Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

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.

“Kill Two Birds with One Stone”: Urban Tree Species Classification Using Bi-Temporal Pléiades Images to Study Nesting Preferences of an Invasive Bird

“…Thus, in urban areas, and in a more general way, single-date imagery may not suffice for urban tree species classification. Li et al [16] explored the identification of five urban tree species based on object-based classification using single-date and bi-temporal WorldView-2 and WorldView-3 images. The overall accuracy increased to more than 11% when using bi-temporal images.…”

Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

“…increased from late summer to high autumn in other bands [7,8]. Therefore, adding the phenology information can contribute to forest type identification.…”

“…A common issue in climate change mitigation is to reduce forest damage and increase forest resources [6], which can be measured by the precise estimation of carbon storage based on accurate mapping of forest types. Traditional forest survey methods involve random sampling and field investigation within each sample plot, a time-consuming and laborious process [7,8]. Remote sensing technology can be used to obtain forest information from areas with rough terrain or that are difficult to reach, complementing traditional methods while at the same time reducing the need for fieldwork.…”

First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

“…Also for forest classifications, images acquired earlier (end of spring) or later (beginning of autumn) in the year would probably lead to higher classification accuracies. For all land cover types, we expect higher classification accuracies with better timing of the acquisitions and in particular by using multi-temporal data [57,[73][74][75].…”