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UID:pretalx-foss4g-europe-2026-BLRKM9@talks.osgeo.org
DTSTART;TZID=EET:20260630T123000
DTEND;TZID=EET:20260630T123500
DESCRIPTION:Temporal and spatial monitoring of geomorphic features associat
 ed with natural hazards are important for disaster prevention\, helping to
  identify vulnerable areas and anticipate potential risks. Remote sensing 
 data has become a cornerstone for natural hazard monitoring\, allowing reg
 ular mapping of remote areas and larger regions with reduced time and cost
 s. The unprecedented and continuously growing volume of Earth Observation 
 (EO) data has prompted the use of EO data cubes (EODCs) for efficient stor
 age\, management\, and analysis (Sudmanns et al.\, 2023). EODCs are mostly
  focused on raster and array structures due to the gridded nature of EO da
 ta. However\, previous work (Abad et al 2022) has shown how raster data cu
 bes are limited by their gridded representation in geomorphic feature dete
 ction. \nWhile pixel-level analysis is valuable for long time series EO da
 tasets\, it often disregards the spatial information (Sudmanns et al.\, 20
 20) essential for geomorphic feature analysis\, treating pixels in isolati
 on rather than as meaningful objects. Segmenting pixels into objects\, or 
 object-based image analysis (OBIA)\, is an established concept that allows
  better representation of natural phenomena with diverse characteristics a
 nd appearances\, such as different types of glacial lakes\, landslides\, o
 r lava flows (Hölbling\, 2022). Individual geomorphic features are treate
 d as aggregates of pixels and are grouped into objects\, providing additio
 nal information on topological relations.\nAdvances in object detection an
 d image segmentation have opened new opportunities for tracking evolving f
 eatures over time. Segmented results can be represented as a time series o
 f evolving vectors within EODCs\, taking advantage of vector data cube inf
 rastructure. Vector data cubes work well with stationary objects\, but the
  varying extent and shape of geomorphic features pose a challenge for exis
 ting data cube structures. Abad et al. (2024) addressed this by introducin
 g summary geometries to define a constant spatial dimension while storing 
 changing geometries as data cube elements\, assigning each feature a uniqu
 e ID based on the centroid of the union and dissolve of all corresponding 
 polygons over time. While effective\, this approach only accounted for spa
 tial extent\, leaving open how to handle other potential spatiotemporal dy
 namics\, such as merging\, splitting\, disappearing\, or reappearing. The 
 method may face difficulties in such cases where feature grouping may diff
 er according to interpretations. For example\, when two nearby glacial lak
 es expand over time and merge\, should they be considered as one lake befo
 re they merge? Or if a lake dries out and a new one appears over time\, sh
 ould both lakes have their own unique ID? In the case of several shape-evo
 lving features\, whether of the same type (e.g.\, glacial lakes)\, or diff
 erent (e.g.\, landslides and landslide-dammed lakes)\, such questions beco
 me important when quantifying geomorphological dynamics. In this study we 
 aim to investigate the implementation of grouping algorithms with features
  experiencing different spatiotemporal dynamics.    \nTo investigate diffe
 rent grouping algorithms\, we first built a vector data cube with a spatio
 temporal polygon dataset of a geomorphic feature. As study area\, we selec
 ted the glacial lakes at the southern margin of the Vatnajökull ice cap i
 n southeast Iceland\, particularly Jökulsárlón\, Breiðárlón\, and Fj
 allsárlón\, due to the lake’s constant evolution. We acquired Landsat 
 4-8 data from 1985 to 2015 and Sentinel-2 data from 2016 to 2025 from Open
 EO and Google Earth Engine. Annual summer composites were created to minim
 ise ice cover and fill gaps caused by frequent cloud cover\, proximity to 
 satellite scene edges (Sentinel-2)\, and stripe errors (Landsat-7)\, which
  partly influenced mapping accuracy\, though exact lake delineation was no
 t essential for this study. The OBIA classification used spectral indices 
 and k-means segmentation to map annual lake extents. The annual glacial la
 ke polygons were used to build a vector data cube based on the notebook by
  Abad et al (2024). Different feature grouping methods were investigated\,
  including the spatial overlap or proximity within a threshold over time\,
  the centroid and the bounding box of the union and dissolve operation of 
 all polygons over time\, as well as a representative point of a feature se
 t.\nAn advantage of the vector data cube over raster representations is th
 e ability to attach attribute information (such as lake area) to individua
 l geometries\, making it easier to visualise and query the temporal dynami
 cs. Results highlighted the importance of feature grouping selection\, as 
 different approaches can lead to meaningfully different interpretations of
  lake evolution. Some methods treated lakes that would later merge as a si
 ngle waterbody from the beginning\, producing a smooth\, continuous growth
  curve. Others assigned separate IDs until the moment of merging\, resulti
 ng in an abrupt jump in area for one lake as it absorbed the other. The la
 tter approach\, however\, is more logically consistent. For example\, smal
 l lakes currently forming above Breiðárlón are clearly distinct feature
 s today\, regardless of whether they will eventually merge with the larger
  lake. Treating them as one lake at their current state would be unreasona
 ble.\nHowever\, when dealing with larger datasets\, we might face difficul
 ties with the number of geometries and scalability. When lakes merge\, the
  other unique ID is still present in the data cube with empty geometries. 
 In a large dataset\, handling such lack of data could become an issue as h
 ighlighted by Abad et al (2024). The scalability issue requires further ex
 ploration in the future. Inconsistent segmentation was another limitation\
 , especially in the 1990s and early 2000s. This prevented reliable detecti
 on of disappearing and reappearing lakes\, as the algorithm would have ass
 igned new IDs to the same lake over time. Temporally consistent input data
  is therefore a prerequisite for accurately capturing the full range of ge
 omorphic dynamics.\nOur work directly addresses the gap of how to structur
 e and analyse evolving vector features over time within data cubes in open
 -source geospatial workflows. The methods were built on open-source tools 
 (OpenEO\, Python) and data (Sentinel and Landsat) when possible and extend
 s on previous work of the community making our work relevant to the FOSS4G
  conference.
DTSTAMP:20260605T010017Z
LOCATION:A01
SUMMARY:Representing spatiotemporal dynamics of glacial lakes with vector d
 ata cubes - Julia Engblom
URL:https://talks.osgeo.org/foss4g-europe-2026/talk/BLRKM9/
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