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These data were compiled for evaluating river-reach level vegetation greenness data in the riparian corridor of the Colorado River delta as specified under Minute 319 of the 1944 Water Treaty. The seven reach areas from the Northerly International Boundary (NIB) to the end of the delta at the Sea of Cortez were defined for research activities. Also, these seven reaches are being monitored under Minute 323 of the 1944 Water Treaty. Additionally, these data were compiled for evaluating restoration-level vegetation greenness data in Reach 2 and Reach 4, as specified under Minute 323 of the 1944 Water Treaty. Objectives of our study were to measure satellite vegetation index data, specifically using the Enhanced Vegetation...
This dataset provides calculated camera-NDVI data for individual regions-of-interest (ROI's) for the phenocam named 'GRCA1PJ' (part of the Phenocam Network, https://phenocam.sr.unh.edu/webcam/). The GRCA1PJ phenocam is within a pinyon-juniper woodland in Grand Canyon National Park. Camera-NDVI refers to a modified version of NDVI calculated by the phenopix package (Filippa et al., 2016). The camera-calculated NDVI data are in the folder FinalOutput. File attributes within that folder are described in detail in the entity and attribute information section of this metadata. It should be possible for the user to use only the ROI definitions, image data downloaded from the phenocam network, and the phenopix R-package...
These datasets provide early estimates of 2021 fractional cover for exotic annual grass (EAG) species and a native perennial grass predicted on July 1 using satellite observation data available no later than June 28th. Four fractional cover maps comprise this release, along with the corresponding confidence maps, for: 1) a group of 17 species of EAGs (i.e., Bromus arvensis L., Bromus briziformis, Bromus catharticus Vahl, Bromus commutatus, Bromus diandrus, Bromus hordeaceus L., Bromus hordeaceus spp. hordeaceus, Bromus japonicus, Bromus madritensis L., Bromus madritensis L. ssp. rubens (L.) Duvin, Bromus L., Bromus racemosus, Bromus rubens L., Bromus secalinus L., Bromus tectorum L., Bromus texensis (Shear) Hitchc.,...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Arizona,
California,
Colorado,
Great Basin,
Harmonized Landsat Sentinel,
These datasets provide early estimates of 2022 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a bi-weekly basis from May to early July. The EAG estimates are developed within one week of the latest satellite observation used for that version. Each bi-weekly release contains four fractional cover maps along with their corresponding confidence maps for: 1) a group of 16 species of EAGs, 2) cheatgrass (Bromus tectorum); 3) medusahead (Taeniatherum caput-medusae); and 4) Sandberg bluegrass (Poa secunda). These datasets were generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) data plots; Harmonized Landsat...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Arizona,
California,
Colorado,
Great Basin,
Harmonized Landsat Sentinel,
The Middle East and North Africa (MENA) region is the most water-scarce region with only two percent of the global average annual rainfall, hence underground aquifers are the major source of water. The need to improve water productivity and increase aquifer storage and recovery (ASR) is driving the efforts for this acceleration of aquifer storage and recovery project. The objective was to model runoff in the study area using multi-source satellite data and identify regions of runoff retention and recharge. Daily runoff is simulated using a saturation excess principle with the VegET model (Senay 2008). It is a spatially explicit (500m grid cell), one-dimensional root-zone water balance model that is driven by precipitation,...
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service,
Raster;
Tags: Jordan,
Land Use Change,
Lebanon,
MENA,
NDVI,
The dataset provides a near real time estimation of 2020 herbaceous mostly annual fractional cover predicted on July 1st with an emphasis on annual exotic grasses Historically, similar maps were produced at a spatial resolution of 250m (Boyte et al. 2019 https://doi.org/10.5066/P96PVZIF., Boyte et al. 2018 https://doi.org/10.5066/P9RIV03D.), but starting this year we are mapping at a 30m resolution (Pastick et al. 2020 doi:10.3390/rs12040725). This dataset was generated using in situ observations from Bureau of Land Management’s (BLM) Assessment, Inventory, and Monitoring data (AIM) plots; weekly composites of harmonized Landsat and Sentinel-2 (HLS) data (https://hls.gsfc.nasa.gov/); relevant environmental, vegetation,...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: California,
Colorado,
Great Basin,
Harmonized Landsat Sentinel,
Idaho,
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the folder there are 8 raster tiffs. 1. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit i. Band 1 of the Tiff is Band 3 (Red) of Landsat ii. Band 2 of the Tiff is Band 4 (NIR) of Landsat iii. Band 3 of...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the folder there are 8 raster tiffs. 1. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit i. Band 1 of the Tiff is Band 3 (Red) of Landsat ii. Band 2 of the Tiff is Band 4 (NIR) of Landsat iii. Band 3 of...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the folder there are 8 raster tiffs. 1. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit i. Band 1 of the Tiff is Band 3 (Red) of Landsat ii. Band 2 of the Tiff is Band 4 (NIR) of Landsat iii. Band 3 of...
