Skip to main content
USGS - science for a changing world
Advanced Search

Filters: Tags: NDVI (X)

86 results (14ms)   

Filters
Date Range
Extensions
Types
Contacts
Categories
Tag Types
Tag Schemes
View Results as: JSON ATOM CSV
thumbnail
This study uses growth in vegetation during the monsoon season measured from LANDSAT imagery as a proxy for measured rainfall. NDVI values from 26 years of pre- and post-monsoon season Landsat imagery were derived across Yuma Proving Ground (YPG) in southwestern Arizona, USA. The LANDSAT imagery (1986-2011) was downloaded from USGS’s GlobeVis website (http://glovis.usgs.gov/). Change in NDVI was calculated within a set of 2,843 Riparian Area Polygons (RAPs) up to 1 km in length defined in ESRI ArcMap 10.2.
thumbnail
This map layer is a grid map of 2001 average vegetation growth for Alaska and the conterminous United States. The nominal spatial resolution is 1 kilometer and the map layer is based on 1-kilometer AVHRR data. The data were compiled by staff at the USGS Center for Earth Resources Observation and Science.
thumbnail
This map layer is a grid map of 1996 average vegetation growth for Alaska and the conterminous United States. The nominal spatial resolution is 1 kilometer and the map layer is based on 1-kilometer AVHRR data. The data were compiled by staff at the USGS Center for Earth Resources Observation and Science.
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...
thumbnail
This map layer is a grid map of 1998 peak vegetation growth for Alaska and the conterminous United States. The nominal spatial resolution is 1 kilometer and the map layer is based on 1-kilometer AVHRR data. The data were compiled by staff at the USGS Center for Earth Resources Observation and Science.
thumbnail
The capacity of ecosystems to provide services such as carbon storage, clean water, and forest products is determined not only by variations in ecosystem properties across landscapes, but also by ecosystem dynamics over time. ForWarn is a system developed by the U.S. Forest Service to monitor vegetation change using satellite imagery for the continental United States. It provides near real-time change maps that are updated every eight days, and summaries of these data also provide long-term change maps from 2000 to the present.Based on the detection of change in vegetation productivity, the ForWarn system monitors the effects of disturbances such as wildfires, insects, diseases, drought, and other effects of weather,...
Quantitative assessment of forest burn severity and determination of its spatial variation are important for post-fire forest restoration and forest fire management. In this paper, we assessed forest burn severity using pre- and post-fire Landsat TM/ETM+ data and field-surveyed data and explored the spatial variation in burn severity and its influencing factors. Our results showed a relatively strong linear relationship between normalized burn ratio (NBR) and composite burn index (CBI) (R2 = 0.63), suggesting that NBR was the best spectral index and could be used to assess forest burn severity in Heilongjiang Province. The forest burn severity showed obvious spatial variation. The majority of heavily burned areas...
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...
thumbnail
These data are aerial image-derived, classification maps of tamarisk (Tamarisk spp.) in the riparian zone of the Colorado River from Glen Canyon Dam to Separation Canyon, a total river distance of 412 km. The classification maps are published in GIS vector format. Two maps are published: 1) a classification of tamarisk from a 0.2 m resolution multispectral image dataset acquired in May 2009 (Tamarisk Classification 2009), and 2) a classification of tamarisk impacted by the tamarisk beetle (Diorhabda carinulata) from a 0.2 m resolution multispectral image dataset acquired in May 2013 (Beetle Impact Classification 2013). Tamarisk presence in 2009 was classified using the Mahalanobis Distance method with a total of...
thumbnail
The study's goal was to downscale 2013 250-m expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) to 30 m (Gu, Y. and Wylie, B.K., 2015, Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations, Remote Sensing of Environment, v. 171, p. 291-298)using 2013 Landsat 8 data. The eMODIS NDVI was downscaled for four periods: mid spring, early summer, late summer and mid fall. The objective was to capture phenologies during periods that correspond to 1) annual grass growth, 2) annual grass senescence, 3) the optimal NDVI profile separation between sagebrush and other shrubs in the region, and...
thumbnail
This dataset provides an estimate of 2015 cheatgrass percent cover in the northern Great Basin at 250 meter spatial resolution. The dataset was generated by integrating eMODIS NDVI satellite data with independent variables that influence cheatgrass germination and growth into a regression-tree model. Individual pixel values range from 0 to 100 with an overall mean value of 9.85 and a standard deviation of 12.78. A mask covers areas not classified as shrub/scrub or grass/herbaceous by the 2001 National Land Cover Database. The mask also covers areas higher than 2000 meters in elevation because cheatgrass is unlikely to exist at more than 2% cover above this threshold. Cheatgrass is an invasive grass that has invaded...
thumbnail
This map layer is a grid map of 2002 peak vegetation growth for Alaska and the conterminous United States. The nominal spatial resolution is 1 kilometer and the map layer is based on 1-kilometer AVHRR data. The data were compiled by staff at the USGS Center for Earth Resources Observation and Science.
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...
thumbnail
This dataset provides a near-real-time estimate of 2018 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatgrass percent cover in the Northern Great Basin, USA, 2015. Rangelands 38:278-284.) This estimate was based on remotely sensed enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) data gathered through July 1, 2018. This is the second iteration of an early estimate of herbaceous annual cover for 2018 over the same geographic area. The previous dataset used eMODIS NDVI data gathered through May 1 (https://doi.org/10.5066/P9KSR9Z4). The pixel values for this most recent estimate ranged from 0 to100% with...
thumbnail
This map layer is a grid map of 1995 peak vegetation growth for Alaska and the conterminous United States. The nominal spatial resolution is 1 kilometer and the map layer is based on 1-kilometer AVHRR data. The data were compiled by staff at the USGS Center for Earth Resources Observation and Science.


map background search result map search result map Peak Vegetation Growth 1995 Average Vegetation Growth 1996 Average Vegetation Growth 2001 Peak Vegetation Growth 1998 Peak Vegetation Growth 2002 Near-real-time cheatgrass percent cover in the northern Great Basin, USA--2015 ForWarn Mean Summer National Difference Vegetation Index 2009-2013 Mean of the Top Ten Percent of NDVI Values in the Yuma Proving Ground during Monsoon Season, 1986-2011 Estimating downscaled eMODIS NDVI using Landsat 8 in the central Great Basin shrub steppe Remote sensing derived maps of tamarisk (2009) and beetle impacts (2013) along 412 km of the Colorado River in the Grand Canyon, Arizona Near-real-time Herbaceous Annual Cover in the Sagebrush Ecosystem, USA, July 2018 Mean of the Top Ten Percent of NDVI Values in the Yuma Proving Ground during Monsoon Season, 1986-2011 Remote sensing derived maps of tamarisk (2009) and beetle impacts (2013) along 412 km of the Colorado River in the Grand Canyon, Arizona Estimating downscaled eMODIS NDVI using Landsat 8 in the central Great Basin shrub steppe Near-real-time cheatgrass percent cover in the northern Great Basin, USA--2015 Near-real-time Herbaceous Annual Cover in the Sagebrush Ecosystem, USA, July 2018 ForWarn Mean Summer National Difference Vegetation Index 2009-2013 Peak Vegetation Growth 1995 Average Vegetation Growth 1996 Average Vegetation Growth 2001 Peak Vegetation Growth 1998 Peak Vegetation Growth 2002