Towards a polyalgorithm for land use change detection

Abstract

One way of analyzing satellite images for land use and land cover change (LULCC) is time series analysis (TSA). Most of the many TSA based LULCC algorithms proposed in the remote sensing community perform well on datasets for which they were designed, but their performance on randomly chosen datasets from across the globe has not been studied. A polyalgorithm combines several basic algorithms, each meant to solve the same problem, producing a strategy that unites the strengths and circumvents the weaknesses of constituent algorithms. The foundation of the proposed TSA based ‘polyalgorithm’ for LULCC is three algorithms (BFAST, EWMACD, and LandTrendR), precisely described mathematically, and chosen to be fundamentally distinct from each other in design and in the phenomena they capture. Analysis of results representing success, failure, and parameter sensitivity for each algorithm is presented. For a given pixel, Hausdorff distance is used to compare the distance between the change times (breakpoints) obtained from two different algorithms. Timesync validation data, a dataset that is based on human interpretation of Landsat time series in concert with historical aerial photography, is used for validation. The polyalgorithm yields more accurate results than EWMACD and LandTrendR alone, but counterintuitively not better than BFAST alone. This nascent work will be directly useful in land use and land cover change studies, of interest to terrestrial science research, especially regarding anthropogenic impacts on the environment.