2023-05-04T13:11:58.897510

2025-02-20T21:21:30.646052

10

10 × 10 m

Amazon basin

Updated daily, image revisit time every 5 days

regional

Use the following credit when this data is displayed:  Source: “GLAD-S2 alerts”. GLAD/UMD, accessed through Global Forest Watch on [date]  Use the following credit when this data is cited:  Pickens, A.H., Hansen, M.C., Adusei, B., and Potapov P. 2020. Sentinel-2 Forest Loss Alert. Global Land Analysis and Discovery (GLAD), University of Maryland. Accessed through Global Forest Watch. [www.globalforestwatch.org](http://www.globalforestwatch.org/) on [date] 

Deforestation alerts (GLAD-S2)

daily, 10 m, Amazon, UMD/GLAD

Pickens, A.H., Hansen, M.C., Adusei, B., and Potapov P. 2020. Sentinel-2 Forest Loss Alert. Global Land Analysis and Discovery (GLAD), University of Maryland. \[https://glad.earthengine.app/view/s2-forest-alerts]\(https://glad.earthengine.app/view/s2-forest-alerts)

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

English

This dataset is a forest loss alert product developed by the GLAD (Global Land Analysis and Discovery) lab at the University of Maryland. GLAD-S2 alerts utilize data from the European Space Agency’s Sentinel-2 mission, which provides optical imagery at a 10 m spatial resolution with a 5-day revisit time. The shorter revisit time, when compared to GLAD Landsat alerts, reduces the time to detect forest loss and between the initial detection of forest loss and classification as high confidence. This is particularly advantageous in wet and tropical regions, where persistent cloud cover may delay detections for weeks to months. GLAD-S2 alerts are available for primary forests in the Amazon basin from January 1st, 2019, to present, updated daily.  New Sentinel-2 images are analyzed as soon as they are acquired. Cloud, shadow, and water are filtered out of each new image, and a forest loss algorithm is applied to all remaining clear land observations. The algorithm relies on the spectral data in each new image in combination with spectral metrics from a baseline period of the previous two years.  Alerts become high confidence when at least two of four subsequent observations are flagged as forest loss (this corresponds to “high,” “medium,” and “low” confidence loss on the GLAD app linked below). The alert date represents the date of forest loss detection. Users can choose to display only high confidence alerts on the map, but keep in mind this will filter out the most recent detections of forest loss. Additionally, forest loss will not be detected again on pixels with high confidence alerts. Alerts that have not become high confidence within 180 days are removed from the dataset.  The GLAD-S2 alerts are available on \*\*Google Earth Engine\*\* with asset ID: projects/glad/S2alert 

Monitor primary forest loss in near-real time using Sentinel-2 imagery

  - 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 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.     - GLAD-S2 alerts are within the primary forest mask of [[Turubanova et al (2018)](https://iopscience.iop.org/article/10.1088/1748-9326/aacd1c/meta)](https://iopscience.iop.org/article/10.1088/1748-9326/aacd1c/meta) in the Amazon river basin, with 2001-present forest loss from \[Hansen et al. (2013)]\(https://www.science.org/doi/10.1126/science.1244693) removed.      - The confidence level may change retroactively as source data is updated; alerts that have not become high confidence within 180 days are removed from the dataset.  For GLAD-S2 alerts, every new alert starts out as "low confidence" when loss is first detected (e.g. one anomalous result is detected). Alerts are then classified as high confidence when forest loss has also been identified at that location in a second satellite image within three additional (4 total) cloud-free observations. Once an alert pixel reaches high confidence, forest loss will not be detected by GLAD-S2 at that location again.     - The accuracy of this product has not been assessed.    - 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.   

Forest Change

https://glad.earthengine.app/view/s2-forest-alerts#lon=-64.47;lat=-9.98;zoom=11;

66d24a0a-e9ce-40d4-bdde-d9cd510c5087