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.

我们通过使用 GEDI、ICESat-2 和 Sentinel-2 的空间 borne LiDAR fusion 卫星图像驱动的深度学习建模，提出了一种在全球级巨树分布区内绘制原始森林冠层高度的方法，并且发现了两个以前未被发现的巨树群落，有 89% 的概率有 80-100 米高的树木，可能比亚洲最高的树还要高，这提供了科学证据，确认了西藏东南部 - 云南西北部是全球级巨树倡议的第四个全球分布中心，并促进了 YTGC 巨树分布区在中国国家公园保护范围内的包含。

亚洲最高树木区原始森林树冠高度首次测绘

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

Biodiversity conservation depends on accurate, up-to-date information about
wildlife population distributions. Motion-activated cameras, also known as
camera traps, are a critical tool for population surveys, as they are cheap and
non-intrusive. However, extracting useful information from camera trap images
is a cumbersome process: a typical camera trap survey may produce millions of
images that require slow, expensive manual review. Consequently, critical
information is often lost due to resource limitations, and critical
conservation questions may be answered too slowly to support decision-making.
Computer vision is poised to dramatically increase efficiency in image-based
biodiversity surveys, and recent studies have harnessed deep learning
techniques for automatic information extraction from camera trap images.
However, the accuracy of results depends on the amount, quality, and diversity
of the data available to train models, and the literature has focused on
projects with millions of relevant, labeled training images. Many camera trap
projects do not have a large set of labeled images and hence cannot benefit
from existing machine learning techniques. Furthermore, even projects that do
have labeled data from similar ecosystems have struggled to adopt deep learning
methods because image classification models overfit to specific image
backgrounds (i.e., camera locations). In this paper, we focus not on automating
the labeling of camera trap images, but on accelerating this process. We
combine the power of machine intelligence and human intelligence to build a
scalable, fast, and accurate active learning system to minimize the manual work
required to identify and count animals in camera trap images. Our proposed
scheme can match the state of the art accuracy on a 3.2 million image dataset
with as few as 14,100 manual labels, which means decreasing manual labeling
effort by over 99.5%.

本文提出了一种结合人工智能和人类智能的主动学习系统，能够快速准确地对相机陷阱图像中的动物进行识别和计数，可将手动标注的工作量减少了 99.5% 以上。