Preliminary Comparison and Accuracy Assessment of Mineral Maps Produced From 1997 AVIRIS Data, Ray Mine, AZ
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Mineral Maps AVIRIS Ray Mine↗
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Verification of land cover maps from LANDSAT data
The application of cluster sampling to verifying the accuracy of maps derived from digital data is discussed. Variants of the cluster sampling technique were used in large scale assessments for areas in excess of one million hectares. Three types of classification errors are possible: commission errors, omission errors, and overall error classification. Commission errors for a particular cover type occur when pixels are classified as that cover type but are found to be some other cover type when the field is checked. Omission errors occur when pixels of fields already visited and known to be of a particular cover type, are classified as some other cover type. Overall error is the proportion of pixels incorrectly classified, without regard to omission or commission. Since the classified image represents the sampling frame, sampling for accuracy assessment was designed to estimate commission error. However, the sample can also provide useful estimates of omission and overall error. Assessment for Alaska, Arizona, and Oklahoma are presented.
Linden, D. S.↗
Spatial quantification of maps or images - Cell size or pixel size implications
The paper discusses spatial quantification of maps or images. Spatial accuracies evaluated by mapping and inventory measures were observed with a range of cell sizes for a 10.36 sq km soil survey segment. The distribution of interboundary distances in a map was evaluated as unique, characterizing information; experimentally observed relationships between accuracy and cell size and interboundary distance distribution parameters and cell size led to a model of the process of spatial quantization. Grid positioning effects were evaluated and were significant only at the level of individual mapping units; two generations of 'universal' process models were derived, implemented, and evaluated.
Wehde, M. E.↗
Temperature Correction of Pressure-Sensitive Paints Simplified
Pressure-sensitive paint (PSP) has become a useful tool to augment conventional pressure taps in measuring the surface pressure distribution of aerodynamic components in wind tunnel testing. Although PSP offers the advantage of nonintrusive global mapping of the surface pressure, one prominent drawback to the accuracy of this technique is the inherent temperature sensitivity of PSP's luminescent intensity. Typical aerodynamic surface PSP tests rely on the coated surface to be both spatially and temporally isothermal, along with conventional instrumentation, to yield the highest accuracy pressure mappings. In some tests, however, spatial and temporal thermal gradients are generated by the nature of the test, as in a blowing jet impinging on a surface. In these cases, high accuracy and reliable data cannot be obtained unless the temperature variations on the painted surface are accounted for. A new temperature-correction technique was developed at the NASA Glenn Research Center at Lewis Field to collapse a "family" of PSP calibration curves to a single curve of intensity ratio versus pressure. This correction allows a streamlined procedure to be followed whether or not temperature information is used in the data reduction of the PSP.
Bencic, Timothy J.↗
Pressure-Sensitive Paint Measurements on Surfaces with Non-Uniform Temperature
Pressure-sensitive paint (PSP) has become a useful tool to augment conventional pressure taps in measuring the surface pressure distribution of aerodynamic components in wind tunnel testing. While the PSP offers the advantage of a non-intrusive global mapping of the surface pressure, one prominent drawback to the accuracy of this technique is the inherent temperature sensitivity of the coating's luminescent intensity. A typical aerodynamic surface PSP test has relied on the coated surface to be both spatially and temporally isothermal, along with conventional instrumentation for an in situ calibration to generate the highest accuracy pressure mappings. In some tests however, spatial and temporal thermal gradients are generated by the nature of the test as in a blowing jet impinging on a surface. In these cases, the temperature variations on the painted surface must be accounted for in order to yield high accuracy and reliable data. A new temperature correction technique was developed at NASA Lewis to collapse a "family" of PSP calibration curves to a single intensity ratio versus pressure curve. This correction allows a streamlined procedure to be followed whether or not temperature information is used in the data reduction of the PSP. This paper explores the use of conventional instrumentation such as thermocouples and pressure taps along with temperature-sensitive paint (TSP) to correct for the thermal gradients that exist in aeropropulsion PSP tests. Temperature corrected PSP measurements for both a supersonic mixer ejector and jet cavity interaction tests are presented.
