Detection of Cropland Change Using Multi-Harmonic Based Phenological Trajectory Similarity

Accurate information on cropland changes is critical for food production and security, sustainable cropland management, and global change studies. The common change detection methods bi-temporal based, using remotely sensed imagery easily generate pseudo changes due to phenological or seasonal differences. Cropland exhibits a distinctive phenological trajectory that has strong periodic characteristics and seasonal paths. This paper proposes the use of phenological trajectory similarity to search for the overall changes between two time-series images instead of single change events between two dates of imagery. Due to the complex spectral–temporal characteristic of cropland, a phenological trajectory was constructed using a multi-harmonic model for capturing intra-annual variations. Then, phenological trajectory similarity was measured using coefficient vector difference (CVD), and used for detecting change/no-change areas when considering both the amplitude and phase difference. Finally, instead of the traditional classification method based on original images, we used the coefficient ratio vector (CRV) as the input for change type discrimination. The distance between the coefficient ratio vector (CRV) of the change pixel and of the reference change type was calculated to identify the exactly changed types. The performance of this proposed approach was tested using two sets of Landsat time-series images from 2010 and 2015. Moreover, the change area detection results of three other methods, namely, the continuous change detection and classification (CCDC), change vector analysis (CVA), and post-classification comparison (PCC), were also calculated for comparison and analysis. The results indicated that the proposed approach acquired the highest accuracy with an overall accuracy of 98.58% and a kappa coefficient of 0.82, which demonstrated that the method provides the capacity to detect real changes and estimate pseudo changes caused by season differences.