Arid
DOI10.1016/j.rse.2007.09.013
Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia
John, Ranjeet1; Chen, Jiquan1,2; Lu, Nan1; Guo, Ke2; Liang, Cunzhu3; Wei, Yafen2; Noormets, Asko4,5; Ma, Keping2; Han, Xingguo2
通讯作者John, Ranjeet
来源期刊REMOTE SENSING OF ENVIRONMENT
ISSN0034-4257
出版年2008
卷号112期号:5页码:2018-2032
英文摘要

Changes in species composition and diversity are the inevitable consequences of climate change, as well as land use and land cover change. Predicting species richness at regional spatial scales using remotely sensed biophysical variables has emerged as a viable mechanism for monitoring species distribution. In this study, we evaluate the utility of MODIS-based productivity (GPP and EVI) and surface water content (NDSVI and LSWI) in predicting species richness in the semi-arid region of Inner Mongolia, China. We found that these metrics correlated well with plant species richness and could be used in biome- and life form-specific models. The relationships were evaluated on the basis of county-level data recorded from the Flora of Inner Mongolia, stratified by administrative (i.e., counties), biome boundaries (desert, grassland, and forest), and grouped by life forms (trees, grasses, bulbs, annuals and shrubs). The predictor variables included: the annual, mean, maximum, seasonal midpoint (EVImid), standard deviation of MODIS-derived GPP, EVI, LSWI and NDSVI. The regional pattern of species richness correlated with GPP(SD) (R-2=0.27), which was also the best predictor for bulbs, perennial herbs and shrubs (R-2=0.36, 0.29 and 0.40, respectively). The predictive power of models improved when counties with >50% of cropland were excluded from the analysis, where the seasonal dynamics of productivity and species richness deviate patterns in natural systems. When stratified by biome, GPPSD remained the best predictor of species richness in grasslands (R-2=0.3 0), whereas the most variability was explained by NDSVImax in forests (R-2=0.26), and LSWIavg in deserts (R-2=0.61). The results demonstrated that biophysical estimates of productivity and water content can be used to predict plant species richness at the regional and biome levels. (C) 2008 Elsevier Inc. All rights reserved.


英文关键词plant species richness MODIS semi-arid regions GPP LSWI EVI NDSVI Inner Mongolia China
类型Article
语种英语
国家USA ; Peoples R China
收录类别SCI-E
WOS记录号WOS:000255370700009
WOS关键词VEGETATION WATER-CONTENT ; SPECIES RICHNESS ; ECOSYSTEM STABILITY ; MULTITEMPORAL NDVI ; SPECTRAL INDEX ; CLIMATE-CHANGE ; SENSOR DATA ; LAND-USE ; CHINA ; MODIS
WOS类目Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
来源机构中国科学院植物研究所
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/159042
作者单位1.Univ Toledo, Dept Environm Sci, Toledo, OH 43606 USA;
2.Chinese Acad Sci, Inst Bot, Beijing 100093, Peoples R China;
3.Inner Mongolia Univ, Dept Ecol & Environm Sci, Hohhot 010021, Peoples R China;
4.N Carolina State Univ, Dept Forestry & Environm Resources, Raleigh, NC 27695 USA;
5.N Carolina State Univ, So Global Change Program, Raleigh, NC 27695 USA
推荐引用方式
GB/T 7714
John, Ranjeet,Chen, Jiquan,Lu, Nan,et al. Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia[J]. 中国科学院植物研究所,2008,112(5):2018-2032.
APA John, Ranjeet.,Chen, Jiquan.,Lu, Nan.,Guo, Ke.,Liang, Cunzhu.,...&Han, Xingguo.(2008).Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia.REMOTE SENSING OF ENVIRONMENT,112(5),2018-2032.
MLA John, Ranjeet,et al."Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia".REMOTE SENSING OF ENVIRONMENT 112.5(2008):2018-2032.
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