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

Jiage Chen,Shu Peng,Jun Chen,Huiping Liu

doi:10.3390/rs10071020

Abstract

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.

Highlights

Cropland is the basic resource and condition of human existence, and its quantity and quality are an important basis for ensuring global food production security [1]
One of the major challenges in cropland change detection is how to detect true changes while reducing false changes caused by seasonal differences and other interference factors
The change detection method based on time series images effectively weakens the influence of seasonal differences and noise because data are collected throughout the growing seasons

Summary

Introduction

Cropland is the basic resource and condition of human existence, and its quantity and quality are an important basis for ensuring global food production security [1]. A variety of change detection methods utilizing remote sensing data have been developed for detecting changes [12,13,14]. Time series analysis has been proven to be superior to the bi-temporal methods for capturing land cover change, since it significantly reduces the impacts of seasonal differences and interference noises [20,21,22]. The Breaks for Additive Seasonal and Trend (BFAST) algorithm developed by Verbesselt et al, (2010) is capable of capturing multiple breakpoint changes by estimating time and magnitude of changes occurring within the seasonal or trend components [24,25,26]. The Continuous Change Detection and Classification (CCDC) estimated the trend of the time-series by using harmonic models and utilized this trend to characterize multiple changes, including abrupt [27]

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Discussion

Conclusion