Abstract
Timely and accurate information about spatial distribution of tree species in urban areas provides crucial data for sustainable urban development, management and planning. Very high spatial resolution data collected by sensors onboard Unmanned Aerial Vehicles (UAV) systems provide rich data sources for mapping tree species. This paper proposes a method of tree species mapping from UAV images over urban areas using similarity in tree-crown object histograms and a simple thresholding method. Tree-crown objects are first extracted and used as processing units in subsequent steps. Tree-crown object histograms of multiple features, i.e., spectral and height related features, are generated to quantify within-object variability. A specific tree species is extracted by comparing similarity in histogram between a target tree-crown object and reference objects. The proposed method is evaluated in mapping four different tree species using UAV multispectral ortho-images and derived Digital Surface Model (DSM) data collected in Shanghai urban area, by comparing with an existing method. The results demonstrate that the proposed method outperforms the comparative method for all four tree species, with improvements of 0.61–5.81% in overall accuracy. The proposed method provides a simple and effective way of mapping tree species over urban area.
Highlights
Urban tree cover plays an important role in sustainable urban development and planning by providing a range of environmental and ecological services, and social and economic benefits [1]
Considering the aforementioned problems, in this paper, we propose a novel method of tree species mapping from Unmanned Aerial Vehicles (UAV) multispectral images and derived height data over urban area using tree-crown object histogram and a simple thresholding method
A specific tree species was extracted by quantitatively comparing similarity in histograms between a target tree-crown object and reference objects, measured using the Variable Bin Size Distance (VBSD) [38], a recently proposed histogram similarity measure
Summary
Urban tree cover plays an important role in sustainable urban development and planning by providing a range of environmental and ecological services, and social and economic benefits [1]. The diversity, structure and spatial distribution of tree species are closely related to the quality of these services and benefits. Many existing studies used different image data, such as satellite and aerial multispectral images [10,11], airborne hyperspectral images [12,13] of very high spatial resolution (VHR), and three-dimensional data from airborne LiDAR (Light Detection and Ranging) data [14,15,16,17]. VHR multispectral images, such as IKONOS, QuickBird and WordView-2, and hyperspectral images have been widely used in distinguishing different tree species [10,11,12,13,18,19]. Hyperspectral data and LiDAR data were jointly used to extract different tree species [14,15,16,17], providing promising results
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
