Estimating the Deforestation Rate in the Amazon Rainforest from Sentinel-1 and TanDEM-X Multi-Temporal Stacks

Abstract

Forested areas strongly influence our ecosystem and play an important role in the carbon cycle, biodiversity richness, and climate regulation. Despite the efforts made by the United Nations with the Reducing Emissions from Deforestation and Forest Degradation (REDD) program to reduce deforestation and forest degradation activities, illegal deforestation, especially in the Amazon rainforest, is still increasing at alarming rates. The continuous monitoring of such areas is of particular importance to preserve them and to better understand their influence on the dynamic processes of our planet. Spaceborne synthetic aperture radar (SAR) represents a unique data source for the forest monitoring at a global scale and independently on weather conditions and solar light. The aim of this work is to exploit multi-temporal SAR data stacks, provided by the SAR sensors TanDEM-X and Sentinel-1, to detect changes on the forested areas. In the case of the bistatic TanDEM-X system, where no temporal decorrelation takes place, the volume correlation factor, which quantifies the amount of decorrelation due to scattering from a volume, is a good indicator to distinguish between forest and non-forest areas. Such classification can be improved by applying an image segmentation approach. In the case of Sentinel-1, the combination of the radar backscattering and of the evolution in time of the repeat-pass interferometric coherence can be used to characterize and detect deforested areas. In both multi-temporal SAR data stacks, clear-cuts can be identified and isolated by means of morphological operators and pixel density indicators. A region growing algorithm, as well as different texture analysis operators, will help to evaluate the evolution of the detected clear cuts in time. An ad-hoc consistency analysis between subsequent images plays an important role in reducing false alarms within the multi-temporal change detection algorithm. By implementing a data fusion approach between image stacks provided by both sensors, the deforestation rate for a large time span can be estimated with an increased accuracy. Finally, by exploiting the sensitivity to the tree height of the bistatic TanDEM-X system and the short revisit-time of the Sentinel-1 satellites constellation, the identification of selective logging will also be addressed.