Bencic, Timothy J.↗
CARETS: A prototype regional environmental information system. Volume 6: Cost, accuracy and consistency comparisons of land use maps made from high-altitude aircraft photography and ERTS imagery
Accuracy analyses for the land use maps of the Central Atlantic Regional Ecological Test Site were performed for a 1-percent sample of the area. Researchers compared Level II land use maps produced at three scales, 1:24,000, 1:100,000, and 1:250,000 from high-altitude photography, with each other and with point data obtained in the field. They employed the same procedures to determine the accuracy of the Level I land use maps produced at 1:250,000 from high-altitude photography and color composite ERTS imagery. The accuracy of the Level II maps was 84.9 percent at 1:24,000, 77.4 percent at 1:100,000, and 73.0 percent at 1:250,000. The accuracy of the Level I 1:250,000 maps produced from high-altitude aircraft photography was 76.5 percent and for those produced from ERTS imagery was 69.5 percent. The cost of Level II land use mapping at 1:24,000 was found to be high ($11.93 per km 2 ). The cost of mapping at 1:100,000 ($1.75) was about 2 times as expensive as mapping at 1:250,000 ($.88), and the accuracy increased by only 4.4 percent. Level I land use maps, when mapped from high-altitude photography, were about 4 times as expensive as the maps produced from ERTS imagery, although the accuracy is 7.0 percent greater. The Level I land use category that is least accurately mapped from ERTS imagery is urban and built-up land in the non-urban areas; in the urbanized areas, built-up land is more reliably map
Accuracy↗
Land cover stratification using Landsat Thematic Mapper data in Sahelian and Sudanian woodland and wooded grassland
A standard methodology for thematic mapping of natural vegetation using remotely sensed imagery and digital image processing was modified to account for the spatial and spectral properties of semi-arid landscapes, and tested in study areas in the Sahelian and Sudanian zones, Mali. A principal components transformation of registered wet and dry season Landsat TM images produced a set of synthetic spectral channels differentiating vegetation cover between seasons, and allowed areas with annual grass growth to be distinguished from areas with woody cover. The transformed data were statistically clustered and clusters were assigned to vegetation type and density categories. In a separate step, the images were manually interpreted to differentiate broad soil classes. Four statistics were compared to evaluate the accuracy of the maps based on sample points from air photos. For the relatively detailed categories initially defined, map accuracies were substandard; however, when vegetation density classes were aggregated, overall accuracy was around 90 percent, and class accuracy was greater than 80 percent for most classes. This method is suitable for stratification and inventory of woody biomass at a regional scale in semi-arid woodland and wooded grassland.
Franklin, J.↗
Geometric Accuracy Assessment of LANDSAT-4 Multispectral Scanner (MSS)
Standard LANDSAT-4 MSS digital image data were analyzed for geometric accuracy using two P-format (UTM projection) images of the Washington, D.C. area, scene day 109 (ID number 4010915140) and scene day 125 (ID number 4012515144). Both scenes were tested for geodetic registration accuracy (scene-to-map), temporal registration accuracy (scene-to-scene), and band-to-band registration accuracy (within a scene). The combined RMS error for geodetic registration accuracy was 0.43 pixel (25.51 meters), well within specifications. The comparison between the 2 scenes was made on a band-by-band basis. The 90 percent error figure for temporal registration was 0.68 (57x57) pixel (38.8 meters). Although this figure is larger than the specification, it can be considered excellent with respect to user application. The best case registration errors between bands 1 and 2, and 3 and 4 were 14.2m and 13.7m, respectively, both within specifications. The worst case registration error was 38.0 m between bands 2 and 3.
