Continuous monitoring of forest disturbance on tropical forests is a fundamental tool to support proactive preservation actions and to stop further destruction of native vegetation. Currently most of the monitoring systems in operation are based on optical imagery, and thus are flaw-prone on areas with frequent cloud cover. As this, several Synthetic Aperture Radar (SAR)-based systems have been developed recently, aiming all-weather disturbance detection. This article presents the main aspects and the results of the first year of operation of the SAR based Near Real-Time Deforestation Detection System (DETER-R), an automated deforestation detection system focused on the Brazilian Amazon. DETER-R uses the Google Earth Engine platform to preprocess and analyze Sentinel-1 SAR time series. New images are treated and analyzed daily. After the automated analysis, the system vectorizes clusters of deforested pixels and sends the corresponding polygons to the environmental enforcement agency. After 12 months of operational life, the system has produced 88,572 forest disturbance warnings. Human validation of the warning polygons showed a extremely low rate of misdetections, with less than 0.2% of the detected area corresponding to false positives. During the first year of operation, DETER-R provided 33,234 warnings of interest to national monitoring agencies which were not detected by its optical counterpart DETER in the same period, corresponding to an area of 105,238.5 ha, or approximately 5% of the total detections. During the rainy season, the rate of additional detections increased as expected, reaching 8.1%.

Early Warning Systems (EWS) for near real-time detection of deforestation are a fundamental component of public policies focusing on the reduction in forest biomass loss and associated CO₂ emissions. Most of the operational EWS are based on optical data, which are severely limited by the cloud cover in tropical environments. Synthetic Aperture Radar (SAR) data can help to overcome this observational gap. SAR measurements, however, can be altered by atmospheric effects on and variations in surface moisture. Different techniques of time series (TS) stabilization have been used to mitigate the instability of C-band SAR measurements. Here, we evaluate the performance of two different approaches to SAR TS stabilization, harmonic deseasonalization and spatial stabilization, as well as two deforestation detection techniques, Adaptive Linear Thresholding (ALT) and maximum likelihood classification (MLC). We set up a rigorous, Amazon-wide validation experiment using the Google Earth Engine platform to sample and process Sentinel-1A data of nearly 6000 locations in the whole Brazilian Amazonian basin, generating more than 8M processed samples. Half of those locations correspond to non-degraded forest areas, while the other half pertained to 2019 deforested areas. The detection results showed that the spatial stabilization algorithm improved the results of the MLC approach, reaching 94.36% global accuracy. The ALT detection algorithm performed better, reaching 95.91% global accuracy, regardless of the use of any stabilization method. The results of this experiment are being used to develop an operational EWS in the Brazilian Amazon.

We investigated the dynamics of savanna clearing and land degradation in the MATOPIBA region, the newest agricultural frontier over the Cerrado in Brazil. For geospatial analysis of the “big data,” we used algorithms available on the Google Earth Engine (GEE) platform along with different satellite products. For the detection of vegetation clearing in the 1985–2018 period, we applied the LandTrendr algorithm to the normalized difference vegetation index (NDVI) obtained from Landsat satellite instruments. In the evaluation of land degradation (2001–2015), we used the Trends.Earth algorithm to analyze NDVI data calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS). By combining different datasets into the analysis, we discussed the relationships among savanna clearings, land degradation, topographic attributes, and fire frequency. The results showed that the largest proportions of savanna clearing in the 1985–1990 period were detected in the south of MATOPIBA (mesoregions Ocidental do Tocantins and Extremo Oeste Baiano). The savanna clearing progressed toward the mesoregions Oriental do Tocantins, Sudoeste Piauiense, and Sul Maranhense between 1991 and 2005. Variations over time in cleared areas ranged from 0.086 Mha to 2.146 Mha per year, showing well-defined peaks in 1986, 1991, and 2012. The clearing events followed the soybean expansion, especially over flat terrains at high elevations. In addition to the spillover effects of the Amazon Soy Moratorium on the savannas, other political and economic factors promoted changes in cleared areas of MATOPIBA. Compared to the decade preceding the signature of the Amazon Soy Moratorium in 2006, the cleared area of MATOPIBA increased 41% in the 10-year post-moratorium period. In the other analysis, our Trends.Earth results showed that the largest proportions of land degradation areas, as proxied by the MODIS NDVI, were the Sudoeste Piauiense and Centro Maranhense. These mesoregions presented distinct patterns of precipitation and soil composition. The number of fire events increased with vegetation clearing. However, the amount of cleared areas correlated moderately with that of burned areas due to the multi-use of fire in the region. Finally, we observed that the amount of land-degraded areas increased with fire frequency over agricultural areas, but remained relatively stable over native vegetated areas. Results highlight the importance of satellite sensors and cloud-computing platforms to inspect long-term trajectories of land occupation and degradation over broad savanna areas.

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.

More than half a decade after the launch of the Sentinel-1A C-band SAR satellite, several near real-time forest disturbances detection systems based on backscattering time series analysis have been developed and made operational. Every system has its own particular approach to change detection. Here, we have compared the performance of the main SAR-based near real-time operational forest disturbance detection systems produced by research agencies (INPE, in Brazil, CESBIO, in France, JAXA, in Japan, and Wageningen University, in the Netherlands), and compared them to the state-of-the-art optical algorithm, University of Maryland’s GLAD-S2. We implemented an innovative validation protocol, specially conceived to encompass all the analysed systems, which measured every system’s accuracy and detection speed in four different areas of the Amazon basin. The results indicated that, when parametrized equally, all the Sentinel-1 SAR methods outperformed the reference optical method in terms of sample-count F1-Score, having comparable results among them. The GLAD-S2 optical method showed superior results in terms of user’s accuracy (UA), issuing no false detections, but had a lower producer accuracy (PA, 84.88%) when compared to the Sentinel-1 SAR-based systems (PA $\sim$90%). Wageningen University’s system, RADD, proved to be relatively faster, especially in heavily clouded regions, where RADD warnings were issued 41 days before optical ones, and the one that better performs on small disturbed patches ( $<$0.25 ha) with a UA of 70.11%. Of all the high-resolution SAR methods, CESBIO’s had the best results regarding UA (99.0%). Finally, we tested the potential of three hypothetical combined optical-SAR systems. The results show that these combined systems would have excellent detection capabilities, exceeding largely the producer’s accuracy of all the tested methods at the cost of a slightly diminished user’s accuracy, and constitute a promising and feasible approach for the forthcoming forest monitoring systems.