Classification and mapping of coal refuse, vegetative cover types, and forest types by digital processing ERTS-1 data
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Borden, F. Y.↗
SEARCH · Engineering Papers
Results for “vegetation classification”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Unsupervised classification of MSS Landsat data for mapping spatially complex vegetation
The various stages in carrying out a monocluster block unsupervised classification using Landsat MSS data are described. Procedures for carrying out these various stages were found to be far from well-established for the type of terrain being investigated, which is rugged and contains many small land cover units. Two particular difficulties were encountered: first, that of precise ground location of pixels; and, secondly, that of objectively evaluating the results. Ways in which these can be surmounted are suggested.
Townshend, J. R. G.↗
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)
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.
Cibula, W. G.↗
Feature Identification and Location Experiment
The Feature Identification and Location Experiment (FILE), which was flown on the second Space Shuttle flight to test a technique for real-time, autonomous classification of water, vegetation and bare land as well as clouds, snow and ice, senses earth radiation in spectral bands centered at 0.65 and 0.85 microns. The radiance ratio classification algorithm has successfully made automatic data selection decisions. A classification image obtained on the mission is providing data needed to evaluate the FILE algorithm and overall system performance.
Sivertson, W. E., Jr.↗
Remote sensing/vegetation classification
The CALVEG classification system for identification of vegetation is described. This hierarchical system responds to classification requirements and to interpretation of vegetation at various description levels, from site description to broad identification levels. The system's major strength is its flexibility in application of remote sensing technology to assess, describe and communicate data relative to vegetative resources on a state-wide basis. It is concluded that multilevel remote sensing is a cost effective tool for assessment of the natural resource base. The CLAVEG system is found to be an economically efficient tool for both existing and potential vegetation.
Parker, I. E.↗
Application of thematic mapper-type data over a porphyry-molybdenum deposit in Colorado
The objective of the study was to evaluate the utility of thematic mapper data as a source of geologically useful information for mountainous areas of varying vegetation density. Much of the processing was done in an a priori manner without prior ground-based information. This approach resulted in a successfull mapping of the alteration associated with the Mt. Emmons molybdenum ore body as well as several other hydrothermal systems. Supervised classification produced a vegetation map at least as accurate as the mapping done for the environmental impact statement. Principal components were used to map zones of general, subtle alteration and to separate hematitically stained rock from staining associated with hydrothermal activity. Decorrelation color composites were found to be useful field mapping aids, easily delineating many lithologies and vegetation classes of interest. The factors restricting the interpretability and computer manipulation of the data are examined.
Rickman, D. L.↗
Capitol Reef Ecological Conservation: Mapping Vegetation Functional Groups to Inform Invasive Vegetation Management, Ecological Conservation and Restoration in Capitol Reef National Park
Invasive exotic plant (IEP) species have been found within the park boundaries of Capitol Reef National Park (CARE) in Utah. Currently, remotely sensed datasets such as the Rangeland Analysis Platform (RAP) from the United States Department of Agriculture (USDA) have been used to investigate IEP species within the park, but validation of the national RAP program is necessary for informing decisions at a local scale. CARE seeks a remote monitoring solution that can precisely target managerial efforts within the park’s challenging terrain and hard-to-reach locations. To fulfill this objective, we harnessed Landsat 8 Operational Land Imager (OLI) imagery and leveraged Random Forest (RF) modeling to generate classification maps characterizing vegetation functional groups for 2013 and 2022 within the park. Subsequently, the Land Change Modeler (LCM) in Idrisi TerrSet facilitated the production of a predicted classification map for 2033. The team also devised an annual grass probability map to accentuate areas impacted by exotic grasses. A comparative assessment between the RF classification map and the RAP map for 2022 revealed an overall agreement of 47.41%, with disparities primarily arising from differences in bare soil and shrub areas. Significantly, the 2022 RF-generated classification map showcased an impressive overall accuracy of 92.17%. In short, the probability map, the land cover change detection spanning 2013 to 2022, and the forecasting of observed trends into the future aids in the evaluation of invasive plant impacts and facilitation of CARE’s preparedness for potential ecological disturbances. Notably, in comparison to the RAP, the RF classification method generates functional group maps that are more representative of the study area.
