Evaluating the utility of remote sensing time series analysis for the identification of grassland conversions in Alberta, Canada

Abstract

Grasslands are an important source of ecosystem services and play a critical role in climate regulation. However, this biome is threatened by agricultural expansion and intensification. Remote sensing offers a unique opportunity to monitor these grassland to cropland conversions through the collection of data at various spatial and temporal scales. This research examines the applicability of remote sensing time series analysis as a tool for identifying grassland to cropland conversions in Alberta, Canada. The Breaks for Additive Seasonal and Trend (BFAST) Seasonal method was the most effective model tested, identifying the correct year of change for 76% of rangeland to cropland conversions and 66% of pasture to cropland conversions. However, the results were strongly dependent on model parameterizations, the datasets used, and the subsequent crop planted. Overall, this research provides a new data-driven approach for identifying grassland to cropland conversions and can be used to improve monitoring of Canada’s grassland resources.