Malaccha: An R-based end-to-end Markov transition matrix extraction for land cover datasets

Abstract

The Markov chain approach is one of the many methods used in analyzing land cover change. The method is widely used for its ease of use and transparency. Malaccha (Markovian Land Cover Change) presents an end-to-end code that processes a land cover dataset into Markov transition matrices, presenting the probability of change from one land cover type to another. In this study, the code was built with NASA’s readily available MODIS HDF dataset in mind. Malaccha breaks down into several steps: land cover data loading, raster reprojection and cropping, data extraction, and transition matrix calculation. The processes were designed to be done in one run. The end results of the software are the land cover classification and the probability transition matrices. Malaccha’s product can be used for subsequent studies, including coupling with other sophisticated methods and future development in understanding land cover change dynamics and projections.