Vanchy Li↗
CropEx Web-Based Agricultural Monitoring and Decision Support
CropEx is a Web-based agricultural Decision Support System (DSS) that monitors changes in crop health over time. It is designed to be used by a wide range of both public and private organizations, including individual producers and regional government offices with a vested interest in tracking vegetation health. The database and data management system automatically retrieve and ingest data for the area of interest. Another stores results of the processing and supports the DSS. The processing engine will allow server-side analysis of imagery with support for image sub-setting and a set of core raster operations for image classification, creation of vegetation indices, and change detection. The system includes the Web-based (CropEx) interface, data ingestion system, server-side processing engine, and a database processing engine. It contains a Web-based interface that has multi-tiered security profiles for multiple users. The interface provides the ability to identify areas of interest to specific users, user profiles, and methods of processing and data types for selected or created areas of interest. A compilation of programs is used to ingest available data into the system, classify that data, profile that data for quality, and make data available for the processing engine immediately upon the data s availability to the system (near real time). The processing engine consists of methods and algorithms used to process the data in a real-time fashion without copying, storing, or moving the raw data. The engine makes results available to the database processing engine for storage and further manipulation. The database processing engine ingests data from the image processing engine, distills those results into numerical indices, and stores each index for an area of interest. This process happens each time new data is ingested and processed for the area of interest, and upon subsequent database entries, the database processing engine qualifies each value for each area of interest and conducts a logical processing of results indicating when and where thresholds are exceeded. Reports are provided at regular, operator-determined intervals that include variances from thresholds and links to view raw data for verification, if necessary. The technology and method of development allow the code base to easily be modified for varied use in the real-time and near-real-time processing environments. In addition, the final product will be demonstrated as a means for rapid draft assessment of imagery.
Harvey. Craig↗
Monitoring tropical vegetation succession with LANDSAT data
The shadowing problem, which is endemic to the use of LANDSAT in tropical areas, and the ability to model changes over space and through time are problems to be addressed when monitoring tropical vegetation succession. Application of a trend surface analysis model to major land cover classes in a mountainous region of the Phillipines shows that the spatial modeling of radiance values can provide a useful approach to tropical rain forest succession monitoring. Results indicate shadowing effects may be due primarily to local variations in the spectral responses. These variations can be compensated for through the decomposition of the spatial variation in both elevation and MSS data. Using the model to estimate both elevation and spectral terrain surface as a posteriori inputs in the classification process leads to improved classification accuracy for vegetation of cover of this type. Spatial patterns depicted by the MSS data reflect the measurement of responses to spatial processes acting at several scales.
Robinson, V. B.↗
Image applications for coastal resource planning: Elkhorn Slough Pilot Project
The purpose of this project has been to evaluate the utility of digital spectral imagery at two levels of resolution for large scale, accurate, auto-classification of land cover along the Central California Coast. Although remote sensing technology offers obvious advantages over on-the-ground mapping, there are substantial trade-offs that must be made between resolving power and costs. Higher resolution images can theoretically be used to identify smaller habitat patches, but they usually require more scenes to cover a given area and processing these images is computationally intense requiring much more computer time and memory. Lower resolution images can cover much larger areas, are less costly to store, process, and manipulate, but due to their larger pixel size can lack the resolving power of the denser images. This lack of resolving power can be critical in regions such as the Central California Coast where important habitat change often occurs on a scale of 10 meters. Our approach has been to compare vegetation and habitat classification results from two aircraft-based spectral scenes covering the same study area but at different levels of resolution with a previously produced ground-truthed land cover base map of the area. Both of the spectral images used for this project were of significantly higher resolution than the satellite-based LandSat scenes used in the C-CAP program. The lower reaches of the Elkhorn Slough watershed was chosen as an ideal study site because it encompasses a suite of important vegetation types and habitat loss processes characteristic of the central coast region. Dramatic habitat alterations have and are occurring within the Elkhorn Slough drainage area, including erosion and sedimentation, land use conversion, wetland loss, and incremental loss due to development and encroachnnent by agriculture. Additonally, much attention has already been focused on the Elkhorn Slough due to its status as a National Marine Education and Research Reserve and as part of the Monterey Bay National Marine Sanctuary. These destinations have resulted in a rich collection of prior spatial and temporal habitat data.
Kvitek, Rikk G.↗
Unmanned Ground Vehicle Perception Using Thermal Infrared Cameras
The ability to perform off-road autonomous navigation at any time of day or night is a requirement for some unmanned ground vehicle (UGV) programs. Because there are times when it is desirable for military UGVs to operate without emitting strong, detectable electromagnetic signals, a passive only terrain perception mode of operation is also often a requirement. Thermal infrared (TIR) cameras can be used to provide day and night passive terrain perception. TIR cameras have a detector sensitive to either mid-wave infrared (MWIR) radiation (3-5?m) or long-wave infrared (LWIR) radiation (8-12?m). With the recent emergence of high-quality uncooled LWIR cameras, TIR cameras have become viable passive perception options for some UGV programs. The Jet Propulsion Laboratory (JPL) has used a stereo pair of TIR cameras under several UGV programs to perform stereo ranging, terrain mapping, tree-trunk detection, pedestrian detection, negative obstacle detection, and water detection based on object reflections. In addition, we have evaluated stereo range data at a variety of UGV speeds, evaluated dual-band TIR classification of soil, vegetation, and rock terrain types, analyzed 24 hour water and 12 hour mud TIR imagery, and analyzed TIR imagery for hazard detection through smoke. Since TIR cameras do not currently provide the resolution available from megapixel color cameras, a UGV's daytime safe speed is often reduced when using TIR instead of color cameras. In this paper, we summarize the UGV terrain perception work JPL has performed with TIR cameras over the last decade and describe a calibration target developed by General Dynamics Robotic Systems (GDRS) for TIR cameras and other sensors.
