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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS1985

Application of AIS Technology to Forest Mapping

Concerns about environmental effects of large scale deforestation have prompted efforts to map forests over large areas using various remote sensing data and image processing techniques. Basic research on the spectral characteristics of forest vegetation are required to form a basis for development of new techniques, and for image interpretation. Examination of LANDSAT data and image processing algorithms over a portion of boreal forest have demonstrated the complexity of relations between the various expressions of forest canopies, environmental variability, and the relative capacities of different image processing algorithms to achieve high classification accuracies under these conditions. Airborne Imaging Spectrometer (AIS) data may in part provide the means to interpret the responses of standard data and techniques to the vegetation based on its relatively high spectral resolution.

Yool, S. R.↗
NASA NTRS2023

Oregon Coast Range Ecological Conservation: Mapping Recent Logging Within Drinking Watersheds of Oregon’s Coastal Range to Support Future Resource Management Policies

Logging operations are widespread across the Oregon Coast Range and conventional logging practices pose a risk of contamination to surface water quality. The NASA DEVELOP Oregon Coast Ecological Conservation team partnered with nonprofit Oregon Wild to quantify the extent of clearcutting and commercial thinning in 80 Coast Range drinking watersheds between 2000 and 2022. This project used all available Landsat data from 1997 through June 2023 in Google Earth Engine. Sensors used include Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus, Landsat 8 Operational Land Imager, and Landsat 9 Operational Land Imager-2. The Continuous Change Detection and Classification (CCDC) algorithm was used with Landsat observations to identify clearcutting patches. Percent change in summer median Landsat Normalized Difference Vegetation Index (NDVI) images were used to identify areas of forest disturbance including commercial thinning. The team concluded that logging, including both clearcutting and commercial thinning, impacted 31% of forested area in drinking watersheds and the intensity of logging remained consistent from year to year. Clearcutting occurred primarily on private land while commercial thinning occurred primarily on state and federal lands. This study showed that CCDC effectively identifies clearcutting, and percent change in NDVI successfully identifies disturbances including commercial thinning. Key constraints included the lack of field validation data and the inability to attribute disturbances to logging with certainty. Ultimately, this study identified the drinking watersheds and communities most likely to be impacted by logging activity. These results can inform legislation aimed at balancing the commercial and environmental benefits of forestlands.

Logging↗
NASA NTRS1985

African land-cover classification using satellite data

Data from the advanced very high resolution radiometer sensor on the National Oceanic and Atmospheric Administration's operational series of meteorological satellites were used to classify land cover and monitor vegetation dynamics for Africa over a 19-month period. There was a correspondence between seasonal variations in the density and extent of green leaf vegetation and the patterns of rainfall associated with the movement of the Intertropical Convergence Zone. Regional variations, such as the 1983 drought in the Sahel of western Africa, were observed. Integration of the weekly satellite data with respect to time for a 12-month period produced a remotely sensed estimate of primary production based upon the density and duration of green leaf biomass. Eight of the 21-day composited data sets covering an 11-month period were used to produce a general land-cover classification that corresponded well with those of existing maps.

Tucker, C. J.↗
NASA NTRS1981

Crop classification using airborne radar and LANDSAT data

Airborne radar data acquired with a 13.3 GHz scatterometer over a test-site near Colby, Kansas were used to investigate the statistical properties of the scattering coefficient of three types of vegetation cover and of bare soil. A statistical model for radar data was developed that incorporates signal-fading and natural within-field variabilities. Estimates of the within-field and between-field coefficients of variation were obtained for each cover-type and compared with similar quantities derived from LANDSAT images of the same fields. The classification accuracy provided by LANDSAT alone, radar alone, and both sensors combined was investigated. The results indicate that the addition of radar to LANDSAT improves the classification accuracy by about 10; percentage-points when the classification is performed on a pixel basis and by about 15 points when performed on a field-average basis.

Ulaby, F. T.↗
NASA NTRS1973

The use of ERTS-1 MSS data for mapping strip mines and acid mine drainage in Pennsyvania

Digital processing of ERTS-I MSS data for areas around the west branch of the Susquehanna River permits identification of stripped areas including ones that are not discernible from visual analysis of ERTS imagery. Underflight data and ground-based observations are used for ground-truth and as a basis for designing more refined operators to make sub-classifications of stripped areas, particularly with regard to manifestations of acid mine drainage; because of associated diagnostic effects on vegetation, seasonal changes in classifiction criteria are being documented as repeated, cloud-free ERTS-I coverage of the same area becomes available. Preliminary results indicate that ERTS data can be used to moniter not only the total extent of stripping in given areas but also the effectiveness of reclamation and pollution abatement procedures.

Alexander, S. S.↗
NASA NTRS1979

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

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.

Tendam, I. M.↗
NASA NTRS2006

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

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.

