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29 records · Page 2Linked to original sources
USGS2017

Creating high-resolution bare-earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for lidar point cloud filtering

For areas of the world that do not have access to lidar, fine-scale digital elevation models (DEMs) can be photogrammetrically created using globally available high-spatial resolution stereo satellite imagery. The resultant DEM is best termed a digital surface model (DSM) because it includes heights of surface features. In densely vegetated conditions, this inclusion can limit its usefulness in applications requiring a bare-earth DEM. This study explores the use of techniques designed for filtering lidar point clouds to mitigate the elevation artifacts caused by above ground features, within the context of a case study of Prince William Forest Park, Virginia, USA. The influences of land cover and leaf-on vs. leaf-off conditions are investigated, and the accuracy of the raw photogrammetric DSM extracted from leaf-on imagery was between that of a lidar bare-earth DEM and the Shuttle Radar Topography Mission DEM. Although the filtered leaf-on photogrammetric DEM retains some artifacts of the vegetation canopy and may not be useful for some applications, filtering procedures significantly improved the accuracy of the modeled terrain. The accuracy of the DSM extracted in leaf-off conditions was comparable in most areas to the lidar bare-earth DEM and filtering procedures resulted in accuracy comparable of that to the lidar DEM.

GIScience and Remote Sensing↗
USGS2019

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (>250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated from such products. Thereby, the overarching goal of this study was to develop high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, ten time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the 3 time-periods over 12 months (monsoon: Julian days 151-300; winter: Julian days 301-365 plus 1-60; and summer: Julian days 61-150), taking the every 8-day data from Landsat-8 and 7 for the years 2013-2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the 5 agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledge-base for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N=2179) in 5 AEZs. Classification was performed on GEE for each of the 5 AEZs using well-established knowledge-based and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N=1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, at www.croplands.org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/

GIScience and Remote Sensing↗
USGS2013

An evaluation of the individual components and accuracies associated with the determination of impervious area

The percentage of impervious surface area in a watershed has been widely recognized as a key indicator of terrestrial and aquatic ecosystem condition. Although the use of the impervious indicator is widespread, there is currently no consistent or mutually accepted method of computing impervious area and the approach of various commonly used techniques varies widely. Further, we do not have reliable information on the components of impervious surfaces, which would be critical in any future planning attempts to remediate problems associated with impervious surface coverage. In cooperation with the USGS Geographic Analysis and Monitoring Program (GAM) and The National Map, and the EPA Landscape Ecology Program, this collaborative research project utilized very high resolution imagery and GIS techniques to map and quantify the individual components of total impervious area in six urban/suburban watersheds in different parts of the United States. These data were served as ground reference, or "truth," for the evaluation for four techniques used to compute impervious area. The results show some important aspects about the component make-up of impervious cover and the variability of methods commonly used to compile this critical emerging indicator of ecosystem condition.

GIScience and Remote Sensing↗
USGS2013

Multiple baseline radar interferometry applied to coastal land cover classification and change analyses

ERS-1 and ERS-2 SAR data were collected in tandem over a four-month period and used to generate interferometric coherence, phase, and intensity products that we compared to a classified land cover coastal map of Big Bend, Florida. Forests displayed the highest intensity, and marshes the lowest. The intensity for fresh marsh and forests progressively shifted while saline marsh intensity variance distribution changed with the season. Intensity variability suggested instability between temporal comparisons. Forests, especially hardwoods, displayed lower coherences and marshes higher. Only marshes retained coherence after 70 days. Coherence was more responsive to land cover class than intensity and provided discrimination in winter. Phase distributions helped reveal variation in vegetation structure, identify broad land cover classes and unique within-class variations, and estimate water-level changes. Copyright ?? 2006 by V. H. Winston & Son, Inc. All rights reserved.

GIScience and Remote Sensing↗
USGS2013

The Vegetation Drought Response Index (VegDRI): A new integrated approach for monitoring drought stress in vegetation

The development of new tools that provide timely, detailed-spatial-resolution drought information is essential for improving drought preparedness and response. This paper presents a new method for monitoring drought-induced vegetation stress called the Vegetation Drought Response Index (VegDRI). VegDRI integrates traditional climate-based drought indicators and satellite-derived vegetation index metrics with other biophysical information to produce a 1 km map of drought conditions that can be produced in near-real time. The initial VegDRI map results for a 2002 case study conducted across seven states in the north-central United States illustrates the utility of VegDRI for improved large-area drought monitoring.

