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dc.contributor.author
Zhang, Shaohui
dc.contributor.author
Korhonen, Lauri
dc.contributor.author
Lang, Mait
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dc.contributor.author
Pisek, Jan
dc.contributor.author
Díaz, Gastón Mauro
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dc.contributor.author
Korpela, Ilkka
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Xia, Zhongyu
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Haapala, Hanna
dc.contributor.author
Maltamo, Matti
dc.date.available
2024-04-04T13:29:36Z
dc.date.issued
2024-01
dc.identifier.citation
Zhang, Shaohui; Korhonen, Lauri; Lang, Mait; Pisek, Jan; Díaz, Gastón Mauro; et al.; Comparison of Semi-Physical and Empirical Models in the Estimation of Boreal Forest Leaf Area Index and Clumping with Airborne Laser Scanning Data; Institute of Electrical and Electronics Engineers; IEEE Transactions on Geoscience and Remote Sensing; 62; 1-2024; 1-12
dc.identifier.issn
1558-0644
dc.identifier.uri
http://hdl.handle.net/11336/231935
dc.description.abstract
Leaf area index (LAI) is an important forest canopy variable that is related to various biophysical processes of forest ecosystems. Airborne laser scanning (ALS) has shown promise in modeling and mapping LAI using different types of ALS metrics. The most common ways of modeling LAI with ALS data are multivariate empirical models and the semi-physical model shape derived from the Beer–Lambert law of radiation attenuation. We tested the utility of ALS-based empirical and semi-physical models in the estimations of effective LAI (LAIe), canopy clumping index (Omega_E), and clumping-corrected LAI at three boreal forest sites in Finland. In semi-physical models, the all echo penetration index (API) showed consistently the best performance in predicting LAIe. It is, therefore, a robust and potentially the most transferable predictor using this model shape. Empirical models overall yielded slightly better model fits compared to the semi-physical models, yet they are also more prone to overfitting. In addition, empirical models had constantly lower accuracies when predicting LAI than LAIe. We also tested the utility of ALS-based multi-angular canopy gap fraction metrics that were derived from polar transformed ALS point clouds. Images derived from polar transformed point clouds can be analyzed similarly to digital hemispherical photographs (DHPs) to obtain canopy gap fractions. The results showed that polar metrics derived from polar transformed ALS data can provide supporting information to empirical models in the estimation of LAIe, LAI, and especially Omega_E. In particular, a combination of ALS penetration indices and polar metrics yielded positive results in Omega_E estimation.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Electrical and Electronics Engineers
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
AIRBORNE LASER SCANNING
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CANOPY CLUMPING
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FOREST CANOPY
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LEAF AREA INDEX
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LIGHT DETECTION AND RANGING
dc.subject.classification
Otras Ingenierías y Tecnologías
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dc.subject.classification
Otras Ingenierías y Tecnologías
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Comparison of Semi-Physical and Empirical Models in the Estimation of Boreal Forest Leaf Area Index and Clumping with Airborne Laser Scanning Data
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-04-03T10:43:31Z
dc.journal.volume
62
dc.journal.pagination
1-12
dc.journal.pais
Estados Unidos
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dc.description.fil
Fil: Zhang, Shaohui. University Of Eastern Finland. Faculty Of Science And Forestry.; Finlandia
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Fil: Korhonen, Lauri. University Of Eastern Finland. Faculty Of Science And Forestry.; Finlandia
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Fil: Lang, Mait. University Of Tartu. Faculty Of Science And Technology. Tartu Observatory.; Estonia
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Fil: Pisek, Jan. University Of Tartu. Faculty Of Science And Technology. Tartu Observatory.; Estonia
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Fil: Díaz, Gastón Mauro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Centro de Investigación y Extensión Forestal Andino Patagónico; Argentina
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Fil: Korpela, Ilkka. University of Helsinki; Finlandia
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Fil: Xia, Zhongyu. University Of Eastern Finland. Faculty Of Science And Forestry.; Finlandia
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Fil: Haapala, Hanna. University Of Eastern Finland. Faculty Of Science And Forestry.; Finlandia
dc.description.fil
Fil: Maltamo, Matti. University Of Eastern Finland. Faculty Of Science And Forestry.; Finlandia
dc.journal.title
IEEE Transactions on Geoscience and Remote Sensing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/TGRS.2024.3353410
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