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Land cover mapping using time series HJ-1/CCD data |
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| Authors: | Bo Zhong Peng Ma AiHua Nie AiXia Yang YanJuan Yao WenBo Lü Hang Zhang QinHuo Liu |
| Institution: | 1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, China 2. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China 3. Satellite Environment Center, Ministry of Environmental Protection, Beijing, 100094, China 4. College of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China 5. School of Land Science and Technology, China University of Geosciences, Beijing, 100083, China |
| Abstract: | It is very difficult to have remote sensing data with both high spatial resolution and high temporal frequency; thus, two categories of land-use mapping methodology have been developed separately for coarser resolution and finer resolution data. The first category uses time series of data to retrieve the variation of land surface for classification, which are usually used for coarser resolution data with high temporal frequency. The second category uses fine spatial resolution data to classify different land surface. With the launch of Chinese satellite constellation HJ-1in 2008, four 30 m spatial resolution CCDs with about 360 km coverage for each one onboard two satellites made a revisit period of two days, which brought a new type of data with both high spatial resolution and high temporal frequency. Therefore, by taking the spatiotemporal advantage of HJ-1/CCD data we propose a new method for finer resolution land cover mapping using the time series HJ-1/CCD data, which can greatly improve the land cover mapping accuracy. In our two study areas, the very high resolution remote sensing data within Google Earth are used to validate the land cover mapping results, which shows a very high mapping accuracy of 95.76% and 83.78% and a high Kappa coefficient of 0.9423 and 0.8165 in the Dahuofang area of Liaoning Province and the Heiquan area of Gansu Province respectively. |
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