Date
Apr 13, 2022
Title

Mapping Conservation Reserve Program Grasslands in Washington, Colorado, and Kansas with Remote Sensing and Machine Learning

Abstract

The USDA Conservation Reserve Program (CRP) works with farmers and landowners to implement
conservation management practices on enrolled lands, with paid contracts ranging from 10 to 15 years in length.
The CRP Grasslands practices target restoration of agricultural grassland systems by augmenting native
vegetation for pollinators, providing habitat for grassland plants and animals, increasing biodiversity, reducing
soil erosion, and improving water quality.


The USDA’s CRP has been successful in improving the conservation value of millions of acres of farmlands;
however, the program currently lacks spatially explicit information on land cover and vegetation within CRPenrolled
tracts. In partnership with the USDA FSA program, the Conservation Biology Institute (CBI) used a
combination of remote sensing and machine learning algorithms deployed on the innovative cloud-computing
platform, Google Earth Engine, to map grassland characteristics. We used a rich suite of enviro-climatic data,
multiple sources of satellite imagery, and Random Forest modeling techniques to predict land cover for study
areas in Washington, Colorado, and Kansas, where CRP Grasslands holdings are most prevalent. We used
machine learning to create predictive maps of vegetation type by leveraging an extensive set of satellite-derived
variables, environmental layers, and federal survey data (from BLM’s AIM and USDA NRCS’s NRI
programs). Our initial investigation utilized Landsat 8 satellite data to model vegetation cover across the
Washington study area and then scaled up to the Colorado-Kansas study area. The Washington study site was
selected for further model enhancements and an in-depth comparison of Landsat 8, Sentinel-2, and MODIS
satellite imagery, to evaluate differences in model development and performance among sensor types. We
generated vegetation cover predictions for the year 2019 using Random Forest classification models. Classified
outputs for the five vegetation cover models - annual grass, perennial grass, annual forb, perennial forb and bare
soil - were post-processed to exclude water and urban land cover and areas that were not relevant for mapping
grasslands.

 

Citation:

Degagne, R., Pizzino, D., Friedrich, H, Gough, M., Joseph, G., Smith, C. and Strittholt, J. 2021.
Mapping Conservation Reserve Program Grasslands in Washington, Kansas, and Colorado with Remote Sensing and
Machine Learning. CBI Technical Report 2022-1. 70 pp. (DOI: 10.6084/m9.figshare.19141853)

 

Full Paper
CBI Authors & Contributors
GIS Analyst
President | Executive Director
Software Engineer
Gwynne Corrigan, M.S.
Director of Communications
GIS Analyst | Data Manager
Senior Spatial Analyst | Project Manager
Senior Science Coordinator
Senior Software Engineer
Chief Project Officer
Senior Scientist
Senior Geospatial Scientist
Software Engineer
CBI Associates
 
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