institute for geospatial research & education eastern michigan university inner mongolia study...
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Institute for Geospatial Research & Education Eastern Michigan University
Inner Mongolia Study Progress
Zhangbao Ma
10/5/2009
http://igre.emich.eduEastern Michigan University
Contents
1. Sites Selection in Inner Mongolia
2. Data preparation
3. Theoratical Research
http://igre.emich.eduEastern Michigan University
1. Sites Selection in Inner Mongolia
Primary production and rain use efficiency across a precipitation gradient on the Mongolia plateau by Bai Yongfei etc.
http://igre.emich.eduEastern Michigan University
1. Sites Selection in Inner Mongolia
http://igre.emich.eduEastern Michigan University
1. Sites Selection in Inner Mongolia
http://igre.emich.eduEastern Michigan University
1. Sites Selection in Inner Mongolia
http://igre.emich.eduEastern Michigan University
1. Sites Selection in Inner Mongolia
http://igre.emich.eduEastern Michigan University
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.
http://igre.emich.eduEastern Michigan University
2.Data preparation-Collected Rs
TM
Xilin River
RS
ETM+
Xilin River
We have collected Xilin River TM and ETM+.
http://igre.emich.eduEastern Michigan University
2.Data preparation-Collected Rs
Xilin River TM
1979
1985
1987
1998
1999
2000
1989
1990
1995
2001
2002
2004
Xilin River TM data include:
http://igre.emich.eduEastern Michigan University
2.Data preparation-Collected Rs
2001.8
Text2002.9
2002.6
2002.6
Xilin River ETM+
Xilin River ETM+ data include:
http://igre.emich.eduEastern Michigan University
2.Data preparation-Collecting Rs
Collect RS data of Ewenke Qi
Continue to collect RS data
Wulatezhong Qi
Ewenke Qi
Xilin River
Collect RS data of Wulatezhong Qi
We will collect the RS data of Wulatezhong Qi, Ewenke Qi and perfect Xilin River RS data.
http://igre.emich.eduEastern Michigan University
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.
http://igre.emich.eduEastern Michigan University
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.
http://igre.emich.eduEastern Michigan University
3. Theoratical Research-Related papers
(3) Using a hybrid fuzzy classifier (HFC) to map typical grassland vegetation inXilin River Basin, Inner Mongolia, China by Zongyao Sha etc
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.
http://igre.emich.eduEastern Michigan University
3. Theoratical Research-Related papers
(4) Primary production and rain use efficiency across a precipitation gradient on the Mongolia plateau by Yongfei Bai etc
(1) 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.
http://igre.emich.eduEastern Michigan University
3. Theoratical Research-Next step
supervised classification
Data processingCalculate GPP
and NPP
Processing ClassificationGPPNPP
Institute for Geospatial Research & Education Eastern Michigan University
http://igre.emich.edu
Discussion and Suggestion