Water body extraction is an important part of water resource management and has been the topic of a number of research works related to remote sensing for over two decades. Extracting water bodies ...from satellite images with a pixel-based method or indexes cannot eliminate other objects that have a low albedo, such as shadows and built-up areas. Since their spectral differences cannot be separated, in this paper a method that combines a pixel-based index and object-based method has been used on a Sentinel-2 satellite image with a resolution of 10 m. The method uses image segmentation on a multispectral image containing 13 bands. It also uses indexes used for extracting water bodies, such as the Normalized Difference Water Index (NDWI). Two study areas with different characteristics have been chosen, one mountainous and one urban region, both of them located in Macedonia. Using object-based techniques and pixel-based indexes, such as NDWI, the results from the NDWI have been improved by a kappa value of more than 0.5.
Rapid urbanization has several negative effects on both the environment and human health. Urbanization has also become an important contributor to global warming. One of these effects is the urban ...heat island (UHI), which is caused by human activities and defined as the temperature difference between urban and surrounding rural areas. With rapid urbanization in the past few decades, Skopje has experienced remarkable UHI effects. To investigate the roles of built-up and green areas in a surface UHI, this article uses satellite data from Landsat ETM+ to analyse the land surface temperature and high-resolution Planet Scope DOVE data to analyse built-up and green areas. For geostatistical analyses, seventeen randomly selected subareas in Skopje were used. e results show a significant correlation between the UHI and built-up areas, and strong correlation between green areas and areas not affected by the UHI, indicating that the UHI effect can be significantly weakened with additional green areas. One of the significant findings in the study is the ideal proportion of built-up (40%) and green areas (60%), where the UHI effect is weak, or in some cases prevented. For future studies, investigating other factors that may contribute to the UHI phenomenon is suggested.
Hitra urbanizacija ima številne negativne vplive na okolje in zdravje ljudi ter pomembno prispeva h globalnemu segrevanju. Eden izmed teh vplivov so tudi mestni toplotni otoki, ki jih povzroca ...clovekova dejavnost in so opredeljeni kot temperaturne razlike med mestnimi in okoliškimi obmocji. Zaradi hitre urbanizacije v zadnjih desetletjih se Skopje spoprijema z izjemno mocnimi ucinki mestnih toplotnih otokov. Za proucevanje vloge pozidanih in zelenih površin pri nastanku površinskega mestnega toplotnega otoka je avtorica uporabila satelitske podatke Landsat ETM+, na podlagi katerih je analizirala temperaturo tal, in posnetke satelita Planet Scope DOVE visoke locljivosti, na podlagi katerih je analizirala pozidane in zelene površine. Za geostatisticne analize je uporabila 17 nakljucno izbranih podobmocij v Skopju. Izsledki raziskave kažejo pomembno povezavo med mestnim toplotnim otokom in pozidanimi površinami ter mocno povezavo med zelenimi površinami in obmocji, na katerih ni mestnega toplotnega otoka, kar kaže, da se lahko ucinek mestnih toplotnih otokov pomembno zmanjša z dodatnimi zelenimi površinami. Eden izmed pomembnih izsledkov raziskave je, da idealno razmerje med deležem pozidanih in zelenih površin, pri katerem je ucinek mestnega toplotnega otoka šibek, v nekaterih primerih pa ga celo ni, znaša 40 : 60. Nadaljnje raziskave bi se morale osredotocati še na druge dejavnike, ki lahko vplivajo na nastanek mestnega toplotnega otoka.
As wetlands are one of the world’s most important ecosystems, their vulnerability necessitates the constant monitoring and mapping of their changes. Satellite-based remote sensing has become an ...essential data source for mapping and monitoring wetlands. As wetlands are dynamic ecosystems, their classification depends on many different parameters. However, considering their complex structure; wetlands tend to be challenging land cover for classification, which sometimes requires the use of multi-sensor remote sensing techniques. The objectives of this study were: (i) to investigate the monthly dynamics of several wetland classes using multi-sensor parameters; (ii) to find correlations between the investigated parameters. Thus, we extracted the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Landsat 8, and extracted dual polarization backscatter values (VH-VV) from the Sentinel-1 satellite at a monthly period over a year. The results showed strong correlation between the LST and the NDVI values of 0.94, and strong correlation between the microwave (VH) and both thermal and optical parameters with a 0.81 correlation coefficient, while there was weak or no correlation between the VV and the other investigated parameters. We strongly recommend that future studies clarify the Sentinel-1 backscatter values in wetland areas, by taking multiple field measurements close to the image acquisition time.
Effective post-event emergency management contributes substantially to communities’ earthquake resilience, and one of the most crucial actions following an earthquake is building damage assessment. ...On-site inspections are dangerous, expensive, and time-consuming. Remote sensing techniques have shown great potential in localizing the most damaged regions and thus guiding aid and rescue operations in recent earthquakes. Furthermore, to prevent post-earthquake casualties, heavily damaged, unsafe buildings must be identified immediately since in most earthquakes, strong aftershocks can cause such buildings to collapse. The potential of the response spectrum concept for being associated with satellite-based remote sensing data for post-earthquake structural damage estimation was investigated in this study. In this respect, a response spectra-based post-earthquake structural damage estimation method aided by satellite-based remote sensing data was proposed to classify the buildings after an earthquake by prioritizing them based on their expected damage levels, in order to speed up the damage assessment process of critical buildings that can cause casualties in a possible strong aftershock. A case study application was implemented in the Bayrakli region in Izmir, Turkey, the most affected area by the Samos earthquake, on 30 October 2020. The damage estimations made in this research were compared with the in situ damage assessment reports prepared by the Republic of Turkey Ministry of Environment and Urbanization experts. According to the accuracy assessment results, the sensitivity of the method is high (91%), and the necessary time spent by the in situ damage assessment teams to detect the critical buildings would have been significantly reduced for the study area.
