Open AccessReview on the Use of Remote Sensing for Urban Forest Monitoring
Author(s) -
Razieh Shojanoori,
Helmi Zulhaidi Mohd Shafri
Publication year - 2016
Publication title -
arboriculture and urban forestry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.222
H-Index - 47
eISSN - 2155-0778
pISSN - 1935-5297
DOI - 10.48044/jauf.2016.034
Subject(s) - urban forest , remote sensing , urbanization , urban ecosystem , lidar , environmental science , geography , urban planning , urban forestry , tree (set theory) , computer science , environmental resource management , environmental planning , forestry , ecology , mathematical analysis , mathematics , biology
Urban forests are vital in urban areas because they clean the air, absorb water, and protect the environment from intense heat. Destruction of the urban forest by increased urbanization is a considerable threat to the ecosystem. Hence, urban planners must obtain and manage information about urban forests, but the complexity of urban areas has made these tasks difficult. With developments in remote-sensing technologies, the monitoring and detection of urban forests can be achieved without performing any field measurements. In this study, different remote-sensing imageries and various methods are evaluated to obtain urban forest information. This review demonstrates that very high resolution (VHR) satellite imagery, such as from WorldView-2, is the most efficient data that can be used 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. An 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 limitations of collecting urban forest information.