Estimating forest inventory information for the talladega national forest using airborne laser scanning systems

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dc.authorid0000-0001-5376-5638en_US
dc.authorid0000-0001-5552-5670en_US
dc.authorid0000-0001-6450-2286en_US
dc.authorid0000-0002-5454-3970en_US
dc.contributor.authorLee, Taeyoon
dc.contributor.authorVatandaşlar, Can
dc.contributor.authorMerry, Krista
dc.contributor.authorBettinger, Pete
dc.contributor.authorPeduzzi, Alicia
dc.contributor.authorStober, Jonathan
dc.date.accessioned2024-12-09T07:03:16Z
dc.date.available2024-12-09T07:03:16Z
dc.date.issued2024
dc.departmentAÇÜ, Orman Fakültesi, Orman Mühendisliği Bölümüen_US
dc.description.abstractAccurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor intensive and time consuming. To address this limitation, remote sensing technology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Shoal Creek and Talladega Ranger Districts of the Talladega National Forest by employing ALS-derived data and aerial photography. The quality of the predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species group models. This study confirms that quality level 2 LiDAR data were sufficient for developing adequate predictive models (R2adj. ranging between 0.71 and 0.82), when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species group predictive models were of higher quality than general predictive models. Lastly, landscape level maps were created from the predictive models and these may be helpful to planners, forest managers, and landowners in their management efforts.
dc.identifier.doi10.3390/rs16162933
dc.identifier.issn2072-4292
dc.identifier.issue16en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttp://dx.doi.org/10.3390/rs16162933
dc.identifier.urihttps://hdl.handle.net/11494/5135
dc.identifier.volume16en_US
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)en_US
dc.relation.ispartofRemote Sensing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAerial Imageryen_US
dc.subjectAirborne Laser Scanningen_US
dc.subjectALASSOen_US
dc.subjectForest Inventoryen_US
dc.subjectLiDARen_US
dc.subjectMixed Pine–Hardwood Foresten_US
dc.titleEstimating forest inventory information for the talladega national forest using airborne laser scanning systemsen_US
dc.typeArticle

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