Machine Learning to Assess Grassland Productivity in Southeastern Arizona

Abstract:

We present preliminary results of machine learning (ML) techniques modeling the combined effects of climate, management, and inherent potential on productivity of grazed semi-arid grasslands in southeastern Arizona. Our goal is to support public land managers determine if agency management policies are meeting objectives and where to focus attention. Monitoring in the field is becoming more and more limited in space and time. Remotely sensed data cover the entire allotments and go back in time, but do not consider the key issue of species composition. By estimating expected vegetative production as a function of site potential and climatic inputs, management skill can be assessed through time, across individual allotments, and between allotments. Here we present the use of Random Forest (RF) as the main ML technique, in this case for the purpose of regression. Our response variable is the maximum annual NDVI, a surrogate for grassland productivity, as generated by the Google Earth Engine cloud computing platform based on Landsat 5, 7, and 8 datasets. PRISM 33-year normal precipitation (1980-2013) was resampled to the Landsat scale. In addition, the GRIDMET climate dataset was the source for the calculation of the annual SPEI (Standardized Precipitation Evapotranspiration Index), a drought index. We also included information about landscape position, aspect, streams, ponds, roads and fire disturbances as part of the modeling process. Our results show that in terms of variable importance, the 33-year normal precipitation, along with SPEI, are the most important features affecting grasslands productivity within the study area. The RF approach was compared to a linear regression model with the same variables. The linear model resulted in an r² = 0.41, whereas RF showed a significant improvement with an r² = 0.79. We continue refining the model by comparison with aerial photography and to include grazing intensity and infrastructure from units/allotments to assess the effect of management practices on vegetation production.