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Improving remote sensing-based net primary production estimation in the grazed land with defoliation formulation model
Ye Hui1,6; Huang Xiao-tao2,6; Luo Ge-ping1,6; Wang Jun-bang3,6; Zhang Miao4,6; Wang Xin-xin5
2019-02-01
Source PublicationJOURNAL OF MOUNTAIN SCIENCE
ISSN1672-6316
Volume16Issue:2Pages:323-336
Corresponding AuthorLuo Ge-ping(luogp@ms.xjb.ac.cn)
AbstractRemote sensing (RS) technologies provide robust techniques for quantifying net primary productivity (NPP) which is a key component of ecosystem production management. Applying RS, the confounding effects of carbon consumed by livestock grazing were neglected by previous studies, which created uncertainties and underestimation of NPP for the grazed lands. The grasslands in Xinjiang were selected as a case study to improve the RS based NPP estimation. A defoliation formulation model (DFM) based on RS is developed to evaluate the extent of underestimated NPP between 1982 and 2011. The estimates were then used to examine the spatiotemporal patterns of the calculated NPP. Results show that average annual underestimated NPP was 55.74 gC.m(-2)yr(-1) over the time period understudied, accounting for 29.06% of the total NPP for the Xinjiang grasslands. The spatial distribution of underestimated NPP is related to both grazing intensity and time. Data for the Xinjiang grasslands show that the average annual NPP was 179.41 gC.m(-2)yr(-1), the annual NPP with an increasing trend was observed at a rate of 1.04 gC.m(-2)yr(-1) between 1982 and 2011. The spatial distribution of NPP reveals distinct variations from high to low encompassing the geolocations of the Tianshan Mountains, northern and southern Xinjiang Province and corresponding with mid-mountain meadow, typical grassland, desert grassland, alpine meadow, and saline meadow grassland types. This study contributes to improving RS-based NPP estimations for grazed land and provides a more accurate data to support the scientific management of fragile grassland ecosystems in Xinjiang.
KeywordRemote sensing Defoliation formulation model Net primary production Grazed land Spatial-temporal patterns Xinjiang
DOI10.1007/s11629-018-5200-2
WOS KeywordDIFFERENCE VEGETATION INDEX ; ESTIMATING ABOVEGROUND BIOMASS ; LEAF-AREA INDEX ; GRAZING INTENSITY ; INNER-MONGOLIA ; NORTHERN CHINA ; USE EFFICIENCY ; CLIMATE-CHANGE ; GRASSLAND ; SATELLITE
Indexed BySCI
Language英语
Funding Projectinternational Partnership Program of the Chinese Academy of Science[131965KYSB20160004] ; National Natural Science Foundation of China[U1803243] ; Network Plan of the Science and Technology Service, Chinese Academy of Sciences (STS Plan) ; Qinghai innovation platform construction project[2017-ZJ-Y20]
Funding Organizationinternational Partnership Program of the Chinese Academy of Science ; National Natural Science Foundation of China ; Network Plan of the Science and Technology Service, Chinese Academy of Sciences (STS Plan) ; Qinghai innovation platform construction project
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000458657000007
PublisherSCIENCE PRESS
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/49414
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLuo Ge-ping
Affiliation1.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Xinjiang, Peoples R China
2.Chinese Acad Sci, Northwest Inst Plateau Biol, Key Lab Restorat Ecol Cold Reg Qinghai, Xining 810008, Qinghai, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China
4.Shaanxi Normal Univ, Northwest Land & Resources Res Ctr, Xian 710119, Shaanxi, Peoples R China
5.Fudan Univ, Inst Biodivers Sci, Key Lab Biodivers Sci & Ecol Engn, Minist Educ, Shanghai 200433, Peoples R China
6.China Univ, Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Ye Hui,Huang Xiao-tao,Luo Ge-ping,et al. Improving remote sensing-based net primary production estimation in the grazed land with defoliation formulation model[J]. JOURNAL OF MOUNTAIN SCIENCE,2019,16(2):323-336.
APA Ye Hui,Huang Xiao-tao,Luo Ge-ping,Wang Jun-bang,Zhang Miao,&Wang Xin-xin.(2019).Improving remote sensing-based net primary production estimation in the grazed land with defoliation formulation model.JOURNAL OF MOUNTAIN SCIENCE,16(2),323-336.
MLA Ye Hui,et al."Improving remote sensing-based net primary production estimation in the grazed land with defoliation formulation model".JOURNAL OF MOUNTAIN SCIENCE 16.2(2019):323-336.
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