IMHE OpenIR  > 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, Jinhu1; Li, Ainong1; Lei, Guangbin1; Zhang, Zhengjian1,2; Nan, Xi1
2020-04-01
Source PublicationISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN0924-2716
EISSN1872-8235
Volume162Pages:63-76
SubtypeArticle
Contribution Rank1
AbstractMountains 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.
KeywordMountain green vegetation index (MGCI) Landsat Sustainable development goals (SDGs) Google Earth Engine (GEE)
DOI10.1016/j.isprsjprs.2020.02.011
Indexed BySCI ; EI
WOS KeywordHJ-1A/B CONSTELLATION ; ONLINE TOOL ; MAP ; CLASSIFICATION ; ELEVATION ; DYNAMICS ; CLOUD ; MODIS
Language英语
Quartile1区
Funding ProjectStrategic 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]
TOP
WOS Research AreaPhysical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000527709200006
Funding OrganizationStrategic 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
PublisherELSEVIER
EI Accession NumberAccession number:20200808196294
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/34570
Collection数字山地与遥感应用中心
Corresponding AuthorLi, Ainong
Affiliation1.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China;
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Corresponding Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
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.
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