Remote Sensing Applications-Society and Environment最新文献

The Egyptian government is grappling with the challenge of sedimentation in Damietta Harbor (DH), leading to expensive and recurring dredging operations for safe vessel navigation. This research examined the changes in the region's coastline, both before and after the port was established between 1977 and 1995. The impact of the government's policy to halt backfilling in the navigation lane on adjacent beaches until 2023 was also assessed using remote sensing (RS) and the Digital Shoreline Analysis System (DSAS) program, revealing varying yearly accretion and erosion rates. Following a shoreline movement tracking procedure, in the period from 1985 to 2023, the total areas undergoing erosion and accretion amounted to −1.36 km² and +2.21 km², respectively.

To address the issue, the research team developed scenarios reflecting regional realities and conducted a numerical simulation using the Coastal Modeling System (CMS). Morphological alterations in the approach channel and pre- and post-port regions were monitored to assess the scenarios. The study also explored how deepening the navigation channel affected the annual sediment volume. Results showed that Scenario 5, involving breakwaters extension and the new western breakwater (NWBW), effectively controlled sediment. However, sedimentation increased with the deepening of the navigation channel. Scenario 7, with a 19 m deepening, exhibited slightly higher sediment volume by 21.75% but stood out for its capacity to handle large vessels. Additionally, the sedimentation in the navigation channel experienced a notable 72.1% decrease in Scenario 8, which portrays the anticipated final state of DH compared to the benchmark case.

The study noted morphological changes from planned coastal constructions on the eastern side, including detached breakwaters (DBW) and Y-groins (Y-gs). Despite significant development costs, these structures were deemed insufficient to stabilize the shoreline. In summary, the research highlights the complexity of addressing sedimentation in DH, emphasizing the need for nuanced approaches in coastal management, and considering the consequences of deepening navigation channels.

The fraction of Photosynthetically Active Radiation (fPAR) plays a pivotal role in determining the carbon flux in ecosystems. Although the MODIS fPAR product has demonstrated effectiveness in the Northern Hemisphere, its validity still needs to be verified in the context of Tropical Dry Forests (TDFs), which constitute 40% of all tropical forests. This study utilized a Wireless Sensor Network (WSN) to generate an in-situ Green fPAR dataset at the Santa Rosa National Park Environmental Monitoring Supersite, aiming to validate MODIS fPAR products from 2013 to 2017. This study employs a 2-flux fPAR estimation approach for the in-situ dataset, followed by Savitzky–Golay derivative-based smoothing, univariate-wavelet transforms, and cross-wavelet analysis to compare phenological variables between the in-situ and MODIS fPAR datasets. Our findings reveal a significant temporal disparity between the MODIS fPAR products and ground-based data, with MODIS consistently lagging in detecting the onset of green-up or senescence in TDFs by 18–55 days. However, the annual and inter-seasonal patterns were statistically significant (p < 0.05) and replicated in the MODIS and in-situ datasets. Notably, these patterns deviate during extreme water conditions (droughts and hurricanes), with MODIS underestimating the effects of drought and failing to represent hurricane impact. Furthermore, MODIS fPAR products do not effectively capture small-scale fPAR variations and intra-seasonal differences. Therefore, this study underscores the limited accuracy of MODIS fPAR observations in the context of TDFs. Consequently, caution is warranted when relying on MODIS fPAR products to monitor rapid phenological changes in Tropical Dry Forests.

