Research On The Grassland Types And Vegetation Coverage In The Source Region Of Yarlung Zangbo River In Xizang Automous Region Based On Remote Sensing

A new earth observation system, which is formed by remote sensing system, global positioning system and geographic information system, has been paid attention to by all industries in the scientific research and application in the national economy.The study area of this paper is the source region of Yarlung Zangbo river, which is the important study area in the environmental protection special public-"Research on Ecological Degradation and Envirmental Management".According to quadrat investigating, we recognize the types of grassland and inversion the vegetation coverage, which server the research on ecological degradation in the source region of Yarlung Zangbo River.We selected the Landsat5 TM images of high quality in August 1st,2009. According to different features of spectral combination, we build the recognition rules of grass identification with the data of 1:400million vegetation map, DEM, and NDVI. We did the research on grass recognition in the source region of Yarlung Zangbo river based on decision tree classification. This paper analyzed the NDVI,RVI,VI3,PVI,DVI,MSAVI,SAVI and TM4/TM5, combining quadrat investigating, and we select the TM4/TM5 as the main factor to construct model with vegetation coverage. We calculate the vegetation coverage of the image with this model and create the image of the distribution of grassland, which is of great important for grasping the desertification of grassland. It was shown that:(1)As a result of different habitat,it is increased the separability in some extent, inflected by soil type and moisture. We can achieve good results of remote sensing recognition of grass on spectral combination features. Using the spectral information,the classification effect of object-oriented classification has been improved greatly overall. It has made up the deficiency of traditional classification based on statistical characteristics of pixels.Compared with traditional supervised classification, the decision tree classification based on spectral combination has high precision of identification, overall classification accuracy has improved by 15.4% and Kappa coefficient has increased by 0.225. Decision tree classification used in this paper only consider the spectral characteristics of images, the inter-band operations, elevation and so on. We did not join the other categories, and theclassification accuracy is not particularly high.If we can consider texture information, the advantage of decision tree classification will be more obvious and its classification accuracy will be higher.(2)It has the same trend with the measured data of grassland coverage and TM4/TM5, which increase the separability of spectral inflection value of different degraded grassland.The grassland coverage estimation model built by TM4/TM5 as the main factor can reflect the distribution of of grassland accurately. The prediction accuracy of model is high:RMSE reaches to 0.074 and relative error is 19.6%. The verification accuracy of model reaches to 0.91 and it can meet the requairement of model validation.