Evaluation of Hyperspectral Multitemporal Information to Improve Tree Species Identification in the Highly Diverse Atlantic Forest

Author(s)

Miyoshi, Gabriela Takahashi ^[1] ; Imai, Nilton Nobuhiro ^{[2, 1]} ; Garcia Tommaselli, Antonio Maria ^{[2, 1]} ; Antunes de Moraes, Marcus Vinicius ^[1] ; Honkavaara, Eija ^[3]

Affiliation

^[1] Sao Paulo State Univ UNESP, Grad Program Cartog Sci, Roberto Simonsen 305, BR-19060900 Presidente Prudente, SP - Brazil

^[2] Sao Paulo State Univ UNESP, Dept Cartog, Roberto Simonsen 305, BR-19060900 Presidente Prudente, SP - Brazil

^[3] Natl Land Survey Finland, Finnish Geospatial Res Inst, Geodeetinrinne 2, Masala 02430 - Finland

• Document type: Journal article
• Source: REMOTE SENSING; v. 12, n. 2 JAN 2 2020.
• Web of Science Citations: 5

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. (AU)

• FAPESP's process: 13/50426-4 - Unmanned airborne vehicle-based 4D remote sensing for mapping Rain Forest biodiversity and its change in Brazil (UAV_4D_Bio)
• Grantee: Antonio Maria Garcia Tommaselli
• Support Opportunities: Regular Research Grants