{ "id": "AIBD-0105", "title": "GLAD deforestation alert subscriptions reduce deforestation probability in Africa by 18%", "summary": "Independent evaluation: subscriptions to GLAD Landsat alerts (bagged decision trees) cut deforestation probability in Africa by 18%.", "date_observed": "2021-01-04", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Global Forest Watch's GLAD deforestation alerts are produced by the University of Maryland from Landsat imagery. Bagged decision trees estimate the per-pixel likelihood of forest loss. The method was published on 2 March 2016. Moffette, Alix-Garcia, Shea and Pickens evaluated the alerts' effect in Nature Climate Change on 4 January 2021, analyzing subscriptions to alerts across 22 tropical countries. They found that subscriptions decreased the probability of deforestation in Africa by 18 percent relative to 2011–2016 baseline levels. Effects were stronger in protected areas and concessions. There was no detectable effect on other continents, and little effect where alerts existed without subscribers. They valued the avoided emissions at USD 149–696 million.", "category": "general benefit", "unexpected": false, "purpose": "Detect humid tropical forest disturbance in near real time from Landsat imagery to support land management and enforcement.", "system": "GLAD (Global Land Analysis and Discovery) Landsat deforestation alerts on Global Forest Watch", "model_version": null, "developer": "University of Maryland GLAD laboratory (Hansen et al.)", "deployer": "World Resources Institute (Global Forest Watch) and alert subscribers", "location": null, "domain": "Conservation and biodiversity", "segment": "Forest monitoring and enforcement", "benefit_type": "Improved Environmental Sustainability", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Aggregate statistical effect across subscribed areas, no discrete attributable event." }, { "group": "organizational", "depth": null, "reason": "Effect measured at the level of subscribed administrative areas and concessions." }, { "group": "societal", "depth": null, "reason": "Continent-level reduction estimate with no single identifiable beneficiary." } ], "ai_role": { "status": "assessed", "statement": "The alerts are a machine-learning product (bagged decision trees). The evaluation attributes the 18 percent reduction to subscriptions to those alerts rather than to their mere availability. The evaluation's lead authors are independent economists, but co-authors are from WRI (operator) and the University of Maryland (producer)." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Moffette, F., Alix-Garcia, J., Shea, K., & Pickens, A. H. (2021). The impact of near-real-time deforestation alerts across the tropics. Nature Climate Change.", "title": "The impact of near-real-time deforestation alerts across the tropics", "url": "https://www.nature.com/articles/s41558-020-00956-w", "date": "2021-01-04", "written_by": "independent", "quote": "subscriptions to alerts in 22 tropical countries decrease the probability of deforestation in Africa by 18%" }, { "kind": "peer-reviewed study", "citation": "Hansen, M. C., Krylov, A., Tyukavina, A., Potapov, P. V., et al. (2016). Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters 11(3).", "title": "Humid tropical forest disturbance alerts using Landsat data", "url": "https://iopscience.iop.org/article/10.1088/1748-9326/11/3/034008", "date": "2016-03-02", "written_by": "makers", "quote": "A set of bagged decision trees was applied to each new Landsat observation and antecedent Landsat metrics." }, { "kind": "press release", "citation": "Berger, A., Carter, S., Schofield, T., Pickens, A., Reiche, J. (2026). Explore GFW's Updated Integrated Disturbance Alerts. Global Forest Watch / Global Nature Watch blog, 13 January 2026.", "title": "Explore GFW's Updated Integrated Disturbance Alerts", "url": "https://globalnaturewatch.org/blog/data-and-tools/integrated-deforestation-alerts/", "date": "2026-01-13", "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "Weekly Landsat coverage and a conservative classifier", "Free public platform with subscription-based alert delivery", "Subscribers with enforcement or management capacity" ], "must_hold": [ "Alerts reach actors who can respond", "Cloud cover does not suppress detections for long periods", "Protected-area or concession managers retain enforcement authority" ], "echoes": [ { "date": "2021-01-04", "setting": "Same study: subscriptions in Asia and Latin America", "model_version": null, "outcome": "did not recur", "source": "Moffette et al. (2021), abstract: no effect on other continents" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1159", "title": "Government‑Backed AI4Peat Mapping Tool Allegedly Misidentifies Granite Outcrops and Quarries as Peat", "url": "https://incidentdatabase.ai/cite/1159", "severity": null, "group": "societal", "basis": "Natural England's ML land-cover mapping from imagery misclassified granite and quarries as peat, misdirecting conservation; GLAD's Landsat classifier maps forest loss for the same users." }, "headline": "GLAD alerts cut deforestation probability in Africa by 18%", "metric": "decreased the probability of deforestation in Africa by 18 percent", "comparator": { "against": "average 2011–2016 deforestation levels, with areas lacking alert subscribers