作者:Wang, J (Wang, Jie)[ 1 ] ; Li, CC (Li, Congcong)[ 2 ] ; Hu, LY (Hu, Luanyun)[ 2 ] ; Zhao, YY (Zhao, Yuanyuan)[ 2 ] ; Huang, HB (Huang, Huabing)[ 1 ] ; Gong, P (Gong, Peng)[ 1,2,3 ]

REMOTE SENSING

卷: 7  期: 1  页: 865-881

DOI: 10.3390/rs70100865

出版年: JAN 2015

摘要

Seasonal dynamic land cover maps could provide useful information to ecosystem, water-resource and climate modelers. However, they are rarely mapped more frequent than annually. Here, we propose an approach to map dynamic land cover types with frequently available satellite data. Landsat 8 data acquired from nine dates over Beijing within a one-year period were used to map seasonal land cover dynamics. A two-step procedure was performed for training sample collection to get better results. Sample sets were interpreted for each acquisition date of Landsat 8 image. We used the random forest classifier to realize the mapping. Nine sets of experiments were designed to incorporate different input features and use of spatial temporal information into the dynamic land cover classification. Land cover maps obtained with single-date data in the optical spectral region were used as benchmarks. Texture, NDVI and thermal infrared bands were added as new features for improvements. A Markov random field (MRF) model was applied to maintain the spatio-temporal consistency. Classifications with all features from all images were performed, and an MRF model was also applied to the results estimated with all features. The best overall accuracies achieved for each date ranged from 75.31% to 85.61%.

通讯作者地址: Gong, P (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 ] Tsinghua Univ, Ctr Earth Syst Sci, Minist Educ, Key Lab Earth Syst Modeling, Beijing 100084, Peoples R China

[ 3 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China