resolution ocean floor maps of the western and southern Pacific Ocean. The centre is part of the joint Nippon Foundation and the General Bathymetric Chart of the Oceans (GEBCO) initiative to produce a definitive map of the World Ocean floor by 2030, empowering the world to make policy decisions, use the ocean sustainability and undertake scientific research based on detailed bathymetric information of the Earth's seabed. The SaWPac Centre is based at NIWA Wellington (New Zealand) and includes a collaborative partnership with GNS Science and Land Information New Zealand. It is responsible for the region from South America to Australia, north of latitude 50°S to 10° north of the Equator and the western part of the Northern Pacific Ocean to Russia. The region includes the world's deepest trenches and also covers some of the remotest oceans where bathymetric data form existing ship tracks is spaced up to 100 km apart. The challenge for the SaWPac Centre is to collate and combine all the available bathymetric data from the numerous nations that have surveyed in the region. The centre will also promote efforts to collect new data and contribute to map products generated by the Seabed 2030 global mapping project.

Crop Identification Technolgy Assessment for Remote Sensing (CITARS). Volume 1: Task design plan

NASA Technical Reports Server (NTRS)

Hall, F. G.; Bizzell, R. M.

1975-01-01

A plan for quantifying the crop identification performances resulting from the remote identification of corn, soybeans, and wheat is described. Steps for the conversion of multispectral data tapes to classification results are specified. The crop identification performances resulting from the use of several basic types of automatic data processing techniques are compared and examined for significant differences. The techniques are evaluated also for changes in geographic location, time of the year, management practices, and other physical factors. The results of the Crop Identification Technology Assessment for Remote Sensing task will be applied extensively in the Large Area Crop Inventory Experiment.