Time series analysis of satellite data to characterize multiple land use transitions: a case study of urban growth and agricultural land loss in India

Urban land use change is one of the most impactful land transitions on the biosphere, resulting in land conversion, habitat loss, and changes in biogeochemical cycling, climate, and hydrology. Thus, understanding it is essential for global change research. Most land change detection algorithms assume linear changes. However, urban land-use changes are often nonlinear, i.e., follow multiple transitions over time. We propose a new methodology to identify multiple transitions due to urbanization with high frequency remote sensing time series. We design, implement, and evaluate a time series approach to detect the timing of urban conversion of agricultural land in India. Results show an overall accuracy of 82.11% in detecting change timing when the algorithm is applied to MODIS normalized difference vegetation index (NDVI) time series. The proposed algorithm yields better results with raw time series than filtered time series. We discuss the usefulness of our algorithm to understand nonlinear land transitions.