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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1973

Identification of phenological stages and vegetative types for land use classification

The author has identified the following significant results. Classification of digital data for mapping Alaskan vegetation has been compared to ground truth data and found to have accuracies as high as 90%. These classifications are broad scale types as are currently being used on the Major Ecosystems of Alaska map prepared by the Joint Federal-State Land Use Planning Commission for Alaska. Cost estimates for several options using the ERTS-1 digital data to map the Alaskan land mass at the 1:250,000 scale ranged between $2.17 to $1.49 per square mile.

Mckendrick, J. D.↗
NASA NTRS1972

Automatic classification of soils and vegetation with ERTS-1 data

Preliminary results of a test of a computerized analysis method using ERTS 1 data are presented. The method consisted of a four-spectral-band supervised, maximum likelihood, Gaussian classifier with training statistics derived through a combination of clustering and manual methods. The multivariate analysis method leads to the assignment of each resolution element of the data to one of a preselected set of discrete classes. The data frame was an area over the Texas-Oklahoma border including Lake Texoma. The study suggests that multispectral scanner data coupled with machine processing shows promise for earth surface cover surveys. Futhermore, the processing time is short and consequently the costs are low; a full frame can be analyzed completely within 48 hours.

Landgrebe, D. A.↗
NASA NTRS1977

Landsat digital data application to forest vegetation and land use classification in Minnesota

Landsat digital data were used to map eleven categories of land cover in north central Minnesota. The classification accuracy of these maps was found to be very low and they were not adequate for use by field level resource managers. A discussion of the advantages and disadvantages of various processing systems, different algorithms, and the problems in selecting training sets, is included.

Mead, R. A.↗
NASA NTRS1986

TIMS data applications in Nebraska

A total of 172 flight-line miles of Thermal Infrared Multispectral Scanner (TIMS) data were acquired in the state of Nebraska; and an additional mission is planned for August of this year. Data collected by the scanner were generally applied to investigations in four general areas: hydrology, geology, soils, and vegetation analysis. Relatively simple manipulations of these thermal-emittance data led to excellent classifications of vegetation communities established along topographic gradients in the Nebraska Sandhills. Similar procedures were used to study variations in soil parameters along those same routes. Proposed geologic applications include mapping of the surficial geology along a portion of the Platte River and the delineation of a segment of the Cambridge Arch, a structural feature in central Nebraska.

Queen, Lloyd↗
NASA NTRS1973

Identification of phenological stages and vegetative types for land use classification

The author has identified the following significant results. A large grassland was located on the Kenai Peninsula which may be a potential grazing land. Two 1:250,000 vegetation maps were constructed from ERTS-1 scenes 1049-20505 and 1066-20453 using 70 mm MSS chips and black and white prints for an area of 3.5 million acres. Another area (464,000 acres) was mapped using digital data. The latter map is the most accurate and detailed vegetation map of that area produced to date. Areal extents of identified vegetation types were derived for the area mapped from digital data. Early spring (prior to leafing out of the deciduous trees) is suspected as being the best time for mapping Alaskan vegetation from MSS data due to the radiometrically distinctness of the vegetation communities at that time.

Branton, C. I.↗
NASA NTRS1973

Identification of phenological stages and vegetative types for land use classification

The author has identified the following significant results. A large grassland was located on the Kenai Peninsula which may be a potential grazing land. Two 1:250 K vegetation maps were constructed from ERTS-1 scenes 1049-20505 and 1066-20453 using 70 mm MSS chips and black and white prints for an area of 3.5 million acres. Another area (464,000 acres) was mapped using digital data. The latter map is the most accurate and detailed vegetation map of that area produced to date. Areal extents of identified vegetation types were derived for the area mapped from digital data. Early spring (prior to leafing out of deciduous trees) is suspected as being the best time for mapping Alaskan vegetation from MSS data due to the best time for mapping Alaskan vegetation from MSS data due to the radiometrically distinctness of the vegetation communities at that time. Vegetative overlays produced at 1:250 K compare favorably with vegetative maps compiled by Lloyd A. Spetzman and assembled by the joint Federal-State Land Use Planning Commission for Alaska.

Branton, C. I.↗
NASA NTRS1973

Identification of phenological stages and vegetative types for land use classification

The author had identified the following significant results. Digital signatures derived from the CDU are comparable to those taken from the printouts. Therefore, using the CDU to derive signatures should be more efficient, since there is considerable time required in turn around with the computer and time required locating vegetation stands on the printout.

