{"corpus_id":228904823,"paper_sha":"e805b91ae5a8858b0f2695add2cbccc3e25d71f8","doi":"10.1016/j.rse.2020.112165","arxiv_id":null,"pmid":null,"pmcid":null,"mag_id":3094643344,"dblp_id":null,"acl_id":null,"title":"Mapping global forest canopy height through integration of GEDI and Landsat data","year":2020,"publication_date":"2020-11-07","venue":"","journal":{"name":"Remote Sensing of Environment","pages":"112165","volume":""},"journal_issn":null,"journal_title":null,"publication_types":[],"pubmed_pub_types":null,"s2_fields_of_study":["Environmental Science"],"reference_count":40,"citation_count":990,"influential_citation_count":93,"is_open_access":true,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":"https://www.sciencedirect.com/science/article/am/pii/S0034425720305381","s2_open_access_landing_url":"https://www.semanticscholar.org/paper/e805b91ae5a8858b0f2695add2cbccc3e25d71f8","s2_open_access_license":"publisher-specific-oa","s2_open_access_status":"BRONZE","pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"Abstract Consistent, large-scale operational monitoring of forest height is essential for estimating forest-related carbon emissions, analyzing forest degradation, and quantifying the effectiveness of forest restoration initiatives. The Global Ecosystem Dynamics Investigation (GEDI) lidar instrument onboard the International Space Station has been collecting unique data on vegetation structure since April 2019. Here, we employed global Landsat analysis-ready data to extrapolate GEDI footprint-level forest canopy height measurements, creating a 30 m spatial resolution global forest canopy height map for the year 2019. The global forest height map was compared to the GEDI validation data (RMSE = 6.6 m; MAE = 4.45 m, R2 = 0.62) and available airborne lidar data (RMSE = 9.07 m; MAE = 6.36 m, R2 = 0.61). The demonstrated integration of GEDI data with time-series optical imagery is expected to enable multidecadal historic analysis and operational forward monitoring of forest height and its dynamics. Such capability is important to support global climate and sustainable development initiatives.","claims":[{"public_id":"cl_e31b5fd92c3fdab1d992ad36e4d9204c","status":"active","text":"The demonstrated integration of GEDI data with time-series optical imagery is expected to enable multidecadal historic analysis and operational forward monitoring of forest height and its dynamics.","confidence":0.9,"contributors":[{"id":32,"public_id":"7c402c1b98","public_label":"뀨 (7c402c1b98)","roles":["extraction"],"url":"https://sah.borca.ai/u/7c402c1b98"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_e31b5fd92c3fdab1d992ad36e4d9204c"},{"public_id":"cl_bb6f9a7c427885c6a2db90dc7d2f2269","status":"active","text":"The global forest height map was compared to GEDI validation data yielding RMSE = 6.6 m, MAE = 4.45 m, and R² = 0.62.","confidence":0.95,"contributors":[{"id":32,"public_id":"7c402c1b98","public_label":"뀨 (7c402c1b98)","roles":["extraction"],"url":"https://sah.borca.ai/u/7c402c1b98"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/claims/cl_bb6f9a7c427885c6a2db90dc7d2f2269"},{"public_id":"cl_e9b4d5566bce696b82bad91c807182b4","status":"active","text":"The global forest height map was compared to available airborne lidar data yielding RMSE = 9.07 m, MAE = 6.36 m, and R² = 0.61.","confidence":0.95,"contributors":[{"id":32,"public_id":"7c402c1b98","public_label":"뀨 (7c402c1b98)","roles":["extraction"],"url":"https://sah.borca.ai/u/7c402c1b98"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous 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map against validation data.","types":["metric"],"aliases":["mean absolute error"],"contributors":[{"id":32,"public_id":"7c402c1b98","public_label":"뀨 (7c402c1b98)","roles":["extraction"],"url":"https://sah.borca.ai/u/7c402c1b98"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["review"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"url":"https://sah.borca.ai/concepts/co_6c89b4f04fa0a5a0781b0f9eea2ed81d"},{"public_id":"co_713afd11ab46ea5ca1f29f78d7be497b","status":"active","name":"time-series optical imagery","description":"Multi-temporal optical satellite images used together with GEDI data to enable historic and forward monitoring of forest height.","types":["dataset"],"aliases":["optical imagery"],"contributors":[{"id":32,"public_id":"7c402c1b98","public_label":"뀨 (7c402c1b98)","roles":["extraction"],"url":"https://sah.borca.ai/u/7c402c1b98"},{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous 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