Abstract
We have developed the world's first canopy height map of the distribution area of world-level giant trees. This mapping is crucial for discovering more individual and community world-level giant trees, and for analyzing and quantifying the effectiveness of biodiversity conservation measures in the Yarlung Tsangpo Grand Canyon (YTGC) National Nature Reserve. We proposed a method to map the canopy height of the primeval forest within the world-level giant tree distribution area by using a spaceborne LiDAR fusion satellite imagery (Global Ecosystem Dynamics Investigation (GEDI), ICESat-2, and Sentinel-2) driven deep learning modeling. And we customized a pyramid receptive fields depth separable CNN (PRFXception). PRFXception, a CNN architecture specifically customized for mapping primeval forest canopy height to infer the canopy height at the footprint level of GEDI and ICESat-2 from Sentinel-2 optical imagery with a 10-meter spatial resolution. We conducted a field survey of 227 permanent plots using a stratified sampling method and measured several giant trees using UAV-LS. The predicted canopy height was compared with ICESat-2 and GEDI validation data (RMSE =7.56 m, MAE=6.07 m, ME=-0.98 m, R^2=0.58 m), UAV-LS point clouds (RMSE =5.75 m, MAE =3.72 m, ME = 0.82 m, R^2= 0.65 m), and ground survey data (RMSE = 6.75 m, MAE = 5.56 m, ME= 2.14 m, R^2=0.60 m). We mapped the potential distribution map of world-level giant trees and discovered two previously undetected giant tree communities with an 89% probability of having trees 80-100 m tall, potentially taller than Asia's tallest tree. This paper provides scientific evidence confirming southeastern Tibet--northwestern Yunnan as the fourth global distribution center of world-level giant trees initiatives and promoting the inclusion of the YTGC giant tree distribution area within the scope of China's national park conservation.
Abstract (translated)
我们已经开发了世界上第一个世界级的树冠高度图，展示了世界顶级大树分布区的范围。这张地图对于发现更多的个体和群落世界级的树冠，以及分析生物多样性保护措施在雅鲁藏布江大峡谷（YTGC）国家公园的有效性至关重要。我们提出了利用空间站的激光雷达融合卫星影像（全球生态系统动态调查（GEDI），ICESat-2 和 Sentinel-2）进行多层次深度学习建模，来绘制世界顶级大树分布区树冠高度的方法。我们还定制了一个可分离的 Pyramid 接收器场深度卷积神经网络 (PRFXception)。 PRFXception 是一个专门为将世界顶级大树的树冠高度映射到足迹水平，推断GEDI 和 ICESat-2 从 Sentinel-2 光学影像（具有10米空间分辨率）的足迹水平进行建模的 CNN 架构。 我们使用分层抽样方法对227个永久性抽样地进行了现场调查，并使用UAV-LS测量了数棵大树。预测的树冠高度与ICESat-2和GEDI验证数据（RMSE = 7.56 m，MAE = 6.07 m，ME = -0.98 m，R^2 = 0.58 m），UAV-LS点云数据（RMSE = 5.75 m，MAE = 3.72 m，ME = 0.82 m，R^2 = 0.65 m）以及地面调查数据（RMSE = 6.75 m，MAE = 5.56 m，ME = 2.14 m，R^2 = 0.60 m）进行了比较。我们绘制了世界顶级大树潜在分布图，并发现了两个之前未被发现的大树群落，有89%的概率具有80-100米高的大树，这棵大树可能会超过亚洲最高树。本文提供了科学证据，证实了西藏东南部--云南省西北部是世界级大树倡议的第四个全球分布中心，并促进了将 YTGC 大树分布区纳入中国国家公园保护范围。
URL
https://arxiv.org/abs/2404.14661
