Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite

Abstract

Rangelands cover ~23 million hectares and support a $3.4 billion annual cattle industry in California. Large variations in forage production from year to year and across the landscape make grazing management difficult. We here developed optimized methods to map high-resolution forage production using multispectral remote sensing imagery. We conducted monthly flights using a Small Unmanned Aerial System (sUAS) in 2017 and 2018 over a 10-ha deferred grazing rangeland. Daily maps of NDVI at 30-cm resolution were first derived by fusing monthly 30-cm sUAS imagery and more frequent 3-m PlanetScope satellite observations. We estimated aboveground net primary production as a product of absorbed photosynthetically active radiation (APAR) derived from NDVI and light use efficiency (LUE), optimized as a function of topography and climate stressors. The estimated forage production agreed well with field measurements having a R2 of 0.80 and RMSE of 542 kg/ha. Cumulative NDVI and APAR were less correlated with measured biomass ( R 2 = 0.68). Daily forage production maps captured similar seasonal and spatial patterns compared to field-based biomass measurements. Our study demonstrated the utility of aerial and satellite remote sensing technology in supporting adaptive rangeland management, especially during an era of climatic extremes, by providing spatially explicit and near-real-time forage production estimates.

Keywords

• Drone
• MicaSense RedEdge
• Commercial satellite
• Light use efficiency
• Data fusion
• Rangeland
• Aboveground biomass
• Environmental stress