Analysing the temporal dimension is becoming increasingly important in Earth observation (EO) and is required by certain application domains. Remote-sensing-based change detection (DC) was conceptually never limited to bi-temporal analysis (i.e. image differencing) although most change detection analyses have been conducted this way in the past. The availability of data sources with high acquisition frequencies, such as Sentinel-2 (e.g. on average every 5 days), or with a long historic archive, such as Landsat (e.g. over 40 years) now make more complex analysis of temporal dynamics possible.
The LIVID (Long Image Time-Series Variability Indicator Description) approach applies a fully automated, object-oriented workflow to calculate the overall variability of content for uniform objects of any shape and size between multiple images. This type of temporal analysis produces a single map of index values based on user-defined weights of their category changes over time, thus indicating regions with high relative variability or stability. The calculated index is based on semantically enriched, data-derived categories and user-defined weights pertaining to natural resource depletion.
The information about the variability of natural resources over a longer period of time is extremely important for many applications such as nature conservation and agriculture. It might be also used as pre-analysis to identify more specific areas that are worth investigating further using additional time series analysis methods or selecting ground reference points. It might also provide additional evidence in combination with other tools and interpretation of temporal changes.
LIVID is currently being developed at Z_GIS as an eCognition app. The task would be to develop the application further and apply it as a use-case to an example area/topic.
Braun, Andreas, and Volker Hochschild. 2017. “A SAR-Based Index for Landscape Changes in African Savannas.” Remote Sensing 9 (4): 359. doi:10.3390/rs9040359
Sudmanns, Martin; Augustin, Hannah; Tiede, Dirk. 2018. „LIVID”. Open Science Framework Project.
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