Spatial associations between NDVI and environmental factors in the Heihe River Basin
- 3 Downloads
The Heihe River Basin is located in the arid and semi-arid regions of Northwest China. Here, the terrestrial ecosystem is vulnerable, making it necessary to identify the factors that could affect the ecosystem. In this study, MODIS-NDVI data with a 250-m resolution were used as a proxy for the terrestrial ecosystem. By combining these with environmental factors, we were able to explore the spatial features of NDVI and identify the factors influencing the NDVI distribution in the Heihe River Basin during the period of 2000–2016. A geographical detector (Geodetector) was employed to examine the spatial heterogeneity of the NDVI and to explore the factors that could potentially influence the NDVI distribution. The results indicate that: (1) the NDVI in the Heihe River Basin appeared high in the southeast while being low in the north, showing spatial heterogeneity with a q-statistic of 0.38. The spatial trend of the vegetation in the three sub-basins generally increased in the growing seasons from 2000 to 2016; (2) The results obtained by the Geodetector (as denoted by the q-statistic as well as the degree of spatial association between the NDVI and environmental factors) showed spatial heterogeneity in the associations between the NDVI and the environmental factors for the overall basin as well as the sub-basins. Precipitation was the dominant factor for the overall basin. In the upper basin, elevation was found to be the dominant factor. The dominant factor in the middle basin was precipitation, closely followed by the soil type. In the lower basin, the dominant factor was soil type with a lower q-statistic of 0.13, and the dominant interaction between the elevation and soil type was nonlinearly enhanced (q-statistic = 0.22).
KeywordsNDVI environmental factors vegetation Geodetector q-statistic spatial heterogeneity Heihe River Basin
Unable to display preview. Download preview PDF.
We would like to thank the high-performance computing support from the Center for Geodata and Analysis, Faculty of Geographical Science, Beijing Normal University [https://gda.bnu.edu.cn/].
- Brunsdon C, Fotheringham S, Charlton M. 1998. Geographically weighted regression-modelling spatial non-stationarity. Journal of the Royal Statistical Society, 47(3): 431–443.Google Scholar
- Gao J B, Fang P, Yuan L H, 2019. Analyses of geographical observations in the Heihe River Basin: Perspectives from complexity theory. Journal of Geographical Sciences, 29(9): 1441–1461.Google Scholar
- Han H, Ma M, Yan P et al., 2011. Periodicity analysis of NDVI time series and its relationship with climatic factors in the Heihe River Basin in China. Remote Sensing Technology & Application, 26(5): 466–471. (in Chinese)Google Scholar
- Mi L N, Xiao H G, Zhu W J et al., 2015. Dynamic variation of the groundwater level in the middle reaches of the Heihe River during 1985–2013. Journal ofGlaciology and Geocryology, 37(2): 461–469. (in Chinese)Google Scholar
- Ning L X, Cheng C X, Shen S, 2019. Spatial-temporal variability of the fluctuation of soil temperature in the Babao River Basin, Northwest China. Journal of Geographical Sciences, 29(9): 1475–1490.Google Scholar
- Wang J F, Xu C D, 2017. Geodetector: Principle and prospective. Acta Geographica Sinica, 72(1): 116–134. (in Chinese)Google Scholar
- Wang W, Feng Q S, Guo N et al., 2015. Dynamic monitoring of vegetation coverage based on long time-series NDVI data sets in northwest arid region of China. Pratacultural Science, 32(12): 1969–1979.Google Scholar
- Xiong Z, 2014. Impact of different convective parameterization on simulation of precipitation for the Heihe River Basin. Advances in Earth Science, 29(5): 290–297. (in Chinese)Google Scholar
- Zhu Y J, Wu B, Lu Q, 2012. Progress in the study on response of arid zones to precipitation change. Forest Research, 25(1): 100–106. (in Chinese)Google Scholar