Remote Sensing Study Of Land Cover Classification And Change In China

Land cover and its natural alteration, which play an important role in climate system and biochemistry pattern in globe scale, have brought tremendous effect to global entironment. The land cover information of the earth's surface is the primary input parameters for globe climate and terrestrial hydrography models. Remote sensing offered an effective method for acquiring surface land cover information. China is a country with a vast territory and diversiform, complicated surface land cover; upon that the research about Chinese surface land cover condition is one of the import parts of globe environment change.The article studied acquiring Chinese surface land cover information which the 1km MODIS NDVI&Ts data product which were used as main remote sensing data. Temperature-Vegetation Angel and Temperature-Vegetation Distance (TVA&TVD) based on NDVI-Ts space were proposed as biophysical parameters which can be thought as a token of surface land cover condition. TVA&TVD were selected as main classification features, and Principal Component Analysis was used to extract the primary information. Artificial Neural Network (ANN) algorithm was adopted to identify the Chinese surface land cover types at coarse spatial scales. One side, BP neural network classifier based on genetic algorithm for remotely-sensed data was proposed; on the other hand, classification of Chinese surface land cover was made with the algorithm, and other algorithms: fuzzy ARTMAP ANN and Maximum likelihood classifier (MLC), subsequently the three algorithms and their classification results were compared and analyzed. The surface vegetation and land cover condition detection using Change-Vector analysis is another important part of the article.Main research results and initiatives in this thesis include as following:1. Two index were proposed as biophysical parameters : TVA&TVDThere is an obvious diversity for distribution of different surface land cover classes in NDVI-Ts space, and TVA&TVD based on NDVI-Ts space can locate the spacial distribution uniquely. TVA&TVD can be in the person of different surface land cover classes much better than NDVI &Ts, which can be demonstrated by the information entropy and the different change trajectory per annum of each class.2. Several suitable features for Chinese surface land cover classification were selected and extracted.After TVA&TVD temporal series data, spacial structure information and Chinese digital elevation model were selected classification features, Primary Components Analysis (PCA) was...