Evaluating the Potentials of Sentinel-2 for Archaeological Perspective

Αγαπίου, Άθως; Alexakis, Dimitrios D.; Sarris, Apostolos; Hadjimitsis, Diofantos G.

doi:10.3390/rs6032176

Cited by 80 publications

(54 citation statements)

References 52 publications

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“…Although some fusion techniques already exist, like pan-sharpening between multi-spectral and panchromatic bands of the same or different satellite sensors [26,27], or as more recently shown, fusion between archive greyscale and recent multispectral satellite images [28], fusion methodologies of more diverse datasets need to be developed and tested for archaeological research. Studies have shown that it is feasible to scale-up ground spectroradiometric data (i.e., ground spectral signatures) to existing [29] or even forthcoming satellite sensors [30].…”

Section: Methodsmentioning

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: Methodsmentioning

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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“…The usefulness of medium and high-resolution satellite data is well established and the procedures for the identification of buried structures are well described in Earth observation studies applied to cultural heritage and aerial archaeology [7,41,48]. In particular, the presence of buried structures or anthropogenic transformation with high impact on the landscape (ditches, trenches, etc.)…”

Section: Sentinel-2 Datamentioning

confidence: 99%

Multitemporal 2016-2018 Sentinel-2 Data Enhancement for Landscape Archaeology: The Case Study of the Foggia Province, Southern Italy

et al. 2020

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This paper is focused on the use of satellite Sentinel-2 data for assessing their capability in the identification of archaeological buried remains. We selected the "Tavoliere delle Puglie" (Foggia, Italy) as a test area because it is characterized by a long human frequentation and is very rich in archaeological remains. The investigations were performed using multi-temporal Sentinel-2 data and spectral indices, commonly used in satellite-based archaeology, and herein analyzed in known archaeological areas to capture the spectral signatures of soil and crop marks and characterize their temporal behavior using Time Series Analysis and Spectral Un-mixing. Tasseled Cap Transformation and Principal Component Analysis have been also adopted to enhance archaeological features. Results from investigations were compared with independent data sources and enabled us to (i) characterize the spectral signatures of soil and crop marks, (ii) assess the performance of the diverse spectral channels and indices, and (iii) identify the best period of the year to capture the archaeological proxy indicators. Additional very important results of our investigations were (i) the discovery of unknown archaeological areas and (ii) the setup of a database of archaeological features devised ad hoc to characterize and categorize the diverse typologies of archaeological remains detected using Sentinel-2 Data.

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“…This activity resulted in several experiments based on simulated S2 datasets [5][6][7][8][9][10][11][12][13][14][15]. These studies reported a high potential of S2 in various fields of application.…”

Section: Introductionmentioning

confidence: 99%

First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

2016

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Abstract:The study presents the preliminary results of two classification exercises assessing the capabilities of pre-operational (August 2015) Sentinel-2 (S2) data for mapping crop types and tree species. In the first case study, an S2 image was used to map six summer crop species in Lower Austria as well as winter crops/bare soil. Crop type maps are needed to account for crop-specific water use and for agricultural statistics. Crop type information is also useful to parametrize crop growth models for yield estimation, as well as for the retrieval of vegetation biophysical variables using radiative transfer models. The second case study aimed to map seven different deciduous and coniferous tree species in Germany. Detailed information about tree species distribution is important for forest management and to assess potential impacts of climate change. In our S2 data assessment, crop and tree species maps were produced at 10 m spatial resolution by combining the ten S2 spectral channels with 10 and 20 m pixel size. A supervised Random Forest classifier (RF) was deployed and trained with appropriate ground truth. In both case studies, S2 data confirmed its expected capabilities to produce reliable land cover maps. Cross-validated overall accuracies ranged between 65% (tree species) and 76% (crop types). The study confirmed the high value of the red-edge and shortwave infrared (SWIR) bands for vegetation mapping. Also, the blue band was important in both study sites. The S2-bands in the near infrared were amongst the least important channels. The object based image analysis (OBIA) and the classical pixel-based classification achieved comparable results, mainly for the cropland. As only single date acquisitions were available for this study, the full potential of S2 data could not be assessed. In the future, the two twin S2 satellites will offer global coverage every five days and therefore permit to concurrently exploit unprecedented spectral and temporal information with high spatial resolution.

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Evaluating the Potentials of Sentinel-2 for Archaeological Perspective

Cited by 80 publications

References 52 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

Multitemporal 2016-2018 Sentinel-2 Data Enhancement for Landscape Archaeology: The Case Study of the Foggia Province, Southern Italy

First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

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