Upward expansion and acceleration of forest clearance in the mountains of Southeast Asia

生物多样性 地理 气候变化 碳储量 雨林 森林覆盖 自然地理学 环境科学 农林复合经营 林业 生态学 生物
作者
Yu Feng,Alan D. Ziegler,Paul R. Elsen,Yang Liu,Xinyue He,Dominick V. Spracklen,Joseph Holden,Xin Jiang,Chunmiao Zheng,Zhenzhong Zeng
出处
期刊:Nature sustainability [Nature Portfolio]
卷期号:4 (10): 892-899 被引量:112
标识
DOI:10.1038/s41893-021-00738-y
摘要

Southeast Asia contains about half of all tropical mountain forests, which are rich in biodiversity and carbon stocks, yet there is debate as to whether regional mountain forest cover has increased or decreased in recent decades. Here, our analysis of high-resolution satellite datasets reveals increasing mountain forest loss across Southeast Asia. Total mean annual forest loss was 3.22 Mha yr−1 during 2001–2019, with 31% occurring on the mountains. In the 2010s, the frontier of forest loss moved to higher elevations (15.1 ± 3.8 m yr−1 during 2011–2019, P < 0.01) and steeper slopes (0.22 ± 0.05° yr−1 during 2009–2019, P < 0.01) that have high forest carbon density relative to the lowlands. These shifts led to unprecedented annual forest carbon loss of 424 Tg C yr−1, accelerating at a rate of 18 ± 4 Tg C yr−2 (P < 0.01) from 2001 to 2019. Our results underscore the immediate threat of carbon stock losses associated with accelerating forest clearance in Southeast Asian mountains, which jeopardizes international climate agreements and biodiversity conservation. Southeast Asia contains half the world’s tropical mountain forests. This study finds increasing mountain forest loss there, with the clearing frontier moving higher in the 2010s and causing unprecedented carbon loss.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
hhh完成签到,获得积分10
1秒前
savesunshine1022完成签到,获得积分10
2秒前
2秒前
窦鞅发布了新的文献求助10
2秒前
千島雪穂完成签到,获得积分10
3秒前
9秒前
Blueblue完成签到,获得积分10
9秒前
共享精神应助桂圆同学采纳,获得10
12秒前
Phoebe发布了新的文献求助10
12秒前
Sylvia完成签到 ,获得积分10
13秒前
16秒前
丁小二完成签到 ,获得积分10
16秒前
欢呼惜文完成签到,获得积分10
16秒前
16秒前
窦鞅完成签到,获得积分10
16秒前
我要发核心完成签到 ,获得积分10
17秒前
solarrrrr完成签到,获得积分10
18秒前
骑着蜗牛去赶集完成签到,获得积分10
19秒前
20秒前
白色完成签到,获得积分10
21秒前
Tiffany发布了新的文献求助10
21秒前
花花屯屯发布了新的文献求助10
21秒前
21秒前
DDDD源完成签到,获得积分10
21秒前
SW冒险家完成签到 ,获得积分10
22秒前
23秒前
24秒前
小二郎应助nextconnie采纳,获得10
24秒前
vera完成签到 ,获得积分10
25秒前
25秒前
wsy发布了新的文献求助10
26秒前
dd发布了新的文献求助10
26秒前
26秒前
28秒前
赘婿应助Tiffany采纳,获得10
30秒前
32秒前
Gauss发布了新的文献求助10
33秒前
33秒前
33秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Various Faces of Animal Metaphor in English and Polish 800
Signals, Systems, and Signal Processing 610
Superabsorbent Polymers: Synthesis, Properties and Applications 500
Photodetectors: From Ultraviolet to Infrared 500
On the Dragon Seas, a sailor's adventures in the far east 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6351755
求助须知:如何正确求助?哪些是违规求助? 8166264
关于积分的说明 17185960
捐赠科研通 5407831
什么是DOI,文献DOI怎么找? 2862981
邀请新用户注册赠送积分活动 1840543
关于科研通互助平台的介绍 1689612