Human health impact assessment due to air pollution is critical for evaluating environmental hazards. In this research, levels of three air pollutants (CO, NO₂, and SO₂) were estimated based on satellite data (sentinel-5p) to assess the air quality over three areas (Al-Mahaad Al-Dinni, Bus Stop, and Oil Company areas) at Banha city, Qalyobia in addition to evaluating the human health risks resulting from exposure to these air pollutants. Unlike the conventional approaches for human health risk assessment, which depend on in-situ air pollution measurements, in this study, the spatial distribution of the pollutants was acquired from satellite remote sensing for the period 2021–2022. The respiratory health assessment of 151 outdoor workers, exposed to air pollution in Banha City was conducted. Results showed that the highest concentration of the pollutants was found in areas of Bus Stop and Oil Company. Consequently, the percentage of predicted pulmonary function tests was significantly lower among those working in the Oil company area than those working in the Bus Stop and Al-Mahaad Al-Dinni areas. Additionally, 47.6 % of the studied group complained of nasal congestion and 67.5 % of them complained of wheezing, 30.5% of them had obstructive airway diseases. In conclusion, remotely sensed air quality data proved its efficiency in health impact studies which could be carried out in different regions globally with less cost than traditional measurement techniques. Urban pollution can influence outdoor workers with varied percentages according to their exposure rate to pollutants and work-related factors.

Wetland ecosystems have experienced several ecological and hydrological impacts in recent decades determined by human activities and natural disturbances. The Lower Delta of the Paraná River, one of the most important wetland ecosystems of South America, has seen significant losses in both the structural and functional components of wetland vegetation. These losses promoted not only a widespread conversion of freshwater marshes into grasslands between 1997 and 2013, but also a decline in ecosystem functional diversity between 2001 and 2015. These processes manifested as abrupt shifts in long-term vegetation dynamics, a distorted, transient, spatially heterogeneous relationship with the hydrologic regime, and altered plant communities. However, recent field observations (2015–2023) have partially challenged previous findings and assumptions. Thus, we ask whether previously observed wetland losses are part of a long-term periodic process, rather than a permanent change. To address this question, we studied land use and land cover conversions through an object-based supervised classification of yearly Landsat composites between 1985 and 2023, trained on 935 ground-truth points. To study the spatial and temporal patterns of wetland gain and loss, we implemented an Intensity Analysis (IA), as well as analyses that capture frequency-specific variations and identify significant shifts in linear trends. We produced a total of 39 land cover maps. The IA revealed non-stationarity at all levels of analysis: interval, category, and transition. The study area exhibited resilient patterns through significant and increasingly short-term, periodic dynamics guiding the gain and loss of freshwater marshes. On the opposite, long-term, negative trends depicted an absolute, sustained loss. These contrasting patterns suggest that despite experiencing absolute loss and degradation, wetland ecosystems thrive by exhibiting transient recovery or adaptation mechanisms. Our study unraveled the complexity of wetland ecosystem dynamics, emphasizing how resilience and degradation interplay in the context of land use intensification.

Arid regions experience climatic stress under climate change: increased drought frequency coupled with intensified storm events. This disruption and lack of precipitation patterns leads to water scarcity and hinders the achievement of sustainable development goals. Egypt drainage basin exhibiting the greatest suitability for the implementation of rainwater harvesting (RWH) strategies. To facilitate the development of sustainable water resource management practices in the region, this study uses a multi-criteria methodology to delineate optimal zones for RWH within the Wadi Safaga. Integration of radar and optical remote sensing data obtained from Sentinel-1&2, Landsat-8, ALOS/PALSAR, and Sentinel-1 Interferometric SAR with climatic Tropical Rainfall Measuring Mission (TRMM), hydrological, and geological datasets emphasizes the hydrologic characteristics of the catchments. Additionally, the analysis of rainfall intensity patterns within the basin was undertaken. Thirteen factors are used in the predicted model including elevation, slope, curvature, depression, lithology, radar, InSAR CCD, drainage density (Dd), distance to river (DR), vegetation, topographic wetness index (TWI), rainfall, and lineament density. A knowledge-driven Geographic Information System (GIS) methodology, including weighted factors based on the Analytical Hierarchy Process (AHP), was implemented to delineate plausible areas for RWH and groundwater potential zones (GWPZs). The resultant map categorized the basin into five GWPZ classes: very low (14%), low (28%), moderate (27%), high (21%), and very high (10%). Furthermore, the study identified optimal locations for constructing reservoirs to store harvested rainwater and provide protection for downstream mining, industrial, and tourism activities. In conclusion, the obtained information is crucial for planners and decision-makers to implement sustainable water resource management strategies within the Wadi Safaga basin.

