Abstract
Accurate land cover mapping and change analysis is essential for natural resource management and ecosystem monitoring. GlobeLand30 is a global land cover product from China with 30 m resolution that provides reliable data for many international scientific programs. Few studies have focused on systematically implementing this global land cover product in regional studies. Therefore, this paper presents an object-based extended change vector analysis (ECVA_OB) and transfer learning method to update the reginal land cover map using GlobeLand30 product. The method is designed to highlight small and subtle changes through the concept of uncertain area analysis. Updating is carried out by classifying changed objects using a change-detection-based transfer learning method. Land cover changes are analyzed and the factors affecting updating results are explored. The method was tested with data from Shanghai, China, a city that has experienced significant changes in the past decade. The experimental results show that: (1) the change detection and classification accuracy of the proposed method are 83.30% and 78.77%, respectively, which are significantly better than the values obtained for the multithreshold change vector analysis (MCVA) and the multithreshold change vector analysis and support vector machine (MCVA + SVM) methods; (2) the updated results agree well with GlobeLand30 2010, especially for cultivated land and artificial surfaces, indicating the effectiveness of the proposed method; (3) the most significant changes over the past decade in Shanghai were from cultivated land to artificial surfaces, and the total area containing artificial surfaces in Shanghai increased by about 55% from 2000 to 2011. The factors affecting the updating results are also discussed, which be attributed to the classification accuracy of the base image, extended change vector analysis, and object-based image analysis.
Highlights
With the rapid growth of the global population and economic developments, urbanization has taken place at an unprecedented rate all over the world [1]
Changes from cultivated land to artificial surfaces accounted for 72.76% of the total, and the total area containing artificial surfaces significantly increased by about 51.11% due to urban expansion
The factors affecting the updating results are the accuracy of the base map, the extended change vector analysis, and the object-based image analysis
Summary
With the rapid growth of the global population and economic developments, urbanization has taken place at an unprecedented rate all over the world [1]. Many global land cover products have been established, such as the University of Maryland land cover dataset (UMD) [8]; DIScover [15]; Moderate-resolution Imaging Spectroradiometer (MODIS) land cover product (MOD12) [16,17]; Global Land Cover Database for the Year 2000 (CLC2000) [18]; GlobeCover 2009 [19]; Global Land Cover by National Mapping Organizations (GLCNMO) [20]; and GlobeLand30 [21] These products have provided reliable data for a series of international scientific research programs. To maintain the value of existing land cover products, consistent and timely updating is essential
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