Jennifer Rover
Jennifer is a research geographer with the US Geological Survey at the USGS Earth Resources Observation and Science (EROS) Center in Sioux Falls, SD. Her research focus includes investigating and characterizing inland lake and wetland dynamics and related ecosystem components.
Jennifer is a research geographer with the US Geological Survey at the USGS Earth Resources Observation and Science (EROS) Center in Sioux Falls, SD. Her research focus includes investigating and characterizing inland lake and wetland dynamics and related ecosystem components. Approaches utilize remotely sensed data and geographic information science to infer hydrological processes in lakes, rivers, and wetlands and assess influences from upland land cover and land cover change. Jennifer also leads the Applied Science component for Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. The LCMAP Applied Science team develops innovative applications with time-series land cover change science products. New applications are relevant to partner and stakeholder needs while providing opportunities that enable user feedback to be incorporated into future research and product development. Engagement with the community by means of various mechanisms are an ongoing and important aspect of LCMAP.
Science and Products
Filter Total Items: 29
MODIS-informed greenness responsesto daytime land surface temperaturefluctuations and wildfire disturbancesin the Alaskan Yukon River Basin
Pronounced climate warming and increased wildfire disturbances are known to modify forest composition and control the evolution of the boreal ecosystem over the Yukon River Basin (YRB) in interior Alaska. In this study, we evaluate the post-fire green-up rate using the normalized difference vegetation index (NDVI) derived from 250 m 7 day eMODIS (an alternative and application-ready type of Modera
Authors
Zhengxi Tan, Shu-Guang Liu, Calli B. Jenkerson, Jennifer Oeding, Bruce K. Wylie, Jennifer R. Rover, Claudia J. Young
Estimating aboveground biomass in interior Alaska with Landsat data and field measurements
Terrestrial plant biomass is a key biophysical parameter required for understanding ecological systems in Alaska. An accurate estimation of biomass at a regional scale provides an important data input for ecological modeling in this region. In this study, we created an aboveground biomass (AGB) map at 30-m resolution for the Yukon Flats ecoregion of interior Alaska using Landsat data and field mea
Authors
Lei Ji, Bruce K. Wylie, Dana R. Nossov, Birgit E. Peterson, Mark P. Waldrop, Jack W. McFarland, Jennifer R. Rover, Teresa N. Hollingsworth
On the terminology of the spectral vegetation index (NIR – SWIR)/(NIR + SWIR)
The spectral vegetation index (ρNIR – ρSWIR)/(ρNIR + ρSWIR), where ρNIR and ρSWIR are the near-infrared (NIR) and shortwave-infrared (SWIR) reflectances, respectively, has been widely used to indicate vegetation moisture condition. This index has multiple names in the literature, including infrared index (II), normalized difference infrared index (NDII), normalized difference water index (NDWI), n
Authors
Lel Ji, Li Zhang, Bruce K. Wylie, Jennifer R. Rover
Classifying the hydrologic function of prairie potholes with remote sensing and GIS
A sequence of Landsat TM/ETM+ scenes capturing the substantial surface water variations exhibited by prairie pothole wetlands over a drought to deluge period were analyzed in an attempt to determine the general hydrologic function of individual wetlands (recharge, flow-through, and discharge). Multipixel objects (water bodies) were clustered according to their temporal changes in water extents. We
Authors
Jennifer R. Rover, C.K. Wright, Ned H. Euliss, David M. Mushet, Bruce K. Wylie
A self-trained classification technique for producing 30 m percent-water maps from Landsat data
Small bodies of water can be mapped with moderate-resolution satellite data using methods where water is mapped as subpixel fractions using field measurements or high-resolution images as training datasets. A new method, developed from a regression-tree technique, uses a 30 m Landsat image for training the regression tree that, in turn, is applied to the same image to map subpixel water. The self-
Authors
Jennifer R. Rover, Bruce K. Wylie, Lei Ji
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Filter Total Items: 29
MODIS-informed greenness responsesto daytime land surface temperaturefluctuations and wildfire disturbancesin the Alaskan Yukon River Basin
Pronounced climate warming and increased wildfire disturbances are known to modify forest composition and control the evolution of the boreal ecosystem over the Yukon River Basin (YRB) in interior Alaska. In this study, we evaluate the post-fire green-up rate using the normalized difference vegetation index (NDVI) derived from 250 m 7 day eMODIS (an alternative and application-ready type of ModeraAuthorsZhengxi Tan, Shu-Guang Liu, Calli B. Jenkerson, Jennifer Oeding, Bruce K. Wylie, Jennifer R. Rover, Claudia J. YoungEstimating aboveground biomass in interior Alaska with Landsat data and field measurements
Terrestrial plant biomass is a key biophysical parameter required for understanding ecological systems in Alaska. An accurate estimation of biomass at a regional scale provides an important data input for ecological modeling in this region. In this study, we created an aboveground biomass (AGB) map at 30-m resolution for the Yukon Flats ecoregion of interior Alaska using Landsat data and field meaAuthorsLei Ji, Bruce K. Wylie, Dana R. Nossov, Birgit E. Peterson, Mark P. Waldrop, Jack W. McFarland, Jennifer R. Rover, Teresa N. HollingsworthOn the terminology of the spectral vegetation index (NIR – SWIR)/(NIR + SWIR)
The spectral vegetation index (ρNIR – ρSWIR)/(ρNIR + ρSWIR), where ρNIR and ρSWIR are the near-infrared (NIR) and shortwave-infrared (SWIR) reflectances, respectively, has been widely used to indicate vegetation moisture condition. This index has multiple names in the literature, including infrared index (II), normalized difference infrared index (NDII), normalized difference water index (NDWI), nAuthorsLel Ji, Li Zhang, Bruce K. Wylie, Jennifer R. RoverClassifying the hydrologic function of prairie potholes with remote sensing and GIS
A sequence of Landsat TM/ETM+ scenes capturing the substantial surface water variations exhibited by prairie pothole wetlands over a drought to deluge period were analyzed in an attempt to determine the general hydrologic function of individual wetlands (recharge, flow-through, and discharge). Multipixel objects (water bodies) were clustered according to their temporal changes in water extents. WeAuthorsJennifer R. Rover, C.K. Wright, Ned H. Euliss, David M. Mushet, Bruce K. WylieA self-trained classification technique for producing 30 m percent-water maps from Landsat data
Small bodies of water can be mapped with moderate-resolution satellite data using methods where water is mapped as subpixel fractions using field measurements or high-resolution images as training datasets. A new method, developed from a regression-tree technique, uses a 30 m Landsat image for training the regression tree that, in turn, is applied to the same image to map subpixel water. The self-AuthorsJennifer R. Rover, Bruce K. Wylie, Lei Ji - News