Improving Deforestation Detection on Tropical Rainforests Using Sentinel-1 Data and Convolutional Neural Networks

Abstract

Detecting early deforestation is a fundamental process in reducing forest degradation and carbon emissions. With this procedure, it is possible to monitor and control illegal activities associated with deforestation. Most regular monitoring projects have been recently proposed, but most of them rely on optical imagery. In addition, these data are seriously restricted by cloud coverage, especially in tropical environments. In this regard, Synthetic Aperture Radar (SAR) is an attractive alternative that can fill this observational gap. This work evaluated and compared a conventional method based on time series and a Fully Convolutional Network (FCN) with bi-temporal SAR images. These approaches were assessed in two regions of the Brazilian Amazon to detect deforestation between 2019 and 2020. Different pre-processing techniques, including filtering and stabilization stages, were applied to the C-band Sentinel-1 images. Furthermore, this study proposes to provide the network with the distance map to past-deforestation as additional information to the pair of images being compared. In our experiments, this proposal brought up to 4% improvement in average precision. The experimental results further indicated a clear superiority of the DL approach over a time series-based deforestation detection method used as a baseline in all experiments. Finally, the study proved the benefits of pre-processing techniques when using detection methods based on time series. On the contrary, the analysis revealed that the neural network could eliminate noise from the input images, making filtering innocuous and, therefore, unnecessary. On the other hand, the stabilization of the input images brought non-negligible accuracy gains to the DL approach.

DETER-R: An Operational Near-Real Time Tropical Forest Disturbance Warning System Based on Sentinel-1 Time Series Analysis

“…Also, experimental techniques using ancillary precipitation data to mask storm-related SAR observations has been developed [41]. Until the date of writing of this article, no NRT system has been able to apply these kind of techniques on an operational basis, although approaches based on deep learning algorithms have shown promising results [42][43][44].…”

SIT.net: SAR Deforestation Classification of Amazon Forest for Land Use Land Cover Application

“…The deforestation analysis using Sentinel-1 requires pre-processing to remove speckle noise and need stabilization used for balancing the radar signals variation in different time series images [12]. Stabilization is useful if you are using Maximum Likelihood detection approach and U-net CNN approach but not useful with Adaptive linear threshold approach [13].…”

Evaluating Visible–Infrared Imaging Radiometer Suite Imagery for Developing Near-Real-Time Nationwide Vegetation Cover Monitoring in Indonesia

“…A comprehensive and dependable system for monitoring forests is essential to offering precise, prompt, and trustworthy information about changes in forest cover to decision-makers. These data can be utilized to prioritize regions for examination and enforcement and to execute policies and measures to prevent, minimize, or restore forest disturbances [8][9][10].…”