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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Outage Cause Classification of Power Distribution Systems with Machine Learning and Real-World Data

Power distribution systems are geographically dispersed by nature. It may be affected by various factors, such as vegetation, weather, animal and human behaviors. Present response procedures to an outage event massively rely on expert experience and thus tend to be time-consuming. Automatic outage event detection and classification will help to reduce the responding and restoration time. However, this issue is less addressed with existing research done in this area. In this applied research, a set of waveform pre-processing techniques are first proposed to prepare the waveform data for being used as inputs to the classification algorithm. Further, a machine learning-based algorithm is proposed to classify the outage events according to their root causes, e.g. tree contact, animal contact, lightning, etc. Available data include three phase current & voltage waveforms and contextual information during the distribution system outages. The proposed machine learning algorithm takes the current and voltage waveforms as direct inputs in search of features that humans are unable to capture. Real data provided by a distribution company in the East Tennessee region is used to test the proposed pre-processing techniques and the classification algorithm.

Sun, Haoyuan↗
NASA NTRS2017

Early Season Large-Area Winter Crop Mapping Using MODIS NDVI Data, Growing Degree Days Information and a Gaussian Mixture Model

Knowledge on geographical location and distribution of crops at global, national and regional scales is an extremely valuable source of information applications. Traditional approaches to crop mapping using remote sensing data rely heavily on reference or ground truth data in order to train/calibrate classification models. As a rule, such models are only applicable to a single vegetation season and should be recalibrated to be applicable for other seasons. This paper addresses the problem of early season large-area winter crop mapping using Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) time-series and growing degree days (GDD) information derived from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) product. The model is based on the assumption that winter crops have developed biomass during early spring while other crops (spring and summer) have no biomass. As winter crop development is temporally and spatially non-uniform due to the presence of different agro-climatic zones, we use GDD to account for such discrepancies. A Gaussian mixture model (GMM) is applied to discriminate winter crops from other crops (spring and summer). The proposed method has the following advantages: low input data requirements, robustness, applicability to global scale application and can provide winter crop maps 1.5-2 months before harvest. The model is applied to two study regions, the State of Kansas in the US and Ukraine, and for multiple seasons (2001-2014). Validation using the US Department of Agriculture (USDA) Crop Data Layer (CDL) for Kansas and ground measurements for Ukraine shows that accuracies of greater than 90% can be achieved in mapping winter crops 1.5-2 months before harvest. Results also show good correspondence to official statistics with average coefficients of determination R(exp. 2) greater than 0.85.

mixture model↗
NASA NTRS1974

Coastal wetlands analysis from ERTS MSS digital data and field spectral measurements

Classification, delineation and evaluation of coastal wetlands can be made on the basis of major vegetative associations. To produce wetland maps, two vegetation-analysis look-up tables were developed for use in the ERTS ANALYSIS System. These look-up tables are based on Earth Resources Technology Satellite (ERTS) digital values in Multispectral Scanner (MSS) bands 4, 5, and 7 and were developed using seasonal spectral reflectance measurements from field observations. Computer-generated maps at an approximate scale of 1:20,000 were produced for the primary test site, Chincoteague Marsh, Virginia. There is a high degree of accuracy in the identification of wetland features and plant associations. The classification was also tested on other Atlantic coast salt marshes and a brackish marsh in the Chesapeake Bay.

Carter, V.↗
NASA NTRS2017

HOTEX: An Approach for Global Mapping of Human Built-Up and Settlement Extent

Understanding the impacts of urbanization requires accurate and updatable urban extent maps. Here we present an algorithm for mapping urban extent at global scale using Landsat data. An innovative hierarchical object-based texture (HOTex) classification approach was designed to overcome spectral confusion between urban and nonurban land cover types. VIIRS nightlights data and MODIS vegetation index datasets are integrated as high-level features under an object-based framework. We applied the HOTex method to the GLS-2010 Landsat images to produce a global map of human built-up and settlement extent. As shown by visual assessments, our method could effectively map urban extent and generate consistent results using images with inconsistent acquisition time and vegetation phenology. Using scene-level cross validation for results in Europe, we assessed the performance of HOTex and achieved a kappa coefficient of 0.91, compared to 0.74 from a baseline method using per-pixel classification using spectral information.

