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 . 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 insular Southeast Asia at a 10m spatial resolution, with coverage from January 2019 to the present for Africa and January 2020 to the present for South 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 if the forest disturbance probability is above 85%. If the forest disturbance probability reaches 97.5% in subsequent imagery within a maximum 90-day period, alerts are then marked as "high confidence". 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 mangroves (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 generated by WUR using Google Earth Engine.*This data product utilizes a special encoding*Each pixel (alert) encodes the date of disturbance and confidence level in one integer value. The leading integer of the decimal representation is 2 for a low-confidence alert and 3 for a high-confidence alert, followed by the number of days since December 31, 2014. 0 is the no-data value. For example:20001 is a low confidence alert on January 1st, 201530055 is a high confidence alert on February 24, 201521847 is a low confidence alert on January 21, 20200 represents no alert
Overviewdeforestationforest change
Additional Information
KeyValue
dcat_issued2022-01-03T21:41:44.000Z
dcat_modified2022-01-03T21:41:51.220Z
dcat_publisher_nameGlobal Forest Watch
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harvest_source_id7d0f9dad-ed66-484f-a5fe-363b700aebe2
harvest_source_titleGlobal Forest Watch Open Data Portal