Flooding is one of the most severe natural hazards, causing widespread environmental, economic, and social disruption. If not managed properly, it can lead to human losses, property damage, and the destruction of livelihoods. The ability to rapidly assess such damages is crucial for emergency management. Near Real-Time (NRT) spatial information on flood-affected areas, obtained via remote sensing, is essential for disaster response, relief, urban and industrial reconstruction, insurance services, and damage assessment. Numerous flood mapping methods have been proposed, each with distinct strengths and limitations. Among the most widely used are machine learning algorithms and spectral indices, though these methods often face challenges, particularly in threshold selection for spectral indices and the sampling process for supervised classification. This study aims to develop an NRT flood mapping approach using supervised classification based on spectral features. The method automatically generates training samples through masks derived from spectral indices. More specifically, this study uses FWEI, NDVI, NDBI, and BSI indices to extract training samples for water/flood, vegetation, built-up areas, and soil, respectively. The Otsu thresholding technique is applied to create the spectral masks. Land cover classification is then performed using the Random Forest algorithm with the automatically generated training samples. The final flood map is obtained by subtracting the pre-flood water class from the post-flood image. The proposed method is implemented using optical satellite images from Sentinel-2, Landsat-8, and Landsat-9. The proposed method’s accuracy is rigorously evaluated and compared with those obtained from spectral indices and machine learning techniques. The suggested approach achieves the highest overall accuracy (OA) of 90.57% and a Kappa Coefficient (KC) of 0.89, surpassing SVM (OA: 90.04%, KC: 0.88), Decision Trees (OA: 88.64%, KC: 0.87), and spectral indices like AWEI (OA: 84.12%, KC: 0.82), FWEI (OA: 88.23%, KC: 0.86), NDWI (OA: 85.78%, KC: 0.84), and MNDWI (OA: 87.67%, KC: 0.85). These results underscore the superior accuracy and effectiveness of the proposed approach for NRT flood detection and monitoring using multi-sensor optical imagery.

Thermal pollution reduces water quality through any process that leads to a change in the water’s ambient temperature. Karun is one of the most relevant sources of water supply in Iran, and its pollution, created by industrial, urban, and agricultural issues, has been one of the most critical challenges throughout the last few years. As the water temperature rises, the amount of dissolved oxygen in it decreases, thereby affecting the entire ecosystem associated with it. Drainage of urban and industrial runoff into surface water sources can increase the water temperature. Dams also constitute a significant part, modifying spatial patterns of temperature along river routes and causing thermal contamination. In this paper, the thermal pollution of the Karun River was assessed, and regions along this river with unusually raised water temperatures were identified and compared over 20 years. By analyzing the results, it can be found that the thermal pollution from dams has a significant impact on the downstream river environment and ecology that is considerably relevant during summer periods, showing average decreases of 3 degrees Celsius immediately beyond the dams’ locations (from 41 degrees Celsius upstream dams to 38 degrees Celsius beyond them) or even bigger (reductions of 13 degrees Celsius in one of the studied dams). Hence, our results showed that water temperature is colder downstream in the hot seasons of the year than upstream of the dams. The results suggest that the usage of remote sensing data effectively could complement collected data from ground-based sensors to estimate water temperature and to identify pollution areas. It provides experts with spatially extensive and highly synchronized data.

Forest fires are natural events that occur in numerous ecosystems worldwide and cause significant damage to human, ecological and socio-economic factors. It is also crucial to obtain useful information on the distribution and density of burned areas on large scale. An efficient way to map large regions is through remote sensing (RS). Nevertheless, the complex scenario and similar spectral signature of features in multispectral bands can lead to many false positives, making it difficult to extract the burned areas accurately. Multispectral data from Sentinel-2 satellite images allow the development of novel burned area indices, as more spectral data is recorded in the Red-Edge region. This research aims to develop a new burned area detection index (BADI) at 20 m spatial resolution in the google earth engine platform to detect the wildfire-affected areas in southwest of Iran using Sentinel-2 satellite imagery. The BADI spectral index has been specially designed to take benefit of the Sentinel-2 spectral bands and use a spectral combination of bands that are reasonable for post-fire burned regions detection. The final results indicated that the proposed index by applying a post-processing stage works well in the case of the study area to identify the burned areas. At the same time, it can satisfactorily suppress the complicated and irrelevant changes in the scene. Furthermore, the BADI index is rapid and can provide the burned areas map in near real-time. According to the Copernicus Emergency Management Service (EMS) reference data, maps of the burned areas were produced with a kappa coefficient of 0.92 and an overall accuracy of 92.15%, which demonstrated a good result in comparison to similar spectral indices.