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Numerous types of solid structures protect coastal activities and cities from the damaging effects of tides and waves all around the globe. Thus, having a three-dimensional digital representation of the physical environment would help decision-makers in understanding the dynamic nature of coastal environments and implementing effective mitigation strategies. Generally speaking, Feature matching, Structure from Motion (SFM) and Multi-View Stereo (MVS) algorithms are used in this order to achieve realistic results. The Literature shows that there is a constant evolution of new techniques and technologies either with learning based or hand-crafted approach, which gives a possibility to integrate different method to optimize each step of the three-dimensional reconstruction process. The aim of this paper is to present the progress of three-dimensional modelling methods that use ground-level images by providing an overview of the latest applications and a comparison of their results. Overall, the state-of-the-art in three-dimensional building modelling using ground-level imagery is rapidly evolving, and new ways are being developed to improve the efficiency, accuracy and scalability of the process

This paper reports a study of bioclimatic dynamics and land use in the region of Tlemcen (north-west of Algeria). GIS (geographic information system) and satellite images (LandSat) were used to conduct a diachronic study based on a series of satellite images from the years 2000 and 2019 to demonstrate changes in the study area while highlighting the importance of environmental conditions, including climate and anthropogenic impact on dynamic vegetation cover. Bioclimatic synthesis has shown an improvement in the rainfall regime and an evolution towards higher bioclimatic atmospheres between the two periods (1985–1999) and (2004–2018). Land use in the study area is under the influence of strong anthropogenic pressures and is experiencing a decline in forest areas from 28.74 to 17.49% but also in agricultural land and lawns from 38.61% to 27.67% in favour of built-up areas which increased from 0.81% to 1.09% but mainly to the benefit of bare soil: an ultimate stage of degradation whose surface area increased from 31.59% in 2000 to 53.46% in 2019. [ABSTRACT FROM AUTHOR]

Coastal zones, as highly dynamic and complex environments, have substantial ecological and territorial implications for both government authorities and coastal managers. This research study investigated the impacts of port construction on shoreline dynamics along the coastal region of Ksar Esghir, located on the northern coast of Morocco, over a 19-year period (2002-2021). This study aimed to characterize the evolution of the coastline using high-resolution satellite images in a geographic information system (GIS) environment. Coastline evolution was assessed using GIS tools, particularly the digital shoreline analysis system (DSAS). Statistical approaches were used to determine the net rates of shoreline change, namely the end point rate (EPR) and net shoreline movement (NSM). Three main sectors were defined for the study area: The Eddallya sector, the western sector of Ksar Esghir and the port sector. As a result, two distinct zones have been identified in the Eddallya sector. Zone I shows an average accretion of +1.46 m/year, while zone II shows an erosion of -0.80 m/year. Analysis of the western sector of Oued Ksar Sghir revealed both erosion and accretion sites. Furthermore, the port sector showed positive values for shoreline evolution, with an average of +9.44 m and a rate of +0.49 m/year, signifying significant shoreline expansion over the study period. These findings highlight the dynamic and highly complex processes involved in coastal development in the study area. The results suggest that sediment dynamics, tidal regimes and potential anthropogenic influences have a significant impact on shoreline evolution, especially where port construction is concerned. The outcomes of this study provide helpful information for better and sustainable coastal management along the coastal area of Ksar Sghir. [ABSTRACT FROM AUTHOR]

