Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery

In this research work, a multi-index-based support vector machine (SVM) classification approach has been proposed to determine the complex and morphologically heterogeneous land cover/use (LCU) patterns of cities, with a special focus on separating bare lands and built-up regions, using Istanbul, Turkey as the main study region, and Ankara and Konya (in Turkey) as the independent test regions. The multi-index approach was constructed using three-band combinations of spectral indices, where each index represents one of the three major land cover categories, green areas, water bodies, and built-up regions. Additionally, a shortwave infrared-based index, the Normalized Difference Tillage Index (NDTI), was proposed as an alternative to existing built-up indices. All possible index combinations and the original ten-band Sentinel-2A image were classified with the SVM algorithm, to map seven LCU classes, and an accuracy assessment was performed to determine the multi-index combination that provided the highest performance. The SVM classification results revealed that the multi-index combination of the normalized difference tillage index (NDTI), the red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI) improved the mapping accuracy of the heterogeneous urban areas and provided an effective separation of bare land from built-up areas. This combination showed an outstanding overall performance with a 93% accuracy and a 0.91 kappa value for all LCU classes. The results of the test regions provided similar findings and the same index combination clearly outperformed the other approaches, with 92% accuracy and a 0.90 kappa value for Ankara, and an 84% accuracy and a 0.79 kappa value for Konya. The multi-index combination of the normalized difference built-up index (NDBI), the NDVIre, and the MNDWI, ranked second in the assessment, with similar accuracies to that of the ten-band image classification

Evaluating the potential of multi-temporal Sentinel-1 and Sentinel-2 data for regional mapping of olive trees

Olives are a crucial economic crop in Mediterranean countries. Detailed spatial information on the distribution and condition of crops at regional and national scales is essential to ensure the continuity of crop quality and yield efficiency. However, most earlier studies on olive tree mapping focused mainly on small parcels using single-sensor, very high resolution (VHR) data, which is time-consuming, expensive and cannot feasibly be scaled up to a larger area. Therefore, we evaluated the performance of Sentinel-1 and Sentinel-2 data fusion for the regional mapping of olive trees for the first time, using the Izmir Province of Türkiye, an ancient olive-growing region, as a case study. Three different monthly composite images reflecting the different phenological stages of olive trees were selected to separate olive trees from other land cover types. Seven land-cover classes, including olives, were mapped separately using a random forest classifier for each year between 2017 and 2021. The results were assessed using the k-fold cross-validation method, and the final olive tree map of Izmir was produced by combining the olive tree distribution over two consecutive years. District-level areas covered by olive trees were calculated and validated using official statistics from the Turkish Statistical Institute (TUIK). The K-fold cross-validation accuracy varied from 94% to 95% between 2017 and 2021, and the final olive map achieved 98% overall accuracy with 93% producer accuracy for the olive class. The district-level olive area was strongly related to the TUIK statistics (R 2 = 0.60, NRMSE = 0.64). This study used Sentinel data and Google Earth Engine (GEE) to produce a regional-scale olive distribution map that can be scaled up to the entire country and replicated elsewhere. This map can, therefore, be used as a foundation for other scientific studies on olive trees, particularly for the development of effective management practices.</p

Evaluation and comparison of MODIS and VIIRS aerosol optical depth (AOD) products over regions in the Eastern Mediterranean and the Black Sea

