Places to Watch

This dataset, assembled by Global Nature Watch, aggregates deforestation alerts from four alert systems (DIST-ALERT, GLAD-L, GLAD-S2, RADD) into a single, integrated disturbance alert layer. This integration allows users to detect deforestation and other vegetation loss faster than any single system alone, as the integrated layer is updated when any of the source alert systems are updated.   The source alert systems are derived from satellites of varying spectral and spatial resolutions. The 30 m GLAD Landsat-based alerts and DIST-ALERTs are up-sampled to match the 10 m spatial resolution of purely Sentinel-based alerts (GLAD-S2, RADD). Using the same 10 m grid avoids the double counting of overlapping alerts, which are instead classified at a higher confidence level, indicated by darker pixels.   Alerts are classified as high confidence when detected multiple times (two or more times for GLAD-L, GLAD-S2 and RADD, and four or more times for DIST-ALERT) by a single alert system. This can occur in areas and at times when only one alert system was operating. Where multiple alert systems are operating, alerts detected by multiple (two or more) of these systems are classified as highest confidence. With multiple sensors picking up change in the same location, we can be more confident that an alert was not a false positive and do not need to wait for additional satellite imagery to increase confidence in detected loss, thus providing more confident alerting faster than with a single system. Alerts from multiple systems which are within 180 days (6 months) of each other are considered a single event. In this case, the earliest date within that 180 day window will appear as the alert date in the integrated alert product. These will be considered highest confidence alerts. The date of the alert remains until another new alert (which is not within 6 months) in the same pixel is registered.   A study conducted by Wageningen University in collaboration with researchers from Global Nature Watch and University of Maryland's GLAD lab found that integrating alert systems results in faster detection of new disturbances by days to months, and also shortens the delay to increase confidence. Combined alerts have a higher producer's accuracy (fewer false negatives), but a lower user's accuracy (more false positives) since the commission errors from each system are combined; however, 'highest confidence' alerts, where more than one system detected the change, effectively eliminated false detections. Learn more: https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82   Monitor vegetation disturbance in near-real-time using integrated alerts from multiple alerting systems

This dataset, assembled by Global Nature Watch, aggregates deforestation alerts from three alert systems (GLAD-L, GLAD-S2, RADD) into a single, integrated deforestation alert layer. This integration allows users to detect deforestation events faster than any single system alone, as the integrated layer is updated when any of the source alert systems are updated. The source alert systems are derived from satellites of varying spectral and spatial resolutions. 30 m GLAD Landsat-based alerts are up-sampled to match the 10 m spatial resolution of Sentinel-based alerts (GLAD-S2, RADD). This avoids the double counting of overlapping alerts, which are instead classified at a higher confidence level, indicated by darker pixels. Alerts are classified as high confidence when detected twice by a single alert system. This can occur in areas and at times when only one alert system was operating. Where multiple alert systems are operating, alerts detected by multiple (two or three) of these systems are classified as highest confidence. With multiple sensors picking up change in the same location, we can be more confident that an alert was not a false positive and do not need to wait for additional satellite imagery to increase confidence in detected loss, thus providing more confident alerting faster than with a single system. A study conducted by Wageningen University in collaboration with researchers from Global Nature Watch and University of Maryland's GLAD lab found that integrating alert systems results in faster detection of new disturbances by days to months, and also shortens the delay to increase confidence. Combined alerts have a higher producer's accuracy (fewer false negatives), but a lower user's accuracy (more false positives) since the commission errors from each system are combined; however, 'highest confidence' alerts, where more than one system detected the change, effectively eliminated false detections. Learn more: https://iopscience.iop.org/article/10.1088/1748-9326/ad2d82 The integrated deforestation alerts are available on **Google Earth Engine** with asset ID: projects/forma-250/assets/gfw_integrated_alerts/default_latest **Download** the raster tiles here: https://data.globalforestwatch.org/datasets/gfw::integrated-deforestation-alerts/about Monitor forest disturbance in near-real-time using integrated alerts from three alerting systems

This data set estimates agriculture-linked deforestation for oil palm, soy, cattle, cocoa and coffee annually for the years 2001-2015. While agriculture is generally recognized to be a major driver of deforestation, few studies have attempted to estimate the role that particular commodities play in global deforestation, and even fewer have been spatially explicit. In this analysis, we estimate the extent to which these commodities are replacing forests and map their impacts using the best available spatially explicit data. We report results globally at the second administrative level (e.g., county, municipality, or other administrative subdivision, depending on the country). To identify the specific commodities that have replaced forested land, we analyzed the overlap of current commodity extent with global annual tree cover loss from 2001 to 2018. We used recent, detailed crop maps for global oil palm and South American soy and supplemented with coarser resolution global data where needed for the other commodities and regions. Provides annual agriculture-linked deforestation estimates for oil palm, soy, cattle, cocoa and coffee for the years 2001-2015 by administrative boundary

