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%.

The main objective of this study is to evaluate different methods to integrate (fusion and combination) Synthetic Aperture Radar (SAR) Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band SAR (PALSAR-1) (Fine Beam Dual mode-FDB) and LANDSAT images in order to identify those which lead to higher accuracy of land-use and land-cover (LULC) mapping in an agricultural frontier region in Amazon. One method used to integrate the multipolarized information in SAR images before the fusion process was also evaluated. In this method, the first principal component (PC1) of SAR data was used. Color compositions of fused data that presented better LULC classification were visually analyzed. Considering the proposed objective, the following fusion methods must be highlighted: Ehlers, Wavelet á trous, Intensity, Hue and Saturation (IHS), and selective principal component analysis (SPC). These latter three methods presented good results when processed using PC1 from ALOS/PALSAR-1 FBD backscatter filtered image or three SAR extracted and selected features. These results corroborate with the applicability of the proposed method for SAR data information integration. Distinct methods better discriminate different LULC classes. In general, densely forested classes were better characterized by the Ehlers_TM6 fusion method, in which at least the polarization HV was used. Intermediate and initial regeneration classes were better discriminated using SPC-fused data with PC1 of ALOS/PALSAR-1 FBD data. Bare soil and pasture classes were better discriminated in optical features and the PC1 of ALOS/PALSAR-1 FBD data fused by the IHS method. Soybean with approximately 40 days from seeding was better discriminated in image classification obtained from ALOS/PALSAR-1 FBD image.

In tropical biomes such as the Amazon, the cloud cover is frequent. The use of synthetic aperture radar (SAR) sensor systems is important to monitor and study these biomes because they can acquire data under cloud coverage. In this paper, an advanced land observing system phased array L-band synthetic aperture radar fine-beam dual (ALOS/PALSAR-FBD) image was evaluated for land use and land cover (LULC) classification of an Amazon test site. The features extracted from this image were also evaluated. To perform this task, a method for feature selection, considering the desired classes, was proposed. In order to better understand the applicability of this type of data in Brazilian Government projects (such as DETER-B and TerraClass), the results obtained with SAR images were compared with those from LANDSAT5/TM. The results show that the PALSAR-FBD image and the features selected are not suitable for the discrimination of densely forested classes. They presented, however, a good discrimination among the group of forested and agropastoral classes, as well as among nondensely forested classes (i.e., pastures, bare soil, and new regeneration). Therefore, these data present good applicability for mapping and monitoring of both deforestation and LULC and they can be used in the above mentioned projects. The classification of selected features, with eight classes of interest, achieved an increase of about 133% and 69% in the Kappa index (0.32) and an Overall Accuracy (0.54) regarding the PALSAR-FBD classification (0.136 and 0.322, respectively). This result shows the applicability of the proposed method. It is also expected that the features selected in this paper will improve the classification of similar study sites.

The Brazilian Amazon Satellite Monitoring Program (PRODES Amazonia) tracks forest loss in the Brazilian Amazon but excludes about 6.6% ( ~ 280,000 km²) of non-forest vegetation (NF). Here we developed the PRODES NF system to address this gap by adapting the well-established methodology of PRODES Amazonia. Initial findings from PRODES NF show that the Brazilian Amazon lost 10.46% ( ~ 30,000 km²) of NF area, mainly in the last two decades, with the estates of Mato Grosso, Roraima, and Amapá being primary hotspots of losses. Savannas were the most affected (13.3% of their extent). NF loss strongly correlates (r = 0.87; p < 0.0001) with deforestation, suggesting a continuum of vegetation loss in the biome regardless of the predominant vegetation type. Combining data from PRODES Amazonia and PRODES NF reveals an official estimate of ~798,000 km² in total primary vegetation loss ( ~ 19% of the entire biome) in the Brazilian Amazon by 2022.

Land Use and Land Cover (LULC) classes defined by subjective criteria can diminish the significance of a study, hindering the reproducibility and the comparison of results with other studies. Having a standard legend for a given study area and objective could benefit a group of researchers focused on long-term or multidisciplinary studies in a given area, in the sense that they would be able to maintain class definition among different works, done by different teams. To allow for reproducibility, it is important that the classes in this legend are described using quantifiable elements of land cover, which can be measured on the ground, as is recommended by Land Cover Meta Language (LCML). The present study aims to propose LCML formalized hierarchical legends for LULC classes, focusing on the southwest of Pará state, within the Brazilian Amazon. In order to illustrate the potential of these legends, a secondary objective of the current study is to analyze classification results using legends derived from a particular Remote Sensing dataset and compare these results with the classification obtained using the LCML hierarchical legend proposed. To perform this analysis, firstly, we proposed a conceptual class model based on existing classification systems for the upland Brazilian Amazon Biome. From this model, 16 LULC classes were described in LCML, using quantifiable and easily recognizable physiognomic characteristics of land cover classes measured on the lower Tapajós river, in Pará state. These classes were grouped into legends with different levels of detail (number of classes), based on our model or on the image and clustering algorithms. All legends were used in supervised classification of a Landsat5/TM image. Results indicate that it is necessary to incorporate multi-temporal knowledge for class definition as well as the proposed thresholds (height and cover proportion of soil, litter, herbaceous vegetation, shrubs, and trees) in order to properly describe classes. However, the thresholds are useful to delimit classes that happen in a successive way. Classification results revealed that classes formed by the same elements of land cover with similar thresholds present high confusion. Additionally, classifications obtained using legends based on the class separability in a given Remote Sensing image tend to be more accurate but not always useful because they can hide or mix important classes. It was observed that the more generalized the legend (those with few details and number of classes), the more accurate the classifications results are for all types of legends.

O governo brasileiro possui três sistemas oficiais de monitoramento por satélite da supressão da vegetação nativa dos biomas brasileiros, concebidos e executados pelo Instituto Nacional de Pesquisas Espaciais (INPE), por meio do Programa de Monitoramento dos Biomas Brasileiros (BiomasBR): 1) o Prodes – Monitoramento Anual da Supressão de Vegetação Nativa; 2) o Deter - Monitoramento Diário da Supressão e Degradação de Vegetação Nativa; e 3) o TerraClass - Monitoramento Sistêmico da Cobertura e Uso da Terra, este em colaboração com a Empresa Brasileira de Agropecuária (EMBRAPA). Neste artigo são apresentadas as principais características de cada um destes sistemas, os resultados mais recentes do monitoramento da vegetação nativa brasileira, as principais políticas públicas baseadas no BiomasBR e o papel do programa no cumprimento dos compromissos climáticos internacionais assumidos pelo país.