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Dynamic Characteristics Analysis Of Vertical Vegetation In Taibai County And Its Relationship With Temperature

Posted on:2020-03-07Degree:MasterType:Thesis
Country:ChinaCandidate:H B YangFull Text:PDF
GTID:2370330590459432Subject:Physical geography
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Located in the heartland of the Qinling Mountains,Taibai County is famous for its main peak,Taibai Mountain.This paper takes Taibai County as the research object,based on the Landsat image,combines actual investigation data,carries on the expert decision tree classification to the vegetation in the area,in order to study the dynamic change of different vegetation types in Taibai County.By means of the NDVI data set extracted from remote sensing images of Taibai County,the temporal and spatial variation of vegetation index as well as the correlation between vegetation and temperature are analyzed synthetically.The pattern of vegetation species transfer in Taibai County from 2018 to 2021 is predicted by CA-Markov model.The main findings and progress are as follows:(1)To obtain the distribution range of vegetation types in Taibai County from 2004 to 2018With the help of remote sensing software,the distribution of vegetation in Taibai County from 2004 to 2018 was completed.The results show that the cultivated land/grassland is mostly distributed in the low area 700m-1400m,the oak forest is distributed in the area 1400m-2100m,The birch forest is mostly distributed in the area of 2100m-2800m above sea level,and the area of birch forest is reduced by 2859.7hm2 compared with 2004;Abies fargesii and Larix chinensis are mainly distributed at the area of 2800m-3400m.The area of Abies fargesii decreases distinctly in 2004-2018 and increases in the area of 2100m-2800m,while the area of Larix chinensis has been increasing gradually in the area of 2800m-3400m in the past ten years.(2)To gain the transfer patterns of different vegetation types in different elevation and slope with time in Taibai CountyBy Using remote sensing images,the vegetation types were analyzed according to different elevations and slope directions.The research shows that at different elevations,the vegetation index is 0.10-0.49,and the vegetation area in the area with altitude is 1400m-1800m.The maximum increase is 29.46%.The vegetation area is 0.50-0.59,and the vegetation area in the area of 2400m-3200m is growing rapidly,with an increase of 45.9%.The vegetation area with vegetation index of 0.70-0.79 is mostly concentrated in the area of 2000m-3000m above sea level.In different slopes,the change of vegetation area at low altitude is not obvious,while the vegetation area of high-altitude vegetation such as Bashan fir has increased by 2.09%compared with that of 2004.(3)To acquire the temporal and spatial variation of NDVI in Taibai County and its correlation with temperatureStudying the annual average and annual temperature values of NDVI from 2010 to 2017,it was found that the vegetation index and temperature of Taibai County increased significantly.The temperature in 2017 increased by 0.24 ? compared with the temperature in 2010.Correlation analysis was carried out between the average annual temperature and the average value of NDVI.The correlation coefficient between vegetation index and annual average temperature was above 0.86.Although the influence of temperature on vegetation in individual months was delayed during the growth period,most of the monthly temperature and vegetation index showed strong correlation in the same month.(4)To Predict vegetation transfer mode based on CA-Markov model in Taibai CountyThe CA-Markov model is used to predict the vegetation species transfer information in 2018.The Kappa index is applied as the simulation precision index,and the Kappa index is 0.8748 by calculation.The patterns of vegetation species transfer in the area from 2018 to 2021 are forecast.It is found that the cultivated land/grassland area decreases further,while the forest vegetation area increases,especially the oak forest.
Keywords/Search Tags:Elevation analysis, Dynamic change of vegetation, Relevance, Temperature correlation
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