Fast Monitoring of Amazonas Deforestation by combining Sentinel-1 and TanDEM-X Interferometric SAR Data

Abstract

The Amazon rainforest plays a paramount role in the Earth’s ecosystem. 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 delicate ecosystems and strongly influencing climate changes. An up-to-date and fast assessment and monitoring of 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, rainforests are almost completely hidden from optical sensors by a consistent clouds coverage during the wet season, which can last up to six months per year. Synthetic aperture radar (SAR), with its capability to acquire data independently from weather or day-light conditions, is therefore the most suitable technique 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 rainforest, by combining Sentinel-1 and TanDEM-X interferometric SAR data. The idea is firstly to exploit the large coverage and short revisit time provided by the constellation of Sentinel-1 satellites in order to cover the entire arch of deforestation at regular temporal intervals. The goal is here to discriminate forest/non-forest by exploiting C-band backscatter signatures in dual polarization and the evolution in time of the interferometric coherence, in order 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 revisit time of 11 days, and used to track fast changes at small scales, aiming at identifying specific on-going deforestation activities. In this case, the unique ability of TanDEM-X to generate interferometric coherences, which are temporal decorrelation-free, allows for an accurate isolation of the decorrelation contribution caused by volume scattering, which is a stable and reliable indicator of the presence of vegetation on ground. The preliminary results are very encouraging, and aim at verifying the feasibility of the development of a semiautomatic tool for a reliable monitoring of on-going deforestation, which could support governmental authorities in stopping illegal deforestation activities. In the final paper, we intent to present the acquisition concept and the developed algorithms for performing forest classification and change detection.