4.0 Article

Assessment of changes in the state of the rangelands of Inner Mongolia, China between 1998 and 2007 using remotely sensed data

Journal

RANGELAND JOURNAL
Volume 34, Issue 1, Pages 103-109

Publisher

AUSTRALIAN RANGELAND SOC
DOI: 10.1071/RJ11055

Keywords

aboveground net primary productivity; Inner Mongolia; land degradation; precipitation-use efficiency; temperate grasslands; vegetation restoration

Categories

Funding

  1. National 973 project [2010CB950603, 2010CB833501]
  2. Natural Sciences Foundation of China [40971027]
  3. Opening Fund of Key Laboratory of Resources Remote Sensing
  4. Digital Agriculture, Ministry of Agriculture [RDA0905]

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The spatial annual patterns of aboveground net primary productivity (ANPP) and precipitation-use efficiency (PUE) of the rangelands of the Inner Mongolia Autonomous Region of China, a region in which several projects for ecosystem restoration had been implemented, are described for the years 1998-2007. Remotely sensed normalised difference vegetation index and ANPP data, measured in situ, were integrated to allow the prediction of ANPP and PUE in each 1 km(2) of the 12 prefectures of Inner Mongolia. Furthermore, the temporal dynamics of PUE and ANPP residuals, as indicators of ecosystem deterioration and recovery, were investigated for the region and each prefecture. In general, both ANPP and PUE were positively correlated with mean annual precipitation, i.e. ANPP and PUE were higher in wet regions than in arid regions. Both PUE and ANPP residuals indicated that the state of the rangelands of the region were generally improving during the period of 2000-05, but declined by 2007 to that found in 1999. Among the four main grassland-dominated prefectures, the recovery in the state of the grasslands in the Erdos and Chifeng prefectures was highest, and Xilin Gol and Chifeng prefectures was 2 years earlier than Erdos and Hunlu Buir prefectures. The study demonstrated that the use of PUE or ANPP residuals has some limitations and it is proposed that both indices should be used together with relatively long-term datasets in order to maximise the reliability of the assessments.

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