Acta Geographica Sinica ›› 2016, Vol. 71 ›› Issue (11): 1948-1966.doi: 10.11821/dlxb201611007

• Ecology and Environment • Previous Articles     Next Articles

Experimental analysis of spatial and temporal dynamics of fractional vegetation cover in Xinjiang

Baozhong HE1,2(), Jianli DING1,2(), Zhe ZHANG1,2, Ghulam Abduwasit3   

  1. 1. College of Resource and Environmental Science, Xinjiang University, Urumqi 830046, China
    2. Key Laboratory for Oasis Ecology, Xinjiang University, Urumqi 830046, China
    3. Center for Sustainability, Saint Louis University, Saint Louis MO 63108, USA
  • Received:2016-05-21 Revised:2016-09-09 Online:2016-11-25 Published:2016-11-29
  • Supported by:
    Key Laboratory of Subject of the Xinjiang Uygur Autonomous Region, No.2016D03001;The Research and Development of the Network Platform for the Monitoring and Early Warning of the Large Scale Soil Salinization in Xinjiang, No.201591101;National Natural Science Foundation of China, No.U1303381, No.41261090, No.41161063;The Ministry of Education to Promote Cooperation with the Mei Da Area of Scientific Research and High-Level Personnel Training Project


This paper presents spatial and temporal dynamics of fraction of vegetation in Xinjiang Uygur Autonomous Region of China. Fractional vegetation cover (FVC) was estimated by using MODIS-NDVI data from 2005 to 2015. The study area was divided into 11 ecological and climate regions according to the altitude and land cover. Slope, variability and linear regression model were used to analyze the present situation and future tendency for FVC in Xinjiang and its sub-regions. The BP-ANN neural network analysis was used to predict FVC from 2016 to 2020, and the FVC trend over the entire study area during 2005-2020 was discussed. The results showed that: From 2005 to 2015, FVC increased in general over time, and spatially, decreased from northwest to southeast; In mountain areas, FVC increased in general; desert system showed no significant change, and multi-average FVC was about 0.10. The dynamic change of FVC was mainly caused by precipitation. We observed an improvement of vegetation cover over oasis and desert ecotone. FVC showed a significant increase over oasis. The year 2009 was the turning point with a historical low value. The variation near areas covered by ice and snow, river and lakes was remarkable, showing a change rate of 150%-316%. This change was probably responded by glacial depletion and fluctuation changes of lakes due to global climate change. The ecosystem in northern Xinjiang is obviously better than that in southern and eastern Xinjiang. In terms of oasis, the northern part is improved remarkably (P = 0.001). There was an obvious FVC fluctuation in Yili region compared to other regions. The mountain area showed an obvious degeneration tendency. The local minima point of FVC was observed in Yili in 2008, while it was in the other three regions in 2009. The lag of local minima occurring in the northern and southern parts of the study areas may have been caused by precipitation and temperature variation across the study area. Predicted average FVC from 2016-2020 demonstrated trends and patterns identical to 2005-2015 with some local differences. For example, FVC increases (P = 0.002) during 2005-2020 in general. In desert areas, the trend is from non-significant decrease during 2005-2015 to non-significant increase for 2005-2020. In oasis region, predicted FVC showed a slightly rising trend compared to the obviously rising trend in 2005-2015. The multi-average FVC is above 0.62 and it showed improvement during 2005-2020. For sub-regions and ecosystems, the trend differs significantly between 2005-2015 and 2005-2020. In northern part, the trend in 2005-2020 was almost the same with that of 2005-2015, while in 2016-2020 the tendency was opposite to 2005-2015, with oasis and mountain FVC showing a decreasing trend. In Yili, the general trend in 2005-2020 was almost the same with that of 2005-2015, but the amplitude of variation became smaller in 2016-2020 when compared to early stage and the mountain area showing a remarkably decreasing trend. Our results demonstrated that BP-ANN model can predict FVC in Xinjiang with statistical significance, the coefficient of determination (R2) of 0.95, root-mean-square error of 0.05, suggesting that this method gained a statisifactory result.

Key words: fractional vegetation cover (FVC), moderate resolution imaging spectroradiometer (MODIS), BP artificial neural network, climate change, Xinjiang