wur_africa_radd_coverage
created_on
2023-05-04T13:11:58.897421
updated_on
2023-05-04T13:11:58.897422
geographic_coverage
Humid tropical forests in: Cameroon, Central African Republic, Democratic Republic of the Congo, Equatorial Guinea, Gabon, Indonesia, Malaysia, Republic of the Congo
update_frequency
Every 6-12 days
citation
Reiche et al. 'RADD alerts'. Accessed through Global Forest Watch on [DATE]. www.globalforestwatch.org.
title
Deforestation alerts (RADD) Coverage
source
Congo Basin: Wageningen University and Research, as described in 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. [YEAR]. Forest disturbance alerts for the Congo Basin using Sentinel-1. [PUBLICATION].
overview
This dataset 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 (RAdar for Detecting Deforestation) alerts use a detection methodology produced by Wageningen University and Research (WUR), with support from the World Resources Institute, and are operationalized by Satelligence for Indonesia and Malaysia through Google Compute Engine (for commercial and public use) and through Google Earth Engine (for scientific and public use) for the Congo Basin. 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 Indonesia, Malaysia, and six Congo Basin countries at a 10m spatial resolution, with coverage from January 2019 to the present (starting January 2018 for Indonesia and Malaysia). The alerts presented here were implemented in two different processing environments, resulting in slight methodological differences as described below. **Indonesia and Malaysia**: The alerts for Indonesia and Malaysia were developed thanks to the support of a coalition of [ten major palm oil producers and buyers](https://www.wri.org/news/2019/10/release-palm-oil-industry-jointly-develop-radar-monitoring-technology-detect). As such, the alerts were particularly tuned to capture forest changes potentially related to palm oil production. In particular, the alerts only detect changes in areas with slope less than 5 degrees and less than 1500 meters of elevation, as oil palm plantations typically occur in low, flat areas. Sentinel-1 images are pre-processed using the SNAP toolbox, after which they are normalized. 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 confirmed with subsequent imagery within a maximum 90-day period if the forest disturbance probability is above 97.5%. The product has a minimum mapping unit of 0.1 ha (equivalent to 10 Sentinel-1 pixels) to minimize false detections. For more information on methodology and validation, please refer to [Reiche et. al. (YEAR)](LINK TO PAPER). **Congo Basin**: 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 confirmed 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://science.sciencemag.org/content/342/6160/850)) 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. (YEAR)](LINK TO PAPER). 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.
function
Near real-time forest disturbance alerts in primary humid tropical forests using Sentinel-1's cloud-penetrating radar sensors
cautions
- This product does not separate human-caused deforestation from other forest disturbances - False detections may occur in swamp forests due to the high sensitivity of C-band (~5.6 cm) radar to moisture variations - Areas with a slope greater than 5 degrees and elevation greater than 1500 meters are excluded from the Indonesia and Malaysia alerts - 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.
id
5fd1bbe6-f021-47b7-9b0b-a1470ae3ed54