Abstract
Urban tree species mapping is an important prerequisite to understanding the value of urban vegetation in ecological services. In this study, we explored the potential of bi-temporal WorldView-2 (WV2, acquired on 14 September 2012) and WorldView-3 images (WV3, acquired on 18 October 2014) for identifying five dominant urban tree species with the object-based Support Vector Machine (SVM) and Random Forest (RF) methods. Two study areas in Beijing, China, Capital Normal University (CNU) and Beijing Normal University (BNU), representing the typical urban environment, were evaluated. Three classification schemes—classification based solely on WV2; WV3; and bi-temporal WV2 and WV3 images—were examined. Our study showed that the single-date image did not produce satisfying classification results as both producer and user accuracies of tree species were relatively low (44.7%–82.5%), whereas those derived from bi-temporal images were on average 10.7% higher. In addition, the overall accuracy increased substantially (9.7%–20.2% for the CNU area and 4.7%–12% for BNU). A thorough analysis concluded that near-infrared 2, red-edge and green bands are always more important than the other bands to classification, and spectral features always contribute more than textural features. Our results also showed that the scattered distribution of trees and a more complex surrounding environment reduced classification accuracy. Comparisons between SVM and RF classifiers suggested that SVM is more effective for urban tree species classification as it outperforms RF when working with a smaller amount and imbalanced distribution of samples.
Highlights
All types of urban vegetation, especially trees, play an important role in the urban ecosystem
For the WV3 image at Capital Normal University (CNU), all Bhattacharyya Distance (BD) values reach a plateau at a scale parameter of 140, 140 was selected as best scale parameter (Figure 4b)
Our results show that using bi-temporal WV2 and WV3 images consistently improve urban tree species mapping accuracy regardless of study area or classifier used
Summary
All types of urban vegetation, especially trees, play an important role in the urban ecosystem. Acquiring timely and detailed information on spatial distribution and structural characteristics of trees within urban areas is critical for a better understanding of their eco-service values, and subsequently for developing strategies for sustainable urban development. Especially high spatial resolution satellite imagery, provide a great opportunity for timely tree species mapping at a considerably lower cost. High spatial resolution satellite images, such as those acquired by IKONOS, QuickBird, WorldView-2 (WV2) or WorldView-3 (WV3) satellites, have been widely used for tree species identification in forested areas [5,6,7,8,9,10]. Compared to the traditional four-band IKONOS and QuickBird, the WV2 satellite (DigitalGlobe Inc.) launched in 2009 has better spectral (eight bands) and spatial (0.5–2 m) resolution. In addition to the eight bands it shares with WV2, it has a 16-band mode which could provide an additional eight short-wave infrared (SWIR) bands that may further benefit vegetation analysis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