These datasets provide early estimates of 2023 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a weekly basis from May to early July. The EAG estimates are developed typically within 7-13 days of the latest satellite observation used for that version. Each weekly release contains four fractional cover maps along with their corresponding confidence maps for: 1) a group of 16 species of EAGs, 2) cheatgrass (Bromus tectorum); 3) medusahead (Taeniatherum caput-medusae); and 4) Sandberg bluegrass (Poa secunda). These datasets were generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) data plots; Harmonized...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Arizona,
California,
Colorado,
Great Basin,
Harmonized Landsat Sentinel,
These datasets provide early estimates of 2023 fractional cover for exotic annual grass (EAG) species and one native perennial grass species on a weekly basis from May to early July. The EAG estimates are developed typically within 7-13 days of the latest satellite observation used for that version. Each weekly release contains four fractional cover maps along with their corresponding confidence maps for: 1) a group of 16 species of EAGs, 2) cheatgrass (Bromus tectorum); 3) medusahead (Taeniatherum caput-medusae); and 4) Sandberg bluegrass (Poa secunda). These datasets were generated leveraging field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) data plots; Harmonized...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Arizona,
California,
Colorado,
Great Basin,
Harmonized Landsat Sentinel,
This dataset supports the following publication: "Solar and sensor geometry, not vegetation response, drive satellite NDVI phenology in widespread ecosystems of the western United States" (DOI:10.1016/j.rse.2020.112013). The data release allows users to replicate, test, or further explore results. The dataset consists of 4 separate items based on the analysis approach used in the original publication 1) the 'Phenocam' dataset uses images from a phenocam in a pinyon juniper ecosystem in Grand Canyon National Park to determine phenological patterns of multiple plant species. The 'Phenocam' dataset consists of scripts and tabular data developed while performing analyses and includes the final NDVI values for all areas...
Locating meadow study sitesMeadow centers as recorded in the ‘Copy of sitecords_areaelev from Caruthers thesis.xls’ file delivered by Debinski in November 2012 were matched to polygons as recorded in files ‘teton97map_area.shp’ and ‘gallatin97map_area.shp’ both also delivered by Debinski in November 2012.In cases where the meadow center did not fall within a meadow polygon, if there was a meadow polygon of the same meadow TYPE nearby (judgment was used here), the meadow center was matched with the meadow polygon of same meadow TYPE. In total, 29 of 30 Gallatin meadow sites and 21 of 25 Teton meadow sites were positively located.Identifying meadow pixels for analysisThe native MODIS 250-meter grid was reprojected...
Categories: Data;
Types: Citation;
Tags: Greater Yellowstone Ecosystem,
MODIS,
NDVI,
North Central CASC,
Plants,
The dataset provides a spatially explicit estimate of 2019 herbaceous annual percent cover predicted on May 1st with an emphasis on annual grasses. The estimate is based on the mean output of two regression-tree models. For one model, we include, as an independent variable amongst other independent variables, a dataset that is the mean of 17-years of annual herbaceous percent cover (https://doi.org/10.5066/F71J98QK). This model's test mean error rate (n = 1670), based on nine different randomizations, equals 4.9% with a standard deviation of +/- 0.15. A second model was developed that did not include the mean of 17-years of annual herbaceous percent cover, and this model's test mean error rate (n = 1670), based...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Bromus tectorum,
California,
Colorado,
Desert,
Ecology,
This dataset release provides historical (2016 - 2022) estimates of fractional cover for exotic annual grass (EAG) species and a native perennial bunch grass in the arid and semi-arid rangelands of the western United States. The dataset includes four fractional cover maps per year, accompanied by corresponding confidence maps, for a group of 16 species of EAGs, cheatgrass (Bromus tectorum); medusahead (Taeniatherum caput-medusae); and Sandberg bluegrass (Poa secunda). The data were generated using a combination of field observations from Bureau of Land Management (BLM) Assessment, Inventory, and Monitoring (AIM) plots; remotely sensed data from the Harmonized Landsat and Sentinel-2 (HLS) product (specifically Normalized...
Categories: Data,
Data Release - Revised;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Arizona,
California,
Colorado,
Great Basin,
Harmonized Landsat Sentinel,
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the folder there are 8 raster tiffs. 1. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit i. Band 1 of the Tiff is Band 3 (Red) of Landsat ii. Band 2 of the Tiff is Band 4 (NIR) of Landsat iii. Band 3 of...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the folder there are 8 raster tiffs. 1. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit i. Band 1 of the Tiff is Band 3 (Red) of Landsat ii. Band 2 of the Tiff is Band 4 (NIR) of Landsat iii. Band 3 of...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the folder there are 8 raster tiffs. 1. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit i. Band 1 of the Tiff is Band 3 (Red) of Landsat ii. Band 2 of the Tiff is Band 4 (NIR) of Landsat iii. Band 3 of...
Categories: Publication;
Types: Citation;
Tags: EVI,
Grasslands and Plains,
Greater Yellowstone Ecosystem,
Landscapes,
NDVI,
In support of mapping ecological conditions (e.g. invasive annual grass) in sagebrush-dominated landscapes of the western United States, we developed weekly (starting from week 7 to week 42 and Week 1 starts January 1 or Day of the year 1 to 7, week 2 is from Day of year 8 to 14, and so on) 30-m cloud-free Normalized Difference Vegetation Index (NDVI) from 2016 to 2019. The data was generated with machine-learning techniques (i.e., regression tree [RT]) and harmonized Landsat and Sentinel -2 (HLS) data. The geographic coverage includes areas in the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. This NDVI collection allows for local-scale detection and analysis such as, fuel breaks...
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