Detection of marine vessels plays an important role in monitoring, managing and securing seas and oceans, and forms the foundation of Maritime Domain Awareness (MDA). Although marine vessel detection has remained an active area of research for many years, unlike other object detectors, techniques of detection have been left far behind and lack systematic robustness. Hence, this study compared the performance of Faster R–CNN, RetinaNet and Single Shot Detector (SSD) across different epochs and complexities of ResNet architectures using Sentinel-1 VH polarization in one of the busiest ports in the Philippines. In particular, the models were created from the training samples dataset derived from Sentinel-1 VH imagery captured on January 12, 2024 in ResNet-34, -50, and −101 backbones, and 20 and 100 epochs. In this study, a total of 18 different object detector models were created for the comparative analysis. The models were tested with respect to different dates but having the same imagery type to determine their applicability across other base maps. Faster R–CNN with the highest F1 score of 0.85 outperformed RetinaNet with a highest F1 score of 0.74 and SSD with the highest F1 score of 0.38. The fastest model created was SSD, with an average speed of 9 to 44 minutes, followed by RetinaNet with an average speed of 8 to 58 minutes; the slowest is Faster R–CNN with an average speed of 25 minutes to 1 hour and 3 minutes. The use of Sentinel-1 VH imagery for marine vessel detection is a viable alternative, but the choice of object detectors should be carefully considered. The presence of geospatial software with advance deep learning tools improves remote sensing applications and allows non-programmers to optimize their competence. This study highlights the potential utilization of other imagery with higher spatial resolution, testing of other deep learning algorithms, finetuning of parameters, and utilization of higher computing infrastructure. The findings of this study can be applied in other areas for MDA, particularly in regions where advanced remote sensing applications have yet to be extensively explored.

This study employs advanced satellite imagery from ASTER and Sentinel-2A to conduct detailed lithological mapping of the Devanur and Manamedu ophiolite complexes in the southern Central Shear Zone (CSZ). The primary focus is on the Manamedu Ophiolite Complex (MOC) and the Devanur Ophiolitic Complex (DOC). Image enhancement techniques such as Color composites, Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) were utilized to differentiate various rock types. RGB band combinations derived from PCA and MNF outputs demonstrated effective discrimination of rock units. Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods were employed on ASTER and Sentinel-2A images, yielding classified lithologies that closely matched existing maps from the Geological Survey of India (GSI) and other studies, validating the accuracy of the findings. Additionally, Laboratory Spectral Signature Studies were conducted on 10 rock samples using an ASD FieldSpec Pro® spectroradiometer, providing reflectance spectra from 350 nm to 2500 nm. These spectra, particularly the continuum-removed reflectance, revealed diagnostic absorption features that were corroborated by geochemical analyses. A detailed analysis investigated how elemental compositions and key minerals influenced absorption bands. Major oxide geochemical compositions of DOC and MOC samples were identified using XRF methods. The aim of this research is to characterize DOC and MOC through remote sensing and spectral signature analysis. Sentinel-2A data proved more effective in lithological discrimination compared to ASTER, with spectral signatures indicating the presence of iron (Fe) and magnesium (Mg) contents. Notably, SVM classification of Sentinel-2A MNF + DEM data achieved an overall accuracy of more than 90% when compared with field investigations. This study underscores the efficacy of processing VNIR and SWIR bands from ASTER and Sentinel-2A satellite imagery alongside DEM data and ground surveys for mapping mafic-ultramafic rocks in the DOC and MOC regions of the CSZ.

