Abstract
Urban areas feature complex and heterogeneous land covers which create challenging issues for tree species classification. The increased availability of high spatial resolution multispectral satellite imagery and LiDAR datasets combined with the recent evolution of deep learning within remote sensing for object detection and scene classification, provide promising opportunities to map individual tree species with greater accuracy and resolution. However, there are knowledge gaps that are related to the contribution of Worldview-3 SWIR bands, very high resolution PAN band and LiDAR data in detailed tree species mapping. Additionally, contemporary deep learning methods are hampered by lack of training samples and difficulties of preparing training data. The objective of this study was to examine the potential of a novel deep learning method, Dense Convolutional Network (DenseNet), to identify dominant individual tree species in a complex urban environment within a fused image of WorldView-2 VNIR, Worldview-3 SWIR and LiDAR datasets. DenseNet results were compared against two popular machine classifiers in remote sensing image analysis, Random Forest (RF) and Support Vector Machine (SVM). Our results demonstrated that: (1) utilizing a data fusion approach beginning with VNIR and adding SWIR, LiDAR, and panchromatic (PAN) bands increased the overall accuracy of the DenseNet classifier from 75.9% to 76.8%, 81.1% and 82.6%, respectively. (2) DenseNet significantly outperformed RF and SVM for the classification of eight dominant tree species with an overall accuracy of 82.6%, compared to 51.8% and 52% for SVM and RF classifiers, respectively. (3) DenseNet maintained superior performance over RF and SVM classifiers under restricted training sample quantities which is a major limiting factor for deep learning techniques. Overall, the study reveals that DenseNet is more effective for urban tree species classification as it outperforms the popular RF and SVM techniques when working with highly complex image scenes regardless of training sample size.
Highlights
Vegetation has aesthetic, environmental, human health, and economic benefits in urban ecosystems
The study reveals that DenseNet is more effective for urban tree species classification as it outperforms the popular Random Forest (RF) and Support Vector Machine (SVM) techniques when working with highly complex image scenes regardless of training sample size
To identify the optimal data fusion approach to use for the comparison of machine learning classifiers, the combination of WV2 Panchromatic and VNIR band, WV3 short-wave infrared (SWIR) and Light Detection and Ranging (LiDAR) datasets were tested for classification accuracy
Summary
Vegetation has aesthetic, environmental, human health, and economic benefits in urban ecosystems. Tree species diversity is a vital parameter to characterize urban ecosystems. It is becoming more and more important for sustainable urban planning. A stratified threshold approach was used to remove background and shadows that were still represented in the image following the LiDAR-derived tree mask. Non-vegetation background was removed using a threshold statement where the two bands compared corresponded to the peaks and valleys representing the standard vegetation spectral curve. Shadows were removed using a bimodal histogram threshold method determined through comparison of the histogram of the NIR1 band image to separate shadowed and non-shadowed pixels. Pixels with NIR1 reflectance values higher than the threshold were retained as the non-shadowed pixels, while shadowed pixels were excluded from the image. Studies have shown that reflectance in shadowed regions is significantly less in the NIR band than sunlit areas [69]
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