Search Results (5)

Soil salinity is a major threat to the continuity of sustainable agriculture and food provision and the soil structure deterioration. In this context, determining, reducing and managing soil salinity is very important for creating sustainable modern agriculture. Determining soil salinity is generally carried out in the laboratory environment and devices used in land plots. Remote sensing is one of the important methods used for precise estimation and mapping of salinity. With remote sensing technology, soil salinity maps for large areas can be obtained with low cost and low effort. This study aims to compare remote sensing soil electrical conductivity from PlanetScope and ground measured data in wheat and beet fields in the farming areas of Alpu, Turkey. For that reason, electrical conductivity was measured at several points in wheat and beet fields using in-situ measurements and compared with various soil salinity indices from PlanetScope imagery acquired on the same day. Linear regression analysis was carried out to correlate the electrical conductivity data with their corresponding soil salinity spectral index values. The results show a high correlation (R² = 0.84) between soil salinity in wheat fields and some of the used indices. This study strengthens the idea that soil salinity maps can be obtained fast and accurately for large areas using remote sensing technology. Full article

Remotely sensed data can reinforce the abilities of water resources researchers and decision-makers to monitor water quality more effectively. In the past few decades, remote sensing techniques have been widely used to measure qualitative water quality parameters. However, the use of moderate resolution sensors may not meet the requirements for monitoring small water bodies. Water quality in a small dam was assessed using high-resolution satellite data from RapidEye and in situ measurements collected a few days apart. The satellite carries a five-band multispectral optical imager with a ground sampling distance of 5 m at its nadir and a swath width of 80 km. Several different algorithms were evaluated using Pearson correlation coefficients for electrical conductivity (EC), total dissolved soils (TDS), water transparency, water turbidity, depth, suspended particular matter (SPM), and chlorophyll-a. The results indicate strong correlation between the investigated parameters and RapidEye reflectance, especially in the red and red-edge portion with highest correlation between red-edge band and water turbidity (r² = 0.92). Two of the investigated indices showed good correlation in almost all of the water quality parameters with correlation higher than 0.80. The findings of this study emphasize the use of both high-resolution remote sensing imagery and red-edge portion of the electromagnetic spectrum for monitoring several water quality parameters in small water areas. Full article

In water resources management, remote sensing data and techniques are essential in watershed characterization and monitoring, especially when no data are available. Water quality is usually assessed through in-situ measurements that require high cost and time. Water quality parameters help in decision making regarding the further use of water-based on its quality. Turbidity is an important water quality parameter and an indicator of water pollution. In the past few decades, remote sensing has been widely used in water quality research. In this study, we compare turbidity parameters retrieved from a high-resolution image with in-situ measurements collected from Borabey Lake, Turkey. Here, the use of RapidEye-3 images (5 m-resolution) allows for detailed assessment of spatio-temporal evaluation of turbidity, through the normalized difference turbidity index (NDTI). The turbidity results were then compared with data from 21 in-situ measurements collected in the same period. The actual water turbidity measurements showed high correlation with the estimated NDTI mean values with an R² of 0.84. The research findings support the use of remote sensing data of RadipEye-3 to estimate water quality parameters in small water areas. For future studies, we recommend investigating different water quality parameters using high-resolution remote sensing data. Full article

With rapid population growth, both urbanization and transportation affect air pollution, population health, and global warming. A number of air pollutants are released from industrial facilities and other activities and may cause adverse effects on human health and the environment. One of the biggest air pollutants, nitrogen dioxide (NO₂), is mainly caused by the combustion of fossil fuels, especially from traffic exhaust gases. Over the years, air pollution has been monitored using satellite remote sensing data. In this study, we investigate the relationship of the tropospheric NO₂ retrieved from the recently launched Sentinel-5 Precursor, a low-earth-orbit atmosphere mission dedicated to monitoring air pollution equipped with the spectrometer Tropomoi (Tropospheric Monitoring Instrument), and the population density over Turkey. For this purpose, we use the mean value of the NO₂ collected from July 2018 to January 2019 and the statistic population data from 2017. The results showed a significant correlation of higher than 0.72 between the population density and the maximum NO₂ values. For future studies, we recommend investigating the correlation of different air pollutants with population and other factors contributing to air and environmental pollution. Full article

An urban heat island (UHI) is an urban area that is significantly warmer than its surrounding rural areas due to antropogenic activities. The urban area of the city of Skopje has been rising rapidly in the past decade. In this study, the effect of UHI is analyzed using Landsat 8 data in the summer period of 2013–2017 as a case study in Skopje, Macedonia. An algorithm was applied to retrieve the land surface temperature (LST) distribution from the Landsat 8 data. In addition, the correlation between land surface temperature and the normalized difference vegetation index (NDVI) and the normalized difference build-up index (NDBI) were analyzed to explore the impacts of the green areas and the build-up land on the urban heat island. The results indicate that the effect of the urban heat island in Skopje is located in many sub-urban areas. The negative correlation between LST and NDVI indicates that the green area can weaken the effect on the urban heat island, while the positive correlation between LST and NDBI means that the built-up land can strengthen the effect of the urban heat island in the study area. Full article