Spruce, Joseph↗
NASA NTRS1993

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

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

Blodget, Herbert W.↗
NASA NTRS1973

Unsupervised classification and areal measurement of land and water coastal features on the Texas coast

Multispectral scanner (MSS) digital data from ERTS-1 was used to delineate coastal land, vegetative, and water features in two portions of the Texas Coastal Zone. Data (Scene ID's 1037-16244 and 1037-16251) acquired on August 29, 1972, were analyzed on NASA Johnson Space Center systems through the use of two clustering algorithms. Seventeen to 30 spectrally homogeneous classes were so defined. Many classes were identified as being pure features such as water masses, salt marsh, beaches, pine, hardwoods, and exposed soil or construction materials. Most classes were identified to be mixtures of the pure class types. Using an objective technique for measuring the percentage of wetland along salt marsh boundaries, an analysis was made of the accuracy of areal measurement of salt marshes. Accuracies ranged from 89 to 99 percent. Aircraft photography was used as the basis for determining the true areal size of salt marshes in the study sites.

Flores, L. M.↗
NASA NTRS1976

Skylab/EREP application to ecological, geological, and oceanographic investigations of Delaware Bay

The author has identified the following significant results. Skylab/EREP S190A and S190B film products were optically enhanced and visually interpreted to extract data suitable for mapping coastal land use; inventorying wetlands vegetation; monitoring tidal conditions; observing suspended sediment patterns; charting surface currents; locating coastal fronts and water mass boundaries; monitoring industrial and municipal waste dumps in the ocean; and determining the size and flow direction of river, bay, and man-made discharge plumes. Film products were visually analyzed to identify and map ten land use and vegetation categories at a scale of 1:125,000. Thematic maps were compared with CARETS land use maps, resulting in classification accuracies of 50 to 98%. Digital tapes from S192 were used to prepare thematic land use maps. The resolutions of the S190A, S190B, and S192 systems were 20-40m, 10-20m, and 70-100m, respectively.

Klemas, V.↗
NASA NTRS1993

Determining successional stage of temperate coniferous forests with Landsat satellite data

Thematic Mapper (TM) digital imagery was used to map forest successional stages and to evaluate spectral differences between old-growth and mature forests in the central Cascade Range of Oregon. Relative sun incidence values were incorporated into the successional stage classification to compensate for topographic induced variation. Relative sun incidence improved the classification accuracy of young successional stages, but did not improve the classification accuracy of older, closed canopy forest classes or overall accuracy. TM bands 1, 2, and 4; the normalized difference vegetation index; and TM 4/3, 4/5, and 4/7 band ratio values for o|d-growth forests were found to be significantly lower than the values of mature forests. The Tasseled Cap features of brightness, greenness, and wetness also had significantly lower old-growth values as compared to mature forest values .

Fiorella, Maria↗
NASA NTRS1993

Linear mixing model applied to coarse spatial resolution data from multispectral satellite sensors

A linear mixing model was applied to coarse spatial resolution data from the NOAA Advanced Very High Resolution Radiometer. The reflective component of the 3.55-3.95 micron channel was used with the two reflective channels 0.58-0.68 micron and 0.725-1.1 micron to run a constrained least squares model to generate fraction images for an area in the west central region of Brazil. The fraction images were compared with an unsupervised classification derived from Landsat TM data acquired on the same day. The relationship between the fraction images and normalized difference vegetation index images show the potential of the unmixing techniques when using coarse spatial resolution data for global studies.

Holben, Brent N.↗
NASA NTRS1995

Determining successional stage of temperate coniferous forests with Landsat satellite data

Thematic Mapper (TM) digital imagery was used to map forest successional stages and to evaluate spectral differences between old-growth and mature forests in the central Cascade Range of Oregon. Relative sun incidence values were incorporated into the successional stage classification to compensate for topographic induced variation. Relative sun incidence improved the classification accuracy of young successional stages, but did not improve the classification accuracy of older, closed canopy forest classes or overall accuracy. TM bands 1, 2, and 4; the normalized difference vegetation index (NDVI); and TM 4/3, 4/5, and 4/7 band ratio values for old-growth forests were found to be significantly lower than the values of mature forests (P less than or equal to 0.010). Wetness and the TM 4/5 and 4/7 band ratios all had low correlations to relative sun incidence (r(exp 2) less than or equal to 0.16). The TM 4/5 band ratio was named the 'structural index' (SI) because of its ability to distinguish between mature and old-growth forests and its simplicity.

