A New Framework for Modelling and Monitoring the conversion of Cultivated Land to Built-up Land based on a Hierarchical Hidden Semi-Markov Model using Satellite Image Time Series
This publication appears in: Remote Sensing
Authors: Y. Yuan, L. Lin, J. Chen, H. Sahli, Y. Chen, C. Wang and B. Wu
Number of Pages: 24
Publication Year: 2019
Large amounts of farmland loss caused by urban expansion has been a severe global environmental problem. Therefore, monitoring urban encroachment upon farmland is a global issue. In this study, we propose a novel framework for modelling and monitoring the conversion of cultivated land to built-up land using a satellite image time series (SITS). The land-cover change process is modelled by a two-level hierarchical hidden semi-Markov model, which is composed of two Markov chains with hierarchical relationships. The upper chain represents annual land-cover dynamics, and the lower chain encodes the vegetation phenological patterns of each land-cover type. This kind of architecture enables us to represent the multilevel semantic information of SITS at different time scales. Specifically, intra-annual series reflect phenological differences and inter-annual series reflect land-cover dynamics. In this way, we can take advantage of the temporal information contained in the entire time series as well as the prior knowledge of land cover conversion to identify where and when changes occur. As a case study, we applied the proposed method for mapping annual, long-term urban-induced farmland loss from Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series in the Jing-Jin-Tang district, China from 2001 to 2010. The accuracy assessment showed that the proposed method was accurate for detecting conversions from cultivated land to built-up land, with the overall accuracy of 97.72% in the spatial domain and the temporal accuracy of 74.60%. The experimental results demonstrated the superiority of the proposed method in comparison with other state-of-the-art algorithms. In addition, the spatial-temporal patterns of urban expansion revealed in this study are consistent with the findings of previous studies, which also confirms the effectiveness of the proposed method.