Haiyan Pan

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.

In the summer and autumn, which is the primary cropland planting preparation and harvest time, cropland burning is very common in China. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra active fire product (MOD14) and GlobeLand30-2010 data are used here to analyze the fire activity of the predominant land cover types. A total of 44,852 scenes of MOD14 images and MOD03 images are used, covering the whole of China from 20 May to 31 October during 2010 to 2014. Agricultural burning is a significant contributor to fire activity in China, and accounts for 60% on average of all the fire activity over the last five years. The spatial and temporal distribution of agricultural burning in seven different geographical regions is analyzed in detail. The experiments showed that the Central and Eastern China regions are the largest contributors to agricultural burning, producing 59%–80% of all the agricultural fires. At the national scale, the number of agricultural fire counts peak in June, which is associated primarily with winter burning of wheat croplands.