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Inner Mongolia Study Progress

Inner Mongolia Study Progress. Zhangbao Ma 10/5/2009. 1. Sites Selection in Inner Mongolia. 2. Data preparation. 3. Theoratical Research. Contents. 1. Sites Selection in Inner Mongolia.

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Inner Mongolia Study Progress

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  1. Inner Mongolia Study Progress Zhangbao Ma 10/5/2009

  2. 1. Sites Selection in Inner Mongolia 2. Data preparation 3. Theoratical Research Contents

  3. 1. Sites Selection in Inner Mongolia Primary production and rain use efficiency across a precipitation gradient on the Mongolia plateau by Bai Yongfei etc.

  4. 1. Sites Selection in Inner Mongolia

  5. 1. Sites Selection in Inner Mongolia

  6. 1. Sites Selection in Inner Mongolia

  7. 1. Sites Selection in Inner Mongolia

  8. 1. Sites Selection in Inner Mongolia Proposed Selection Results: 1.EwenkeQi meadow steppe 119o41’17” 48o27’40 ” 2.Xilinhaote typical steppe 116o33’32” 43o32’31 ” 3.WulatezhongQi desert steppe 108o28’12” 41o46’26 ” We can collect these related data of the three county.

  9. RS TM Xilin River ETM+ Xilin River 2.Data preparation-Collected Rs We have collected Xilin River TM and ETM+.

  10. 2.Data preparation-Collected Rs Xilin River TM data include: 1979 1998 1985 1999 1987 2000 Xilin River TM 1989 2001 1990 2002 1995 2004

  11. 2.Data preparation-Collected Rs Xilin River ETM+ data include: Text 2002.9 Xilin River ETM+ 2001.8 2002.6 2002.6

  12. 2.Data preparation-Collecting Rs We will collect the RS data of Wulatezhong Qi, Ewenke Qi and perfect Xilin River RS data. Wulatezhong Qi Collect RS data of Wulatezhong Qi Collect RS data of Ewenke Qi Ewenke Qi Continue to collect RS data Xilin River

  13. 3. Theoratical Research-Related papers (1) A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia, China by Yichun Xie etc In this paper, two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4,5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass.

  14. 3. Theoratical Research-Related papers (2) Primary production of Inner Mongolia, China, between 1982 and 1999 estimated by a satellite data-driven light use efficiency model by Sara Brogaard etc The aim of this study is to develop and adapt a satellite data-driven gross primary production model called Lund University light use efficiency model (LULUE) to temperate conditions in order to map gross primary production (GPP) for the Grasslands of Inner Mongolia Autonomous Region (IMAR), China, from 1982 to 1999. The water stress factor included in the original model has been complemented with two temperature stress factors. In addition, algorithms that allocate the proportions of C3/C4 photosynthetic pathways used by plants and that compute temperature-based C3 maximum efficiency values have been incorporated in the model.

  15. 3. Theoratical Research-Related papers (3) Using a hybrid fuzzy classifier (HFC) to map typical grassland vegetation in Xilin River Basin, Inner Mongolia, China by Zongyao Shaetc In this paper, a hybrid fuzzy classifier (HFC) for vegetation classification with Landsat ETM+ imagery on the typical grassland in Xilinhe River Basin, Inner Mongolia, China has been developed. The overall result of the HFC was much better than that of the CSC. We can use this method to make supervised classification.

  16. 3. Theoratical Research-Related papers (4) Primary production and rain use efficiency across a precipitation gradient on the Mongolia plateau by Yongfei Bai etc • ANPP increased while the interannual variability of ANPP declined • (2) plant species richness increased and the relative abundance of key functional groups shifted predictably • (3) RUE increased in space across different ecosystems but decreased with increasing annual precipitation within a given ecosystem. These findings have important implications for understanding and predicting ecological impacts of global climate change and for management practices in arid and semiarid ecosystems in the Inner Mongolia steppe region and beyond.

  17. 3. Theoratical Research-Next step GPP NPP Processing Classification Data processing supervised classification Calculate GPP and NPP

  18. Discussion and Suggestion Thank You ! http://igre.emich.edu

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