Forest fires have become a major concern in the northern parts of Morocco, particularly in the Tangier-Tetouan-Al Hoceima (TTA) region, causing significant damage to the environment and human lives. To address this pressing issue, this study proposes an approach that utilizes remote sensing (RS) and machine learning (ML) techniques to detect burned areas in the TTA region within the Google Earth Engine platform. The study focuses on burned areas resulting from forest fires in three specific locations in the TTA region that have experienced such fires in recent years, namely Tangier-Assilah in 2017, M'diq Fnideq in 2020, and Chefchaouen in 2021. In our study, we extensively explored multiple combinations of spectral indices, such as normalized burn ratio (dNBR), normalized difference vegetation index (dNDVI), soil-adjusted vegetation index (dSAVI), and burned area index (dBAI), in conjunction with Sentinel-2 (S2) satellite images. These combinations were employed within the Random Forest (RF) algorithm, allowing us to draw important conclusions. Initially, we assess the individual effectiveness of the dNBR index, which yields accuracy rates of 83%, 90%, and 82% for Tangier-Assilah, Chefchaouen, and M'diq Fnideq, respectively. Recognizing the need for improved outcomes, we expand our analysis by incorporating spectral indices and S2 bands. However, the results obtained from this expanded combination lack consistency and stability across different locations. While Tangier-Assilah and M'diq Fnideq experience accuracy improvements, reaching 95% and 88%, respectively, the inclusion of Sentinel bands has an adverse effect on Chefchaouen, resulting in a decreased accuracy of 87%. To achieve optimal accuracy, our focus shifted towards the combination of dNBR and the other spectral indices. The results were truly remarkable, with accuracy rates of 96%, 97%, and 97% achieved for Tangier-Assilah, Chefchaouen, and M'diq Fnideq, respectively. Our decision to prioritize the spectral indices was based on the feature importance method, which highlights the significance of each feature in the classification process. The practical implications of our study extend to fire management and prevention in the TTA region. The insights gained from our analysis can inform the development of effective policies and strategies to mitigate the impact of forest fires. By harnessing the potential of RS and ML techniques, along with the utilization of spectral indices, we pave the way for enhanced fire monitoring and response capabilities in the region. [ABSTRACT FROM AUTHOR]

Climate change poses a major challenge in terms of urban planning management for the sake of a sustainable future. It is affecting the hydrological cycle around the world, leading to extreme weather conditions. Floods rank as the most frequent and widespread disaster in the world, they adversely affect inhabitants in terms of property damage and threat to human safety (and lives, in the worst cases). Uncontrolled urban sprawl also exacerbates floods by expanding impervious surfaces and affecting flow paths. Other factors that trigger flooding (apart from the rainfall intensity) are human involvement in the main waterways, thereby significantly impacting the hydraulic flow characteristics, structural engineering breakdowns, compounded by potential deforestation. For the purpose of monitoring the aftermath of floods experienced by the cities of Casablanca and Tetouan (Morocco) respectively in January and March 2021 and estimating their damages, optical and radar satellite images derived from the Google Earth Engine (GEE) cloud platform were used along with the Geographic Information System (GIS). In this study, a novel technique for extracting flooded areas from high-resolution Synthetic Aperture Radar (SAR) time series images has been developed. A comparison was carried out subsequently between the time-series approach and other traditional approaches including radiometric thresholding method, spectral indices namely Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) as well as Flood Water Index (FWI). Based on the above approach, the water levels were estimated and the damages were assessed and mapped, notably the number of people exposed to flood hazard and the amount of built-up areas and cropland affected. The results demonstrated that Casablanca city has witnessed a higher flood level than Tetouan city, putting a large number of people at risk and affecting a significant area of land use. The findings can also provide local authorities with a comprehensive view of flooding and enable them to make decisions on preparedness, mitigation, and adaptation to flood-related disasters. [ABSTRACT FROM AUTHOR]

Leachate from public landfill in the city of Tangier, which is neither collected nor treated, could constitute a probable source of pollution of the groundwater table and of those of the Mlalah and Khandak Bou Hajjar wadis in the east, and the Mghogha and Ghir Boudra wadis to the south. This work aims to analyze the level of contamination at Tangier's municipal waste disposal. The leachate samples were collected and analyzed during the period from 2016 to 2019 and the physicochemical parameters (humidity, pH, organic matter, etc.) were determined on these samples as well as the contents of five heavy metals (lead, cadmium, iron, chrome and zinc). Analysis of the well water shows the presence of polluting elements in the leachate water and a high concentration of metals (especially iron) that exceeds standards. This makes the effluent extremely toxic and thus presents a permanent threat to the health of the local population and the surrounding environment. [ABSTRACT FROM AUTHOR]