Land use/Land Cover Classification and Change Detection in S

发布时间：2021-01-04 16:04

　　In April,1990,Chinese government made a grand strategic decision of developing and opening Pudong,in order to take the development and opening of Shanghai Pudong as the dragon-head in developing the economy of the Changjiang River Delta . It was made clear that,in the following ten years,this decision will be put into effect with all concentrated efforts to build Pudong into a world-class modernized new region,an enable of new Shanghai in the 21st century. Therefore,Shanghai will be built into o... 

【文章来源】：华东师范大学上海市 211工程院校 985工程院校 教育部直属院校

【文章页数】：90 页

【学位级别】：博士

【文章目录】：
英文摘要
Chapter 1: Introduction
    1. The importance of land use determination
    2. The Importance of Remote Sensing for Land use/Land cover
        2.1. Brief introduction to remote sensing
        2.2. Role of remote sensing data in development
Chapter 2: Land Use/Land Cover Classification
    1. Land use/land cover classification systems
    2. Conventional land use classification methods
    3. Classification of land use based on satellite data
        3.1. Satellite data
        3.2. Computer-assisted classification
            3.2.1. Unsupervised Classification Method
            3.2.2. Supervised Classification Method
    4. Accuracy Assessment
Chapter 3: Land use/Land cover change detection
Chapter 4: Study Area
    1. General Background of Shanghai
        1.1 Location, Area and Population
        1.2. Climate
        1.3. Industry and Agriculture
        1.4. Commerce and Finance
        1.5. Science and technology, Culture and Education
        1.6. Foreign Trade and absorption of Foreign Capital
        1.7. Transportation and Post & Telecommunication
    2. The Present Situation of Shanghai Pudong New Area
    3. Broad Outline of Overall Plan of Shanghai Pudong Area
    4. Division of the Land Grades
Chapter 5: Methods
    1. Data
    2. Image processing
        2.1. Image Enhancement
        2.2. False Color Composite
    3. Image classification
        3.1. Information classes
        3.2. Training sample
            3.2.1 Select Training Sample Using IDRISI
            3.2.2. Select training regions using ER Mapper
            3.2.3 Purification of training samples
        3.3. Classification
            3.3.1. Classification using IDRISI
            3.3.2. Classification using ER Mapper:
    4. Classification accuracy
    5. Change detection
Chapter 6: Results
    1. Image Classification Using IDRISI
    2. Classification Using ER Mapper
    3. Classification Accuracy
        3.1. Classification Accuracy （IDRISI）
        3.2. Classification Accuracy （ER Mapper）
    4. Change detection
Chapter 7: Discussion
References
Acknowledgments



本文编号：2957037