evaluation of hyperspectral multitemporal information to improve tree species identification in the highly diverse atlantic forest

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ID: 130126
2020
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Abstract
The monitoring of forest resources is crucial for their sustainable management, and tree species identification is one of the fundamental tasks in this process. Unmanned aerial vehicles (UAVs) and miniaturized lightweight sensors can rapidly provide accurate monitoring information. The objective of this study was to investigate the use of multitemporal, UAV-based hyperspectral imagery for tree species identification in the highly diverse Brazilian Atlantic forest. Datasets were captured over three years to identify eight different tree species. The study area comprised initial to medium successional stages of the Brazilian Atlantic forest. Images were acquired with a spatial resolution of 10 cm, and radiometric adjustment processing was performed to reduce the variations caused by different factors, such as the geometry of acquisition. The random forest classification method was applied in a region-based classification approach with leave-one-out cross-validation, followed by computing the area under the receiver operating characteristic (AUCROC) curve. When using each dataset alone, the influence of different weather behaviors on tree species identification was evident. When combining all datasets and minimizing illumination differences over each tree crown, the identification of three tree species was improved. These results show that UAV-based, hyperspectral, multitemporal remote sensing imagery is a promising tool for tree species identification in tropical forests.
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miyoshi2020remoteevaluation Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Gabriela Takahashi Miyoshi;Nilton Nobuhiro Imai;Antonio Maria Garcia Tommaselli;Marcus Vinícius Antunes de Moraes;Eija Honkavaara
Journal Journal of pharmacological sciences
Year 2020
DOI
10.3390/rs12020244
URL
https://www.mdpi.com/2072-4292/12/2/244
Keywords Keywords not found

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