Spatial Inventory and Economic Analysis of Grassland Practices in Texas, Kansas and Washington State and Enhancement to the Accuracy of Biometric Estimates for Tree-based Practices in the State of Mississippi.
Chief Project Officer
Full title: Spatial Inventory and Economic Analysis of Grassland Practices in Texas, Kansas and Washington State and Enhancement to the Accuracy of Biometric Estimates for Tree-based Practices in the State of Mississippi.
Our goal for this project is to test the ability of advanced and scalable remote sensing technology to enhance grassland biometric and economic assessments for CRP grassland enrollments, and also to enhance the accuracy of biometric and economic assessments of CRP tree enrollments (enrolled in CRP for at least eight years). We will focus our pilot study for CRP grassland enrollments in the states of Texas, Kansas and Washington. These states all contain large acreage under CRP grassland practices, and provide contrasting geographies to test the advanced methodologies being deployed to estimate biometric and economic variables. In Mississippi we will update existing biometric models for forested areas through the use of LIDAR data to enhance the accuracy of the modelling outputs for tree-based practices across the state.
Freely-available, recent (2017-2019) Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (Multispectral) data from the European Space Agency will be processed using the Google Earth Engine platform. This cutting-edge approach facilitates analysis of large study areas, provides access to additional data archives, and allows data to be processed at higher spatial resolution, which could be especially important to discriminating patterns of land cover/use in small CRP polygons. This approach also leverages our ability to perform remote sensing analysis at high temporal resolution (e.g. to examine phenomena monthly, rather than seasonally); carrying out analysis at increased temporal resolution will be especially important to differentiate various grassland characteristics. Employing Google Earth Engine sets us up to pursue integration of near-real time data to monitor USDA CRP targets with satellite imagery. Future work could provide a means for USDA to perform active monitoring and agile management of CRP practices/targets.
Economic analysis of grassland practices will be done in collaboration with researchers at the University of North Carolina and the University of Tennessee. This analysis will focus on the non-market valuation of the benefits of grassland related CRP programs such as Contour Grass Strips, Filter Strip and Grass Waterway by utilizing existing and historical regional CRP practice data. All relevant biometric and economic outputs will be incorporated into the USDA CRP Decision support tool (crptool.org) that CBI has designed and built. The tool is designed in such a way to scale up to more states and CRPs.