A Novel Concept for Monitoring Amazonas Deforestation by Combining Sentinel-1a/b and TanDEM-X SAR Data

Abstract

The Amazon Rain forest plays a key-role in the Earth’s ecosystem. For example, it helps regulating carbon dioxide levels in the atmosphere, it is a large repository of biodiversity, and a shelter for many native local tribes. In the last decades, illegal deforestation has drastically increased, threatening its ecosystems and strongly influencing climate changes. An up-to-date assessment and monitoring of the forest resources is therefore of crucial importance. Given the huge extend of the Amazon basin, spaceborne remote sensing represents a unique instrument for providing consistent, timely, and high-resolution products at a global scale, without need for on-ground activities. Moreover, Synthetic Aperture Radar (SAR), with its capability to acquire data independently from weather or day-light conditions, is likely to become the most suitable technology to achieve such a high demanding task. In this paper, we present a novel approach to monitor the evolution of deforested areas in the Amazon rain forest by combining Sentinel-1A/B and TanDEM-X data. The idea is firstly to exploit the large coverage and short revisit time provided by the constellation of Sentinel-1A/B satellites in order to cover the entire deforestation arch about ones per month. The goal is to discriminate forest/non-forest by exploiting C-band backscatter signatures in dual polarization and to identify the so-called deforestation hot-spots: local areas characterized by a significant amount of on-going deforestation activities. Secondly, high-resolution time series of bistatic TanDEM-X data can be acquired over these hot spots with a repeat-cycle of 11 days, and used to track fast changes at small scales, aiming at identifying specific on-going deforestation activities. The underlying principle is that a radar wave penetrates into the canopy, leading to volume scattering. Interferometric SAR acquisitions are therefore subjected to volume decorrelation. Single-pass bistatic TanDEM-X data does not suffer from temporal decorrelation and provides a very reliable classification, by exploiting the volume decorrelation contribution only. This TanDEM-X capability is so far unique, and opens the way for an effective and reliable monitoring of the Amazon rain forest with unprecedented accuracy. Finally, the proposed technique will be supported by preliminary results from a simulated acquisition scenario, in order to demonstrate the high potentials of single-pass interferometry for long-term forest change monitoring.