Multi-spectral satellite imagery is an ideal data source for comprehensive, real-time Earth observation (EO) due to its extensive coverage of Earth and regular updates. It has a wide range of applications in environment monitoring, disaster management, urban planning, weather forecasting etc. Yet, the visual aspect of these images and thus the possibility to extract useful information using image processing techniques is often degraded due to fog, rain, dust, cloud, etc. Satellite image enhancement denotes a set of techniques designed to improve the quality of a satellite image such that the result is more useful for image analysis. The image enhancement aims to improve the quality of an image such that the enhanced image is more useful for image analysis than the original image for a particular remote sensing application. This study introduces a multi-spectral satellite image enhancement architecture called Residual Dense Connection-based UNet++ (RDC-UNet++). The unique design can improve multi-spectral images by enhancing their color and texture details. Extensive experimental studies on multi-spectral image datasets containing more than 150 images prove that the proposed architecture performs better than recent state-of-the-art satellite image enhancement algorithms.

Saudi Arabia has one of the biggest water shortages and the least vegetation in the world, which is presumed to provoke this problem further due to climate change. Therefore, the present study investigates the water, vegetation, and temperature over Al-Asfar Lake region, Al Ahsa, Eastern province of Saudi Arabia using the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST: °C) from Landsat-8 based operational land imager (OLI) measurements for the period 2013 to 2023. This study presented annual and seasonal (dry months: June–September and wet months: December–April) spatiotemporal distribution and variations, calculated their absolute change and trends, and examined their relationship. Results showed positive NDWI values over Al-Asfar Lake, indicating waterbodies; while positive NDVI values on the lake's bank, signifying vegetation. Notably, there were significant temporal variations in water and vegetation observed on annual, seasonal, and monthly scales. The study also found an overall decrease in vegetation areas of 5.36 km² in 2023 compared to 2013, while waterbodies increased by 8.83 km². The trend analysis using area-averaged data demonstrated that NDWI increased on annual (0.0075/year) and seasonal (dry: 0.0083/year and wet: 0.0049/year) scales, while NDVI decreased (annual: 0.0066/year, dry: 0.0083/year, and wet: 0.0009/year). Moreover, LST was recorded least amount over waterbodies (28.23 °C) and vegetation (32.45 °C) covered areas compared to the entire lake region (38.43 °C), respectively. Remark, LST displayed decreasing trends over waterbodies (−0.05/year), followed by vegetation (−0.17/year), and the entire lake region (−0.0001/year), signifying that water and vegetation are vital components to controlling land surface temperature in this region. Finally, the LST showed a positive correlation with NDVI and negative correlation with NDWI. There may be a direct and indirect impact of climate change upon NDVI, LST, and NDWI as shown by the decreases in NDVI and LST and an increase in NDWI. This study can be considered as a base document to monitor waterbodies, vegetation cover, and temperature changes using remote sensing measurements of NDWI, NDVI, and LST, which will assist policymakers in developing water resource management, irrigation planning, and environmental monitoring strategies.

Accurate aboveground biomass (AGB) estimations over large areas are essential for assessing carbon stocks and forest resources. This study evaluated machine learning approaches for AGB modeling in Pakistan's mountainous region of Diamir district using freely available Sentinel-1 and Sentinel-2 data and 171 field-measured AGB training points. Random Forest, Gradient Tree Boosting, CatBoost, LightGBM, and XGBoost algorithms were implemented and optimized. Models were developed using individual and combined datasets. Sentinel-2 optical data outperformed Sentinel-1 radar data, but the fusion of both sensors achieved the highest accuracy (R2 > 0.7, RMSE = 105.64 Mg/ha, MAE = 85.34 Mg/ha). Tree canopy height was the most informative predictor for this data, besides terrain variables and radar textures. The machine learning models significantly improved AGB estimates compared to traditional regression techniques, and gradient boosters outperformed Random Forest. This research demonstrates the potential of multi-sensor remote sensing data and advanced algorithms for forest biomass mapping in complex terrain, with modeling accuracies reaching root mean squared errors below 90 Mg/ha. The framework provides an effective solution for monitoring biomass using freely available satellite data. Further refinements include integrating higher-resolution optical data and additional field samples for better validation. This study contributes to remote sensing capabilities for assessing vegetation carbon stocks and dynamics.