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First Mapping the Canopy Height of Primeval Forests in the Tallest Tree Area of Asia

Guangpeng Fan, Fei Yan, Xiangquan Zeng, Qingtao Xu, Ruoyoulan Wang, Binghong Zhang, Jialing Zhou, Liangliang Nan, Jinhu Wang, Zhiwei Zhang, Jia Wang

TL;DR

This work maps high-resolution canopy height in the fourth global giant-tree distribution area by fusing spaceborne LiDAR (GEDI, ICESat-2) with Sentinel-2 imagery and regressing height with PRFXception, a pyramid receptive field depth-separable CNN. The method achieves strong agreement with ground-based and UAV-LS references, revealing two previously unknown giant-tree communities with high potential heights (80–100 m) and supporting Southeast Tibet–Northwest Yunnan as a new world-level giant-tree center. The approach addresses the limitations of single-source can height mapping in complex primeval forests and demonstrates robust geographic generalization across regions, with implications for biomass estimation, carbon stocks, and biodiversity conservation. The resulting high-resolution canopy-height map provides a valuable tool for climate, conservation, and land-management planning, including informing national park protection and biodiversity integrity assessments.

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.

First Mapping the Canopy Height of Primeval Forests in the Tallest Tree Area of Asia

TL;DR

This work maps high-resolution canopy height in the fourth global giant-tree distribution area by fusing spaceborne LiDAR (GEDI, ICESat-2) with Sentinel-2 imagery and regressing height with PRFXception, a pyramid receptive field depth-separable CNN. The method achieves strong agreement with ground-based and UAV-LS references, revealing two previously unknown giant-tree communities with high potential heights (80–100 m) and supporting Southeast Tibet–Northwest Yunnan as a new world-level giant-tree center. The approach addresses the limitations of single-source can height mapping in complex primeval forests and demonstrates robust geographic generalization across regions, with implications for biomass estimation, carbon stocks, and biodiversity conservation. The resulting high-resolution canopy-height map provides a valuable tool for climate, conservation, and land-management planning, including informing national park protection and biodiversity integrity assessments.

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.
Paper Structure (40 sections, 10 equations, 22 figures, 3 tables)

This paper contains 40 sections, 10 equations, 22 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Discovery and survey of the fourth world giant tree area
  3. LiDAR remote sensing of forest canopy height
  4. Optical remote sensing of forest canopy height
  5. Challenges of remote sensing of canopy height in primary forest
  6. Depth regression model in remote sensing
  7. Our contribution and innovation
  8. Study area and data set
  9. Study area
  10. We investigate the world's largest trees and communities
  11. Data
  12. Field data
  13. UAV-LS data
  14. GEDI L1B Waveform data
  15. GEDI L2A data
  16. ...and 25 more sections

Figures (22)

  • Figure 1: Study area map
  • Figure 2: Drone photography and LiDAR scanning of Asia's tallest tree and its giant tree community (a) The tall cypress community in Tongmai Town, Bomi County, represents part of China's most pristine forest; (b) Close-up of different parts of the tallest tree in Asia; (c) Full-length photographs and close-up images of the tallest tree in Asia at 102.3 metres; (d) The tallest UAV-LS point clouds in Asia; (e) TLS point clouds of the tallest tree in Asia.
  • Figure 3: We conducted a field survey of Asia's tallest individual trees and their giant tree communities. The landscape of the fourth world-level giant tree distribution area, from the snow peak of more than 7,000 meters to the Zangpo River Valley of 700 meters, has a vertical range of 7,000 meters, supporting nine vegetation zones and rich and complete biodiversity. One of the authors (Guangpeng Fan) shows the scale of the tallest tree in Asia at the base of the tree and shows the view shot upward within the giant tree community. When we stand in front of these world-level giant trees, we are deeply shocked by their tall and upright posture, feeling like in front of the monument of nature, they seem to be hidden in the primitive forest of ancient giants.
  • Figure 4: Architecture diagram for PRFXception.
  • Figure 5: Geographical cross-validation region selection distribution map
  • ...and 17 more figures