Sensors | |
Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object-Oriented Approach | |
Matthew Voss1  | |
关键词: remote sensing; object oriented; hyperspectral; LiDAR; tree species; urban; | |
DOI : 10.3390/s8053020 | |
来源: mdpi | |
【 摘 要 】
The objective of the current study was to analyze the seasonal effect on differentiating tree species in an urban environment using multi-temporal hyperspectral data, Light Detection And Ranging (LiDAR) data, and a tree species database collected from the field. Two Airborne Imaging Spectrometer for Applications (AISA) hyperspectral images were collected, covering the Summer and Fall seasons. In order to make both datasets spatially and spectrally compatible, several preprocessing steps, including band reduction and a spatial degradation, were performed. An object-oriented classification was performed on both images using training data collected randomly from the tree species database. The seven dominant tree species (
【 授权许可】
CC BY
© 2008 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190058978ZK.pdf | 633KB | download |