Imhoff, M. L.↗
Achieving Accuracy Requirements for Forest Biomass Mapping: A Data Fusion Method for Estimating Forest Biomass and LiDAR Sampling Error with Spaceborne Data
The synergistic use of active and passive remote sensing (i.e., data fusion) demonstrates the ability of spaceborne light detection and ranging (LiDAR), synthetic aperture radar (SAR) and multispectral imagery for achieving the accuracy requirements of a global forest biomass mapping mission. This data fusion approach also provides a means to extend 3D information from discrete spaceborne LiDAR measurements of forest structure across scales much larger than that of the LiDAR footprint. For estimating biomass, these measurements mix a number of errors including those associated with LiDAR footprint sampling over regional - global extents. A general framework for mapping above ground live forest biomass (AGB) with a data fusion approach is presented and verified using data from NASA field campaigns near Howland, ME, USA, to assess AGB and LiDAR sampling errors across a regionally representative landscape. We combined SAR and Landsat-derived optical (passive optical) image data to identify forest patches, and used image and simulated spaceborne LiDAR data to compute AGB and estimate LiDAR sampling error for forest patches and 100m, 250m, 500m, and 1km grid cells. Forest patches were delineated with Landsat-derived data and airborne SAR imagery, and simulated spaceborne LiDAR (SSL) data were derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (L VIS). At both the patch and grid scales, we evaluated differences in AGB estimation and sampling error from the combined use of LiDAR with both SAR and passive optical and with either SAR or passive optical alone. This data fusion approach demonstrates that incorporating forest patches into the AGB mapping framework can provide sub-grid forest information for coarser grid-level AGB reporting, and that combining simulated spaceborne LiDAR with SAR and passive optical data are most useful for estimating AGB when measurements from LiDAR are limited because they minimized forest AGB sampling errors by 15 - 38%. Furthermore, spaceborne global scale accuracy requirements were achieved. At least 80% of the grid cells at 100m, 250m, 500m, and 1km grid levels met AGB density accuracy requirements using a combination of passive optical and SAR along with machine learning methods to predict vegetation structure metrics for forested areas without LiDAR samples. Finally, using either passive optical or SAR, accuracy requirements were met at the 500m and 250m grid level, respectively.
Montesano, P. M.↗
The effect of Thematic Mapper spectral properties on land cover mapping for hydrologic modeling
The accuracy of unsupervised land-cover classification from all seven Landsat TM bands and from six combinations of three or four bands is evaluated using images of the Clinton River Basin, a suburban watershed near Detroit. Data from aerial TMS photography, USGS topographic maps, and ground surveys are employed to determine the classification accuracy. The mapping accuracy of all seven bands is found to be significantly better (6 percent overall, 12 percent for residential areas, and 13 percent for commercial districts) than that with bands 2, 3, and 4; but almost the same accuracy is obtained by including at least one band from each major spectral region (visible, NIR, and mid-IR).
Gervin, J. C.↗
Achieving Accuracy Requirements for Forest Biomass Mapping: A Spaceborne Data Fusion Method for Estimating Forest Biomass and Lidar Sampling Error
The synergistic use of active and passive remote sensing (i.e., data fusion) demonstrates the ability of spaceborne light detection and ranging (LiDAR), synthetic aperture radar (SAR) and multispectral imagery for achieving the accuracy requirements of a global forest biomass mapping mission (+/-20 Mg/ha or 20%, the greater of the two, for at least 80% of grid cells). A data fusion approach also provides a means to extend 3D information from discrete spaceborne LiDAR measurements of forest structure across scales much larger than that of the LiDAR footprint. For estimating biomass, these measurements mix a number of errors including those associated with LiDAR footprint sampling over regional-global extents. A general framework for mapping above ground live forest biomass density (AGB) with a data fusion approach is presented and verified using data from NASA field campaigns near Howland, ME, USA, to assess AGB and LiDAR sampling errors across a regionally representative landscape. We combined SAR and Landsat-derived optical (passive optical) image data to identify contiguous areas (>0.5 ha) that are relatively homogenous in remote sensing metrics (forest patches). We used this image-derived data with simulated spaceborne LiDAR derived from orbit and cloud cover simulations and airborne data from NASA's Laser Vegetation Imaging Sensor (LVIS) to compute AGB and estimate LiDAR sampling error for forest patches and 100 m, 250 m, 500 m, and 1 km grid cells. At both the patch and grid scales, we evaluated differences in AGB estimation and sampling error from the combined use of LiDAR with both SAR and passive optical and with either SAR or passive optical alone. First, this data fusion approach demonstrates that incorporating forest patches into the AGB mapping framework can provide sub-grid forest information for coarser grid-level AGB reporting. Second, a data fusion approach for estimating AGB using simulated spaceborne LiDAR with SAR and passive optical image combinations reduced forest AGB sampling errors 12%-38% from those where LiDAR is used with SAR or passive optical alone. In absolute terms, sampling errors were reduced from 14-40 Mg/ha to 11-28 Mg/ha across all grid scales and prediction methods, where minimum sampling errors were 11, 15, 18, and 22 Mg/ha for 1 km, 500 m, 250 m, and 100 m grid scales, respectively. Third, spaceborne global scale accuracy requirements were achieved whereby at least 80% of the grid cells at 100 m, 250 m, 500 m, and 1 km grid levels met AGB accuracy requirements using a combination of passive optical and SAR along with machine learning methods to predict vegetation structure metrics for forested areas without LiDAR samples. Finally, using either passive optical or SAR, accuracy requirements were met at the 500 m and 250 m grid level, respectively..
