Examining The Effect of Conflicts in a Semantic EO DC

For agricultural fire and burned area estimation from conflict situations or otherwise, in situations where conventional active fire/burned area products may be insufficient due to spatio-temporal limitations, having an alternative with higher spatial resolution such as Sentinel 2 imagery, may prove more useful. Currently, research is being conducted into using Sentinel 2 imagery data for such purposes, e.g. using  SWIR bands  to detect active fires and recently burned areas. If these can in future be considered in sen2cube, then it will prove more applicable in this type of conflict scenario than it is currently.

Semantic EO Data Cubes such as Sen2Cube, although still in its early stages of development, a powerful tool for EO data analytics, as using it has the potential to make EO analysis fun and interactive (model building process) especially for non-programmers, while getting much the same benefits as other major tools out there. One particularly important benefit, I reiterate, is the ability to query by AOI alone. Due to SCBIR, valuable information is not lost due to cloud cover, even for passive sensors like Sentinel 2.

However, there are limitations. Some "current" limitations of the Sen2Cube are:

• Spatial Extent: Currently, the Factbase is limited to 3, with Austria being the only country whose entire extent is contained within it.
• The computational requirement of setting on up is also high, however, there are plans by the team to automate this process (as soon as first quarter 2023). Therefore it will be easier to instantiate data cubes in other regions.
• This issue also cause a limitation for my analysis of the drought affected areas of the western provinces of Afghanistan, which where most affected by the drought.
• The platform is state of the art and relatively small scale, at the moment and therefore may present hitches while processing large extents. It is advised to use small AOIs for now.

Major Updates: to improve functionality and possibly temporal limit (2022 imagery will be back). There is also talks to automate the process of establishing new semantic data cubes, to make it less time consuming. It will soon be easier to set up semantic data cubes for anywhere in the world automatically, which would greatly improve its applicability to current conflict scenarios such as Ukraine.

Gates, S., Hegre H., Nygard H. M. & Strand H. (2012). Development Consequences of Armed Conflict. World Development, 40(9), 1713-1722.

Tiede, Dirk & Sudmanns, Martin & Augustin, Hannah & Lang, Stefan & Baraldi, A.. (2019). Sentinel-2 Semantic Data & Information Cube Austrai. (Link)

Tiede, D.; Lüthje, F.; Baraldi, A. (Eds.) (2014): Automatic post-classification land cover change detection in Landsat images: Analysis of changes in agricultural areas during the Syrian crisis. Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation. Potsdam. DGPF (23).  (Link )

Xikun Hu, Yifang Ban, Andrea Nascetti, Sentinel-2 MSI data for active fire detection in major fire-prone biomes: A multi-criteria approach, International Journal of Applied Earth Observation and Geoinformation, Volume 101, 2021, 102347, ISSN 1569-8432, https://doi.org/10.1016/j.jag.2021.102347. ( Link )

Jan Hofinger (2022) Crisis-Related Agricultural Changes in Northwest Syria: Big EO Data Analysis Using a Sentinel-2 Semantic Data Cube.

Sen2Cube ( Website ,  App ,  Manual )

 FAO, Food Insecurity in Afghanistan