Detecting grassland cover changes through spatiotemporal outlier analysis using remotely sensed time-series data: a case study from Xilingol, China

Abstract

Significant land cover change (SLCC) in grassland ecosystem includes both conversions in land cover types and critical modifications to specific land cover properties without land cover conversions. A statistics-based framework, known as spatiotemporal outlier analysis (STOA), was proposed to detect SLCC from time-series remote sensing data considering both spatial and temporal contexts. The proposed STOA combines local spatial association analysis (spatial context) and temporal variation analysis (temporal context) to extract spatiotemporal outliers. As the case study, STOA was applied to mapping SLCC on the grassland of Xilinhot, China using time-series MODIS vegetation index during 2000–2015. The results clearly revealed the anisotropic characteristics and spatio-temporal variations in the extracted SLCC, demonstrating meaningful patterns in the land cover changes (LCC). Considering the scale-effect, it is inferred that the detected SLCC is most likely attributed to human-induced impact. We conclude that STOA is a promising tool to quantify LCC for grassland ecosystems.

Keywords:

• Spatiotemporal patterns
• remote sensing
• change detection
• local spatial association
• grassland

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Funding support for this study included the National Natural Science Foundation of China (No. 41871296) and Open Fund of Key Laboratory of Geographic Information Science (Ministry of Education) East China Normal University (No. KLGIS2017A05).