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Research Of County NPP Spatial-temporal Changes In Medium High Mountain Valley Region Based On CASA Model

Posted on:2016-12-24Degree:MasterType:Thesis
Country:ChinaCandidate:H R DuFull Text:PDF
GTID:2180330470456528Subject:Physical geography
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Ximeng is situated to the southwest of Yunnan province, and adjacent to Burma, that is located in Hengduan Mountains Valley in southern region, the relative altitude is quite imbalance, belongs to the medium high mountain valley region. After entering the21st century, the areas of eucalyptus, rubber, tea and other cash tree were grown rapidly, this made land Cover Change. It brought an important impact on the ecological environment. As an important index, net primary productivity (NPP) can reflect the changes of ecological environment; determine the ecosystem carbon source/sink effect. Explores the temporal and spatial variation of NPP, Analysis the influence of land cover change on NPP, in order to learn more about the ecological environment of regional, and provide support and reference proposal for formulating the corresponding strategy of social and economic development.Under the support of Excel software, Arcgisl0.2software, ENV14.8, Combining the remote sensing data, meteorological data in2000,2005and2010.we selected Normalized Difference Vegetation Index and Simple Ratio Index, Enhanced Vegetation Index and Simple Ratio Index to calculate Vegetation’s fraction of photosynthetically active radiation, used CASA model for estimating NPP.the calculated results were verified by compared the existing research findings and Moidds-NPP. Analysis of the characteristics of temporal and spatial changes of NPP, discusses the influence of land cover change on NPP, to summarize the conclusions are as follows:(1) The CASA model based on NDVI、SR has better adaptable on NPP estimation of Ximeng. By comparing the existing research, differences between the validation data and the NPP which were estimated by NDVI and SR is reasonable. By comparing Modis-NPP data, it proved that CASA model was finer on the spatial scale.(2) In2000,2005and2010, NPP of Ximeng was first decreased and then increased,NPP in2010was higher than NPP in2000.Seasonal variation of NPP showed the highest in autumn, and lowest in winter, spring was slightly higher than summer. The comprehensive effects of climatic factors cover type change, vegetation growth, and human activities had caused the annual variation of NPP in Ximeng. Seasonal variation of NPP (monthly variation) related to the seasonal variation of solar radiation, precipitation and precipitation time distribution.(3) It was also found that NPP values of different cover types varied greatly with time. The total NPP of deciduous broad-leaved forest and woodland increased, meanwhile, the mean NPP of deciduous broad-leaved forest and woodland was first decreased and then increased. The mean NPP and the total NPP of evergreen broad-leaved forest, farmland, urban land, grassland and waters decreased first and then increased NPP monthly variation of all cover types and NPP monthly variation of Ximeng was accordant.(4) The differences of NPP among different cover types were marked. From2000to2005, NPP of Lisuo in the middle and west, NPP of Wenga Ke in the south decreased significantly, while NPP of Xinchang in the northwest increased. From2005to2010, NPP of Mengka in the west and Lisuo in the middle increased obviously, NPP of Mengsuo in the east had no obvious changes from2005to2010. The NPP variation of evergreen broad-leaved forest, farmland, deciduous broad-leaved forest and woodland were the main factors to change the NPP of Ximeng.(5) The area of cash tree increased from19.77km2to222.51km2, the most prominent feature was a large area of forest, woodland change for commercial forest, mainly concentrated in the central, western region of Ximeng. Land cover change was an important factor affecting the variation of NPP, The total value of NPP of commercial forest increased mainly with planting area expanded, age growth, and the changes in mean NPP associated with age growth.
Keywords/Search Tags:Net primary productivity, Carnegie-Ames-Stanford-Approach (CASA)model, South-west areas, Vegetation index, Spatial and temporal characteristics, landuse cover/change
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