Wang, Panshi↗
NASA NTRS1987

Landsat classification of the barren hydrolittoral areas of Lake Yli-Kitka, north-eastern Finland

As a part of the project 'Landsat-studies for Mapping the Variables within Water Areas' this study deals with the classification possibilities of evaluating and mapping depth relations and bottom materials within the barren and clear-watered shores of Lake Yli-Kitka, North-Eastern Finland. It has been discovered that it is possible to distinguish open water areas with a water depth of more than about half of the Secchi disk depth from those of shallower hydrolittoral areas. The morainic, sandy and only slightly vegetated subareas of the shallow shores and shoals can possibly be identified by using a simple classification procedure. The data used were recorded by the coarse-resolution Landsat MSS imagery system, and better results are expected after the experiences of the Landsat TM data and the availability of the SPOT material.

Raitala, J.↗
NASA NTRS1975

Utilization of Skylab EREP system for appraising changes in continental migratory bird habitat

The author has identified the following significant results. Surface water statistics using data obtained by supporting aircraft were generated. Signature extraction and refinement preliminary to wetland and associated upland vegetation recognition were accomplished, using a selected portion of the aircraft data. Final classification mapping and analysis of surface water trends will be accomplished.

Gilmer, D. S.↗
NASA NTRS1975

A scheme for the uniform mapping and monitoring of earth resources and environmental complexes: An assessment of natural vegetation, environmental, and crop analogs

The author has identified the following significant results. A study was performed to develop and test a procedure for the uniform mapping and monitoring of natural ecosystems in the semi-arid and wood regions of the Sierra-Lahontan and Colorado Plateau areas, and for the estimating of rice crop production in the Northern Great Valley (Ca.) and the Louisiana Coastal Plain. ERTS-1 and high flight and low flight aerial photos were used in a visual photointerpretation scheme to identify vegetation complexes, map acreages, and evaluate crop vigor and stress. Results indicated that the vegetation analog concept is valid; that depending on the kind of vegetation and its density, analogs are interpretable at different levels in the hierarchical classification from second to the fourth level. The second level uses physiognomic growth form-structural criteria, and the fourth level uses floristic or taxonomic criteria, usually at generic level. It is recommended that analog comparisons should be made in relatively small test areas where large homogeneous examples can be found of each analog.

Poulton, C. E.↗
NASA NTRS1993

Investigations on the 1.7 micron residual absorption feature in the vegetation reflection spectrum

The detection and interpretation of the weak absorption features associated with the biochemical components of vegetation is of great potential interest to a variety of applications ranging from classification to global change studies. This recent subject is also challenging because the spectral signature of the biochemicals is only detectable as a small distortion of the infrared spectrum which is mainly governed by water. Furthermore, the interpretation is complicated by complexity of the molecules (lignin, cellulose, starch, proteins) which contain a large number of different and common chemical bonds. In this paper, we present investigations on the absorption feature centered at 1.7 micron; these were conducted both on AVIRIS data and laboratory reflectance spectra of leaves.

Verdebout, J.↗
NASA NTRSDate not supplied

Impact of Anthropogenic Activity, Climate Change, and Urbanization on Wetland Habitat in the Platte River Basin​

