Tropical Land Use Land Cover Mapping in Par\'{a} (Brazil) using Discriminative Markov Random Fields and Multi-temporal TerraSAR-X Data

TL;DR

This paper introduces a novel machine learning framework combining Discriminative Markov Random Fields and Import Vector Machines with multi-temporal TerraSAR-X data to improve land cover mapping in dynamic regions like the Amazon.

Contribution

It presents a new approach integrating spatio-temporal priors and machine learning for accurate land cover classification amidst high temporal variability.

Findings

Achieved up to 79% overall accuracy in land cover mapping.

Effective in regions with high-frequency temporal changes.

Limitations remain in differentiating pasture types.

Abstract

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial-temporal variability is a challenging task. We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely Discriminative Markov Random Fields with spatio-temporal priors, and Import Vector Machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Par\'{a} with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to $79\%$ in a five class setting, yet limitations for the differentiation of different pasture types remain. The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.