Land Cover Change Detection Based on Spatial-Temporal Sub-Pixel Evolution Mapping: A Case Study for Urban Expansion

Da He, Yanfei Zhong, Liangpei Zhang

Abstract: In the past decades, land cover change detection (LCCD) has been dramatically developed, since it provides corroborative support for policy decision, regulatory actions, and subsequent urban-rural activities. Satellite remote sensing image is the major source of LCCD since it is able to revisit the Earth's surface regularly and provide time series images for monitoring and space-time analysis. However, there is always a trade-off between spatial scale and temporal scale, i.e., finer spatial resolution image generally has a lower revisit frequency, leading to an observation omission; while higher revisit frequency image usually has a lower spatial resolution, resulting in a deficiency in detecting finer scale change information. In this paper, a spatial-temporal sub-pixel mapping (SSM) algorithm is proposed on the premise that one pair of fine spatial resolution image with low frequency revisit period and coarse spatial resolution with high frequently revisit period are available, and SSM is taken to restore the coarse image to a finer scale thematic map which can be then compared to the fine image, realizing a frequency and detailed LCCD. SSM is an extension of traditional mono-temporal sub-pixel mapping (SPM) algorithm, and is improved by incorporating temporally fine distribution patterns for a more appropriate restoration of coarse image. A study case for urban expansion LCCD were carried out to verify the ability of the proposed algorithm to handle change detection based on one pair of china-made Gaofen-2 image (GF-2) and Landsat-8 image, the result demonstrate that the proposed SSM algorithm outperform the other traditional SPM, achieving both fine temporal resolution and spatial resolution LCCD for further applications.