GIScience and Remote Sensing↗
USGS2013

Estimation of perennial vegetation cover distribution in the Mojave Desert using MODIS-EVI data

This paper details a method to create regional models of perennial vegetation cover using pre-existing field data and satellite imagery. Total cover of perennial vegetation is an important ecological attribute of desert ecosystems, including the Mojave Desert, USA, an area of 125,000 km2. Moderate-Resolution Imaging Spectroradiometer Enhanced Vegetation Index (MODIS-EVI) data were coupled with measurements of total perennial cover and plot elevation using stepwise linear regression and linear regression techniques to create two models of cover. The final models produced R2 of 0.82 and 0.81, respectively, and yielded maps of perennial cover distribution in the Mojave Desert at 250 m spatial resolution. Copyright ?? 2008 by Bellwether Publishing, Ltd. All rights reserved.

GIScience and Remote Sensing↗
USGS2013

Effects of sampling interval on spatial patterns and statistics of watershed nitrogen concentration

This study investigates how spatial patterns and statistics of a 30 m resolution, model-simulated, watershed nitrogen concentration surface change with sampling intervals from 30 m to 600 m for every 30 m increase for the Little River Watershed (Georgia, USA). The results indicate that the mean, standard deviation, and variogram sills do not have consistent trends with increasing sampling intervals, whereas the variogram ranges remain constant. A sampling interval smaller than or equal to 90 m is necessary to build a representative variogram. The interpolation accuracy, clustering level, and total hot spot areas show decreasing trends approximating a logarithmic function. The trends correspond to the nitrogen variogram and start to level at a sampling interval of 360 m, which is therefore regarded as a critical spatial scale of the Little River Watershed. Copyright ?? 2009 by Bellwether Publishing, Ltd. All right reserved.

GIScience and Remote Sensing↗
USGS2013

An evaluation of object-oriented image analysis techniques to identify motorized vehicle effects in semi-arid to arid ecosystems of the American West

Human disturbance is a leading ecosystem stressor. Human-induced modifications include transportation networks, areal disturbances due to resource extraction, and recreation activities. High-resolution imagery and object-oriented classification rather than pixel-based techniques have successfully identified roads, buildings, and other anthropogenic features. Three commercial, automated feature-extraction software packages (Visual Learning Systems' Feature Analyst, ENVI Feature Extraction, and Definiens Developer) were evaluated by comparing their ability to effectively detect the disturbed surface patterns from motorized vehicle traffic. Each package achieved overall accuracies in the 70% range, demonstrating the potential to map the surface patterns. The Definiens classification was more consistent and statistically valid. Copyright ?? 2010 by Bellwether Publishing, Ltd. All rights reserved.

GIScience and Remote Sensing↗
USGS2013

Landscape indicators and land cover change in the Mid-Atlantic Region of the United States, 1973-2001

Landscape indicators, derived from land use and land cover data as well as other data, were used to calculate the ecological consequences of land cover change in terms of nitrate loading and physical bird habitat. Both were modeled from 1973, 1992, and 2001 land cover data in the Mid-Atlantic region of the United States. Land cover statistics and trends are calculated for three time periods. In general, forest gain and agricultural loss was found in areas of improving landscape indicators and forest loss and agricultural gain was found to occur in areas of declining indicators, which was confirmed by high-resolution aerial photographic analysis.

Delaware, Maryland, New Jersey, New York, North Ca↗
USGS2013

Accuracy evaluation of an ASTER-Derived Global Digital Elevation Model (GDEM) Version 1 and Version 2 for two sites in western Africa

This study compares the ASTER Global DEM version 1 (GDEMv1) and version 2 (GDEMv2) for two study sites with distinct terrain and land cover characteristics in western Africa. The effects of land cover, slope, relief, and stack number are evaluated through both absolute and relative DEM statistical comparisons. While GDEMv2 at times performed better than GDEMv1, this improvement was not consistent, revealing the complex nature and interaction of terrain and land cover characteristics, which influences the accuracy of GDEM tiles on local and regional scales.

Akwatia;K�ni�ba↗
USGS2011

The U.S. Geological Survey cartographic and geographic information science research activities 2006-2010

The U.S. Geological Survey (USGS) produces geospatial databases and topographic maps for the United States of America. A part of that mission includes conducting research in geographic information science (GIScience) and cartography to support mapping and improve the design, quality, delivery, and use of geospatial data and topographic maps. The Center of Excellence for Geospatial Information Science (CEGIS) was established by the USGS in January 2006 as a part of the National Geospatial Program Office. CEGIS (http://cegis.usgs.gov) evolved from a team of cartographic researchers at the Mid-Continent Mapping Center. The team became known as the Cartographic Research group and was supported by the Cooperative Topographic Mapping, Geographic Analysis and Monitoring, and Land Remote Sensing programs of the Geography Discipline of the USGS from 1999-2005. In 2006, the Cartographic Research group and its projects (http://carto-research.er.usgs.gov/) became the core of CEGIS staff and research. In 2006, CEGIS research became focused on The National Map (http://nationalmap.gov).

Cartography and Geographic Information Science↗