as the counterfactual across 22 tropical countries", "before": "average 2011–2016 levels", "after": "probability of deforestation in Africa decreased by 18%; no effect on other continents", "basis": "measured", "quote": "Here, we show that subscriptions to alerts in 22 tropical countries decrease the probability of deforestation in Africa by 18% relative to the average 2011–2016 levels. There is no effect on other continents, and the availability of alerts does not significantly change deforestation outcomes.", "url": "https://www.nature.com/articles/s41558-020-00956-w" }, "later": { "status": "persisted", "date": "2026-01-13", "note": "Global Forest Watch kept the Landsat-based GLAD alerts (GLAD-L) running and in 2022 folded them into an integrated disturbance alerts layer with GLAD-S2, RADD and, from January 2026, DIST-ALERT.", "quote": "GLAD-L is GFW's longest-standing alert product from the UMD Global Land Analysis and Discovery (GLAD) lab and uses imagery from NASA's Landsat satellites to detect tree cover loss at 30-meter resolution.", "url": "https://globalnaturewatch.org/blog/data-and-tools/integrated-deforestation-alerts/" }, "value": { "amount": "between US$149 million and US$696 million", "what": "value of avoided deforestation in Africa, priced at the social cost of carbon", "who": "society, through avoided carbon emissions", "estimate": true, "quote": "Calculated using the social cost of carbon for avoided deforestation in Africa, we estimate the alert system’s value to be between US$149 million and US$696 million.", "url": "https://www.nature.com/articles/s41558-020-00956-w" }, "developer_country": "United States", "deployer_country": "United States", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1159 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1159", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.nature.com/articles/s41558-020-00956-w; https://globalnaturewatch.org/blog/data-and-tools/integrated-deforestation-alerts/; https://www.nature.com/articles/s41558-020-00956-w", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://glad.umd.edu/; https://en.wikipedia.org/wiki/World_Resources_Institute", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent peer-reviewed study, the makers' peer-reviewed study and the deployer's press release. 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Global Forest Watch / Global Nature Watch blog, 13 January 2026.", "title": "Explore GFW's Updated Integrated Disturbance Alerts", "url": "https://globalnaturewatch.org/blog/data-and-tools/integrated-deforestation-alerts/", "date": "2026-01-13", "written_by": "deployer" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Independent evaluation: subscriptions to GLAD Landsat alerts (bagged decision trees) cut deforestation probability in Africa by 18%.", "account": "Global Forest Watch's GLAD deforestation alerts are produced by the University of Maryland from Landsat imagery. 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They valued the avoided emissions at USD 149–696 million." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "GLAD (Global Land Analysis and Discovery) Landsat deforestation alerts on Global Forest Watch" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "University of Maryland GLAD laboratory (Hansen et al.)" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "GLAD deforestation alert subscriptions reduce deforestation probability in Africa by 18%", "headline": "GLAD alerts cut deforestation probability in Africa by 18%" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "37e5d9f524fe3cd7cc54508a97a4988c9794e794016ba1511c8e9c3b9a34783f" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Global Forest Watch's GLAD deforestation alerts are produced by the University of Maryland from Landsat imagery. Bagged decision trees estimate the per-pixel likelihood of forest loss. The method was published on 2 March 2016. Moffette, Alix-Garcia, Shea and Pickens evaluated the alerts' effect in Nature Climate Change on 4 January 2021, analyzing subscriptions to alerts across 22 tropical countries. They found that subscriptions decreased the probability of deforestation in Africa by 18 percent relative to 2011–2016 baseline levels. Effects were stronger in protected areas and concessions. There was no detectable effect on other continents, and little effect where alerts existed without subscribers. They valued the avoided emissions at USD 149–696 million.", "replication": { "produced_by": [ "Weekly Landsat coverage and a conservative classifier", "Free public platform with subscription-based alert delivery", "Subscribers with enforcement or management capacity" ], "must_hold": [ "Alerts reach actors who can respond", "Cloud cover does not suppress detections for long periods", "Protected-area or concession managers retain enforcement authority" ], "echoes": [ { "date": "2021-01-04", "setting": "Same study: subscriptions in Asia and Latin America", "outcome": "did not recur", "source": "Moffette et al. (2021), abstract: no effect on other continents" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 38, "reviewed": 0, "record_mapped": 38, "not_assessed": 53, "complete": false, "by_state": { "value_present": 38, "not_reported": 0, "unresolved": 0, "not_assessed": 53, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }