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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

An improvement in land cover classification achieved by merging microwave data with Landsat multispectral scanner data

The improvement in land cover classification achieved by merging microwave data with Landsat MSS data is examined. To produce a merged data set for analysis and comparison, a registration procedure by which a set of Seasat SAR digital data was merged with the MSS data is described. The Landsat MSS data and the merged Landsat/Seasat data sets were processed using conventional multichannel spectral pattern recognition techniques. An analysis of the classified data sets indicates that while Landsat data delineate different forest types (i.e., deciduous/coniferous) and allow some species separation, SAR data provide additional information related to plant canopy configuration and vegetation density as associated with varying water regimes, and therefore allow for further subdivision in the classification of forested wetlands of the coastal region of the southern United States.

Wu, S. T.↗
NASA NTRS1985

Assessment of Computer-based Geologic Mapping of Rock Units in the LANDSAT-4 Scene of Northern Death Valley, California

Geologists obtain low accuracy levels when maps derived from LANDSAT MSS data are compared with those made by conventional methods. Procedures developed for the IDIMS computer system and used to classify a subset of a TM image of the Death Valley, California - Nevada border are described. Despite the superior resolution, broader spectral coverage, and greater sensitivity inherent to the TM, the actual recorded measured accuracy was in the same narrow range (30 to 60%) recorded for MSS data from earlier LANDSATs. The supervised classification approach appears to be superior to the unsupervised approach when applied to vegetation-sparse surfaces composed of spectrally contrasting rock/soil units distributed in relatively flat to low relief terrain. As spatial resolution improves and optimal spectral bands for identifying rock materials are specified, use of classified multispectral remote sensing data from air and space when coupled with supporting field calibration and checks should become the dominant way in which geologic mapping is carried out in future decades.

Short, N. M.↗
NASA NTRS1987

Remote sensing of submerged aquatic vegetation in lower Chesapeake Bay - A comparison of Landsat MSS to TM imagery

Landsat MSS and TM imagery, obtained simultaneously over Guinea Marsh, VA, as analyzed and compares for its ability to detect submerged aquatic vegetation (SAV). An unsupervised clustering algorithm was applied to each image, where the input classification parameters are defined as functions of apparent sensor noise. Class confidence and accuracy were computed for all water areas by comparing the classified images, pixel-by-pixel, to rasterized SAV distributions derived from color aerial photography. To illustrate the effect of water depth on classification error, areas of depth greater than 1.9 m were masked, and class confidence and accuracy recalculated. A single-scattering radiative-transfer model is used to illustrate how percent canopy cover and water depth affect the volume reflectance from a water column containing SAV. For a submerged canopy that is morphologically and optically similar to Zostera marina inhabiting Lower Chesapeake Bay, dense canopies may be isolated by masking optically deep water. For less dense canopies, the effect of increasing water depth is to increase the apparent percent crown cover, which may result in classification error.

Ackleson, S. G.↗
NASA NTRS2006

The Relationship of Forest Fires Detected by MODIS and SRTM Derived Topographic Features in Central Siberia

Fires are a common occurrence in the Siberian boreal forest. The MOD14 Thermal anomalies product of the Terra MODIS Moderate Resolution Spectroradiometer) product set is designed to detect thermal anomalies (i.e. hotspots or fires) on the Earth's surface. Recent field studies showed a dependence of fire occurrence on topography. In this study MODIS thermal anomaly data and SRTM topography data were merged and analyzed to evaluate if forest fires are more likely to occur at certain combinations of elevation, slope and aspect. Using the satellite data over a large area can lead to better understanding how topography and forest fires are related. The study area covers a 2.5 Million krn(exp 2) portion of the Central Siberian southern taiga from 72 deg to 110 deg East and from 50 deg to 60 deg North. About 57% of the study area is forested and 80% of the forest grows between 200 and 1000 m. Forests with pine (Pinus sylvestris), larch (Larix sibirica, L. gmelinii), Siberian pine (Pinus sibirica), spruce (Picea obovata.) and fir (Abies sibirica) cover most of the landscape. Deciduous stands with birch (Betula pendula, B. pubescens) and aspen (Populus tremula) cover the areas of lower elevation in this region. The climate of this area is distinctly continental with long, cold winters and short hot summers. The tree line in this part of the world is around 1500 m in elevation with alpine tundra, snow and ice fields and rock outcrops extending up to over 3800 m. A 500 m resolution landcover map was developed using 2001 MODIS MOD13 Normalized Vegetation Index (NDVI) and Middle Infrared (MIR) products for seven 16-day periods. The classification accuracy was over 87%. The SRTM version 2 data, which is distributed in 1 degree by 1 degree tiles were mosaiced using the ENVI software. In this study, only those MODIS pixels were used that were flagged as "nominal or high confidence fire" by the MODIS fire product team. Using MODIS data from the years 2000 to 2005 along with the improved Shuttle Radar Topographic Mission (SRTM) version 2 data at 100 m resolution, the distribution of hot spots was examined by elevation, slope and aspect as well as by forest type. The results show that more forest area burns at lower elevations but a larger percentage of the available forest area burns at higher elevations. This is probably because steep slopes occur at higher elevations. Fires are only more common on slopes with a southern exposure if the slope is steeper than 15 degrees. The next step in this study will be to monitor areas where the risk of fire is high (steep slopes with a southern exposure) and to refine this method by incorporating anthropogenic features for more accurate fire disturbance monitoring.

Ranson, Jon K.↗
NASA NTRSDate not supplied

Coronado Ecological Conservation: Assessing Vegetation Change Due to Border Wall Construction and Shifting Social Trails

Species monitoring is essential in mitigating the impacts of plant invasion, such as radical changes in an area’s ecosystem, degraded soil health, increased wildfire severity, landslides, and increased flooding. NASA DEVELOP partnered with the National Park Service (NPS) to investigate invasive species in disturbed lands: specifically, areas affected by off-trail walking and US-Mexico border construction activities. The team assessed how construction has impacted the distribution of Lehmann’s lovegrass and Russian thistle invasives throughout Coronado National Memorial, AZ from 1986 to 2022. Using data from Landsat 5 and 8, Sentinel-2, the National Agriculture Imagery Program, and PlanetScope, the team computed vegetation indices including the Normalized Difference Vegetation Index, Normalized Difference Moisture Index, Modified Soil Adjusted Vegetation Index 2, Enhanced Vegetation Index, and Tasseled Cap Wetness, Brightness, and Greenness transformations as vegetation health indicators to input into various machine learning algorithms. To minimize noise, the team conducted Principal Component Analysis on the vegetation indices and spectral bands before running k-means++ clustering and random forest classification algorithms. Between all datasets, we found the median area fully overtaken by invasive plants was 5.37% of the park’s total area in 2022. The NPS will use the end products to help increase restoration efforts in disturbed areas with high concentrations of invasive plants. The NPS’s collection of ground data for 2022–2023, in conjunction with future data collection, will notably improve the accuracy of classification models, leading to more precise monitoring of invasive spread over time.

Carson Schuetze↗
NASA NTRS2023

Coronado Ecological Conservation: Assessing Vegetation Change Due to Border Wall Construction and Shifting Social Trails

Species monitoring is essential for mitigating the impacts of plant invasion, such as radical changes in an area’s ecosystem, degraded soil health, increased wildfire severity, landslides, and increased flooding. For this project, NASA DEVELOP partnered with the National Park Service (NPS) to investigate invasive species in disturbed lands: specifically, areas affected by off-trail travel and U.S.-Mexico border construction activities. The team assessed how construction has impacted the distribution of Lehmann’s lovegrass and Russian thistle invasives throughout Coronado National Memorial, AZ from 1986-2022. Using data from Landsat 5 and 8, Sentinel-2, NAIP, and PlanetScope, the team computed NDVI, NDMI, MSAVI2, EVI, and Tasseled Cap Wetness, Brightness, and Greenness transformations as vegetation health indicators to input into various machine learning algorithms. To minimize noise, the team conducted Principal Component Analysis on vegetation indices and spectral bands before running k-means clustering and random forest classification algorithms. Between all datasets, the team found that the median area fully overtaken by invasive plants was 5.37% of the park’s total area in 2022. The NPS will use end products to help increase restoration efforts in disturbed areas with high concentrations of invasive plants, and this project can serve as a jumping off point for future invasive species monitoring. The NPS’s collection of ground data for 2022-2023, in conjunction with future data collection, will notably improve the accuracy of classification models, leading to more precise monitoring of invasive species spread over time.