terrain classification↗
NASA plans for future earth resources missions
Development activities underway to improve LANDSAT sensors and observing systems are reported. Data cover high resolution imagery of surface temperatures for improving crop and other vegetation and soil classifications, and heat capacity mapping for recording surface temperatures at hours of maximum and minimum heating. Automatic identification and classification methods designed to extract information such as crop and forage acreages, amounts of water run-off, and types of land use directly from the satellite observations were also studied. A number of applications verification tests were conducted, particularly in the areas of crop and land use inventory and water run-off prediction, to demonstrate the direct transfer of space acquired information to end users.
Nordberg, W.↗
Land use surveys by means of automatic interpretation of LANDSAT system data
Analyses for seven land-use classes are presented. The classes are: urban area, industrial area, bare soil, cultivated area, pastureland, reforestation, and natural vegetation. The automatic classification of LANDSAT MSS data using a maximum likelihood algorithm shows a 39% average error of emission and a 3.45 error of commission for the seven classes.
Dejesusparada, N.↗
Remote sensing education at the University of British Columbia
The development, current status, and organization of the University of British Columbia's interdisciplinary graduate program in remote sensing are described. Specialized programs are tailored to meet student's needs and interest and can range from the theoretical development of technology (sensor development, modelling, and computer analysis) to specialized application of remote sensing in resource analysis such as the determination of vegetation damage, land classification, and land use. The courses and faculty members are listed.
Murtha, P. A.↗
What are the best radar wavelengths, incidence angles and polarizations for geologic applications? A statistical approach
Linear discriminant analysis of multifrequency and multipolarization radar scatterometer data of lava flows and sedimentary rocks indicates that the lava flows can be separated by age and the sedimentary rocks can be discriminated from one another. The optimum wavelengths, polarizations and incidence angles among those available for these problems was determined by the discriminant analysis program. For separation of the lava flows, shorter wavelengths, smaller incidence angles and horizontal polarization are best. A SIR-C radar configuration could provide nearly complete discrimination of these lava flows. Conversely, the longer wavelengths, larger incidence angles and vertical polarization was preferred for sedimentary rocks, perhaps due to the slight vegetation cover. Satisfactory classification of sedimentary rocks requires more radar data than for the lavas. These results are potentially useful both for radar system configuration and for geological applications. The method developed here may provide a rationale for user specification of imaging system parameters.
Blom, R.↗
Seasat over-land scatterometer data. II - Selection of extended area land-target sites for the calibration of spaceborne scatterometers
The post-launch performance verification for future scatterometers can use extended area land targets to calibrate antenna gain patterns and to verify and monitor deployment configurations. For the Ku-band Seasat scatterometer, a region of tropical rain forest in the Amazon basin was used as a homogeneous extended-area land target. As this region is continuously being deforested, other regions are investigated for calibrating scatterometers. The global backscatter coefficients (sigma0) are compared to classifications of natural vegetation and cultivation intensity and the variability with time during the three-month mission is studied. The statistical variability of sigma0 is compared with prior estimates resulting from the known variability of the instrument parameters and communication noise. Data from selected forested regions with relatively homogeneous sigma0 and little time-dependence are presented.
Kennett, Rosemary G.↗
Lunar and Planetary Science XXXV: Image Processing and Earth Observations
The titles in this section include: 1) Expansion in Geographic Information Services for PIGWAD; 2) Modernization of the Integrated Software for Imagers and Spectrometers; 3) Science-based Region-of-Interest Image Compression; 4) Topographic Analysis with a Stereo Matching Tool Kit; 5) Central Avra Valley Storage and Recovery Project (CAVSARP) Site, Tucson, Arizona: Floodwater and Soil Moisture Investigations with Extraterrestrial Applications; 6) ASE Floodwater Classifier Development for EO-1 HYPERION Imagery; 7) Autonomous Sciencecraft Experiment (ASE) Operations on EO-1 in 2004; 8) Autonomous Vegetation Cover Scene Classification of EO-1 Hyperion Hyperspectral Data; 9) Long-Term Continental Areal Reduction Produced by Tectonic Processes.
Source record↗
New Data-Driven Estimation of Terrestrial CO2 Fluxes in Asia Using a Standardized Database of Eddy Covariance Measurements, Remote Sensing Data, and Support Vector Regression
The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial carbon dioxide fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem carbon dioxide exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated carbon dioxide fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8 days are reproduced (e.g., r (exp 2) =0.73 and 0.42 for 8 day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r (exp 2)=1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land carbon dioxide fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land carbon dioxide fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land carbon dioxide fluxes. These data-driven estimates can provide a new opportunity to assess carbon dioxide fluxes in Asia and evaluate and constrain terrestrial ecosystem models.
chlorophyll fluorescence↗