{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:19:42Z","timestamp":1767831582583,"version":"3.49.0"},"reference-count":65,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["813360"],"award-info":[{"award-number":["813360"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["13-002"],"award-info":[{"award-number":["13-002"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CEMarin","award":["813360"],"award-info":[{"award-number":["813360"]}]},{"name":"CEMarin","award":["13-002"],"award-info":[{"award-number":["13-002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utr\u00eda National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks.<\/jats:p>","DOI":"10.3390\/rs15133334","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T01:02:41Z","timestamp":1688086961000},"page":"3334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Seeing the Forest for the Trees: Mapping Cover and Counting Trees from Aerial Images of a Mangrove Forest Using Artificial Intelligence"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0213-9324","authenticated-orcid":false,"given":"Daniel","family":"Sch\u00fcrholz","sequence":"first","affiliation":[{"name":"Max Planck Institute for Marine Microbiology, 28359 Bremen, Germany"},{"name":"Leibniz Centre for Tropical Marine Research (ZMT), 28359 Bremen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7849-5205","authenticated-orcid":false,"given":"Gustavo","family":"Castellanos-Galindo","sequence":"additional","affiliation":[{"name":"Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), 12587 Berlin, Germany"},{"name":"Institute of Biology, Freie Universit\u00e4t Berlin, 14195 Berlin, Germany"},{"name":"Smithsonian Tropical Research Institute, Balboa 0843, Panama"}]},{"given":"Elisa","family":"Casella","sequence":"additional","affiliation":[{"name":"Leibniz Centre for Tropical Marine Research (ZMT), 28359 Bremen, Germany"},{"name":"Department of Environmental Sciences, Informatics and Statistics, Ca\u2019 Foscari University of Venice, 30172 Venice, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7388-2820","authenticated-orcid":false,"given":"Juan","family":"Mej\u00eda-Renter\u00eda","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n en Ecolog\u00eda de Estuarios y Manglares, Departamento de Biolog\u00eda, Universidad del Valle, Cali 25360, Colombia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0389-5589","authenticated-orcid":false,"given":"Arjun","family":"Chennu","sequence":"additional","affiliation":[{"name":"Leibniz Centre for Tropical Marine Research (ZMT), 28359 Bremen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5844","DOI":"10.1111\/gcb.15275","article-title":"Global declines in human-driven mangrove loss","volume":"26","author":"Goldberg","year":"2020","journal-title":"Glob. 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