Abstract
Understanding the spatial patterns of plant communities is important for sustainable wetland ecosystem management and biodiversity conservation. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral data with high spatial resolution have become ideal for accurate classification of wetland plant communities. In this article, four dominant plant communities (Phragmites australis, Typha orientalis, Suaeda glauca, and Scirpus triqueter) and two unvegetated cover types (water and bare land) in the Momoge Ramsar wetland site were classified. This was achieved using UAV hyperspectral images and three object- and pixel-based machine-learning classification algorithms [random forest (RF), convolutional neural network (CNN), and support vector machine (SVM)]. First, spectral derivative analysis, logarithmic analysis, and continuum removal analysis identified the wavelength at which the greatest difference in reflectance occurs. Second, dimensionality reduction of hyperspectral images was conducted using principal component analysis. Subsequently, an optimal feature combination for community mapping was formed based on data transformation (spectral features, vegetation indices, and principal components). Image objects were obtained by segmenting the optimum object feature subsets. Finally, distribution maps of communities were produced by using three machine-learning classification algorithms. Our results reveal that object-based image analysis outperforms pixel-based methods, with overall accuracies (OAs) of 80.29-87.75%; RF has the highest OA of 87.75% (Kappa = 0.864), followed consecutively by CNN (OA = 83.31%, Kappa = 0.829) and SVM (OA = 80.29%, Kappa = 0.813). Phragmites australis dominates the plant community (55.9%) at the study area, followed by Typha orientalis (16.2%), Suaeda glauca (16.2%), and Scirpus triqueter (4.6%). The results highlight the importance of spectral transformation features in red-edge regions. The mapping results will help establish basic information for subsequent studies involving habitat suitability assessment at this study site.
Highlights
WETLAND plants act as sentinels for ecological changes and provide early signs of physical or chemical degradation, such as reduction of wetland area and wetland plant diversity, decreased and damaged rare and endangered waterfowl habitats, water eutrophication, as well as decreased of organic matter accumulation and primary productivity [1, 2]
The main objectives of this study are to (i) make full use of the high spatial characteristics and hyperspectrality of unmanned aerial vehicle (UAV) hyperspectral images to realize the fine identification of different wetland plant community types and (ii) examine the effectiveness of various machine-learning classification algorithms [random forest (RF), convolutional neural network (CNN), and support vector machine (SVM)] for mapping wetland plant communities
According to the relevant literature screening, it was found that these vegetation indices selected in this paper indicated the difference in leaf, canopy structure, chlorophyll content, and water content of different wetland plant communities, these VIs were used in the plant-community classification to improve feature discrimination and accuracy of our target community classes [43]
Summary
WETLAND plants act as sentinels for ecological changes and provide early signs of physical or chemical degradation, such as reduction of wetland area and wetland plant diversity, decreased and damaged rare and endangered waterfowl habitats, water eutrophication, as well as decreased of organic matter accumulation and primary productivity [1, 2]. Once their health and functions are disturbed, the effects are detrimental on all life forms supported by wetlands [3]. Since the late 1980s, the rapid development of satellite remote sensing technology has greatly enhanced our ability to delineate wetlands [6, 7]
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