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dc.contributor.authorKent, Rafien
dc.contributor.authorLindsell, Jeremy Aen
dc.contributor.authorLaurin, Gaia Vaglioen
dc.contributor.authorValentini, Riccardoen
dc.contributor.authorCoomes, Daviden
dc.date.accessioned2015-06-25T12:04:00Z
dc.date.available2015-06-25T12:04:00Z
dc.date.issued2015-06-26en
dc.identifier.citationRemote Sensing 2015, 7(7), 8348-8367. doi: 10.3390/rs70708348
dc.identifier.issn2072-4292
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/248707
dc.description.abstractIdentifying historical forest disturbances is difficult, especially in selectively logged areas. LiDAR is able to measure fine-scale variations in forest structure over multiple kilometers. We use LiDAR data from ca. 16 km2 of forest in Sierra Leone, West Africa, to discriminate areas of old-growth from areas recovering from selective logging for 23 years. We examined canopy height variation and gap size distributions. We found that though recovering blocks of forest differed little in height from old-growth forest (up to 3 m) they had a greater area of canopy gaps (average 10.2% gap fraction in logged areas, compared to 5.6% in unlogged area); and greater numbers of gaps penetrating to the forest floor (162 gaps at 2 m height in logged blocks, and 101 in an unlogged block). Comparison of LiDAR measurements with field data demonstrated that LiDAR delivered accurate results. We found that gap size distributions deviated from power-laws reported previously, with substantially fewer large gaps than predicted by power-law functions. Our analyses demonstrate that LiDAR is a useful tool for distinguishing structural differences between old-growth and old-secondary forests. That makes LiDAR a powerful tool for REDD+ (Reduction of Emissions from Deforestation and Forest Degradation) programs implementation and conservation planning.
dc.description.sponsorshipThis research was funded by the European Union under the EuropeAid Programme, as a part of the Across the River Transboundary Peace Park Project DCI/ENV/2008/151-577; by a Cambridge Conservation Initiative Collaborative Fund grant “Applications of airborne remote sensing to the conservation management of a West African National Park”; and by the ERC grant Africa GHG #247349. We would also like to thank the British Technion Society for the generous funding of the post-doctoral Coleman-Cohen fellowship of R. Kent.
dc.languageEnglishen
dc.language.isoenen
dc.publisherMDPI
dc.rightsAttribution 2.0 UK: England & Wales
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/
dc.subjectgap size frequency distributionen
dc.subjectold growth foresten
dc.subjectre-growth foresten
dc.subjectselective loggingen
dc.subjectmoist tropical foresten
dc.subjectGola Rainforest National Parken
dc.subjectSierra Leoneen
dc.subjectMCMCen
dc.subjectpower-lawen
dc.subjectLiDARen
dc.titleAirborne LiDAR Detects Selectively Logged Tropical Forest Even in an Advanced Stage of Recoveryen
dc.typeArticle
dc.description.versionThis is the final version of the article. It first appeared from MDPI via http://dx.doi.org/10.3390/rs70708348en
prism.endingPage8367
prism.publicationDate2015en
prism.publicationNameRemote Sensingen
prism.startingPage8348
prism.volume7en
rioxxterms.versionofrecord10.3390/rs70708348en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2015-06-26en
dc.contributor.orcidCoomes, David [0000-0002-8261-2582]
dc.identifier.eissn2072-4292
rioxxterms.typeJournal Article/Reviewen


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Attribution 2.0 UK: England & Wales
Except where otherwise noted, this item's licence is described as Attribution 2.0 UK: England & Wales