Coronado National Memorial↗
NASA NTRS2023

Bandelier Ecological Conservation: Mapping Invasive Species Along the Rio Grande Corridor in Bandelier National Monument

The Southwest U.S. has experienced a growth of invasive riparian species, specifically Elaeagnus angustifolia (Russian olive), Tamarix ramosissima (saltcedar), and Ulmus pumila (Siberian elm), which alter local soil chemistry and outcompete native species. Locating these exotic species is critical for ecological conservation; however, field identification can be resource intensive. NASA DEVELOP partnered with the National Park Service (NPS) at Bandelier National Monument (BAND) to assess the feasibility of using Earth observation data to map invasive species along the Rio Grande corridor of the park. The team used Landsat 8 OLI, Sentinel-2 MSI, and ISS DESIS imagery to compute principal components based on spectral bands, vegetation indices, and terrain indices. Using the first five principal components, the team created classification maps using both a k-means classification algorithm and a random forest algorithm to differentiate between native and non-native species. The team derived maps for the three invasive riparian species in the region for the last five years. The team found that invasive species covered 33% of the park's river corridor in 2023, and the invasive species extent has increased by 5.7% from 2019 to 2023. The methods will serve as a guide for aiding historic and present invasive species identification in riparian regions, and the NPS staff at BAND will use the results to inform local mitigation practices and advocate for invasive species removal.

Evan Barrett↗
NASA NTRS1981

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

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

Tracy, C.↗
NASA NTRS1974

The utility of ERTS-1 data for applications in land use classification

A comprehensive study has been undertaken to determine the extent to which conventional image interpretation and computer-aided (spectral pattern recognition) analysis techniques using ERTS-1 data could be used to detect, identify (classify), locate, and measure current land use over large geographic areas. It can be concluded that most of the level 1 and 2 categories in the USGS Circular no. 671 can be detected in the Houston-Gulf Coast area using a combination of both techniques for analysis. These capabilities could be exercised over larger geographic areas, however, certain factors such as different vegetative cover, topography, etc. may have to be considered in other geographic regions. The best results in identification (classification), location, and measurement of level 1 and 2 type categories appear to be obtainable through automatic data processing of multispectral scanner computer compatible tapes.

Dornbach, J. E.↗
NASA NTRS1990

Information analysis of a spatial database for ecological land classification

An ecological land classification was developed for a complex region in southern California using geographic information system techniques of map overlay and contingency table analysis. Land classes were identified by mutual information analysis of vegetation pattern in relation to other mapped environmental variables. The analysis was weakened by map errors, especially errors in the digital elevation data. Nevertheless, the resulting land classification was ecologically reasonable and performed well when tested with higher quality data from the region.

Davis, Frank W.↗
NASA NTRS1993

Linear mixing model applied to AVHRR LAC data

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 - 3.93 microns channel was extracted and used with the two reflective channels 0.58 - 0.68 microns and 0.725 - 1.1 microns to run a Constraine Least Squares model to generate vegetation, soil, and shade fraction images for an area in the Western region of Brazil. The Landsat Thematic Mapper data covering the Emas National park region was used for estimating the spectral response of the mixture components and for evaluating the mixing model results. 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 resolution data for global studies.

Holben, Brent N.↗
NASA NTRS1982

LANDSAT Applications for the State of Delaware

The first phase of a cooperative demonstration project between the Eastern Regional Remote Sensing Applications Center and the Delaware Department of Natural Resources and Environmental Control is summarized. Separate land cover classifications were performed for three counties in Delaware using multiseasonal LANDSAT data from April 3 and July 20, 1974. The objective of the New Castle County and Kent County classifications was to provide general land cover information with special emphasis on existing farmland and deciduous and coniferous forest, respectively. A detailed study of existing inland and coastal wetland vegetation communities was attempted for Sussex County. Detailed accuracy assessments were conducted for general land cover in New Castle and Kent Counties and for wetland communities in Sussex County. The classifications were combined to provide a statewide Level I land cover map and acreage statistics. Based on these results, the participating state agencies determined that LANDSAT is a viable tool for mapping and monitoring land cover within the state.

Gervin, J. C.↗
NASA NTRS1970

Potentially efficient forest and range applications of remote sensing using earth orbital space craft, circa 1980

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.

