{"id":4511,"date":"2025-06-19T09:47:39","date_gmt":"2025-06-19T09:47:39","guid":{"rendered":"https:\/\/scientificworld.org\/?p=4511"},"modified":"2025-06-19T09:47:42","modified_gmt":"2025-06-19T09:47:42","slug":"space-lasers-and-ai-revolutionize-forest-carbon-measurement-aiding-climate-research","status":"publish","type":"post","link":"https:\/\/scientificworld.org\/?p=4511","title":{"rendered":"Space Lasers and AI Revolutionize Forest Carbon Measurement, Aiding Climate Research"},"content":{"rendered":"\n<p>Geospatial scientist Hamdi Zurqani from the University of Arkansas at Monticello has pioneered a novel method to measure forest biomass using satellite data and artificial intelligence. Published in&nbsp;<a href=\"http:\/\/dx.doi.org\/10.1016\/j.ecoinf.2025.103052\"><em>Ecological Informatics<\/em><\/a>, this approach combines NASA\u2019s spaceborne LiDAR with European satellite imagery to enhance the accuracy&nbsp;and efficiency of carbon cycle tracking\u2014a critical tool in combating climate change.<\/p>\n\n\n\n<p>Forests play a vital role in Earth\u2019s carbon cycle, storing approximately 80% of terrestrial carbon. Traditional ground-based methods for estimating forest biomass are slow and labor-intensive, limiting their scalability. Zurqani\u2019s research leverages open-access satellite data, processed via Google Earth Engine, to overcome these challenges.<\/p>\n\n\n\n<p>The study integrated 3D canopy measurements from NASA\u2019s GEDI LiDAR\u2014mounted on the International Space Station\u2014with optical imagery from the Copernicus Sentinel satellites. Four machine learning algorithms were tested, with gradient tree boosting delivering the highest accuracy in predicting biomass. Random forest also performed well, while support vector machines struggled, underscoring the importance of selecting the right AI tools for ecological analysis.<\/p>\n\n\n\n<p>Zurqani emphasized that combining multi-source data, such as vegetation indices and topographic features, was key to achieving reliable results. This method enables large-scale biomass mapping, even in remote or inaccessible regions.<\/p>\n\n\n\n<p>\u201cForests are the lungs of our planet,\u201d said Zurqani. \u201cThis technology allows us to monitor their health and carbon storage potential with unprecedented precision, informing global climate policies.\u201d<\/p>\n\n\n\n<p>The study marks a significant advancement in environmental monitoring, though challenges like weather interference and limited LiDAR coverage persist. Future research may explore advanced AI models, such as neural networks, to further refine predictions. As climate change escalates, such innovations will be indispensable for sustainable forest management and climate action.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Geospatial scientist Hamdi Zurqani from the University of Arkansas at Monticello has pioneered a novel method to measure forest biomass using satellite data and artificial intelligence. Published in&nbsp;Ecological Informatics, this approach combines NASA\u2019s spaceborne LiDAR with European satellite imagery to enhance the accuracy&nbsp;and efficiency of carbon cycle tracking\u2014a critical tool in combating climate change. Forests [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1117],"tags":[2012,1425,2013],"class_list":["post-4511","post","type-post","status-publish","format-standard","hentry","category-environmental-science","tag-ai-revolutionize","tag-environmental-science","tag-forest-carbon"],"_links":{"self":[{"href":"https:\/\/scientificworld.org\/index.php?rest_route=\/wp\/v2\/posts\/4511","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scientificworld.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/scientificworld.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/scientificworld.org\/index.php?rest_route=\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/scientificworld.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4511"}],"version-history":[{"count":1,"href":"https:\/\/scientificworld.org\/index.php?rest_route=\/wp\/v2\/posts\/4511\/revisions"}],"predecessor-version":[{"id":4512,"href":"https:\/\/scientificworld.org\/index.php?rest_route=\/wp\/v2\/posts\/4511\/revisions\/4512"}],"wp:attachment":[{"href":"https:\/\/scientificworld.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4511"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/scientificworld.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4511"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/scientificworld.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4511"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}