| 光谱学与光谱分析 |
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| Crop Classification Based on Time Series MODIS EVI and Ground Observation for Three Adjoining Years in Xinjiang |
| Shakir Muhammad1,2, NIU Zheng1*, WANG Li1,2, Abdullah Aablikim3, HAO Peng-yu1,2, WANG Chang-yao1 |
1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. National Bureau of Statistic China Survey Office in Xinjiang, Urumqi 830001 |
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Abstract There is a regular use of Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter EVI to classify the crops on a regional level throughout the world. A rapid agricultural land use change attributed to new Chinese agriculture policy is attracting many researchers to focus. The objective of this study is to present a more straightforward multiyear classification methodology using time series MODIS EVI with 250 meters spatial resolution and subsequent field data in Xinjiang, China. An extensive polygon based ground reference annual crop data were collected for the years 2011, 2012 and 2013 throughout the study area. The most pure pixel within each polygon was selected which eases crop differentiation. Artificial Immune Network (ABNet) was used to classify cotton, maize, wheat/others, rice and grapes, dominating most of the study area. The data of two different years were used together to classify the crop of next year, as 2011 and 2012 were used to classify crops of 2013. Classification results were validated using the same year ground data. Results showed the classification accuracy above 80% for each year with kappa coefficient of 0.7 and above. However more research and additional ground reference data are needed to classify a range of crops in the study area which will give a more detailed view of the land use land cover change strengthening agriculture decisions practices in the future.
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Received: 2014-06-13
Accepted: 2014-09-28
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Corresponding Authors:
NIU Zheng
E-mail: niuzheng@radi.ac.cn
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