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Peru’s Southeastern Amazon deforestation trends can be attributed to alluvial gold mining. Illegal mining occurring in forestry concessions, national parks, and the territories of Indigenous People Organizations is of particular concern. We present a methodology to create near real-time alerts of deforestation caused by alluvial gold mining. A time series of Sentinel-1 Synthetic Aperture Radar (SAR) data from February to December 2022 is created in Google Earth Engine (GEE) and assessed using Morton Canty’s Omnibus Q-test change detection algorithm. Resulting detections are validated with high-resolution optical imagery from Planet NICFI’s monthly basemaps and Planet Scope daily imagery. The alerts identify the location and timing of large areas (group pixels of

Mangrove forests are a biodiverse ecosystem known for a wide variety of crucial ecological services, including carbon sequestration, coastal erosion control, and prevention of saltwater intrusion. Given the ecological importance of mangrove forests, a comprehensive and up-to-date mangrove extent mapping at broad geographic scales is needed to define mangrove forest changes, assess their implications, and support restoration activities and decision making. The main objective of this study is to evaluate mangrove classifications derived from a combination of Landsat-8 OLI, Sentinel-2, and Sentinel-1 observations using a random forest (RF) machine learning (ML) algorithm to identify the best approach for monitoring Guyana’s mangrove forests on an annual basis. Algorithm accuracy was tested using high-resolution planet imagery in Collect Earth Online. Results varied widely across the different combinations of input data (overall accuracy, 88–95%; producer’s accuracy for mangroves, 50–87%; user’s accuracy for mangroves, 13–69%). The combined optical–radar classification demonstrated the best performance with an overall accuracy of 95%. Area estimates of mangrove extent ranged from 908.4 to 3645.0 hectares. A ground-based validation exercise confirmed the extent of several large, previously undocumented areas of mangrove forest loss. The results establish that a data fusion approach combining optical and radar data performs marginally better than optical-only approaches to mangrove classification. This ML approach, which leverages free and open data and a cloud-based analytics platform, can be applied to mapping other areas of mangrove forests in Guyana. This approach can also support the operational monitoring of mangrove restoration areas managed by Guyana’s National Agricultural and Research Extension Institute (NAREI).

The Amazon is the largest expanse of tropical rainforest globally and deforestation resulting from land use changes poses a major concern for sustainable resource management. Synthetic aperture radar (SAR) data have all-weather and all-day capability, and thus are well-suited for mapping land use land cover (LULC) in tropical regions, which are seasonally influenced by cloud cover. Understanding modified land uses and drivers of deforestation is fundamental for the development of policies and measures to reduce emissions and for developing forest reference levels. Sentinel-1 C-band SAR data present unprecedented potential since the observations are free and openly available, providing for the first regular and standardize SAR data. This study analyzes the applicability of Sentinel-1 data for LULC classification as an effort to differentiate modified land uses, which is a current need for early-warning deforestation systems. The study area covers a deforestation frontier in the Peruvian Amazon where the landscape is characterized by a mosaic of LULC types. Collect Earth Online is used for reference LULC data collection, and seven classes are defined for this study: forest, secondary vegetation, agriculture, pasture, urban, mining, water. Amplitude γo time-series spanning 2017–2019 are analyzed along with statistical metrics for each class, and a classification decision tree is developed in Google Earth Engine. Overall accuracy obtained is considered low (52%). Results show high user's accuracy for forest and water classification, a lot of confusion between agriculture, secondary vegetation, and forest, and the use of the polarization ratio VV/VH is suggested to be useful for pasture classification. The orientation of streets in a urban environment is confirmed to have high influence on backscattering response. This study provides information for future research on LULC and the identification of drivers in deforestation monitoring systems that could result in additional actionable information for decision-making.

Over the past decades, the Peruvian Amazon has experienced a rapid change in forest cover due to the expansion of agriculture and extractive activities. This study uses spectral mixture analysis (SMA) in a cloud-computing platform to map forest loss within and outside indigenous territories, protected areas, mining concessions, and reforestation concessions within the Madre de Dios Region in Peru. The study area is focused on key areas of forest loss in the western part of the Tambopata National Reserve and surrounding the Malinowski River. Landsat 8 Operational Land Imager and Landsat 7 Enhanced Thematic Mapper Plus surface reflectance data spanning 2013–2018 were analyzed using cloud-based SMA to identify patterns of forest loss for each year. High-resolution Planet Dove (3m) and RapidEye (5m) imagery were used to validate the forest loss map and to identify the potential drivers of loss. Results show large areas of forest loss, especially within buffer zones of protected areas. Forest loss also appears in the Kotsimba Native Community within a 1 km buffer of the Malinowski River. In addition to gold mining, agriculture and pasture fields also appear to be major drivers of forest loss for our study period. This study also suggests that gold mining activity is potentially not restricted to the legal mining concession areas, with 49% of forest loss occurring outside the mining concessions. Overall accuracy obtained for the forest loss analysis was 96%. These results illustrate the applicability of a cloud-based platform not only for land use land cover change detection but also for accessing and processing large datasets; the importance of monitoring not only forest loss progression in the Madre de Dios, which has been increasing over the years, especially within buffer zones, but also its drivers; and reiterates the use of SMA as a reliable change detection classification approach.