RAdar for Detecting Deforestation (RADD) is a deforestation alert product that uses data from the European Space Agency’s Sentinel-1 satellites to detect forest disturbances in near-real-time and to confirm alerts within weeks. The RADD alerts use a detection methodology produced by Wageningen University and Research (WUR), Laboratory of Geo-information Science and Remote Sensing. These alerts are particularly advantageous in monitoring tropical forests, as Sentinel-1’s cloud-penetrating radar and frequent revisit times (6-12 days) allow for more consistent monitoring than alert products based on optical satellite images. Alerts are available for the primary humid tropical forest areas of South America, sub-Saharan Africa and Southeast Asia at 10 m spatial resolution, with coverage from January 2019 to the present for Africa and January 2020 to the present for South America, Central America, and Southeast Asia. Pre-processed Sentinel-1 images are collected from Google Earth Engine, then quality controlled and normalized using historical time-series metrics. Forest disturbance alerts are then detected using a probabilistic algorithm. Each disturbance alert is detected from a single observation in the latest image, and then marked as high confidence with subsequent imagery within a maximum 90-day period if the forest disturbance probability is above 97.5%. Unconfirmed alerts are provided for forest disturbance probabilities above 85%. The product has a minimum mapping unit of 0.1 ha (equivalent to 10 Sentinel-1 pixels) to minimize false detections. Alerts are detected within areas of primary humid tropical forest, defined by  Turubanova et al (2018) and with 2001-2018 forest loss (Hansen et al. (2013)) and mangrove (Bunting et al. 2018)  removed. For more information on methodology and validation, please refer to Reiche et. al. (2021). The version presented here (v1) has been updated from that described in the paper (v0), with changes to the forest mask and a reduction of the minimum mapping unit.   The RADD alerts were made possible thanks to the support of a coalition of ten major palm oil producers and buyers. Under the project, Wageningen University and Research (WUR) developed the detection method and Satelligence first scaled the system in Indonesia and Malaysia and provided additional prioritization of alerts for on-the-ground follow up. Additional support was provided by the US Forest Service and Norway’s International Climate and Forest Initiative. The alerts are currently operated by WUR using Google Earth Engine.   The RADD alerts are available on \*\*Google Earth Engine\*\* with asset ID: projects/radar-wur/raddalert/v1  Monitor primary forest disturbance in near-real-time using Sentinel-1’s cloud-penetrating radar sensors

The Universal Mill List (UML) is a collection of palm oil mill locations across the worldwith associated group, company, and mill names, RSPO certification status and unique “universal IDs”. The UML is based on data contributed to the authors from palm oil buyer companies, the Roundtable on Sustainable Palm Oil (RSPO), and FoodReg, as well as data gathered from government records and through extensive supply chain research. The objective of the UML is to provide a comprehensive, common dataset for the palm oil industry that can be used to easily identify mills across various platforms and reporting efforts. The harmonization of data and the verification of each of the mill locations was a joint effort from a consortium of partners at the World Resources Institute (WRI), Rainforest Alliance, Proforest, Daemeter, Trase, Earthworm Foundation, Auriga, CIFOR, Transitions, the University of California, Santa Barbara, and the University of Hawai’i. The UML is created and updated according to a standardized process, as detailed in the “Universal Mill List Methodology” (WRI and Rainforest Alliance in press, update pending). All mill locations are manually verified using high resolution satellite imagery according to criteria for mill infrastructure, including the presence of buildings, settling ponds, and nearby palm oil plantations. Duplicate mills are identified and removed according to an analysis to identify exact and nearby GPS location duplicates. Verified, unique mill locations are assigned “universal IDs” and added to the UML. Mill name, parent company, and group name information are based on the best available data as gathered by Trase (for Indonesia) and Earthworm Foundation (for most other mills) but may still have gaps or potential inaccuracies. Great care is taken to ensure no duplication of attribute information in the database. The RSPO certification status is the status of each mill at a specific cut-off date, available in the attribute data. RSPO certification statuses are updated every day, so current statuses may be different than the ones listed in the attribute table. Please check for up-to-date mill certification status at RSPO Palm Trace, here. The dataset is updated roughly every 6 months as new data becomes available. Displays the location of palm oil mills.

This dataset is a derivative of the OPERA’s DIST-ALERT product (OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product), which is derived through a time-series analysis of harmonized data from the NASA/USGS Landsat and ESA Sentinel-2 satellites (known as the HLS dataset). The product identifies and continuously monitors vegetation cover change in 30-m pixels across the globe. It can be accessed through the LPDAAC website here: https://search.earthdata.nasa.gov/search?q=C2746980408-LPCLOUD, and on Google Earth Engine (GEE) with asset ID: projects/glad/HLSDIST/current The DIST-ALERT on GNW is a derivative of this, and additional data layers not used in the GNW product are available through the LPDAAC and GEE such as initial vegetation fraction, and disturbance duration. Specifically, three layers are used to create the GNW DIST-ALERT. The VEG-DIST-DATE provides the date of the alert, the VEG-DIST-COUNT is used to determine the confidence (2-3 consecutive observations for low confidence and 4+ observations for high confidence) and VEG-ANOM-MAX is used as a threshold for determining loss (>30%). While the version on the LPDAAC is updated every 2-4 days, the data is updated weekly on GNW. The product detects notable reductions in vegetation cover (measured as “vegetation fraction” or the percent of the ground that is covered by vegetation) for every pixel every time the new satellite data is acquired and the ground is not obscured by clouds or snow. The current vegetation fraction estimate is compared to the minimum fraction for the same time period (within 15 days before and after) in the previous 3 years, and if there is a reduction, then the system identifies an alert in that pixel. Anomalies of at least a 10% reduction from the minimum become alerts in the original product, and on GNW, a higher threshold of 30% is used, to reduce noise, and false alerts in the dataset. Because the product compares each pixel to the minimum for the same time period in previous years, it takes into account regular seasonal variation in vegetation cover. As the product is global and detects vegetation anomalies, much of the data may not be applicable to GNW users monitoring forests. Therefore, we mask the alerts with UMD’s tree cover map, allowing users to view only alerts within 30% canopy cover. Monitors global vegetation disturbance in near-real-time using harmonized Landsat-Sentinel-2 (HLS) imagery