Wilson, R. C.↗
NASA NTRS1997

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

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

radar interferometry vegetation land cover↗
NASA NTRS1984

Use of Landsat-derived profile features for spring small-grains classification

The present model for the temporal behavior of agricultural greenness is applied to the extraction of Landsat-derived profile features, distinguishing small from large grain crops. An additional feature derivable from the temporal behavior of the ratio of greenness to brightness is noted which aids in the separation of crops from other vegetation. A limited training set of 20 pure pixels/class, obtained from ground data, is subjected to the Ho-Kashyap (1965) linear classifier. The initial correct classification value for pure pixels of about 85 percent drops to 75 percent for all Landsat pixels.

Badhwar, G. D.↗
NASA NTRSDate not supplied

Mapping Inundation from Hurricane Florence (2018) with L-Band Synthetic Aperture Radar, Commercial Imagery, and Ancillary Data via Machine Learning Classification

During and after flooding events, mapping the extent of floodwaters aids in the distribution of resources, recovery efforts, and damage assessment practices. Development of a land cover classification system focused on mapping inundation after major hurricane events using synthetic aperture radar (SAR) data could allow for the production of near-real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of a developing situation. Complimentary optical and SAR images from domestic and foreign entities are brought together through activations of the International Charter: Space and Major Disasters to support response efforts, from true-color, near-infrared, and thermal remote sensing data obtained by NASA, NOAA, and international satellites to the collection of high-resolution true color aerial photography by NOAA and the National Geodetic Survey. In response to Hurricane Florence of 2018, NASA JPL collected numerous swaths of quad-pol L-band SAR data with the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument observing the record-setting river stages across North and South Carolina. The resulting fully-polarized SAR images allow for mapping of inundation extent at a high spatial resolution with a unique advantage over optical imaging stemming from the sensor’s ability to penetrate cloud cover and dense vegetation. In this study, true-color NOAA aerial and commercial satellite imagery are used in conjunction with four UAVSAR data swaths centered on the Lumberton and Cape Fear River basins in southeastern North Carolina to develop a Random Forest classification model focused on mapping open water and floodwater otherwise obscured by vegetation or lingering cloud cover. Ancillary building footprint, transportation route, and population data will also be incorporated into the classification scheme to estimate the societal impacts of flooding based on the proximity of features to detected inundation. Preliminary results from the Hurricane Florence case study will be discussed in addition to the limitations of available validation data for assessment of the classifier’s accuracy.

Alexander M Melancon↗
NASA NTRS1984

Estimating vegetation coverage in St. Joseph Bay, Florida with an airborne multispectral scanner

A four-channel multispectral scanner (MSS) carried aboard an aircraft was used to collect data along several flight paths over St. Joseph Bay, FL. Various classifications of benthic features were defined from the results of ground-truth observations. The classes were statistically correlated with MSS channel signal intensity using multivariate methods. Application of the classification measures to the MSS data set allowed computer construction of a detailed map of benthic features of the bay. Various densities of segrasses, various bottom types, and algal coverage were distinguished from water of various depths. The areal vegetation coverage of St. Joseph Bay was not significantly different from the results of a survey conducted six years previously, suggesting that seagrasses are a very stable feature of the bay bottom.

Savastano, K. J.↗
NASA NTRS1983

Continental land cover classification using meteorological satellite data

The use of the National Oceanic and Atmospheric Administration's advanced very high resolution radiometer satellite data for classifying land cover and monitoring of vegetation dynamics over an extremely large area is demonstrated for the continent of Africa. Data from 17 imaging periods of 21 consecutive days each were composited by a technique sensitive to the in situ green-leaf biomass to provide cloud-free imagery for the whole continent. Virtually cloud-free images were obtainable even for equatorial areas. Seasonal variation in the density and extent of green leaf vegetation corresponded to the patterns of rainfall associated with the inter-tropical convergence zone. Regional variations, such as the 1982 drought in east Africa, were also observed. Integration of the weekly satellite data with respect to time produced a remotely sensed assessment of biological activity based upon density and duration of green-leaf biomass. Two of the 21-day composited data sets were used to produce a general land cover classification. The resultant land cover distributions correspond well to those of existing maps.

Tucker, C. J.↗