Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2195-2199, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2195-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2195-2199, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2195-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

MONITORING FARMLAND LOSS CAUSED BY URBANIZATION IN BEIJING FROM MODIS TIME SERIES USING HIERARCHICAL HIDDEN MARKOV MODEL

Y. Yuan1, Y. Meng2, Y. X. Chen1, C. Jiang1, and A. Z. Yue2 Y. Yuan et al.
  • 1Dept. of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing, China
  • 2Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Bejing, China

Keywords: Urban Encroachment onto Farmland, Time Series, Change Detection, Hierarchical Hidden Markov Model (HHMM)

Abstract. In this study, we proposed a method to map urban encroachment onto farmland using satellite image time series (SITS) based on the hierarchical hidden Markov model (HHMM). In this method, the farmland change process is decomposed into three hierarchical levels, i.e., the land cover level, the vegetation phenology level, and the SITS level. Then a three-level HHMM is constructed to model the multi-level semantic structure of farmland change process. Once the HHMM is established, a change from farmland to built-up could be detected by inferring the underlying state sequence that is most likely to generate the input time series. The performance of the method is evaluated on MODIS time series in Beijing. Results on both simulated and real datasets demonstrate that our method improves the change detection accuracy compared with the HMM-based method.