Caucasia top-down: Remote sensing data for survey in a high altitude mountain landscape

Reinhold, Sabine; Belinskiy, Andrey; Коробов, Д. С.

doi:10.1016/j.quaint.2015.10.106

Cited by 22 publications

(11 citation statements)

References 10 publications

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“…The scientific field of remote sensing has exhibited great potential over the last years for archaeological investigations, even in challenging environments [1][2][3][4][5][6][7]. Optical and radar satellite datasets have been used to reconstruct the archaeo-environment [8,9], to map recent land use and land cover changes in areas with archaeological interest [10][11][12], to detect traces linked with archaeological remains [13,14] or even to investigate spatial patterns between archaeological sites [15].…”

Section: Introductionmentioning

confidence: 99%

Fusion of Satellite Multispectral Images Based on Ground-Penetrating Radar (GPR) Data for the Investigation of Buried Concealed Archaeological Remains

et al. 2017

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Abstract:The paper investigates the superficial layers of an archaeological landscape based on the integration of various remote sensing techniques. It is well known in the literature that shallow depths may be rich in archeological remains, which generate different signal responses depending on the applied technique. In this study three main technologies are examined, namely ground-penetrating radar (GPR), ground spectroscopy, and multispectral satellite imagery. The study aims to propose a methodology to enhance optical remote sensing satellite images, intended for archaeological research, based on the integration of ground based and satellite datasets. For this task, a regression model between the ground spectroradiometer and GPR is established which is then projected to a high resolution sub-meter optical image. The overall methodology consists of nine steps. Beyond the acquirement of the in-situ measurements and their calibration (Steps 1-3), various regression models are examined for more than 70 different vegetation indices (Steps 4-5). The specific data analysis indicated that the red-edge position (REP) hyperspectral index was the most appropriate for developing a local fusion model between ground spectroscopy data and GPR datasets (Step 6), providing comparable results with the in situ GPR measurements (Step 7). Other vegetation indices, such as the normalized difference vegetation index (NDVI), have also been examined, providing significant correlation between the two datasets (R = 0.50). The model is then projected to a high-resolution image over the area of interest (Step 8). The proposed methodology was evaluated with a series of field data collected from the Vésztő-Mágor Tell in the eastern part of Hungary. The results were compared with in situ magnetic gradiometry measurements, indicating common interpretation results. The results were also compatible with the preliminary archaeological investigations of the area (Step 9). The overall outcomes document that fusion models between various types of remote sensing datasets frequently used to support archaeological research can further expand the current capabilities and applications for the detection of buried archaeological remains.

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Section: Introductionmentioning

confidence: 99%

Fusion of Satellite Multispectral Images Based on Ground-Penetrating Radar (GPR) Data for the Investigation of Buried Concealed Archaeological Remains

et al. 2017

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“…Smith et al 2009; Badalyan et al . 2010; Lindsay et al 2010, 2014; Hammer 2014; Reinhold et al 2016). Further work is necessary to assess the variability and coherence of this fortress-building phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Investigating the spatial organisation of Bronze and Iron Age fortress complexes in the South Caucasus

et al. 2019

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“…They also provide vital economic and ecological resources to the world's population, thanks to their rich biodiversity, water-storing capacity, mineral reserve, etc (Price, 1998). Therefore, mountain areas are more and more surveyed with remote sensing (RS) data (Boori et al, 2015;Barrachina et al 2015;Reinhold et al, 2016;Chimner et al, 2019). However, RS data are usually contaminated by topographic effects, since slopes combined with the observation configuration (e.g., viewing and sun geometry, atmosphere composition) impact the direct, diffuse and multi-scattered irradiance, which in turn affects the RS signals and radiative budget (RB) of mountain regions (Schaaf et al, 1994;Mousivand et al, 2015;Pellarin et al, 2016;Wen et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

DART radiative transfer modelling for sloping landscapes

Wang

Lauret

Gastellu-Etchegorry

2020

Remote Sensing of Environment

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Topography is one of the key factors that impact remotely sensed data and their interpretation. Indeed, combined with the viewing geometry and neighbour effects, it strongly affects the direct, diffuse and multi-scattered scene irradiance, which in turn impacts the radiative budget and remote sensing signals of the landscapes. The increased availability of digital elevation models (DEM) and the advancement of 3D radiative transfer (RT) models allow us to better address these topographic effects. DART (Discrete Anisotropic Radiative Transfer) is one of the most accurate and comprehensive 3D RT models that simulate remote sensing observations of natural and urban landscapes with topography and atmosphere. It simulates environmental effects (i.e., impact of adjacent landscape on the observed landscape) using a so-called infinite slope mode that infinitely duplicates the observed landscape while ensuring the continuity of slope and altitude at the DEM edges. Up to DART version 5.7.4, this mode was slightly inaccurate and computer intensive, depending on the topography. This paper presents an innovative modelling strategy that greatly improves it in terms of accuracy, image quality and 2 computer efficiency. For that, a fictive auxiliary oblique plane, adapted to the landscape topography, is introduced for managing the scene illumination, the Earth-Atmosphere coupling and the storage of the radiation that exits the scene before being projected onto the sensor plane.Improvements and validations are illustrated both visually and quantitatively by DART images, radiometric products and radiative budget. For example, the observed reflectance of a Lambertian slope is equal to the expected analytical value. In addition, the solar plane reflectance of a forest on a mountain slope (experimental scene) has an average error of about 0.01% relative to the reflectance of the same forest stand in the reference scene (i.e., nine duplications of the experimental scene). This new modelling is already integrated in the official DART version (https://dart.omp.eu).

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Caucasia top-down: Remote sensing data for survey in a high altitude mountain landscape

Cited by 22 publications

References 10 publications

Fusion of Satellite Multispectral Images Based on Ground-Penetrating Radar (GPR) Data for the Investigation of Buried Concealed Archaeological Remains

Fusion of Satellite Multispectral Images Based on Ground-Penetrating Radar (GPR) Data for the Investigation of Buried Concealed Archaeological Remains

Investigating the spatial organisation of Bronze and Iron Age fortress complexes in the South Caucasus

DART radiative transfer modelling for sloping landscapes

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