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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]

Worldwide urbanization drives rural-urban transformation (RUT) which has major consequences in many countries of the Global South where there is an urgent need to better understand and manage the underlying processes and consequences for ecosystem services. To fill existing knowledge gaps on the extent and time course of RUT in Morocco, this study focused on (i) analyzing the spatial patterns of rural-urban transformation in the Rabat-Sale-Kenitra (RSK) region from 1972 to 2020, (ii) identifying key mechanisms of change, and (iii) defining the main driving forces behind the spatial transformation patterns. To this end, we processed data of the Landsat free archive, historical grayscale Corona images, and nighttime lights datasets on Google Earth Engine (GEE) using machine learning classifiers and LandTrendr spectral-temporal segmentation algorithms. With an overall accuracy (OA) ranging from 88-95%, the results revealed that during the study period the RSK region experienced a 473% growth of horizontal built-up reflected in an area increase from 63.4 km2 to 299.9 km2. The main changes occurred along the Kenitra-Rabat-Temara axis and in central cities connected to the main road network. The horizontal expansion of large and medium-sized cities led to the formation of a Rural-Urban Interface (RUI) on the outskirts. The urban sprawl of some cities has affected the surrounding rural lands within the RUI. Environmental, social, economic, and political forces have interacted in shaping the changes in rural-urban landscapes., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Loulad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Horse mackerel is a semi-pelagic species found in abundance in the Moroccan coasts and occupies the first ranks in the catches landed by the coastal fleet. In this study, we investigated the ingestion of Polyamide, Acrylic and Polystyrene by Atlantic horse mackerel, in the Moroccan Atlantic coastal area located between Larache (35°30'N) and Boujdour (26°30'N). The objective is to map the spatial distribution of horse Mackerel containing microplastics (MPs) in their stomachs and identify hot spot areas. We also aim to verify the most ingested polymer by this fish characterized by significant daily vertical migrations. The results show that the three studied polymers were detected in the stomach contents of more than 73% of studied fishes. The hot spot areas are located more in the northern part where urbanization and fishing activity are important. Polyamide, the densest polymer, is the most abundant (86% of cases), followed by acrylic. These two polymers were found in association in 47% of cases. No correlation between the presence of MPs in the stomach contents and the size of the individual fishes was noted. Interestingly, the group of mature specimens ingested more MPs than the immature group., (Copyright © 2021 Elsevier Ltd. All rights reserved.)