Object-based landform delineation and classification from DEMs for archaeological predictive mapping

Verhagen, Philip; Drăguţ, Lucian

doi:10.1016/j.jas.2011.11.001

Cited by 75 publications

(50 citation statements)

References 14 publications

(11 reference statements)

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“…In an OBIA approach, neighboring cells with similar values are segmented into objects of varying sizes, which are then used as units of classification. Researchers have traditionally used the pixel-based classification of remotely-sensed data [14], but the OBIA is gaining popularity in various disciplines recently as it is considered more accurate and versatile [49][50][51][52].…”

Section: Obia Vegetation Classification With Lidar Datamentioning

confidence: 99%

Archaeological Application of Airborne LiDAR with Object-Based Vegetation Classification and Visualization Techniques at the Lowland Maya Site of Ceibal, Guatemala

et al. 2017

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Abstract:The successful analysis of LiDAR data for archaeological research requires an evaluation of effects of different vegetation types and the use of adequate visualization techniques for the identification of archaeological features. The Ceibal-Petexbatun Archaeological Project conducted a LiDAR survey of an area of 20 × 20 km around the Maya site of Ceibal, Guatemala, which comprises diverse vegetation classes, including rainforest, secondary vegetation, agricultural fields, and pastures. We developed a classification of vegetation through object-based image analysis (OBIA), primarily using LiDAR-derived datasets, and evaluated various visualization techniques of LiDAR data. We then compared probable archaeological features identified in the LiDAR data with the archaeological map produced by Harvard University in the 1960s and conducted ground-truthing in sample areas. This study demonstrates the effectiveness of the OBIA approach to vegetation classification in archaeological applications, and suggests that the Red Relief Image Map (RRIM) aids the efficient identification of subtle archaeological features. LiDAR functioned reasonably well for the thick rainforest in this high precipitation region, but the densest parts of foliage appear to create patches with no or few ground points, which make the identification of small structures problematic.

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Section: Obia Vegetation Classification With Lidar Datamentioning

confidence: 99%

Archaeological Application of Airborne LiDAR with Object-Based Vegetation Classification and Visualization Techniques at the Lowland Maya Site of Ceibal, Guatemala

et al. 2017

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“…For example, Verhagen and Drăguţ (2012) used Object Based Image Analysis of a DEM from lidar data (5m x 5m posting) to create a landscape geomorphometric classification model to use within an archaeological predictive model; Vaughn and Crawford (2009) used key environmental variables defined from contemporary remotely-sensed data (e.g. vegetation colour and topography/slope) to predictively model Maya settlement in northwest Belize, whilst Rua (2009) attempted to identify potential locations of rural Roman villae in Portugal using a range of topographic, hydrological and slope variables.…”

Section: Archaeological Predictive Modellingmentioning

confidence: 99%

Using geoarchaeological deposit modelling as a framework for archaeological evaluation and mitigation in alluvial environments

Carey

Howard

Jackson

et al. 2017

Journal of Archaeological Science: Reports

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The pace of anthropogenic development on floodplains and adjacent valley floors is still increasing and in many countries this is accompanied by a requirement for heritage mitigation and management.The result is an increased demand for effective and efficient archaeological evaluation and mitigation strategies, which can only be achieved in alluvial environments through the application of geoarchaeological methods. This paper uses lidar data combined with deep geophysical survey (electrical resistivity), gouge coring and limited borehole data to derive a three dimensional geoarchaeological deposit model, which provided a vehicle for archaeological evaluation and mitigation. Significantly, the results of this deposit model are compared to the results from the subsequent archaeological evaluation trenching, a methodological next step that has not received sufficient attention within the (geo)archaeological literature. The deposit model is refined using radiocarbon dating and artefactual evidence derived from the archaeological evaluation trenching.The results demonstrate how geoarchaeological deposit modelling can be integrated with archaeological evaluation trenching and provides discussion of the importance and difficulties of integrating geoarchaeological sediment units (archives) with archaeological contextual excavation data, with conventional stratigraphic matrices.

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“…The neighbourhood size selected for a TPI implementation is of utmost significance since different sizes reveal different landforms (De Reu et al, 2013;Ilia et al, 2013;Verhagen and Dragut, 2012;Weiss, 2001). …”

Section: Tpi Landform Classificationmentioning

confidence: 99%

“…In particular, landform classification has been proved a valuable processing tool for studies related to archaeology, ecology, agriculture, forestry, rural planning, hazards, etc. (Ho and Umitsu, 2011;Hoersch et al, 2002;Macmillan et al, 2003;Martin-Duque et al, 2003;Mcnab, 1993;Verhagen and Dragut, 2012).…”

Section: Introductionmentioning

confidence: 99%

Landform Analysis Using Terrain Attributes. A Gis Application on the Island of Ikaria (Aegean Sea, Greece)

Skentos

Anagnostopoulou

2017

Annals of Valahia University of Targoviste, Geographical Series

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The main objective of this study is to classify the landforms of Ikaria Island by conducting morphometric analysis. The whole classification process is based on the calculation of the Topographic Position Index (TPI). The delivered TPI landform classes are spatially correlated with the geology, slope, valley depth and the topographic ruggedness of the island. The results of this study indicate the presence of two distinctive landform units, affected mainly by the local geological setting.

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Object-based landform delineation and classification from DEMs for archaeological predictive mapping

Cited by 75 publications

References 14 publications

Archaeological Application of Airborne LiDAR with Object-Based Vegetation Classification and Visualization Techniques at the Lowland Maya Site of Ceibal, Guatemala

Archaeological Application of Airborne LiDAR with Object-Based Vegetation Classification and Visualization Techniques at the Lowland Maya Site of Ceibal, Guatemala

Using geoarchaeological deposit modelling as a framework for archaeological evaluation and mitigation in alluvial environments

Landform Analysis Using Terrain Attributes. A Gis Application on the Island of Ikaria (Aegean Sea, Greece)

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