Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia
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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 (EVI mid ), 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, GPP SD remained the best predictor of species richness in grasslands (R 2 = 0.30), whereas the most variability was explained by NDSVI max in forests (R 2 = 0.26), and LSWI avg 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. © 2008 Elsevier Inc. All rights reserved.
author list (cited authors)
John, R., Chen, J., Lu, N., Guo, K. e., Liang, C., Wei, Y., ... Han, X.