Karst wetlands, recognized for their unique hydrology and remarkable biodiversity, play a crucial role in global carbon sequestration and the terrestrial carbon cycle. However, understanding the relationships between hydrology and the spatial distribution, functional traits, and diversity of karst wetland vegetation is challenging. This study proposes a novel self-supervised deep learning method, the Hyperspectral Point cloud Projection model (HPProj), for creating a Hyperspectral Point Cloud (HSPC) using the asynchronously acquired Hyperspectral Image (HSI) and LiDAR Point Cloud, which further conducts a fine-grained 3D mapping of vegetation species in karst wetland. We also establish a new 3D Vegetation-parameter Quantitative Analysis Framework (3D-VQAF). This framework is the first to quantify variations in vegetation functional traits along gradients of flooding frequency and distance-to-water based on a 3D vegetation map. This study confirmed that the HPProj fusion generated high-quality HSPC with a high consistency of spectral reflectance with the original HSI (R = 0.99). We demonstrated that HSPC improved the accuracy of 3D vegetation mapping for 24 categories compared to traditional 2D methods and achieved an 80.47% mean F1-score. 3D-VQAF revealed the distribution of functional traits for each species along hydrologic gradients and indicated that in high-flood-frequency areas displayed elevated levels of chlorophyll, carotenoids, and nitrogen indices. Besides, its volume distributions were roughly normal and centered around 10 m offshore. We found that vegetation diversity exhibited a rapid increase followed by a gradual decrease with rising flooding frequency and distance-to-water, peaking at 6% flooding frequency and 7 m to water, which is critical for karst wetland protection and sustainable development.

中文翻译：

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基于无人机高光谱点云 3D 测绘量化喀斯特湿地水文梯度植被物种功能特征

喀斯特湿地以其独特的水文和显着的生物多样性而闻名，在全球固碳和陆地碳循环中发挥着至关重要的作用。然而，了解水文与喀斯特湿地植被的空间分布、功能特征和多样性之间的关系具有挑战性。本研究提出了一种新颖的自监督深度学习方法，即高光谱点云投影模型（HPProj），用于使用异步获取的高光谱图像（HSI）和激光雷达点云创建高光谱点云（HSPC），进一步进行精细喀斯特湿地植被物种的粒度 3D 绘图。我们还建立了新的3D植被参数定量分析框架（3D-VQAF）。该框架是第一个根据 3D 植被图量化植被功能特征随洪水频率梯度和距水距离的变化的框架。这项研究证实，HPProj 融合生成了高质量的 HSPC，其光谱反射率与原始 HSI 具有高度一致性（R = 0.99）。我们证明，与传统的 2D 方法相比，HSPC 提高了 24 个类别的 3D 植被测绘的准确性，并实现了 80.47% 的平均 F1 分数。 3D-VQAF 揭示了每个物种的功能性状沿水文梯度的分布，并表明在高洪水频率地区，叶绿素、类胡萝卜素和氮指数水平升高。此外，其体积分布大致呈正态，集中在离岸10 m附近。我们发现，随着洪水频率和距水体距离的增加，植被多样性呈现出快速增加后逐渐减少的趋势，在洪水频率为6%、距水体距离为7 m时达到峰值，这对于喀斯特湿地保护和可持续发展至关重要。