An automated algorithm to detect timing of urban conversion of agricultural land with high temporal frequency MODIS NDVI data

Urban expansion is one of the major drivers of agricultural lands loss. However, current remote sensing-based efforts to monitor this process are limited to small scale case studies that require much user input. Given the rate and magnitude of contemporary urbanization, there is a need to develop a land change algorithm that can characterize the loss of agricultural land at large scales over long time periods. Moreover, characterizing agricultural land conversion trajectories from remote sensing images is complex due to farm size, climatic variability, changes in cropping patterns, and variations in the rate of development processes. Here we propose an econometric time series approach to identify agricultural land loss due to urban expansion, utilizing high temporal frequency MODIS NDVI data between 2000 and 2010. The algorithm is comprised of two main components: 1) detrending the time series, and 2) testing for the presence of a breakpoint in the detrended time series and estimating the date of the breakpoint. Evaluations of the algorithm with simulated and actual MODIS NDVI data confirm that the method can successfully detect when and where urban conversions of agricultural lands occur. The algorithm is simple, robust, and highly automated, thus is valuable for monitoring agricultural land loss at regional and even global scales.