The Platte River Basin (PRB) is a dynamic ecosystem where wetlands play a pivotal role as essential habitats for various flora and fauna, including local and migratory birds. It provides many crucial ecosystem services that benefit humans directly and indirectly. However, anthropogenic activity, climate change, and urbanization have resulted in decline in wildlife habitat, elevated flood risk, and wetland loss. To address this issue, NASA DEVELOP partnered with Audubon Great Plains (AGP) to address the vital habitats within urban areas to protect bird species, reduce flood hazards, and analyze the potential impact of future development on wetlands. We utilized remotely sensed data from Landsat 8 Operational Land Imager (OLI), Sentinel-2 Multispectral Instrument (MSI), and Sentinel-1 Synthetic Aperture Radar (SAR) to assess land use and land cover (LULC) change. Nighttime lights data from Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) as well as NASA Socioeconomic Data and Applications Center (SEDAC) population data were also used as inputs to simulate urban growth potential up to 2050 using the open-source FUTure Urban-Regional Environment Simulation (FUTURES) model. A broad scale analysis across 13 focal cities showed varied changes in land use patterns across the PRB, with the most notable being a decrease in agricultural land coverage and an increase in vegetation and grassland coverage. We overlaid a flood extent map with the LULC classifications in Grand Island to identify possible restoration sites under AGP’s Urban Woods and Prairies Initiative. The results for two proposed scenarios showed that at least 51 counties out of 81 in the PRB would experience growth by year 2050. The first scenario (all wetlands are protected) showed that there will be no loss of wetlands by 2050. However, the second scenario (no wetlands are protected) showed a decrease in wetland area and loss of habitat for bird conservation. The results will help AGP to lead awareness workshops for communities about wetland protection and to form impactful conservation strategies in the future.​

Nancee Uniyal↗
DOE OSTI2020

NEON AOP foliar trait maps, maps of model uncertainty estimates, and conifer map, East River, CO 2018

This data package contains mapped trait estimates and their uncertainties, and conifer map, for the National Ecological Observatory Network's Airborne Observation Platform survey data acquired over the Upper East River, Colorado in 2018. For full details, please see associated reference. in brief, trait models were developed independently for needle and non-needle leaf species using partial least squares regression (PLSR) using ground data from additional datasets: doi:10.15485/1618130, doi:10.15485/1618132, and doi:10.15485/1631278, merged with extracted spectral data from doi:10.15485/1618131. We separated vegetated pixels into needle and non-needle classes in order to generate a classification map based on the spectral differences between these leaf types (conifer.tif). We trained a deep learning model with custom architecture, detailed in Chadwick et al. In Press. The model performed with 0.998 true positive rate and 0.982 true negative rate, with ‘positives’ being non-needle identification. We then utilized PLSR to generate models of foliar traits for each leaf type. So that we could also map uncertainty in these predictions, we generated ten different models for needle and non-needle leaf species using different testing holdout sets of discrete sites. Each of these models was developed with a 100-fold cross validation procedure that utilized a 70% training set and 30% validation set with each fold, and then assessed based on the 10% of testing sites that were not included in that model’s development. The mean predicted value across the 10 models is used for the trait estimate in each pixel across the study area. The models are applied according to the leaf type designation in the conifer.tif map. The errors are the standard deviation across the 10 different models developed, with high error suggesting instability in model prediction and areas where values may not be reliable for ecological inference. These maps are only applied to areas with a NDVI > 0.5 to exclude non-vegetated areas. Shade masks could be applied to these data (doi:10.15485/1618131), but have not been for this data package. These data are also available on Google Earth Engine: https://code.earthengine.google.com/?asset=users/kdc/ER_NEON

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

The ERTS-1 investigation (ER-600). Volume 4: ERTS-1 range analysis

The Range Analysis Team conducted an investigation to determine the utility of using LANDSAT 1 data for mapping vegetation-type information on range and related grazing lands. Two study areas within the Houston Area Test Site (HATS) were mapped to the highest classification level possible using manual image interpretation and computer aided classification techniques. Rangeland was distinguished from nonrangeland (water, urban area, and cropland) and was further classified as woodland versus nonwoodland. Finer classification of coastal features was attempted with some success in differentiating the lowland zone from the drier upland zone. Computer aided temporal analysis techniques enhanced discrimination among nearly all the vegetation types found in this investigation.

Erb, R. B.↗
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↗