An updated Vegetation Map of China (1:1000000)

植被(病理学) 中国 地理 地图学 医学 考古 病理
作者
Yanjun Su,Qinghua Guo,Tianyu Hu,Hongcan Guan,Shichao Jin,An Shazhou,Xuelin Chen,Ke Guo,Zhanqing Hao,Yuanman Hu,Yongmei Huang,Mingxi Jiang,Jiaxiang Li,Zhenji Li,Xiankun Li,Xiaowei Li,Cunzhu Liang,Liu Renlin,Qing Liu,Hongwei Ni
出处
期刊:中国科学通报:英文版 卷期号:65 (13): 1125-1136 被引量:169
标识
DOI:10.1016/j.scib.2020.04.004
摘要

Vegetation maps are important sources of information for biodiversity conservation, ecological studies, vegetation management and restoration, and national strategic decision making. The current Vegetation Map of China (1:1000000) was generated by a team of more than 250 scientists in an effort that lasted over 20 years starting in the 1980s. However, the vegetation distribution of China has experienced drastic changes during the rapid development of China in the last three decades, and it urgently needs to be updated to better represent the distribution of current vegetation types. Here, we describe the process of updating the Vegetation Map of China (1:1000000) generated in the 1980s using a “crowdsourcing-change detection-classification-expert knowledge” vegetation mapping strategy. A total of 203,024 field samples were collected, and 50 taxonomists were involved in the updating process. The resulting updated map has 12 vegetation type groups, 55 vegetation types/subtypes, and 866 vegetation formation/sub-formation types. The overall accuracy and kappa coefficient of the updated map are 64.8% and 0.52 at the vegetation type group level, 61% and 0.55 at the vegetation type/subtype level and 40% and 0.38 at the vegetation formation/sub-formation level. When compared to the original map, the updated map showed that 3.3 million km2 of vegetated areas of China have changed their vegetation type group during the past three decades due to anthropogenic activities and climatic change. We expect this updated map to benefit the understanding and management of China’s terrestrial ecosystems.
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
Loti完成签到,获得积分10
1秒前
1秒前
xuan完成签到,获得积分10
2秒前
可爱的函函应助D调的华丽采纳,获得10
2秒前
霸天狂龙发布了新的文献求助10
2秒前
lkl完成签到,获得积分10
3秒前
3秒前
文静元霜完成签到,获得积分10
4秒前
4秒前
5秒前
6秒前
pp完成签到,获得积分10
6秒前
我是老大应助阿涛采纳,获得10
6秒前
6秒前
顶刊大王发布了新的文献求助40
7秒前
抑浠完成签到 ,获得积分10
7秒前
7秒前
lgs发布了新的文献求助10
7秒前
YC完成签到,获得积分10
8秒前
8秒前
9秒前
liyali完成签到,获得积分10
9秒前
英吉利25发布了新的文献求助10
10秒前
congcong完成签到,获得积分20
10秒前
YengFing发布了新的文献求助10
10秒前
Fx发布了新的文献求助10
11秒前
11秒前
大个应助欢呼妙菱采纳,获得10
11秒前
霸天狂龙完成签到,获得积分10
12秒前
微笑猎豹发布了新的文献求助10
12秒前
lrene完成签到,获得积分10
12秒前
温水完成签到,获得积分10
13秒前
13秒前
xxx完成签到,获得积分10
14秒前
14秒前
韶邑发布了新的文献求助10
14秒前
今后应助小金猪采纳,获得10
15秒前
15秒前
淡然发布了新的文献求助10
15秒前
15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
APA handbook of comparative psychology: Basic concepts, methods, neural substrate, and behavior 1000
Child and Adolescent Mental Health 600
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The fast track to determining transfer functions of linear circuits: The student guide 500
Römisch-Germanische Forschungen 500
Electric machines: theory, operating applications, and controls 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 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7600424
求助须知:如何正确求助?哪些是违规求助? 9176595
关于积分的说明 19649353
捐赠科研通 7176375
什么是DOI,文献DOI怎么找? 3268680
关于科研通互助平台的介绍 2433062
邀请新用户注册赠送积分活动 2262267