This study presents an analysis of the mean atmospheric column nitrogen dioxide (NO2) and carbon monoxide (CO) over the Republic of North Macedonia during a six-month period. Measurements of NO2 and ...CO obtained from the recently launched Sentinel-5 Precursor spacecraft with TROPOspheric Monitoring Instrument (Sentinel-5P TROPOMI) have been used. The aim of this study was to use relatively high-resolution satellite data for local air quality/air pollution monitoring and to investigate the relation of the pollutants with geographical and demographical data of the study area. For that purpose, along with CO and NO2 data from TROPOMI, population statistics, digital elevation model and vegetation cover have been used for geo-spatial and statistical analyses. The findings show significantly high CO and NO2 values in several parts of the study area, especially high CO values in the Vardar and Polog Valleys, and high NO2 values in the densely populated cities. According to the analyses, there is high positive correlation between the NO2 and the population statistics (r = 0.78; R2 = 0.61) and high negative correlation (r = -0.9; R2 = 0.80) between the altitude and the CO values of the study area. The overall results of this study confirmed the capability of Sentinel-5P TROPOMI data to be used in monitoring the air quality and air pollution over local areas.
With the developing technologies, the use of unmanned aerial vehicles’s (UAV) is increasing in all areas. Compared with the conventional photogrammetry and remote sensing sensors, UAVs are more ...convenient to collect data for small areas. In this study, the accuracy of UAV products was investigated in the archeological area of Eskişehir Şarhöyük. In order to produce reference data for the orthophoto and DTM accuracy analysis, a digital map from the test area was produced using in-situ measurements. Also, for the comparison of the point cloud, a small test area was determined and reference point cloud data was collected with terrestrial laser scanner. The comparison of the results showed significant difference between the UAV images and images collected by conventional methods. Thus, while there was 1 m difference between the data without the use of control points, and the use of control points significantly improved the results.
Predicting soil chemical properties such as soil organic carbon (SOC) and available phosphorus (Ava-P) content is critical in areas where different land uses exist. The distribution of SOC and Ava-P ...is influenced by both natural and anthropogenic factors. This study aimed at (1) predicting SOC and Ava-P in a piedmont plain of Northeast Iran using the Random Forests (RF) and Cubist mathematical models and hybrid models (Regression Kriging), (2) comparing the models’ results, and (3) identifying the key variables that influence the spatial dynamics of soil SOC and Ava-P under different agricultural practices. The machine learning models were trained with 201 composite surface soil samples and 24 ancillary data, including climate (C), organism (O), topography- relief (R), parent material (P) and key soil features (S) according to the SCORPAN digital soil mapping framework, which can predictively represent soil formation factors spatially. Clay, one of the most critical soil properties with a well-known relationship to SOC, was the most important predictor of SOC, followed by open-access multispectral satellite images-based vegetation and soil indices. Ava-P had a similar set of effective variables. Hybrid approaches did not improve model accuracy significantly, but they did reduce map uncertainty. In the validation set, Ava-P was calculated using the RF algorithm with a normalized root mean square (NRMSE) of 96.8, while SOC was calculated using the Cubist algorithm with an NRMSE of 94.2. These values did not change when using the hybrid technique for Ava-P; however, they changed just by 1% for SOC. The management of SOC content and the supply of Ava-P in agricultural activities can be guided by SOC and Ava-P digital distribution maps. Produced digital maps in which the soil scientist plays an active role can be used to identify areas where concentrations are high and need to be protected, where uncertainty is high and sampling is required for further monitoring.
Oil contamination is a major source of pollution in the environment. It may take decades for oil-contaminated soils to be remedied. This study models oil-contaminated soils using one of the world’s ...greatest environmental disasters, the onshore oil spill in the desert of Kuwait in 1991. This work uses state-of-art remote sensing technologies and machine learning to investigate the oil spills during the first Gulf War. We were able to identify oil-contaminated and clear locations in Kuwait using unsupervised classification over pre- and post-oil spill data. The research area’s pre-war and post-war circumstances, in terms of oil spills, were discovered by developing spectral signatures with different wavelengths and several spectral indices utilized for oil-contamination detection. Following that, we use this data for sampling and training to model various oil-contaminated soil levels. In addition, we analyze two separate datasets and used three modeling methodologies, Random Tree (RT), Support Vector Machine (SVM) and Random Forest (RF). The results show that the suggested approach is effective in detecting oil-contaminated soil. As a result, the location and degree of contamination may be established. The results of this analysis can be a valid support to the studies of an appropriate remediation.
The extraction of building inventory information is vital for damage assessment and planning and modelling studies. In the last few years, the conventional data extraction for building inventory was ...overcome using various remote sensing data and techniques. The main objectives of this study were to supply the necessary data for the structural engineers to calculate the seismic performance of existing structures. Thus, we investigated light detection and ranging (LiDAR) derivatives data to classify buildings and extract building inventory information, such as different heights of the buildings and footprint area. The most important data to achieve this was also investigated and classified using machine learning methods, such as Random Forest, Random Tree, and Optimized Forest, over the object-based segmentation results. All of the machine learning methods successfully classified the buildings with high accuracy, whereas the other methods outperformed RT. The height and footprint area results show that the archived sensitivity of the building inventory information is sufficient for the data to be further used in different applications, such as detailed structural health monitoring. Overall, this study presents a methodology that can accurately extract building information. In light of the results, future studies can be directed for investigations on determining the construction year using remote sensing data, such as multi-temporal satellite imagery.
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