In addition to the direct and indirect effects of aerosol loading on radiative forcing and climate, exposure to high aerosol concentrations has adverse effects on human health. Several inversion algorithms are used to retrieve aerosol properties, primarily aerosol optical depth (AOD). Satellite aerosol products are available from several moderate spatial resolution sensors and provide infor-mation on the spatial and temporal dynamics of aerosol concentration. This study provides an eval-uation and inter-comparison of multiple aerosol products from Terra and Aqua Moderate Resolu-tion and Imaging Spectroradiometer (MODIS) and Suomi National Polar-Orbiting Partnership (Suo-mi NPP) Visible-Infrared Imager Radiometer Suite (VIIRS) between 2014 and 2018 in the Eastern Mediterranean and the Black Sea. This evaluation focuses on four MODIS aerosol products, which including the 10-km and 3-km Dark Target (DT) AOD retrievals at 10-km and 3-km spatial resolu-tions, Deep Blue (DB) AOD retrievals at the 10-km spatial resolution, Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals at the 1-km spatial resolution, and VIIRS DB AOD retrievals at 6-km spatial resolution. Satellite-based AOD retrievals are validated against in-situ AOD measurements from three AERONET ground stations located in urban/land, rural/coastal, and ocean surfaces. The results show that Pearson’s correlation coefficient (r) between the satellite AOD retrievals and AERONET AOD measurements range between 0.45 and 0.93 whilst the Root-Mean-Squared Errors (RMSE) varies from 0.047 to 0.159 which highlights the reliability of satellite AOD retriev-als’ in the region. The best performing products over the urban/land surface are the MODIS MA-IAC and VIIRS DB aerosol products with the RMSE of 0.048 to 0.061 and a higher percentage (81% to 89%) of retrievals falling within expected error for land. Over the ocean, the MODIS DT products have the lowest errors (RMSE 0.047-0.061) and the highest percentage (70% to 80%) of retrievals falling within the expected error. The performance of all algorithms over rural/coastal regions is poorer in terms of over- and under-estimations of AOD with a small percentage of re-trievals (48% to 87%) within expected error. Among all satellite product retrievals and their respec-tive AOD measurements, the VIIRS aerosol product performs better over coastal areas in terms of high correlation coefficient (r= 0.88), low error (RMSE = 0.059), and the large percentage of re-trievals within expected error (88%). The poor performance found over the more complex ru-ral/coastal regions suggests further improvements are needed in land-water-sediment masks, surface reflectance estimation, and aerosol model assumptions over mixed surface types. The aerosol products show the highest agreement (r ~ 0.77-0.98) over the ocean surface whilst the coastal site aerosol products verified the greatest dissimilarity in aerosol loading variations (r ~ 0.50-0.91). Lastly, the good correlation (r &gt; 0.73) between the VIIRS and MODIS AOD products illustrates the potential of VIIRS to provide aerosol data continuity with MODIS. Keywords: Aerosols, MODIS, VIIRS, AERONET, DB, DT, MAIAC, Validation, Inter-comparison

The Influence of Titanium Surfaces in Cultures of Neonatal Rat Calvarial Osteoblast-Like Cells: An Immunohistochemical Study

Background: The goal of this present study was to evaluate the behavior of neonatal rat calvarial osteoblast-like cells cultured on different implant surfaces

Scour patterns around isolated vegetation elements

The complex multi-directional interactions between hydrological, biological and fluvial processes govern the formation and evolution of river landscapes. In this context, as key geomorphological agents, riparian trees are particularly important in trapping sediment and constructing distinct landforms, which subsequently evolve to larger ones. The primary objective of this paper is to experimentally investigate the scour/deposition patterns around different forms of individual vegetation elements. Flume experiments were conducted in which the scour patterns around different representative forms of individual in-stream obstructions (solid cylinder, hexagonal array of circular cylinders, several forms of emergent and submerged vegetation) were monitored by means of a high-resolution laser scanner. The three dimensional scour geometry around the simulated vegetation elements was quantified and discussed based on the introduced dimensionless morphometric characteristics. The findings reveal that the intact vegetation forms generated two elongated scour holes at the downstream with a pronounced ridge. For the impermeable form of the plant, the scour got localized, more deposition was detected within the monitoring zone, and the distance between the obstruction and deposition zone became shorter. It is also shown that with the effect of bending and the subsequent decrease of the projected area of the plant and the increase of bulk volume, the characteristic scour values decrease compared to the intact version, and the scour zone obtains a more elongated form and expands in the downstream direction