Seasonal Land Cover Dynamics in Beijing Derived from Landsat 8 Data Using a Spatio-Temporal Contextual Approach

Remote Sensing

Jie Wang¹  Congcong Li²  Luanyun Hu²  Yuanyuan Zhao²  Huabing Huang¹  Peng Gong¹  Heiko Balzter³ 

[1] State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; E-Mails:;Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China; E-Mails:State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;

关键词: land cover;    random forest;    Markov random field;    image sequence;    entropy;   

DOI  :  10.3390/rs70100865

来源: mdpi

【 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%.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.