Crop Classification Based on Time Series MODIS EVI and Ground Observation for Three Adjoining Years in Xinjiang

作者:Muhammad, S (Muhammad, Shakir)[ 1,2 ] ; Niu, Z (Niu Zheng)[ 1 ] ; Wang, L (Wang Li)[ 1,2 ] ; Aablikim, A (Aablikim, Abdullah)[ 3 ] ; Hao, PY (Hao Peng-yu)[ 1,2 ] ; Wang, CY (Wang Chang-yao)[ 1 ]

SPECTROSCOPY AND SPECTRAL ANALYSIS

卷: 35  期: 5  页: 1345-1350

DOI: 10.3964/j.issn.1000-0593(2015)05-1345-06

出版年: MAY 2015

摘要

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.

通讯作者地址: Niu, Z (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

[ 3 ] Natl Bur Stat China Survey Off Xinjiang, Urumqi 830001, Peoples R China