IGSNRR OpenIR
improvingremotesensingbasednetprimaryproductionestimationinthegrazedlandwithdefoliationformulationmodel
Ye Hui1; Huang Xiaotao2; Luo Geping1; Wang Junbang3; Zhang Miao4; Wang Xinxin5
2019
Source Publicationjournalofmountainscience
ISSN1672-6316
Volume16Issue:2Pages:323
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.
Language英语
Funding Project[international 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]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/76996
Collection中国科学院地理科学与资源研究所
Affiliation1.中国科学院新疆生态与地理研究所
2.中国科学院西北高原生物研究所
3.中国科学院地理科学与资源研究所
4.陕西师范大学
5.复旦大学
Recommended Citation
GB/T 7714
Ye Hui,Huang Xiaotao,Luo Geping,et al. improvingremotesensingbasednetprimaryproductionestimationinthegrazedlandwithdefoliationformulationmodel[J]. journalofmountainscience,2019,16(2):323.
APA Ye Hui,Huang Xiaotao,Luo Geping,Wang Junbang,Zhang Miao,&Wang Xinxin.(2019).improvingremotesensingbasednetprimaryproductionestimationinthegrazedlandwithdefoliationformulationmodel.journalofmountainscience,16(2),323.
MLA Ye Hui,et al."improvingremotesensingbasednetprimaryproductionestimationinthegrazedlandwithdefoliationformulationmodel".journalofmountainscience 16.2(2019):323.
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