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Im, Jungho
Intelligent Remote sensing and geospatial Information Science (IRIS) Lab
Research Interests
  • Remote sensing, Geospatial modeling, Disaster monitoring and management, Climate change

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Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification

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Title
Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification
Author
Ke, YinghaiQuackenbush, Lindi J.Im, Jungho
Keywords
Decision tree; Forest classification; High spatial resolution multispectral imagery; LIDAR; Object-based classification
Issue Date
2010-06
Publisher
ELSEVIER SCIENCE INC
Citation
REMOTE SENSING OF ENVIRONMENT, v.114, no.6, pp.1141 - 1154
Abstract
This study evaluated the synergistic use of high spatial resolution multispectral imagery (i.e., QuickBird, 2.4 m) and low-posting-density LIDAR data (3 m) for forest species classification using an object-based approach. The integration of QuickBird multispectral imagery and LIDAR data was considered during image segmentation and the subsequent object-based classification. Three segmentation schemes were examined: (1) segmentation based solely on the spectral image layers; (2) segmentation based solely on LIDAR-derived layers; and (3) segmentation based on both the spectral and LIDAR-derived layers. For each segmentation scheme, objects were generated at twelve different scales in order to determine optimal scale parameters. Six categories of classification metrics were generated for each object based on spectral data alone, LIDAR data alone and the combination of both data sources. Machine learning decision trees were used to build classification rule sets. Quantitative segmentation quality assessment and classification accuracy results showed the integration of spectral and LIDAR data, in both image segmentation and object-based classification, improved the forest classification compared to using either data source independently. Better segmentation quality led to higher classification accuracy. The highest classification accuracy (Kappa = 91.6%) was acquired when using both spectral- and LIDAR-derived metrics based on objects segmented from both spectral and LIDAR layers at scale parameter 250, where best segmentation quality was achieved. Optimal scales were analyzed for each segmentation-classification scheme. Statistical analysis of classification accuracies at different scales revealed that there was a range of optimal scales that provided statistically similar accuracy.
URI
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DOI
10.1016/j.rse.2010.01.002
ISSN
0034-4257
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