2024-07-11T15:09:26.087954

2025-03-14T16:35:14.123787

10 × 10 m

Humid tropical forest in South America, Central America, sub-Saharan Africa and Southeast Asia

Updated weekly, image revisit time every 6-12 days

regional

Use the following credit when this data is displayed:  Source: "RADD alerts". WUR, accessed through Global Forest Watch on [date]  Use the following credit when this data is cited:  Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., Herold, M. 2021. Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters. [https://doi.org/10.1088/1748-9326/abd0a8](https://doi.org/10.1088/1748-9326/abd0a8) 

Deforestation alerts (RADD)

weekly, 10 m, tropics, WUR

Reiche, J., Mullissa, A., Slagter, B., Gou, Y., Tsendbazar, N.E., Braun, C., Vollrath, A., Weisse, M.J., Stolle, F., Pickens, A., Donchyts, G., Clinton, N., Gorelick, N., Herold, M. 2021. Forest disturbance alerts for the Congo Basin using Sentinel-1. Environmental Research Letters. [https://doi.org/10.1088/1748-9326/abd0a8](https://doi.org/10.1088/1748-9326/abd0a8) 

[CC by 4.0](https://creativecommons.org/licenses/by/4.0/)

English

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)](https://iopscience.iop.org/article/10.1088/1748-9326/aacd1c/meta) and with 2001-2018 forest loss [(Hansen et al. (2013))](https://www.science.org/doi/10.1126/science.1244693) and mangrove [(Bunting et al. 2018) ](https://www.mdpi.com/2072-4292/10/10/1669) removed. For more information on methodology and validation, please refer to [Reiche et. al. (2021)](https://iopscience.iop.org/article/10.1088/1748-9326/abd0a8). 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](https://www.wri.org/news/release-palm-oil-industry-jointly-develop-radar-monitoring-technology-detect-deforestation). 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

  - Although called ‘deforestation alerts’ these alerts detect forest or tree cover disturbances. This product does not distinguish between human-caused and other disturbance types. Where alerts are detected within plantation forests (more likely to happen in the GLAD-L system), alerts may indicate timber harvesting operations, without a conversion to a non-forest land use.     - The term deforestation is used because these are potential deforestation events, and alerts could be further investigated to determine this.     - We do not recommend using deforestation alerts for global or regional trend assessment, nor for area estimates. Rather, we recommend using the annual tree cover loss data for a more accurate comparison of the trends in forest change over time, and for area estimates. Recent alerts will include false positives that have yet to raise their confidence level and may eventually be removed. Past alerts may have been removed in error from the database if rapid canopy closure precedes the additional unobscured satellite observations within 6 months. Additionally, updates to the methodologies, differing number of systems (in the case of the integrated alerts), and variation in cloud cover between months and years pose additional risks to using deforestation alerts for inter/intra-annual comparison.     - The alerts can be ‘curated’ to identify those alerts of interest to a user, such as those alerts which are likely to be deforestation and might be prioritized for action. A user can do this by overlaying other contextual datasets, such as protected areas, or planted trees. The non-curated data are provided here in order that users can define their own prioritization approaches. Curated alert locations are provided in the Places to Watch data layer.     - False detections may occur in swamp forests due to the high sensitivity of short wavelength C-band radar to moisture variations    - Small-scale changes (e.g., logging roads, small-scale agriculture) are typically detected in a timely manner as forest edges are relatively straightforward to detect using short wavelength C-band radar. Large-scale patches (e.g., plantation expansion) may take longer to reach a high enough probability to be flagged as alerts. Those large patches may appear similar to undisturbed forest in the radar image due to conditions like wet soil or remaining woody debris.    - The product is constrained by the global forest baseline used, which may result in inconsistencies at the local level. In areas that are incorrectly labelled as primary forest in the baseline, there may be some commission errors in the alerts. In areas where forest loss occurred prior to the start of the RADD alerts but was missed by the baseline input data (and thus not removed from the forest baseline), alerts may be detected well after the disturbance occurred. This will only affect alerts from early 2019 (Africa) and early 2020 (other geographies).    - RADD alerts are within primary humid forests. Forest loss is defined as complete or partial removal of tree cover within a pixel, and a minimum-mapping unit of 0.1 ha is used.     - The confidence level may change retroactively as source data is updated; alerts that have not become high confidence within 90 days are removed from the dataset. For RADD, researchers use 2 years of data to create historical image metrics showing previous forest condition, preprocess every new Sentinel-1 image, and apply a forest disturbance detection algorithm which calculates the probability that a pixel is disturbed. If the probability of disturbance is greater than 0.85, it becomes a low confidence alert. Subsequent observations within the next 90 days are used to update the probability that the forest was disturbed. When the probability reaches above 0.975, the alert becomes classified as high confidence.    - Once an alert pixel reaches high confidence, forest loss will not be detected by the RADD alert system at that location again.     - A validation of confirmed alerts in the Congo Basin indicated a high level of accuracy (2% false positives, 5% false negatives) for disturbances greater than 0.2 ha.     - When zoomed out, this data layer displays some degree of inaccuracy because the data points must be collapsed to be visible on a larger scale. Zoom in for greater detail.   

https://www.wur.nl/en/Research-Results/Chair-groups/Environmental-Sciences/Laboratory-of-Geo-information-Science-and-Remote-Sensing/Research/Sensing-measuring/RADD-Forest-Disturbance-Alert.htm

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