Filters: Tags: Landsat 8 (X) > partyWithName: U.S. Geological Survey - ScienceBase (X)
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We produced a time series of maps of habitat structure within wetlands of the Central Valley of California. The structure of open water and tall emergent vegetation, such as Typha spp. and Schoenoplectus spp., is critical for migratory birds. Through field observation and digitization of high resolution imagery we identified the locations of tall emergent vegetation, water, and other land cover. Using a random forest classification, we classified multispectral Landsat 8 imagery 2013-2017. We used images from the fall when most wetlands are flooded and the summer to separate trees and tall emergent vegetation. The final maps show the distribution and extent of tall emergent vegetation within wetlands. Final time...
A study to develop an earth observation monitoring system to detect harmful algal bloom (HABs) presence within selected north Texas reservoirs using satellite imagery was conducted by the U.S. Geological Survey (USGS). This data release provides the data collected for this study including site locations, water-quality, phytoplankton, and hyperspectral water surface reflectance data. Data were collected between September of 2019 and December of 2020 at 39 sites distributed across 16 reservoirs and 1 stream. The stream site was sampled in response to an algal bloom in August of 2019 in Austin Tex., and is included herein but the reservoir sites are the focus of this data release. The datasets include all routine and...
In this study, we developed a method that identifies an optimal sample data usage strategy and rule numbers that minimize over- and underfitting effects in regression tree mapping models. A LANDFIRE tile (r04c03, located mainly in northeastern Nevada), which is a composite of multiple Landsat 8 scenes for a target date, was selected for the study. To minimize any cloud and bad detection effects in the original Landsat 8 data, the compositing approach used cosine-similarity-combined pixels from multiple observations based on data quality and temporal proximity to a target date. Julian date 212, which yielded relatively low "no data and/or cloudy” pixels, was used as the target date with Landsat 8 observations from...
The Upper Missouri River headwaters (UMH) basin (36 400 km2 ) depends on its river corridors to support irrigated agriculture and world-class trout fisheries. We evaluated trends (1984–2016) in riparian wetness, an indicator of the riparian condition, in peak irrigation months (June, July and August) for 158 km2 of riparian area across the basin using the Landsat normalized difference wetness index (NDWI). We found that 8 of the 19 riparian reaches across the basin showed a significant drying trend over this period, including all three basin outlet reaches along the Jefferson, Madison and Gallatin rivers. The influence of upstream climate was quantified using per reach random forest regressions. Much of the interannual...
Aquatic features critical to watershed hydrology range widely in size from narrow, shallow streams to large, deep lakes. In this study we evaluated wetland, lake, and river systems across the Prairie Pothole Region to explore where pan-sharpened high-resolution (PSHR) imagery, relative to Landsat imagery, could provide additional data on surface water distribution and movement, missed by Landsat. We used the monthly Global Surface Water (GSW) Landsat product as well as surface water derived from Landsat imagery using a matched filtering algorithm (MF Landsat) to help consider how including partially inundated Landsat pixels as water influenced our findings. The PSHR outputs (and MF Landsat) were able to identify...
Scientists and engineers from the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS) Cal/Val Center of Excellence (ECCOE) collected in situ measurements using field spectrometers to support the validation of surface reflectance products derived from Earth observing remote sensing imagery. Data provided in this data release were collected during selected Earth observing satellite overpasses and tests during the months of May through November 2021. These data were collected at six field sites during 2021. These field sites included South Dakota State University (SDSU) Research Park in Brookings, SD, near the Brookings Airport in Brookings, SD, a private land holding in Sanborn County,...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Arizona,
Brookings,
California,
Coconino National Forest,
Edwards Air Force Base,
This data set is combination of imagery from the Advanced Land Imager and Landsat 8 Surface reflectance data product covering three USGS gauge stations, Middle Haddam Connecticut (01193050), Potomac River at Little Falls (01646500), and the Susquehanna River at Darlington, Maryland (01579550) and the Sandy Hook to Jamaica Bays areas of the New York and New Jersey coast. Raw and corrected water quality data was collected from the NWIS database for the corresponding dates of Landsat 8 overpasses of all three sites. Calculations and functions from the Landsat bands were compared, such as band ratios and power functions were calculated and compared against the corrected data for Colored Dissolved Organic Matter. These...
Categories: Data,
pre-SM502.8;
Types: Citation;
Tags: CDOM, fDOM, Surface Reflectance, Landsat Imagery,
Landsat 8,
New York New Jersey Shore,
USGS Science Data Catalog (SDC)
Scientists and engineers from the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS) Cal/Val Center of Excellence (ECCOE) collected in situ measurements using field spectrometers to support the validation of surface reflectance products derived from Earth observing remote sensing imagery. Data provided in this data release were collected during select Earth observing satellite overpasses and tests during the months of May through October 2022. Data was collected at three field sites: the ground viewing radiometer (GVR) site on the USGS EROS facility in Minnehaha County, South Dakota, a private land holding near the City of Arlington in Brookings County, South Dakota, and a private...
Landsat Normalised Difference Vegetation Index (NDVI) is commonly used to monitor post-fire green-up; however, most studies do not distinguish new growth of conifer from deciduous or herbaceous species, despite potential consequences for local climate, carbon and wildlife. We found that dual season (growing and snow cover) NDVI improved our ability to distinguish conifer tree presence and density. We then examined the post-fire pattern (1984–2017) in Landsat NDVI for fires that occurred a minimum of 20 years ago (1986–1997). Points were classified into four categories depending on whether NDVI, 20 years post-fire, had returned to pre-fire values in only the growing season, only under snow cover, in both seasons...
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