A crop phenology knowledge-based approach for monthly monitoring of construction land expansion using polarimetric synthetic aperture radar imagery

Synthetic aperture radar (SAR) remote sensing, which is independent of weather conditions, can monitor construction land expansion at short intervals for early prevention of unauthorized land use. However, seasonal crop growth creates land cover changes that are hardly distinguishable from land developments by using the traditional approach that employs two SAR images for detection. This study proposes a knowledge-based approach based on crop phenology to detect monthly construction land expansion by using consecutive polarimetric SAR imagery. The innovation of the proposed approach is the utilization of crop phenology knowledge to remove errors introduced by seasonal crop growth. In this approach, using crop phenology knowledge as a basis, a knowledge-based system is built to automatically determine when seasonal crop growth yields considerable errors. Monthly land developments are normally detected by comparing two consecutive images, but in the periods when the errors from crop growth are considerable, monthly detection results are calibrated using an additional third consecutive image, which is utilized to identify the errors based on the difference in temporal land cover change between land development and crop growth. A comparison was made between the proposed approach and the traditional approach for the monthly monitoring of construction land expansion. We found that seasonal paddy growth created many errors by using the traditional approach. The proposed approach substantially reduced these errors. Compared with the traditional approach, the proposed approach reduced errors by up to 87.33% with an average overall error rate of only 0.24%. The results indicated that the proposed approach outperforms the traditional approach in monitoring monthly construction land expansion and suppressing the disturbance from seasonal crop growth.