LiDAR↗
Integrating very-high-resolution imagery, Sentinel-2 time-series data, and machine learning to map shrub fractional abundance across arid and semi-arid ecosystems in China
Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km 2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R 2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R 2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 °C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.
Arid and semi-arid landscapes↗
Verification of vegetation maps made from remote sensing
Verification of vegetation maps is discussed, including a map of the vegetation of the Mt. Washington area of New Hampshire. This area was chosen to determine the accuracy of mapping by remote sensing at the boundary between two major forest biomass. Verification was carried out by ground observation and through the use of low altitude 70 mm infrared photographs. Two verification sampling schemes were used: a point method and a transect method. Resulting confidence limits gave an area weighted sampling accuracy of 89 pct. Spatial patterns in terrestrial vegetation must be understood in order to choose appropriate spatial resolutions required for remote sensing instruments, and to relate vegetation dynamics to climate dynamics and biogeochemical cycles.
Botkin, Daniel B.↗
Accuracy, Bias, and Improvements in Mapping Crops and Cropland across the United States Using the USDA Cropland Data Layer
The U.S. Department of Agriculture’s (USDA) Cropland Data Layer (CDL) is a 30 m resolution crop-specific land cover map produced annually to assess crops and cropland area across the conterminous United States. Despite its prominent use and value for monitoring agricultural land use/land cover (LULC), there remains substantial uncertainty surrounding the CDLs’ performance, particularly in applications measuring LULC at national scales, within aggregated classes, or changes across years. To fill this gap, we used state- and land cover class-specific accuracy statistics from the USDA from 2008 to 2016 to comprehensively characterize the performance of the CDL across space and time. We estimated nationwide area-weighted accuracies for the CDL for specific crops as well as for the aggregated classes of cropland and non-cropland. We also derived and reported new metrics of superclass accuracy and within-domain error rates, which help to quantify and differentiate the efficacy of mapping aggregated land use classes (e.g., cropland) among constituent subclasses (i.e., specific crops). We show that aggregate classes embody drastically higher accuracies, such that the CDL correctly identifies cropland from the user’s perspective 97% of the time or greater for all years since nationwide coverage began in 2008. We also quantified the mapping biases of specific crops throughout time and used these data to generate independent bias-adjusted crop area estimates, which may complement other USDA survey- and census-based crop statistics. Our overall findings demonstrate that the CDLs provide highly accurate annual measures of crops and cropland areas, and when used appropriately, are an indispensable tool for monitoring changes to agricultural landscapes.
54 ENVIRONMENTAL SCIENCES↗
Proposal for a study of computer mapping of terrain using multispectral data from ERTS-A for the Yellowstone National Park test site
The author has identified the following significant results. A terrain map of Yellowstone National Park showed plant community types and other classes of ground cover in what is basically a wild land. The map comprised 12 classes, six of which were mapped with accuracies of 70 to 95%. The remaining six classes had spectral reflectances that overlapped appreciably, and hence, those were mapped less accurately. Techniques were devised for quantitatively comparing the recognition map of the park with control data acquired from ground inspection and from analysis of sidelooking radar images, a thermal IR mosaic, and IR aerial photos of several scales. Quantitative analyses were made in ten 40 sq km test areas. Comparison mechanics were performed by computer with the final results displayed on line printer output. Forested areas were mapped by computer using ERTS data for less than 1/4 the cost of the conventional forest mapping technique for topographic base maps.
Smedes, H. W.↗
Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records
Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6) and VIIRS Collection 1 (C1) represent the state-of-the-art global snow cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map.The increased data content allows flexibility in using the datasets for specific regions and end-user applications.Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16C year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms.These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.
