Abstract
Reliable multi-temporal landslide detection over longer periods of time requires multi-sensor time series data characterized by high internal geometric stability, as well as high relative and absolute accuracy. For this purpose, a new methodology for fully automated co-registration has been developed allowing efficient and robust spatial alignment of standard orthorectified data products originating from a multitude of optical satellite remote sensing data of varying spatial resolution. Correlation-based co-registration uses world-wide available terrain corrected Landsat Level 1T time series data as the spatial reference, ensuring global applicability. The developed approach has been applied to a multi-sensor time series of 592 remote sensing datasets covering an approximately 12,000 km2 area in Southern Kyrgyzstan (Central Asia) strongly affected by landslides. The database contains images acquired during the last 26 years by Landsat (E)TM, ASTER, SPOT and RapidEye sensors. Analysis of the spatial shifts obtained from co-registration has revealed sensor-specific alignments ranging between 5 m and more than 400 m. Overall accuracy assessment of these alignments has resulted in a high relative image-to-image accuracy of 17 m (RMSE) and a high absolute accuracy of 23 m (RMSE) for the whole co-registered database, making it suitable for multi-temporal landslide detection at a regional scale in Southern Kyrgyzstan.
Highlights
Landslides are a world-wide occurring natural hazard leading to severe loss of life and infrastructure
The completeness and quality of remote sensing-based landslide inventories depend on the used multi-temporal image database, whereas a high temporal repetition rate over the longest possible time period of data availability is required in order to perform longer term analysis of landslide occurrence, which is necessary for objective landslide hazard assessment [3,4,5]
These results show that the developed approach is capable of handling a wide range of offsets occurring in images of various spatial resolutions ranging between 5 m for RapidEye and 30 m for Landsat data
Summary
Landslides are a world-wide occurring natural hazard leading to severe loss of life and infrastructure. The completeness and quality of remote sensing-based landslide inventories depend on the used multi-temporal image database, whereas a high temporal repetition rate over the longest possible time period of data availability is required in order to perform longer term analysis of landslide occurrence, which is necessary for objective landslide hazard assessment [3,4,5] The presented study aims at the development of a robust and globally applicable methodology for automated co-registration, which is suitable for efficient correction of spatial offsets between orthorectified standard data products representing multi-sensor time series In this context, a spatially and temporally consistent spatial reference system is required, allowing spatial alignment of all datasets with sufficient relative and absolute accuracy.
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