{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:00:41Z","timestamp":1761897641104,"version":"build-2065373602"},"reference-count":84,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T00:00:00Z","timestamp":1644883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"KESS2","award":["KESS2-Stoddart"],"award-info":[{"award-number":["KESS2-Stoddart"]}]},{"name":"EBG","award":["CNPq 301661\/2019-7"],"award-info":[{"award-number":["CNPq 301661\/2019-7"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Current LiDAR-based methods for detecting forest change use a host of statistically selected variables which typically lack a biological link with the characteristics of the ecosystem. Consensus of the literature indicates that many authors use LiDAR to derive ecosystem morphological traits (EMTs)\u2014namely, vegetation height, vegetation cover, and vertical structural complexity\u2014to identify small-scale changes in forest ecosystems. Here, we provide a conceptual, biological model for predicting forest aboveground biomass (AGB) change based on EMTs. We show that through use of a multitemporal dataset it is possible to not only identify losses caused by logging in the period between data collection but also identify regions of regrowth from prior logging using EMTs. This sensitivity to the change in forest dynamics was the criterion by which LiDAR metrics were selected as proxies for each EMT. For vegetation height, results showed that the top-of-canopy height derived from a canopy height model was more sensitive to logging than the average or high percentile of raw LiDAR height distributions. For vegetation cover metrics, lower height thresholds for fractional cover calculations were more sensitive to selective logging and the regeneration of understory. For describing the structural complexity in the vertical profile, the Gini coefficient was found to be superior to foliage height diversity for detecting the dynamics occurring over the years after logging. The subsequent conceptual model for AGB estimation obtained a level of accuracy which was comparable to a model that was statistically optimised for that same area. We argue that a widespread adoption of an EMT-based conceptual approach would improve the transferability and comparability of LiDAR models for AGB worldwide.<\/jats:p>","DOI":"10.3390\/rs14040933","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T22:44:47Z","timestamp":1644965087000},"page":"933","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits"],"prefix":"10.3390","volume":"14","author":[{"given":"Jaz","family":"Stoddart","sequence":"first","affiliation":[{"name":"School of Natural Sciences, Bangor University, Bangor LL57 2DG, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danilo Roberti Alves","family":"de Almeida","sequence":"additional","affiliation":[{"name":"School of Natural Sciences, Bangor University, Bangor LL57 2DG, UK"},{"name":"Department of Forest Sciences, \u201cLuiz de Queiroz\u201d College of Agriculture, University of S\u00e3o Paulo (USP\/ESALQ), Piracicaba 13418-900, SP, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7844-3560","authenticated-orcid":false,"given":"Carlos Alberto","family":"Silva","sequence":"additional","affiliation":[{"name":"Forest Biometrics and Remote Sensing Lab (Silva Lab), School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2517-0279","authenticated-orcid":false,"given":"Eric Bastos","family":"G\u00f6rgens","sequence":"additional","affiliation":[{"name":"Department of Forest Engineering, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Alto da Jucuba, Campus JK, Diamantina 39100-000, MG, Brazil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Keller","sequence":"additional","affiliation":[{"name":"USDA-Forest Service International Institute of Tropical Forestry, Jardin Botanico Sur, 1201 Calle Ceiba, San Juan, PR 00926, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0493-7581","authenticated-orcid":false,"given":"Ruben","family":"Valbuena","sequence":"additional","affiliation":[{"name":"School of Natural Sciences, Bangor University, Bangor LL57 2DG, UK"},{"name":"Division of Forest Remote Sensing, Department of Forest Resource Management, Swedish University of Agricultural Sciences (SLU), Skogsmarksgr\u00e4nd 17, SE-901 83 Ume\u00e5, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1038\/nature14283","article-title":"Long-term decline of the Amazon carbon sink","volume":"519","author":"Brienen","year":"2015","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13021-016-0069-2","article-title":"Carbon uptake by mature Amazon forests has mitigated Amazon nations\u2019 carbon emissions","volume":"12","author":"Phillips","year":"2017","journal-title":"Carbon Balance Manag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s10669-013-9444-7","article-title":"Socio-economic, environmental, and governance impacts of illegal logging","volume":"33","author":"Reboredo","year":"2013","journal-title":"Environ. 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