Earth System Data Record↗
Spinoff 2015
Topics covered include: 3D Endoscope to Boost Safety, Cut Cost of Surgery; Audio App Brings a Better Night's Sleep Liquid Cooling Technology Increases Exercise Efficiency; Algae-Derived Dietary Ingredients Nourish Animals; Space Grant Research Launches Rehabilitation Chair; Vision Trainer Teaches Focusing Techniques at Home; Aircraft Geared Architecture Reduces Fuel Cost and Noise; Ubiquitous Supercritical Wing Design Cuts Billions in Fuel Costs; Flight Controller Software Protects Lightweight Flexible Aircraft; Cabin Pressure Monitors Notify Pilots to Save Lives; Ionospheric Mapping Software Ensures Accuracy of Pilots' GPS; Water Mapping Technology Rebuilds Lives in Arid Regions; Shock Absorbers Save Structures and Lives during Earthquakes; Software Facilitates Sharing of Water Quality Data Worldwide; Underwater Adhesives Retrofit Pipelines with Advanced Sensors; Laser Imaging Video Camera Sees through Fire, Fog, Smoke; 3D Lasers Increase Efficiency, Safety of Moving Machines; Air Revitalization System Enables Excursions to the Stratosphere; Magnetic Fluids Deliver Better Speaker Sound Quality; Bioreactor Yields Extracts for Skin Cream; Private Astronaut Training Prepares Commercial Crews of Tomorrow; Activity Monitors Help Users Get Optimum Sun Exposure; LEDs Illuminate Bulbs for Better Sleep, Wake Cycles; Charged Particles Kill Pathogens and Round Up Dust; Balance Devices Train Golfers for a Consistent Swing; Landsat Imagery Enables Global Studies of Surface Trends; Ruggedized Spectrometers Are Built for Tough Jobs; Gas Conversion Systems Reclaim Fuel for Industry; Remote Sensing Technologies Mitigate Drought; Satellite Data Inform Forecasts of Crop Growth; Probes Measure Gases for Environmental Research; Cloud Computing Technologies Facilitate Earth Research; Software Cuts Homebuilding Costs, Increases Energy Efficiency; Portable Planetariums Teach Science; Schedule Analysis Software Saves Time for Project Planners; Sound Modeling Simplifies Vehicle Noise Management; Custom 3D Printers Revolutionize Space Supply Chain; Improved Calibration Shows Images' True Colors; Micromachined Parts Advance Medicine, Astrophysics, and More; Metalworking Techniques Unlock a Unique Alloy; Low-Cost Sensors Deliver Nanometer-Accurate Measurements; Electrical Monitoring Devices Save on Time and Cost; Dry Lubricant Smooths the Way for Space Travel, Industry; and Compact Vapor Chamber Cools Critical Components.
Source record↗
A spatiotemporally explicit and scalable indicator of intact lands across the conterminous United States, 1986–2023
Globally, ecologically intact areas are increasingly scarce. Agricultural expansion into previously uncultivated areas drives the loss of intact lands that might otherwise exhibit high levels of ecological integrity. Thus, the absence of cultivation can be an indicator of intact lands as measured from remote sensing data and thematic maps. Our objective for this study was to develop and compare tractable approaches based on remotely sensed satellite data to map spatial patterns of potentially intact lands across the conterminous U.S. (CONUS). Using annual cultivation probabilities derived from satellite observations, we classified and mapped potentially intact lands across CONUS from 1986 to 2023 at 30 m resolution. We created three maps, first by applying a constant cultivation probability threshold across CONUS, second by varying the threshold state-by-state to maximize state-level overall accuracies, and third by equalizing the state-level user's and producer's accuracies to minimize classification bias. Validation against 800,000+ independent ground samples resulted in CONUS-level overall accuracies ≥85% for the roughly 660 million ha of potentially intact land. Map accuracy varied with the proportion of potentially intact lands across regions, with the Pacific-Mountain and Great Plains regions exhibiting the highest accuracies, while Eastern CONUS exhibited a greater mix of potentially intact and non-intact lands and more moderate map accuracies. These novel maps and approaches can be adapted to different spatiotemporal extents to support conservation and production decisions ranging from species and ecosystems protection to reducing land conversion and climate mitigation.
agriculture↗