Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/47650
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dc.contributor.authorVaglio Laurin, Gaiait
dc.contributor.authorPuletti, Nicolait
dc.contributor.authorChen, Qiit
dc.contributor.authorCorona, Piermariait
dc.contributor.authorPapale, Darioit
dc.contributor.authorValentini, Riccardoit
dc.date.accessioned2022-05-04T14:13:58Z-
dc.date.available2022-05-04T14:13:58Z-
dc.date.issued2016it
dc.identifier.issn1872-826Xit
dc.identifier.urihttp://hdl.handle.net/2067/47650-
dc.description.abstractEstimates of forest aboveground biomass are fundamental for carbon monitoring and accounting; delivering information at very high spatial resolution is especially valuable for local management, conservation and selective logging purposes. In tropical areas, hosting large biomass and biodiversity resources which are often threatened by unsustainable anthropogenic pressures, frequent forest resources monitoring is needed. Lidar is a powerful tool to estimate aboveground biomass at fine resolution; however its application in tropical forests has been limited, with high variability in the accuracy of results. Lidar pulses scan the forest vertical profile, and can provide structure information which is also linked to biodiversity. In the last decade the remote sensing of biodiversity has received great attention, but few studies focused on the use of lidar for assessing tree species richness in tropical forests. This research aims at estimating aboveground biomass and tree species richness using discrete return airborne lidar in Ghana forests. We tested an advanced statistical technique, Multivariate Adaptive Regression Splines (MARS), which does not require assumptions on data distribution or on the relationships between variables, being suitable for studying ecological variables. We compared the MARS regression results with those obtained by multilinear regression and found that both algorithms were effective, but MARS provided higher accuracy either for biomass (R2 = 0.72) and species richness (R2 = 0.64). We also noted strong correlation between biodiversity and biomass field values. Even if the forest areas under analysis are limited in extent and represent peculiar ecosystems, the preliminary indications produced by our study suggest that instrument such as lidar, specifically useful for pinpointing forest structure, can also be exploited as a support for tree species richness assessment.it
dc.titleAbove ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forestsit
dc.typearticle*
dc.identifier.doi10.1016/j.jag.2016.07.008it
dc.identifier.scopus2-s2.0-84996587387it
dc.identifier.urlhttps://dspace.unitus.it/handle/2067/29738it
dc.relation.journalINTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATIONit
dc.relation.firstpage371it
dc.relation.lastpage379it
dc.relation.volume52it
dc.type.miur262*
item.fulltextWith Fulltext-
item.openairetypearticle-
item.grantfulltextopen-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.journal.journalissn1872-826X-
crisitem.journal.anceE236035-
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