Mapping Tree Species In Tropical Seasonal Semi-deciduous Forests With Hyperspectral And Multispectral Data

dc.creatorFerreira
dc.creatorMatheus Pinheiro; Zortea
dc.creatorMaciel; Zanotta
dc.creatorDaniel Capella; Shimabukuro
dc.creatorYosio Edemir; de Souza Filho
dc.creatorCarlos Roberto
dc.date2016
dc.datejun
dc.date2017-11-13T11:34:08Z
dc.date2017-11-13T11:34:08Z
dc.date.accessioned2018-03-29T05:48:28Z
dc.date.available2018-03-29T05:48:28Z
dc.identifierRemore Sensing Of Environment. Elsevier Science Inc, v. 179, p. 66 - 78, 2016.
dc.identifier0034-4257
dc.identifier1879-0704
dc.identifierWOS:000375506100006
dc.identifier10.1016/j.rse.2016.03.021
dc.identifierhttp://www-sciencedirect-com.ez88.periodicos.capes.gov.br/science/article/pii/S0034425716301134
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/326369
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1363375
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionAccurately mapping the spatial distribution of tree species in tropical environments provides valuable insights for ecologists and forest managers. This process may play an important role in reducing fieldwork costs, monitoring changes in canopy biodiversity, and locating parent trees to collect seeds for forest restoration efforts. However, mapping tree species in tropical forests with remote sensing data is a challenge because of high floristic and spectral diversity. In this research, we discriminated and mapped tree species in tropical seasonal semi-deciduous forests (Brazilian Atlantic Forest Biome) by using airborne hyperspectral and simulated multispectral data in the 450 to 2400 nm wavelength range. After quantifying the spectral variability within and among individual tree crowns of eight species, three supervised machine learning classifiers were applied to discriminate the species at the pixel level. Linear Discriminant Analysis outperformed Support Vector Machines with Linear and Radial Basis Function (RBF-SVMs) kernels and Random Forests in almost all the tested cases. An average classification accuracy of 70% was obtained when using the visible/near-infrared (VNIR, 450-919 nm) bands. The inclusion of shortwave infrared bands (SWIR, 1045-2400 nm) increased the accuracy to 84%. Narrow-band vegetation indices (VIs) were also tested and increased the classification accuracy by up to 5% when combined with VNIR features. Furthermore, the spectral bands of the WorldView-3 (WV-3) satellite sensor were simulated for classification purposes. WV-3 VNIR bands provided an accuracy of 57.4%, which increased to 74.8% when using WV-3 SWIR bands. We also tested the production of species maps by using an object-oriented approach that integrated a novel segmentation algorithm that was tailored to delineate tree crowns and label high class membership pixels inside each object. In this scenario, RBF-SVMs produced the best species maps, correctly identifying 84.9% of crowns with hyperspectral data and 78.5% with simulated WV-3 data. The use of a reduced set of hyperspectral bands, which were selected with stepwise regression, did not significantly affect the classification accuracies but allowed us to depict the most important wavelengths to discriminate the species. These wavelengths were located around the green reflectance peak (550 nm), at the red absorption feature (650 nm) and in the SWIR range at 1200,1700, 2100 and 2300 nm. These encouraging results suggest the feasibility of the proposed approach for mapping pioneering and climax tree species in the Brazilian Atlantic Forest Biome, highlighting its potential use in forest recovery and inventory initiatives. (C) 2016 Elsevier Inc. All rights reserved.
dc.description179
dc.description66
dc.description78
dc.descriptionSao Paulo Research Foundation (FAPESP) [2013/11.589-5]
dc.descriptionCoordination for the Improvement of Higher Education Personnel (CAPES) [3424/2015-04]
dc.descriptionBrazilian National Council for Scientific and Technological Development (CNPq) [303563/2008-7]
dc.descriptionCNPq [314625/2014-3]
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.languageEnglish
dc.publisherElsevier Science INC
dc.publisherNew York
dc.relationRemore Sensing of Environment
dc.rightsfechado
dc.sourceWOS
dc.subjectBrazilian Atlantic Forest
dc.subjectImaging Spectroscopy
dc.subjectWorldview-3
dc.subjectClassification
dc.subjectIndividual Tree Crowns
dc.titleMapping Tree Species In Tropical Seasonal Semi-deciduous Forests With Hyperspectral And Multispectral Data
dc.typeArtículos de revistas


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