DOI: 10.1016/j.isprsjprs.2020.02.011
论文题名: Global high-resolution mountain green cover index mapping based on Landsat images and Google Earth Engine
作者: Bian J. ; Li A. ; Lei G. ; Zhang Z. ; Nan X.
刊名: ISPRS Journal of Photogrammetry and Remote Sensing
ISSN: 9242716
出版年: 2020
卷: 162 语种: 英语
英文关键词: Google Earth Engine (GEE)
; Landsat
; Mountain green vegetation index (MGCI)
; Sustainable development goals (SDGs)
Scopus关键词: Behavioral research
; Biodiversity
; Climate change
; Conservation
; Data Sharing
; Decision making
; Ecosystems
; Engines
; Environmental protection
; Mapping
; Planning
; Sustainable development
; Vegetation
; Cloud computing platforms
; Food and agriculture organizations
; Google earths
; High spatial resolution
; LANDSAT
; Mean absolute differences
; Operational land imager
; Vegetation index
; Landforms
; biodiversity
; conservation status
; Landsat
; mountain environment
; satellite altimetry
; software
; spatial resolution
; Sustainable Development Goal
; vegetation cover
; vegetation index
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 decision-making 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 R2 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. © 2020 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/158446
Appears in Collections: 气候变化与战略
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作者单位: Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, Sichuan 610041, China; University of Chinese Academy of Sciences, Beijing, 100049, China
Recommended Citation:
Bian J.,Li A.,Lei G.,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,2020-01-01,162