Time series segmentation techniques for land cover change detection

Ecosystem-related observations from remote sensors on satellites offer a significant possibility for understanding the location and extent of global land cover change. In this study, we focus on time series segmentation techniques in the context of land cover change detection. We propose a model based time series segmentation algorithm inspired by an event detection framework proposed in the field of statistics. We also present a novel model free change detection algorithm for detecting land cover change that is computationally simple, efficient, non-parametric and takes into account the inherent variability present in the remote sensing data. A key advantage of this method is that it can be applied globally for a variety of vegetation without having to identify the right model for specific vegetation types. We evaluate the change detection capacity of the proposed techniques on both synthetic and MODIS EVI data sets. We illustrate the importance and relative ability of different algorithms to account for the natural variation in the EVI data set.

Author dc:creator

• Garg, Ashish

• Change detection
• Data mining
• Land cover
• Segmentation
• Time series

Language dc:language.iso

en_US

Repository record dc:identifier.uri

http://purl.umn.edu/156621

OAI identifier oai:identifier

oai:conservancy.umn.edu:11299/156621