RESEARCH YOU NEED TO READ:Abstracts from the November 2016 issue of the scientific journal

摘要

In this article, various remote-sensing methods are reviewed for urban forest information extraction. This review demonstrates that very-high-resolution (VHR) satellite imagery, such as from WorldView-2, is the most efficient type of data that can beused to obtain urban forest information. The use of the combination of LiDAR data with VHR imagery increases the accuracy of information, particularly about tree crown delineation. Traditional pixel-based classification methods are not effectively applicable to obtain urban tree information because of significant spectral variability in urban areas. The object-based classification technique, which uses spatial, textural,and color information, can be a potential method to detect urban forest and tree species discrimination. The new VHR imaging method, which uses the object-based technique, is recommended to overcome the limitation of collecting urban forest information.