Analyzing the effect of view factors on surface heat flux, surface temperature, and vegetation cover.

With the increase in population in cities, economic, social, and environmental problems continue to increase, and it is thought that the microclimatic conditions created by these problems will cause more environmental problems. One of the factors affecting the urban climate in urban areas is urban geometry. The climate of a region changes due to the shape of the land surface, human activities, atmospheric movements, and latent and sensible heat fluxes. Sky view factor (SVF), building view factor (BVF), and tree view factor (TVF) are actively used in the determination of urban geometry. However, studies on how these factors affect the thermal state of the urban environment are insufficient. Determining the land surface changes and thermal condition characteristics (LST, NDVI, SHF, and LHF) depending on urban growth and examining how these properties affect thermal conditions are very important in the construction of sustainable urban planning. In the study, SVF, BVF, TVF, LST, NDVI, SHF, and LHF values of 55 points determined for three different areas with different urban geometries were calculated. How these values affect each other and their situation on urban outdoor thermal comfort is evaluated. In the study, statistical analysis was performed to evaluate the relationship between surface temperature, surface heat fluxes, different view factors, and vegetation. As a result of ANOVA analysis, it was determined as very significant (p > 0.01) in all regions. Both SHF and LHF values differ with SVF. The SHF value has a direct relationship with the SVF value. LHF is inversely proportional to the SVF value. The situation is reversed for SHF variation concerning vegetation. LHF and NDVI are directly related. SHF and NDVI are inversely proportional. SVF and NDVI values also vary according to the LST value. It has been observed that LST has a direct relationship with SVF and an inverse relationship with NDVI.

Changes in air quality over different land covers associated with COVID-19 in Turkey aided by GEE.

With the increased urbanization, the rise of the manufacturing industry, and the use of fossil fuels, poor air quality is one of the most serious and pressing problems worldwide. The COVID-19 outbreak prompted absolute lockdowns in the majority of countries throughout the world, posing new research questions. The study's goals were to analyze air and temperature parameters in Turkey across various land cover classes and to investigate the correlation between air and temperature. For that purpose, remote sensing data from MODIS and Sentinel-5P TROPOMI were used from 2019 to 2021 over Turkey. A large amount of data was processed and analyzed in Google Earth Engine (GEE). Results showed a significant decrease in NO2 in urban areas. The findings can be used in long-term strategies for lowering global air pollution. Future research should look at similar investigations in various study sites and evaluate changes in air metrics over additional classes.

Extracting urban impervious surfaces from Sentinel-2 and Landsat-8 satellite data for urban planning and environmental management.

Impervious surface is mainly defined as any surface which water cannot infiltrate the soil. Due to the impact of urban impervious surfaces (UIS) on environmental issues, the amount of impervious surfaces has been recognized as the most significant index of environmental quality. Detection and analysis of impervious surfaces within a watershed is one of the developing areas of scientific interest. This study evaluates and compares the accuracy and performance of five classification algorithms-supervised object-based nearest neighbour (NN) classifier, supervised pixel-based maximum likelihood classifier (MLC), supervised pixel-based spectral angle mapper (SAM), band ratioing normalized difference built-up index (NDBI), and normalized difference impervious index (NDII)-in extracting urban impervious surfaces. Our first aim was to identify the most effective method for mapping UIS using Sentinel-2A and Landsat-8 satellite data. The second aim was to compare and reveal the efficiency of the spatial and spectral resolution of Sentinel-2A and Landsat-8 data in extracting UIS. The results revealed that the supervised object-based NN approach using the visible and near-infrared bands of both satellite imagery produced the most homogenous and accurate map among the other methods. The object-based NN algorithm achieved an overall classification accuracy of 90.91% and 88.64%, and Kappa coefficient of 0.82 and 0.77 for Sentinel-2 and Landsat-8 images, respectively. The study also showed that the Sentinel-2 image yielded better results than the Landsat-8 pan-sharpened image in extracting detail and classification accuracy. Comparing these methods in the selected challenging study area can provide insight into the selection of the classification method for rapid and reliable extraction of UIS.

Thermal map assessment under climate and land use changes; a case study for Uzundere Basin.

Satellite images are commonly used for defining thermal urban environment and heat islands in urban areas. The objective of this study was to analyze and evaluate thermal environment of Uzundere-Erzurum using satellite images provided by the Landsat Thematic Mapper (TM) sensor on board Landsat 5 satellite. In respect to land use and urban surface features, spatial characterization of thermal urban environment was defined. In order to define the link between surface emissivity, land surface temperatures (LST), and urban surface characteristics more effectively, the CORINE Land Cover (CLC) database for Uzundere was also used. Based on the Landsat satellite images, different land cover categories are defined, and the alterations of heat islands are identified according to these categories. The minimum and maximum surface temperatures were obtained from open spaces, non-vegetated bare rocky areas (- 1.8 and 29.1 °C, respectively). The surface temperature in the water areas was much better (7.5 and 21.9 °C) and followed by green areas surface temperature (- 0.9 and 28 °C). Surface temperatures should be considered energy-focused planning.