Mckendrick, J. D.↗
NASA NTRS1985

1985 International Geoscience and Remote Sensing Symposium (IGARSS '85), University of Massachusetts, Amherst, October 7-9, 1985, Digest. Volumes 1 & 2

The present conference on remote sensing instrumentation considers topics in water resources research, planetary remote sensing and mathematical geophysics, sea ice behavior in view of the Cold Regions Research and Engineering Laboratories pond measurements, Shuttle Imaging Radar-B (SIR-B) system performance and calibration results, geological applications of remote sensing, and microwave scattering from vegetation. Further attention is given to atmospheric probing with lasers, image processing for remote sensing, forest inventory and condition assessment, atmospheric remote sensing, SIR-B geological results, the reflectance, emission, and scattering characteristics of vegetation, sea ice sensing, multipolarization SAR results, the theoretical modeling of surfaces and volumes, crop condition assessment and productivity estimates, and SIR-B results for vegetation cover. Also considered are the microwave remote sensing of soil moisture, advanced sensors, advanced information extraction, wind and wave remote observations, results from the ESA Remote Sensing satellite, large area crop and land cover statistics, SIR-B results for directional ocean wave spectra, seasonal snow cover, ice sheets, and lake ice, fundamental research in terrain remote sensing, image classification methods, vegetation stress detection, remote sensing of coastal processes, SAR systems and calibration, electromagnetic geophysical methods and signal processing, image processing techniques, and SAR internal wave measurements.

Carver, K. R.↗
NASA NTRS1972

Identification of phenological stages and vegetative types for land use classification

The author has identified the following significant results. Two procedures for examining ERTS-1 imagery were investigated in cooperation with the photographic services section. Positive, 10X enlargements were produced of spectral bands 4, 6, and 7, for a portion of photo 1033-21020. Adequate detail remained to recognize physical features such as streams, glaciers, ice, and snow. Also the entire image was studied using transparencies produced by the 3M color-key process. A combination of three complementary color combinations produced visually recognizable shades apparently indicating vegetation differences. Also lakes and glacial streams of sufficient size, ice and snow, and drainage patterns can be recognized. A need is indicated for oblique low level color aerial photography in the vicinity of identifiable terrain features to assist in the positive location of vegetative communities appearing in the analysis process. In the primary areas of concern to this project identifiable manmade features are conspicuous by their absence. In image 1049-20505 band 7 produced distinct tonal differences on a mountain slope to river bottom gradient.

Branton, C. I.↗
NASA NTRS1995

Mapping Vegetative Ground Cover From Polarimetric SAR Data

Three-frequency quad-polarization data used to distinguish among different types of cover. MAPVEG computer program performs first-level classification of NASA/JPL AIRSAR polarimetric synthetic-aperture-radar (SAR) data into simple ground-cover types. Classification images generated used to separate vegetated from nonvegetated areas; useful in determining soil moisture in areas free of vegetation. Classification separates forests from surrounding vegetation, and used to identify clear-cut areas. Urban areas and potential marshlands examples of other identifiable classes. Written in FORTRAN 77.

Chapman, Bruce↗
DOE OSTI2023

How deep should we go to understand roots at the top of the world?

Harsh environmental conditions and the short summers of northern, high-latitude biomes impose unique constraints on the plants that live in the arctic tundra and the boreal forest. To escape the harsh aboveground environment, plants in these habitats often allocate a large portion of their biomass belowground to facilitate nutrient acquisition. In turn, the proximity of living plant roots to vast stores of sequestered soil carbon in these biomes means that shifts in rooting depth distribution and the size of the root–soil interface could significantly contribute to ongoing climate change. Indeed, plant ‘priming’ of rhizosphere decomposition via root exudation, particularly from shallowly distributed roots, can lead to losses of carbon from tundra soils. While we have a hard-won understanding of the distribution of plant communities across the arctic tundra and the boreal forest from direct field observations scaled to the landscape level using climate-informed mapping techniques (i.e. the Circumpolar Arctic Vegetation Map (CAVM); Walker et al. , 2005), these vegetation maps are only the tip of the iceberg (Iversen et al. , 2015). Root form and function remain hidden beneath the land surface. In an article recently published in New Phytologist , Blume-Werry et al. (2023, 10.1111/nph.18998) asked whether rooting depth distribution, and ensuing carbon emissions, could be inferred from commonly used vegetation mapping classifications across the pan-Arctic. An important question to guide our understanding, mapping, and prediction of belowground characteristics and ecosystem feedbacks at the top of the world. Unfortunately, they found that the answer was ‘not quite’. While rooting depth distribution varied demonstrably, in turn causing substantial changes in modeled carbon emissions via rhizosphere ‘priming’, variation across rooting depth profiles did not correspond with vegetation mapping classes. If we are unable to predict belowground rooting depth distributions across large spatial scales by leveraging aboveground vegetation community distributions, how then should belowground researchers proceed?

