NDLI: Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping

Content Provider	Taylor & Francis Online
Author	Shafri, Helmi Zulhaidi Mohd Mohamed, Thamer Ahmed Zahidi, Izni Yusuf, Badronnisa Hamedianfar, Alireza
Abstract	This paper assessed the performance of object-based supervised support vector machine (SVM) and rule-based techniques in classifying tropical vegetated floodplain using 0.6m QuickBird image and LIDAR dataset of 1.4 points per square meter (PPSM). This is particularly significant in hydraulic modelling in which vegetation roughness is a big uncertainty and largely relies on land cover classification. The supervised classification resulted in 79.40% overall accuracy whilst the results improved by 8% with rule-based classification. 40 sample plots of trees and shrubs were measured to be compared to obtain the best classification results. The results showed a linear relationship between tree diameters and NDVI with a high Pearson correlation of 0.76 and coefficient of determination (r2) of 0.58. The canopy areas of shrubs were found to be representative spatially with an even higher Pearson correlation of 0.98 and r2 of 0.95. The study concluded that the fusion of QuickBird image and low point density LIDAR in rule-based classification together with field data were useful in quantifying tropical trees and shrubs.
Starting Page	423
Ending Page	446
Page Count	24
File Format	PDF HTM / HTML
ISSN	22797254
DOI	10.5721/EuJRS20154824
Journal	European Journal of Remote Sensing
Volume Number	48
Issue Number	1
Language	English
Publisher	Taylor & Francis
Publisher Date	2017-02-17
Access Restriction	Open
Subject Keyword	Low point density LIDAR QuickBird OBIA Rule-based classification Tropical vegetated floodplain Vegetation parameter
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Atmospheric Science Computers in Earth Sciences Environmental Science

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