Fiorella, Maria↗
NASA NTRS2011

Characterizing Vegetation Cover in Global Savannas with an Annual Foliage Clumping Index Derived from the MODIS BRDF Product

The global savanna biome is characterized by enormous diversity in the physiognomy and spatial structure of the vegetation. The foliage clumping index can be calculated from bidirectional reflectance distribution function (BRDF) data. It measures the response of the darkspot reflectance to increased shadow associated with clumped vegetation and is related to leaf area index. Clumping index theoretically declines with increasing woody cover until the tree canopy begins to become uniform. In this study, clumping index is calculated for Moderate Resolution Imaging Spectroradiometer BRDF data for the Australian tropical savanna, the tropical savannas of South America, and the tropical savannas of east, west and southern Africa and compared with site-based measurements of tree canopy cover, and with area-based classifications of land cover. There were differences in sensitivity of clumping index between red and near-infrared reflectance channels, and between savanna systems with markedly different woody vegetation physiognomy. Clumping index was broadly related to foliage cover from historical site data in Australia and in West Africa and Kenya, but not in Southern Africa nor with detailed site-based demographic data in the cerrado of Brazil. However, clumping index decreased with proportion of woody cover in land cover datasets for east Africa, Australia and the Colombian Llanos. There was overlap in the range of clumping index values for forest, cerrado and campo land covers in Brazil. Clumping index was generally negatively correlated with percentage tree cover from the MODIS Vegetation Continuous Fields product, but regional differences in the relationship were evident. There were large differences in the frequency distributions of clumping index from savanna, woody savanna and grassland land cover classes between global ecoregions. The clumping index shows differing sensitivity to savanna woody cover for red and NIR reflectance, and requires regional calibration for application as a universal indicator.

Hill, Michael J.↗
NASA NTRS2017

Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS-national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91 percent was achieved, which is a 6 percent improvement in unmixing based classification relative to per-pixel-based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

Subpixel↗
NASA NTRS1980

Satellite monitoring of vegetation and geology in semi-arid environments

The possibility of mapping various characteristics of the natural environment of Tanzania by various LANDSAT techniques was assessed. Interpretation and mapping were carried out using black and white as well as color infrared images on the scale of 1:250,000. The advantages of several computer techniques were also assessed, including contrast-stretched rationing, differential edge enhancement; supervised classification; multitemporal classification; and change detection. Results Show the most useful image for interpretation comes from band 5, with additional information being obtained from either band 6 or band 7. The advantages of using color infrared images for interpreting vegetation and geology are so great that black and white should be used only to supplement the colored images.

Kihlblom, U.↗
NASA NTRS1978

Spectral Measurement of Watershed Coefficients in the Southern Great Plains

The author has identified the following significant results. It was apparent that the spectra calibration of runoff curve numbers cannot be achieved on watersheds where significant areas of timber were within the drainage area. The absorption of light by wet soil conditions restricts differentiation of watersheds with regard to watershed runoff curve numbers. It appeared that the predominant factor influencing the classification of watershed runoff curve numbers was the difference in soil color and its associated reflectance when dry. In regions where vegetation grown throughout the year, where wet surface conditions prevail or where watersheds are timbered, there is little hope of classifying runoff potential with visible light alone.

Blanchard, B. J.↗
NASA NTRS1993

Mapping of taiga forest units using AIRSAR data and/or optical data, and retrieval of forest parameters

A maximum a posteriori Bayesian classifier for multifrequency polarimetric SAR data is used to perform a supervised classification of forest types in the floodplains of Alaska. The image classes include white spruce, balsam poplar, black spruce, alder, non-forests, and open water. The authors investigate the effect on classification accuracy of changing environmental conditions, and of frequency and polarization of the signal. The highest classification accuracy (86 percent correctly classified forest pixels, and 91 percent overall) is obtained combining L- and C-band frequencies fully polarimetric on a date where the forest is just recovering from flooding. The forest map compares favorably with a vegetation map assembled from digitized aerial photos which took five years for completion, and address the state of the forest in 1978, ignoring subsequent fires, changes in the course of the river, clear-cutting of trees, and tree growth. HV-polarization is the most useful polarization at L- and C-band for classification. C-band VV (ERS-1 mode) and L-band HH (J-ERS-1 mode) alone or combined yield unsatisfactory classification accuracies. Additional data acquired in the winter season during thawed and frozen days yield classification accuracies respectively 20 percent and 30 percent lower due to a greater confusion between conifers and deciduous trees. Data acquired at the peak of flooding in May 1991 also yield classification accuracies 10 percent lower because of dominant trunk-ground interactions which mask out finer differences in radar backscatter between tree species. Combination of several of these dates does not improve classification accuracy. For comparison, panchromatic optical data acquired by SPOT in the summer season of 1991 are used to classify the same area. The classification accuracy (78 percent for the forest types and 90 percent if open water is included) is lower than that obtained with AIRSAR although conifers and deciduous trees are better separated due to the presence of leaves on the deciduous trees. Optical data do not separate black spruce and white spruce as well as SAR data, cannot separate alder from balsam poplar, and are of course limited by the frequent cloud cover in the polar regions. Yet, combining SPOT and AIRSAR offers better chances to identify vegetation types independent of ground truth information using a combination of NDVI indexes from SPOT, biomass numbers from AIRSAR, and a segmentation map from either one.

Rignot, Eric↗