Evaluation of Landsat 8 OLI/TIRS Level-2 and Sentinel 2 Level-1C Fusion Techniques Intended for Image Segmentation of Archaeological Landscapes and Proxies

Abstract

The use of medium resolution, open access, and freely distributed satellite images, such as those of Landsat, is still understudied in the domain of archaeological research, mainly due to restrictions of spatial resolution. This investigation aims to showcase how the synergistic use of Landsat and Sentinel optical sensors can efficiently support archaeological research through object-based image analysis (OBIA), a relatively new scientific trend, as highlighted in the relevant literature, in the domain of remote sensing archaeology. Initially, the fusion of a 30 m spatial resolution Landsat 8 OLI/TIRS Level-2 and a 10 m spatial resolution Sentinel 2 Level-1C optical images, over the archaeological site of “Nea Paphos” in Cyprus, are evaluated in order to improve the spatial resolution of the Landsat image. At this step, various known fusion models are implemented and evaluated, namely Gram–Schmidt, Brovey, principal component analysis (PCA), and hue-saturation-value (HSV) algorithms. In addition, all four 10 m available spectral bands of the Sentinel 2 sensor, namely the blue, green, red, and near-infrared bands (Bands 2 to 4 and Band 8, respectively) were assessed for each of the different fusion models. On the basis of these findings, the next step of the study, focused on the image segmentation process, through the evaluation of different scale factors. The segmentation process is an important step moving from pixel-based to object-based image analysis. The overall results show that the Gram–Schmidt fusion method based on the near-infrared band of the Sentinel 2 (Band 8) at a range of scale factor segmentation to 70 are the optimum parameters for the detection of standing visible monuments, monitoring excavated areas, and detecting buried archaeological remains, without any significant spectral distortion of the original Landsat image. The new 10 m fused Landsat 8 image provides further spatial details of the archaeological site and depicts, through the segmentation process, important details within the landscape under examination.

Unpacking Layers of Space –Time Complexity in Land–Use Dynamics. A Case Study from the Olive Agrosystems of Sicily (Italy)

“…Techniques for the extraction of spatial-relational data from remotely sensed images had not before been used to track land-use dynamics over the long term (i.e. over centuries or millennia) (Agapiou, 2020;Davis, 2019). Nor had the identification of contextual relations been used before to semantically uncover land-use categories and formally define them as conceptual spaces (Ahlqvist et al, 2012) correlated to different time periods.…”

“…With roots in archaeology and anthropology, historical ecology is a cross-disciplinary approach fostering the in-depth study of human-environmental heterarchical interactions happening at multiple spatial and temporal scales (Ray and Fernández-Götz, 2019). The use of remote sensing to analyse past human-nature systems (Pricope et al, 2019) and the application of object-based image analysis with geographic components (Hay and Castilla, 2008;Lang et al, 2019) in archaeology and related disciplines (Davis, 2019;Agapiou, 2020;Luo et al, 2019;Tapete, 2018) have focused so far on the semi-automatic digitalization of heritage maps (Gobbi et al, 2019), the extraction of information from remotely sensed data, mainly for feature detection (Sevara et al, 2016, Lasaponara andMasini, 2014), and the identification of surface and sub-surface remains (Lambers and Traviglia, 2016;Traviglia and Torsello, 2017). In many applications, a pre-defined ontology (Schuurman, 2006) of the imageobjects detected is used as a tool to model real-world objects (Blaschke et al, 2014).…”

Soil toxic elements determination using integration of Sentinel-2 and Landsat-8 images: Effects of fusion techniques on model performance

“…HSV is applied by transforming the low spatial resolution RGB image to the HSV color space and replacing the value band with a high spatial resolution image. Hue and saturation bands are then re-sampled to the pixel size of the high resolution and the resulting image is transformed back to the RGB color space (Saberioon et al, 2009;Agapiou, 2020).…”

“…The exploitation of optical Landsat 8-OLI and Sentinel-2 images provides a global median average revisit of 2.9 days, a global median minimum of 14 min (±1 min), and maximum revisit interval of 7.0 days (Li & Roy, 2017). Therefore, their synergistic use is expected to improve timely and accurate observations of the Earth's surface and dynamics as well as their usage in different disciplines of remote sensing including environmental research Agapiou (2020).…”

Mapping Sandy Areas and their changes using remote sensing. A Case Study at North-East Al-Muthanna Province, South of Iraq

“…Landsat satellite images level-2 data were atmospherically corrected and surface reflectance was generated at the data center of USGS using Landsat Ecosystem Disturbance Adaptive Processing System LEDAPS (Version 3.4) for Landsat-5 and Landsat-7 (Landsat, 2019), and Landsat-8 Surface Reflectance Code LaSRC (Version 1.5) for Landsat-8 (USGS, 2018). These images are provided and available as ready products, which means these datasets series can be retrieved without any preprocessing from the endusers (Agapiou, 2020). More information about Landsat Level-2 can be found on the following website (https://www.usgs.gov/land-resources/ nli/landsat/landsat-collection-2-level-2-scienceproducts).…”