IMHE OpenIR  > 数字山地与遥感应用中心
Hyperspectral database prediction of ecological characteristics for grass species of alpine grasslands
Yu, Huan1,6; Kong, Bo2; Wang, Guangxing3; Sun, Hua4; Wang, Lu5
2018
Source PublicationRANGELAND JOURNAL
ISSN1036-9872
Volume40Issue:1Pages:19-29
SubtypeArticle
Contribution Rank2
AbstractAlpine grasslands are being degraded because of human activities and associated global climate change. Mapping the spatial distributions and ecological characteristics of grass species is essential for scientific management of grasslands. However, traditional field-survey methods are costly or even impossible owing to poor accessibility. Hyperspectral remote sensing provides solutions for the purpose. This study was conducted in Shenzha County of the Qiangtang Plateau, north-western Qinghai-Tibet Plateau, to examine the potential of using hyperspectral data for identifying the grass species and predicting their ecological characteristics in the alpine grasslands dominated by Stipa purpurea with coexisting species Leontopodium nanum and Oxytropis microphylla. Hyperspectral data were collected in 106 sample quadrats and the ecological characteristics of each quadrat (number and height of plants, vegetation cover, etc.) were measured. The results of spectral data analysis and regression modelling showed the following. (i) The near-and middle-infrared region was more appropriate than the visible region for discriminating the grass species. (ii) The enhanced spectral variables had much higher correlations with the ecological characteristics than the original bands. (iii) Most of the 23 derived enhanced spectral variables were significantly correlated with the number and height of the dominant species plants within the quadrats. (iv) The vegetation cover could be accurately predicted by using the models based on the enhanced spectral variables of the fieldcollected hyperspectral data with the relativeRMSEvalues < 28%. (v) The ecological characteristics of the dominant species could be more accurately estimated than of co-existing species. Overall, this study suggests that the hyperspectral database method provided great potential to predict the ecological characteristics of grass species in alpine grasslands.
Keyworddominant species ecosystem monitoring prediction
DOI10.1071/RJ17084
Indexed BySCI
WOS KeywordFIELD SPECTROMETRY ; LAND-USE ; REMOTE ; CHINA ; VEGETATION ; GIS ; EVAPOTRANSPIRATION ; DISCRIMINATION ; CONSERVATION ; ENVIRONMENT
Language英语
Funding ProjectNational Natural Science Foundation of China[41101174] ; National Natural Science Foundation of China[41301094] ; Young and Middle-aged Excellent Teacher Training Program[KYGG201401] ; Lead Strategic Project of the Chinese Academy of Sciences[XDB03030507] ; Institute of Mountain Hazards and Environment[SDSQB-2015-02] ; Open Fund for Key Laboratory of Geoscience Spatial Information Technology of Ministry of Land and Resources[KLGSIT2016-01]
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEcology
WOS IDWOS:000428045400003
Funding OrganizationNational Natural Science Foundation of China ; Young and Middle-aged Excellent Teacher Training Program ; Lead Strategic Project of the Chinese Academy of Sciences ; Institute of Mountain Hazards and Environment ; Open Fund for Key Laboratory of Geoscience Spatial Information Technology of Ministry of Land and Resources
PublisherCSIRO PUBLISHING
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/22845
Collection数字山地与遥感应用中心
Corresponding AuthorYu, Huan
Affiliation1.Chengdu Univ Technol, Coll Earth Sci, Chengdu 610059, Sichuan, Peoples R China;
2.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China;
3.Southern Illinois Univ, Dept Geog & Environm Resources, Carbondale, IL 62901 USA;
4.Cent South Univ Forestry & Technol, Res Ctr Forestry Remote Sensing & Informat Engn, Changsha 410004, Hunan, Peoples R China;
5.South China Agr Univ, Coll Nat Resources & Environm, Guangzhou 510642, Guangdong, Peoples R China;
6.Minist Land & Resources, Key Lab Geosci Spatial Informat Technol, Chengdu 610059, Sichuan, Peoples R China
Recommended Citation
GB/T 7714
Yu, Huan,Kong, Bo,Wang, Guangxing,et al. Hyperspectral database prediction of ecological characteristics for grass species of alpine grasslands[J]. RANGELAND JOURNAL,2018,40(1):19-29.
APA Yu, Huan,Kong, Bo,Wang, Guangxing,Sun, Hua,&Wang, Lu.(2018).Hyperspectral database prediction of ecological characteristics for grass species of alpine grasslands.RANGELAND JOURNAL,40(1),19-29.
MLA Yu, Huan,et al."Hyperspectral database prediction of ecological characteristics for grass species of alpine grasslands".RANGELAND JOURNAL 40.1(2018):19-29.
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