{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T16:17:59Z","timestamp":1740154679299,"version":"3.37.3"},"reference-count":96,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000270","name":"Natural Environment Research Council","doi-asserted-by":"publisher","award":["NE\/L002604\/1"],"id":[{"id":"10.13039\/501100000270","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Nottingham"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Effective planning and management strategies for restoring and conserving tropical peat swamp ecosystems require accurate and timely estimates of aboveground biomass (AGB), especially when monitoring the impacts of restoration interventions. The aim of this research is to assess changes in AGB and evaluate the effectiveness of restoration efforts in the North Selangor Peat Swamp Forest (NSPSF), one of the largest remaining peat swamp forests in Peninsular Malaysia, using advanced remote sensing techniques. A Random Forest machine learning method was employed to upscale AGB estimates, derived from a \u2018LiDAR AGB model\u2019, to larger landscape-scale areas with Sentinel-2 spectral and textural variables. The time period under investigation (2015\u20132018) marked a concentrated phase of restoration and regeneration efforts in NSPSF. The results demonstrate an overall increase in tropical peat swamp AGB during these years, where the total amount of estimated AGB stored in NSPSF increased from 19.3 Tg in 2015 to an estimated 19.8 Tg in 2018. The research found that a tailored variable selection approach improved predictions of AGB, with optimised input variables (n = 62) and parameter adjustments producing a good plausible result (R2 = 0.80; RMSE = 55.2 Mg\/ha). This paper concludes by emphasizing the importance of long-term studies (>5 years) for analyzing the success of tropical peat swamp restoration methods, with a potential for integrating remote sensing technology.<\/jats:p>","DOI":"10.3390\/rs16152690","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T15:40:36Z","timestamp":1721749236000},"page":"2690","source":"Crossref","is-referenced-by-count":2,"title":["Remote Sensing for Restoration Change Monitoring in Tropical Peat Swamp Forests in Malaysia"],"prefix":"10.3390","volume":"16","author":[{"given":"Chloe","family":"Brown","sequence":"first","affiliation":[{"name":"School of Geography, University of Nottingham, Nottingham NG7 2QL, UK"}]},{"given":"Sofie","family":"Sj\u00f6gersten","sequence":"additional","affiliation":[{"name":"School of Biosciences, University of Nottingham, Loughborough LE12 5RD, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5279-0858","authenticated-orcid":false,"given":"Martha J.","family":"Ledger","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, University of Hong Kong, Hong Kong"}]},{"given":"Faizal","family":"Parish","sequence":"additional","affiliation":[{"name":"Global Environment Centre, Petaling Jaya 47300, Selangor, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3040-552X","authenticated-orcid":false,"given":"Doreen","family":"Boyd","sequence":"additional","affiliation":[{"name":"School of Geography, University of Nottingham, Nottingham NG7 2QL, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1111\/gcb.13422","article-title":"Keep wetlands wet: The myth of sustainable development of tropical peatlands\u2013implications for policies and management","volume":"23","author":"Evers","year":"2017","journal-title":"Glob. 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