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A large portion of Morocco's forest ecosystem is being damaged by human activity and climate change, which makes it crucial for the country to monitor forest dynamics and develop measures to counter these effects. The objective of this study was to determine how the forest cover has changed over the past four decades in Bouskoura forest, Morocco. Based on Remote Sensing (RS), Geographic Information System (GIS) and machine learning algorithms. Throughout the process, Spatial data such as Landsat 5 TM, Sentinel-2B, and spectral indices, including NDVI, NDWI, NDBI, and MSAVI2 were used to train/validate Random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) classifiers. By comparing the performance of the three classifiers for all four periods, the RF method was the most effective with an overall accuracy of 0.99 and kappa coefficient of 0.99 for 1991, 2001 and 2011, and an overall accuracy of 0.99 and kappa coefficient of 0.98 for 2021. Therefore, the RF was selected as a method of examining time variations. The results indicated that forests covered an area of 20,41 km2 in 1991 which has decreased to 18,96 km2 in 2021, a loss of 1,45 km2 (7.10%) in four decades. The highest forest loss was 2,69 km2 during 1991–2001, 2,12 km2 during 2001–2011, 1,40 km2 during 2011–2021. And the highest forest gain was found to be 3,75 km2 during 2011–2021, 0,61 km2 during 2001–2011, 0,43 km2 during 1991-2001. Recent declines in forest degradation attest to the benefits of initiatives to conserve the environment taken by the country. [ABSTRACT FROM AUTHOR]

Globally, Increasing greenhouse production is causing producing environmental issues. Monitoring the number and determining the locations of greenhouses are essential for the modern agricultural land management. The aim of this study was to detect plastic greenhouses using remote sensing data and the YOLOv5 CNN-based object detection algorithm. In the process, 426 images of plastic greenhouses were manually downloaded from Google and Mohammed 6 satellites and were divided into three datasets, training (63%), validation (15%), and testing (22%). Plastic greenhouse images in the training and validation sets were manually labeled, with the training set being used to train the YOLO-V5 Model. Throughout the training, the validation dataset was used to evaluate how well the network performed. According to the performance results the system reached its highest level of accuracy at epoch 71, with a relatively brief training time of 40 minutes, The model's average precision was 84.1 percent, Using satellite images. In detecting plastic greenhouses, YOLOv5 can deliver exceptional accuracy and computational performance. [ABSTRACT FROM AUTHOR]

Harmful algal blooms (HABs) are becoming widely distributed and more frequent, threatening socioecosystems and human health. We determined species composition, abundance and spatial distribution of dinoflagellate cysts in the upper sediment of the Oualidia Lagoon located on the Atlantic coast of Morocco. Sediment samples were collected in 2017 at 51 stations, and environmental parameters were measured together with microphytoplankton abundance. Sediment characteristics including water percentage, organic matter content and grain size were determined. Fourteen dinoflagellate cyst morphotypes were identified, with Lingulodinium polyedrum (50%) and Gonyaulax spinifera (22%) dominating the assemblages. Total cyst densities ranged from 0 to 293 cysts g−1 dry sediment. Cyst densities were positively correlated with water content and organic matter content and increased with decreasing sediment grain size. We revealed the presence of three neurotoxic dinoflagellate species: Alexandrium minutum, the Alexandrium tamarense species complex, and Gymnodinium catenatum. Numerous cysts had accumulated in the sediment, and, because they are likely responsible for the initiation of HABs in Oualidia Lagoon, they should be monitored. [ABSTRACT FROM AUTHOR]

The accumulation of human debris in the marine ecosystem is one of the main hidden negative impacts of the economic and social development that humanity has witnessed after industrialization era which comes accompanied with the evolution of productions, the consumerism, as well as the emergence of the throw-away culture. In this paper, we studied the marine debris pollution on the seafloor of the North and Central Moroccan Atlantic waters from Tangier (35° N) to Sidi Ifni(29° N). The data were collected during two scientific trawling surveys conducted aboard the Charif Elidrissi Vessel in February–March 2015 and March 2018. The prospection operations allowed to collect a total of 329 kg of Seafloor Marine Debris (SMD) with an average abundance ranging between 16.84 ± 20 kg/km2 to 30.15 ± 43 kg/km2. The plastic materials represented the higher percentage of collected items with more than 40% followed by textile debris which mainly composed of knotted fishing ropes used by the longliners in the area. The analysis of SMD composition and their Spatio-temporal pattern combined with the environmental and socio-economic parameters of the prospected area using GIS tools showed that the majority of collected items comes from fishing activities, and the rest comes from the coastal anthropogenic activities. Then the analysis of hydrodynamic properties of the area using AVISO data showed that the horizontal current in area that controls the exchange of water from the coastal area to the ocean interior plays a major role in scattering debris in this area far from their origins to the open ocean. [ABSTRACT FROM AUTHOR]