Global high-resolution mountain green cover index mapping based on Landsat images and Google Earth Engine

doi:10.1016/j.isprsjprs.2020.02.011

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 数字山地与遥感应用中心

	Global high-resolution mountain green cover index mapping based on Landsat images and Google Earth Engine
	Bian, Jinhu 1; Li, Ainong 1; Lei, Guangbin 1; Zhang, Zhengjian 1,2; Nan, Xi 1
刊名	ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
	2020-04-01
卷号	162 页码:63-76
关键词	Mountain green vegetation index (MGCI) Landsat Sustainable development goals (SDGs) Google Earth Engine (GEE)
ISSN号	0924-2716
DOI	10.1016/j.isprsjprs.2020.02.011
产权排序	1
文献子类	Article
英文摘要	Mountains provide essential ecosystem services to billions of people and are home to a majority of the global biodiversity hotspots. However, mountain ecosystems are particularly sensitive to climate and environmental changes. The protection and sustainable management of mountain ecosystems are thus of great importance and are listed as a Sustainable Development Goal (SDG 15.4) of the United Nations 2030 Agenda for sustainable development. The mountain green cover index (MGCI, SDG 15.4.2), which is defined by measuring the changes of green vegetation in mountain areas, is one of the two SDG indicators for monitoring the conservation status of mountain environments. However, as a country indicator, it is challenging to use the current MGCI data to quantify the detailed changes in highly heterogeneous mountain areas within each country, and correspondingly, the measures is limited when supporting sustainable development and protection strategy decisionmaking for mountain environments. In this paper, a new global high resolution gridded-MGCI calculation method that depicts the varying details in the MGCI from both the spatial and temporal domains was proposed based on 30-m Landsat-8 Operational Land Imager (OLI) images and the Google Earth Engine (GEE) cloud computing platform. In the method, first, a grid-based MGCI calculation model was proposed by that considers the true surface area instead of the planimetric area of each mountain pixel. The global green vegetation cover was then extracted using all available 30-m Landsat-8 satellite observations within the calendar year on the GEE platform via a new frequency- and phenology-based algorithm. The mountain true surface area was finally calculated and introduced into the MGCI calculation model for global MGCI mapping. The results showed that the green vegetation cover extracted from 30 m Landsat images can reach an overall accuracy of 95.56%. In general, 69.73% of the global mountain surface had 1.05 times more surface area than planimetric area. The average difference between the MGCIs considering the surface area and planimetric area can reach 11.89%. According to the statistics of the global grid MGCI, 68.79% of the global mountain area had an MGCI higher than 90%, 16.94% of the global mountain area had no vegetation cover and 3.81% of mountain area had an MGCI lower than 10%. The proposed MGCIs were further aggregated at the country level and compared with the Food and Agriculture Organization (FAO) MGCI baseline data from 2017. The comparison indicated good consistency between the two datasets, with an R-2 of 0.9548 and a mean absolute difference of 4.26%. The new MGCI calculation method was based all available Landsat-8 observations from a year, which reduced the dependence of the MGCI on the updating frequency of the land cover product. Furthermore, the method has great potential for getting the spatio-temporal continuous MGCI with a high spatial resolution for characterizing explicit mountain vegetation dynamics and vegetation-climate change interactions to advance our understanding of global mountain changes. The new MGCI data will be available on the CASEarth data-sharing platform.
电子版国际标准刊号	1872-8235
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19030303] ; National Natural Science Foundation of China[41701432] ; National Natural Science Foundation of China[41631180] ; National Natural Science Foundation of China[41571373] ; National Key Research and Development Program of China[2016YFA0600103] ; National Key Research and Development Program of China[2016YFC0500201-06] ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS[SDS-135-1708] ; CAS Light of West China Program ; Youth Innovation Promotion Association CAS[2019365]
WOS关键词	HJ-1A/B CONSTELLATION ; ONLINE TOOL ; MAP ; CLASSIFICATION ; ELEVATION ; DYNAMICS ; CLOUD ; MODIS
WOS研究方向	Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000527709200006
资助机构	Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; 135 Strategic Program of the Institute of Mountain Hazards and Environment, CAS ; CAS Light of West China Program ; Youth Innovation Promotion Association CAS
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/34569]
专题	成都山地灾害与环境研究所_数字山地与遥感应用中心
通讯作者	Li, Ainong
作者单位	1.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Bian, Jinhu,Li, Ainong,Lei, Guangbin,et al. Global high-resolution mountain green cover index mapping based on Landsat images and Google Earth Engine[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2020,162:63-76.
APA	Bian, Jinhu,Li, Ainong,Lei, Guangbin,Zhang, Zhengjian,&Nan, Xi.(2020).Global high-resolution mountain green cover index mapping based on Landsat images and Google Earth Engine.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,162,63-76.
MLA	Bian, Jinhu,et al."Global high-resolution mountain green cover index mapping based on Landsat images and Google Earth Engine".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 162(2020):63-76.