Abstract
The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R2Margalef = 0.4562, root-mean-square error RMSEMargalef = 0.5629; R2Shannon–Wiener = 0.7948, RMSEShannon–Wiener = 0.7202; R2Simpson = 0.7907, RMSESimpson = 0.1038; and R2Pielou = 0.5875, RMSEPielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity.
Highlights
There is much evidence to support the importance of tree species diversity for maintaining wetland ecosystems, according to Schäfer et al [1]
VGG16 achieved the highest precision with an overall accuracy of 73.25%, ResNet50 achieved a slightly lower accuracy with an overall accuracy of 72.93%, while AlexNet performed the worst with an overall accuracy of
The reason is probably because the number of training samples of silk floss tree and camphor tree was small, and the convolutional neural networks (CNNs) could not learn the features of the two classes well, as a training model is often profitable for species with a large amount of samples
Summary
There is much evidence to support the importance of tree species diversity for maintaining wetland ecosystems, according to Schäfer et al [1]. Traditional biodiversity measurement is often conducted by field work or monitoring systems [4] These means cannot provide spatially distributed and updated information [5]. Zhao et al [12] used a species-driven leaf optical trait method called “spectranomics” for forest species diversity mapping. They identified interspecies variations in terms of biochemical and structural properties from airborne hyperspectral images. A minimum noise fraction transformation was employed for spectral dimensionality reduction Classification approaches such as maximum likelihood (ML) and spectral angle mapper (SAM) were used for tree species distinguishing, and ML showed good capabilities in their study
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