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L. Guo, N. Chehata, C. Mallet and S. Boukir, “Relevance of Airborne Lidar and Multispectral Image Data for Urban Scene Classification Using Random Forests,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 66, No. 1, 2011, pp. 56-66. doi:10.1016/j.isprsjprs.2010.08.007

has been cited by the following article:

• TITLE: Urban Vegetation Mapping from Fused Hyperspectral Image and LiDAR Data with Application to Monitor Urban Tree Heights

  AUTHORS: Fatwa Ramdani

  KEYWORDS: Fusion; LiDAR; Urban Vegetation; Hyperspectral; Mapping

  JOURNAL NAME: Journal of Geographic Information System, Vol.5 No.4, July 30, 2013

  ABSTRACT: Urban vegetations have infinite proven benefits for urban inhabitants including providing shade, improving air quality, and enhancing the look and feel of communities. But creating a complete inventory is a time consuming and resource intensive process. The extraction of urban vegetation is a challenging task, especially to monitor the urban tree heights. In this study we present an efficient extraction method for mapping and monitoring urban tree heights using fused hyperspectral image and LiDAR data. Endmember distribution mapping using the spectral angle mapper technique is employed in this study. High convenience results achieved using fused hyperspectral and LiDAR data from this semiautomatics technique. This method could enable urban community organizations or local governments to map and monitorurban’s tree height and its spatial distribution.