Semi-automated approach for mapping urban trees from integrated airborne based digital image and lidar point cloud datasets

Abstract

Mapping of trees plays an important role in modern urban spatial data management, as many benefits and applications inherit from this detailed up-to-date data sources. Timely and accurate acquisition of information on the condition of urban trees serves as a tool for decision makers to better appreciate urban ecosystems and their numerous values which are critical to building up strategies for sustainable development. The conventional techniques used for extracting trees feature include ground surveying and interpretation of the aerial photography. However, these techniques are associated with some constraint, such as labour intensive field work and a lot of financial requirement which can be overcome by means of integrated LiDAR and digital image datasets. Compared to predominant studies on trees extraction mainly in purely forested areas, this study concentrates on urban areas, which have a high structural complexity with a multitude of different objects. This study presented a semi-automated approach for extracting urban trees from integrated airborne based LiDAR and multispectral digital image datasets over Istanbul city of Turkey. The presented approach includes extraction of shadow free vegetation areas from digital images using shadow index and NDVI techniques, automated extraction of 3D information about vegetation areas from integrated processing of the datasets, extraction of tree objects from the vegetation based on various LiDAR attributes and finally, accuracy assessment of the extracted trees. The quality measures of this approach reveals that the extracted result is 83% complete and 80% correct. The developed algorithms have shown a promising result which proved that the integrated datasets is a suitable technology and viable source of information for urban trees management. Furthermore, the approach has also proved to be an accurate, fast and cost effective technique for estimating and delineating 3D information about trees. As a conclusion, therefore, the extracted information provides a snapshot of location, and extent of trees in the study area which will be useful to city planners and decision makers to understand how much canopy cover exists, identify new planting, removal, or reforestation opportunities and what locations have the greatest need or potential to maximize benefits of return on investment. It can also help track trends or changes to the urban trees over time and inform future management decisions.