Digital Elevation Model generation for historical landscape analysis based on LiDAR data, a case study in Flanders (Belgium)

Werbrouck, Ilke; Antrop, Marc; Eetvelde, Veerle Van; Stal, Cornelis; Maeyer, Philippe De; Bats, Machteld; Bourgeois, Jean; Court-Picon, Mona; Crombe, P.; Reu, Jeroen De; Smedt, Philippe De; Finke, Peter; Meirvenne, Marc Van; Verniers, Jacques; Zwertvaegher, Ann

doi:10.1016/j.eswa.2010.12.162

Cited by 49 publications

(44 citation statements)

References 26 publications

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“…Based on the digital elevation model (DEM) (Werbrouck et al, 2011), a test site of 0.45 ha was selected at the boundary of the palaeomire (Fig. 1).…”

Section: Study Areamentioning

confidence: 99%

Reconstructing palaeochannel morphology with a mobile multicoil electromagnetic induction sensor

et al. 2011

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“…Based on the digital elevation model (DEM) (Werbrouck et al, 2011), a test site of 0.45 ha was selected at the boundary of the palaeomire (Fig. 1).…”

Section: Study Areamentioning

confidence: 99%

Reconstructing palaeochannel morphology with a mobile multicoil electromagnetic induction sensor

et al. 2011

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“…After filtering out artificial features and artefacts by using topographical vector data, manually checking the data, and automatically filtering ditches and field borders through slope analysis, thematic (e.g. soil and geomorphological maps) and historical maps were used as ancillary data in order to estimate the natural pre-19 th century topography (Werbrouck et al, 2011). The result was a 2 x 2 m gridded digital elevation model (DEM), excluding the main present-day anthropogenic features (Fig.…”

Section: Survey Design and Methodologymentioning

confidence: 99%

“…The basis for the field surveys in the area were high precision airborne LIDAR data, made available by the Flemish GIS agency (AGIV) (Werbrouck et al, 2011), which had an average sample density of 1 point per 2 m 2 and an altimetric accuracy ranging from 7 to 20 cm (AGIV, 2003). After filtering out artificial features and artefacts by using topographical vector data, manually checking the data, and automatically filtering ditches and field borders through slope analysis, thematic (e.g.…”

Section: Survey Design and Methodologymentioning

confidence: 99%

A multidisciplinary approach to reconstructing Late Glacial and Early Holocene landscapes

Smedt

Meirvenne

Davies

et al. 2013

Journal of Archaeological Science

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Understanding palaeotopographical variability forms the basis for understanding prehistoric societies.Alluvial and lacustrine environments, in particular, are key areas with both a high archaeological and palaeoecological potential. However, the often deep stratification of these sites, the high water table and the complex sedimentological variations can hamper a detailed reconstruction of the spatial relationship between prehistoric settlement and their environment. Combining different remote and proximal sensing techniques and coring data, can offer detailed insight into such landscapes. More specifically, the integration of mobile geophysical methods allows the collection of unprecedented continuous information on large-scale palaeolandscape variability. In this study we present a combined approach in order to map and model prehistoric landscapes and river systems in and around a Late Glacial palaeolake in north-western Belgium. Based on filtered and unfiltered digital elevation models, a survey area of 60 ha was selected, in which detailed mobile multi-receiver electromagnetic induction survey was conducted. The results allowed for the delineation of palaeochannels in the area and enabled modelling the depth of these features in the survey area, providing insight into their flow characteristics.14 C sampling enabled the dating of the evolving river system to the transition between the Late Glacial and the Early Holocene. Through additional coring, this river system could be traced further through the palaeolake area. Based on these results a detailed reconstruction was made of the palaeotopography that harboured the Final Palaeolithic andEarly Mesolithic occupation of the study site.

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“…A present DEM, representative of the actual topography was originally produced by Werbrouck et al (2011) on a 2 x 2 m² resolution and applied as predictor map for years between 1804 CE (Common Era) till present. A pre-medieval DEM, delivered by Werbrouck et al (2011), in which anthropogenic artifacts were removed, was used for the years between 1114 CE -1803 CE. For the years before 1114 CE, a (pre)historic DEM was used, in which plaggen layers and also marine sediments deposited during medieval storm surges and floodings were removed (Zwertvaegher et al, in review).…”

Section: From Point Scale To Full-coveragementioning

confidence: 99%

Spatio-temporal modeling of soil characteristics for soilscape reconstruction

et al. 2013

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Full-coverage maps for several specific soil characteristics were produced at particular time-intervals over a time span of 12,716 years for a 584 km² large study area located in Belgium. The pedogenetic process model SoilGen2 was used to reconstruct the evolution of several soil variables at specific depths in the soil profile at various point locations (96 in total). The time span covered by the simulations encompassed the final part of the Younger Dryas and the Holocene up till present. Time series on climate, organisms and groundwater table were reconstructed and supplied to the model as boundary conditions. Model quality optimization was performed by calibrating the solubility constant of calcite by comparison of the simulated time necessary for decarbonisation with literature values and evaluating the calibrated value over a wide range of precipitation surpluses representative for the regarded time period. The simulated final state was evaluated against measurements collected in a database representing the historic state of the soil at ~1 950. The simulated specific soil characteristics at the point locations were then used to produce full-coverage maps at the particular time-intervals by regression kriging. Such maps are believed to provide useful information for geoarchaeological studies and archaeological land evaluations.2 Highlights -We modelled the evolution of soil variables using the model SoilGen2 -We produced maps at points in time using model outcomes and regression kriging.-A better reconstruction of the boundary conditions can improve model quality.-Adding the podzolisation process can improve model quality.

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Digital Elevation Model generation for historical landscape analysis based on LiDAR data, a case study in Flanders (Belgium)

Cited by 49 publications

References 26 publications

Reconstructing palaeochannel morphology with a mobile multicoil electromagnetic induction sensor

Reconstructing palaeochannel morphology with a mobile multicoil electromagnetic induction sensor

A multidisciplinary approach to reconstructing Late Glacial and Early Holocene landscapes

Spatio-temporal modeling of soil characteristics for soilscape reconstruction

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