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Data, scripts, and figures associated with a manuscript studying impact of climate and topography on post-fire vegetation recovery.

This data package is associated with the publication “Impact of Topography and Climate on Post-fire Vegetation Recovery Across Different Burn Severity and Land Cover Types through Machine Learning” submitted to Remote Sensing of Environment (Zahura et al. 2023). In this research, a machine learning algorithm, random forest (RF), was utilized to examine the impact of climate and topography on post-fire vegetation recovery. We used enhanced vegetation index (EVI) to examine varying burn severity and land cover types. The data package includes the input files for RF model training, outputs from model predictions and analysis, and python scripts to run the model, analyze the results to understand model performance and interpretability, and plot manuscript figures. This data package contains three folders (Data, Scripts, and Figures), a file-level metadata (FLMD) csv, and a data dictionary (dd) csv. Please see Postfire_recovery_flmd.csv for a list of all files contained in this data package and descriptions for each. The data dictionary (Postfire_recovery_dd.csv) describes the csv column headers. The “Data” folder provides all the inputs and outputs to train the RF model, evaluate performance, and interpret predictions. The “Scripts” folder contains python scripts and jupyter notebooks for model training and result analysis. The “Figures” folder includes the figures used in the manuscript in “.png” and “.jpg” format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Observations and Machine-Learned Models of Near-Surface Permafrost along the Koyukuk River, Alaska, USA

This dataset contains GeoTIFs (raster) and GeoPackages (vector) that map observations of near-surface permafrost and not-permafrost from a field campaign conducted near the village of Huslia, AK along the Koyukuk River and its floodplain in July 2018. These data were collected as part of a campaign to understand if and how permafrost impacts riverbank erosion. This problem cannot be assessed without knowing where permafrost exists. Permafrost was observed via frost probing (to a maximum depth of one meter), coring (to a maximum depth of two meters) and bank/bar excavations. An additional boat survey was performed wherein expert (Joel Rowland) judgment assessed the presence or absence of distinctive permafrost features (e.g., overhanging tundra mats, thermoerosional niching, ice wedges, active drainage of ice melt from soils). This dataset also contains the input features and results of two machine learning models (random forest and convolutional neural network) that extrapolate the observations to the full floodplain that may be useful for building, testing, or validating other machine-learned permafrost models. Permafrost data are provided as georasters of the same shape and geovectors (polylines/polygons) and are all projected into EPSG:32605. All data can be visualized with a GIS (QGIS, ArcGIS, etc.).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

Identification of phenological stages and vegetative types for land use classification

The author has identified the following significant results. Multispectral reflectance signatures have been obtained from computer printouts from scene 1049-20505. Those signatures have been grouped for 13 dominant physical features in the Matanuska Susitna Valley area of Alaska. The derived signatures will be validated and tested by requesting another symbol-coded computer printout for the same area. It is expected that ERTS-1 imagery collected during late autumn and early spring will provide separations of signatures that are confounded in the 1049-20505 scene. Separating bogs and forests should be much improved when either snow covered or when grasses are brown. Photographs have been made of projections on the color additive viewer, each scene being projected with six different filter combinations. The products will be analyzed and those best suited used for producing color transparencies at a scale of 1:250,000 for overlaying on standard quadrangle maps. Color prints will be processed for use in the field. From digital MSS signatures derived to date, identification has been made of which bands are the most powerful for automated classification of dominant physical features. It has been found that separations among density ranges increase with longer wavelengths. Deriving MSS digital signatures has proven valuable to both digital and visual processing of ERTS-1 data.

Branton, C. I.↗
NASA NTRS1974

Identification of phenological stages and vegetative types for land use classification

The author has identified the following significant results. Recent signature identification and refinement techniques indicate that with automated classification of MSS CCT data commercial stands of cottonwood and white spruce can be identified with 80% accuracy in the Bonanza Creek experimental forest. Since that forest is representative of the vast interior Alaska forests, this finding has substantial economic importance to public and private forestry interests in Alaska.

Mckendrick, J. D.↗