Combination of LiDAR's Multiple Attributes for Wetland Classification: A Case Study of Yellow River Delta. Issue 6 (1st November 2020)

• Record Type: Journal Article
• Authors: Ding, Qiong; Liu, Jingbin; Yu, Junpeng; Tang, Long; Guo, Bo
• Is Part Of: Canadian journal of remote sensing. Volume 46:Issue 6(2020)
• Page Start: 753
• Page End: 764
• Publication Date: 2020-11-01
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2020.1867833
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: Wetlands have received intensive interdisciplinary attention as unique ecosystems with valuable resources. As a new technology, the airborne LiDAR system has recently been applied in wetland research. However, most of the studies have focused on one or two LiDAR observations to extract either terrain or vegetation information from wetlands. This research aims at evaluating the combination of LiDAR's spatial, spectral, and derived information to improve the mapping and classification of wetlands. Six LiDAR data derived attributes (DSM, DTM, off-ground features, slope map, multiple pulse returns, and normalized intensity) have been utilized for wetland classification based on an object-oriented classification method. The overall classify accuracy of the proposed LiDAR's multi-attribute approach reaches 91.2%.