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Jesslyn Brown

LCMAP CONUS Reference Data Products
The Land Change Monitoring, Assessment, and Projection (LCMAP) products were validated using an independently collected series of reference data. The intial collection included 25,000 reference data plots across the conterminous United States (CONUS) that included land cover, land use, and change process attributes collected from 1984-2018. These collected attributes and plot locations are available as a seperate data collection product. Future collections related to additional years, geographic regions, etc. may occur.
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To describe calling activity of Pseudacris crucifer in relation to temperature, precipitation, and wetland water levels, we programmed an acoustic recorder (Wildlife Acoustics) to sample seasonal amphibian calls remotely at study site SC4DAI2 in the St. Croix National Scenic Riverway from 2008 to 2012. We programmed the recorder to sample for five minutes at the top of every hour of every day from late winter/early spring through late summer. We used the Songscape option in Songscope software to generate annual summaries of all of our acoustic samples from SC4DAI2. These summaries included a median dB level for each prescribed frequency within each recording. Pseudacris crucifer, the spring peeper, inhabited SC4DAI2...
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USGS Land Cover
The USGS Land Cover project has combined concepts and methodology from the legacy LCMAP and NLCD projects, along with modern deep learning convolutional neural networks, to produce promising prototypes of next generation land cover products. The new land cover algorithm will serve as the new baseline for USGS land cover production. Annual NLCD is a U.S. Geological Survey (USGS) science initiative implemented at the Earth Resources Observation and Science (EROS) Center that harnesses the remotely sensed Landsat data record to provide state-of-the-art land surface change information needed by scientists, resource managers, and decision-makers. Annual NLCD uses a modernized, integrated approach to map, monitor, synthesize,...
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It is well understood that plant phenology is sensitive to climate, however it is not so clear exactly how climate change might alter the spatial and temporal patterns of vegetation phenology. Satellite remote sensing offers a unique vantage point from which to observe phenological cycles across large regions. Although ubiquitous, cycles of green-up and brown-down are complex and exhibit great variability across space and time. Herbivore species such as elk, moose, and deer depend on the availability of herbaceous plants and deciduous shrubs for forage. These vegetation types are most nutritious for herbivores from early season to peak green-up, so characterizing vegetation cycles, or phenology, over the long term...
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Defining site potential for an area establishes its possible long-term vegetation growth productivity in a relatively undisturbed state, providing a realistic reference point for ecosystem performance. Modeling and mapping site potential helps to measure and identify naturally occurring variations on the landscape as opposed to variations caused by land management activities or disturbances (Rigge et al. 2020). We integrated remotely sensed data (250-m enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) (https://earthexplorer.usgs.gov/)) with land cover, biogeophysical (i.e., soils, topography) and climate data into regression-tree software (Cubist®). We...
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