Continuous Change Detection of Forest/Grassland and Cropland in the Loess Plateau of China Using All Available Landsat Data

• Main Authors: Zhihui Wang, Wenyi Yao, Qiuhong Tang, Liangyun Liu, Peiqing Xiao, Xiangbing Kong, Pan Zhang, Fangxin Shi, Yuanjian Wang
• Format: Article
• Language: English
• Published: MDPI AG 2018-11-01
• Series: Remote Sensing
• Subjects: continuous change detection; land cover classification; Landsat NDVI time series; forest/grassland and cropland; Loess Plateau
• Online Access: https://www.mdpi.com/2072-4292/10/11/1775

Accurate identification of the spatiotemporal distribution of forest/grassland and cropland is necessary for studying hydro-ecological effects of vegetation change in the Loess Plateau, China. Currently, the accuracy of change detection of land cover using Landsat data in the loess hill and gully areas is seriously affected by insufficient temporal information from observations and irregular fluctuations in vegetation greenness caused by precipitation and human activities. In this study, we propose a method for continuous change detection for two types of land cover, mosaic forest/grassland and cropland, using all available Landsat data. The period with vegetation coverage is firstly identified using normalized difference vegetation index (NDVI) time series. The intra-annual NDVI time series is then developed at a 1-day resolution based on linear interpolation and S-G filtering using all available NDVI data during the period when vegetation types are stable. Vegetation type change is initially detected by comparing the NDVI of intra-annual composites and the newly observed NDVI. Finally, the time of change and classification for vegetation types are determined using decision tree rules developed using a combination of inter-annual and intra-annual NDVI temporal metrics. Validation results showed that the change detection was accurate, with an overall accuracy of 88.9% ± 1.0%, and a kappa coefficient of 0.86, and the time of change was successfully retrieved, with 85.2% of the change pixels attributed to within a 2-year deviation. Consequently, the accuracy of change detection was improved by reducing temporal false detection and enhancing spatial classification accuracy.