11 research outputs found
Characterization of Fire Severity in the Moroccan Rif Using Landsat-8 and Sentinel-2 Satellite Images.
No full textForest ecosystems are exposed increasingly to a variety of human activities and accentuated by climate change. With its Mediterranean climate, Northern Morocco is very hot, which exposes forests to widespread fires. This work aims at the delineation of wildfires and the spectral characterization of burnt vegetation as well as the characterization of the fire severity in the North of Morocco by using Landsat-8, Sentinel-2 spectral data, and topographic data. The methods used include the derivation of wildfires spectral indices and the computation of topographic parameters (elevation, slope, exposure) from SRTM and PALSAR digital elevation models. Then, the Spectral Angle Mapper (SAM) classification was used to map forest fires' severity. Furthermore, we have compared the severity classes obtained from the SAM method applied to Landsat 8 and Sentinel 2 data, with different spectral indices specialized in detecting wildfires, on the one hand, and topographic data, on the other hand. Results showed that MIRBI and NBR indices allow a better characterization of burned areas than BAI index. For its part, SAM classification provides a fair characterization of the severity classes of burnt forests. It has also been shown that the MIRBI index and sun exposure are strongly correlated with severity classes. The obtained maps show the spatial heterogeneity of burns severity and how they interact with topography. These maps may help land resource managers and fire officials predict areas of potential fire hazards and study vegetation regrowth areas after fires
Robustness of Optimized Decision Tree-Based Machine Learning Models to Map Gully Erosion Vulnerability
No full textGully erosion is a worldwide threat with numerous environmental, social, and economic impacts. The purpose of this research is to evaluate the performance and robustness of six machine learning ensemble models based on the decision tree principle: Random Forest (RF), C5.0, XGBoost, treebag, Gradient Boosting Machines (GBMs) and Adaboost, in order to map and predict gully erosion-prone areas in a semi-arid mountain context. The first step was to prepare the inventory data, which consisted of 217 gully points. This database was then randomly subdivided into five percentages of Train/Test (50/50, 60/40, 70/30, 80/20, and 90/10) to assess the stability and robustness of the models. Furthermore, 17 geo-environmental variables were used as potential controlling factors, and several metrics were examined to evaluate the performance of the six models. The results revealed that all of the models used performed well in terms of predicting vulnerability to gully erosion. The C5.0 and RF models had the best prediction performance (AUC = 90.8 and AUC = 90.1, respectively). However, according to the random subdivisions of the database, these models exhibit small but noticeable instability, with high performance for the 80/20% and 70/30% subdivisions. This demonstrates the significance of database refining and the need to test various splitting data in order to ensure efficient and reliable output results
Dam Siltation in the Mediterranean Region Under Climate Change: A Case Study of Ahmed El Hansali Dam, Morocco
No full textDams are vital for irrigation, power generation, and domestic water needs, but siltation poses a significant challenge, especially in areas prone to water erosion, potentially shortening a dam’s lifespan. The Ahmed El Hansali Dam in Morocco faces heightened siltation due to its upstream region being susceptible to erosion-prone rocks and high runoff. This study estimates the siltation at the dam from its construction up to 2014 using bathymetric data and the Brown model, which is a widely-used empirical model that calculates reservoir trap efficiency. Additionally, the study evaluates the impact of Land Use and Land Cover (LULC) changes and projected future rainfall until around 2076 based on siltation rates. The results indicate that changes in LULC, particularly temporal variations in precipitation, have a significant impact on the siltation of the Ahmed El Hansali dam. Notably, rainfall is strongly correlated with the siltation rate, with an R2 of 0.92. The efficiency of sediment trapping (TE) is 97.64%, meaning that 97.64% of the sediment in the catchment area is trapped or deposited at the bottom of the dam. The estimated annual specific sediment yield is about 32,345.79 tons/km2/yr, and the sediment accumulation rate is approximately 4.75 Mm3/yr. The dam’s half-life is estimated to be around 2076, but future precipitation projections may extend this timeframe due to the strong correlation between siltation and precipitation. Additionally, soil erosion driven by land management practices plays a crucial role in future siltation dynamics. Hence, this study offers a comprehensive assessment of the siltation dynamics at the Ahmed El Hansali dam, providing essential information on the long-term effects of erosion, land use changes, and climate projections. These findings may assist decision makers in managing dam reservoir sedimentation more effectively, ensuring the durability of the dam and extending the reservoir life
Élever la Résilience Agricole à l'Ère Numérique : Les Avantages et les limites de l'Agriculture Digitale
Get PDFLe Maroc, pays méditerranéen, fait face à des plusieurs types de défis dont les défis géopolitiques, sanitaire, économique et sociaux dont les défis agricoles croissants dus aux changements climatiques. Cependant, l'adoption de l’agriculture digitale offre des solutions
prometteuses pour renforcer l’efficience et l’efficacité en termes d’utilisation des ressources, et par conséquent, la résilience agricole. L'agriculture digitale utilise des technologies telles que les drones et les capteurs pour collecter des données en temps réel sur les conditions des cultures, optimisant ainsi l'utilisation des ressources et minimisant les impacts environnementaux. L'agriculture
de précision va encore plus loin en utilisant l'intelligence artificielle pour analyser ces données et prendre des décisions éclairées sur la gestion des cultures, de la détection précoce des maladies à l'optimisation de l'irrigation. Cette approche innovante augmente l'efficacité et l’efficience des agriculteurs, réduit les gaspillages et améliore la durabilité globale des systèmes de production.
En adoptant ces technologies, les agriculteurs marocains peuvent accroître leur résilience aux conditions climatiques changeantes et aux autres défis (exemples pour aider le lecteur), tout en contribuant à une agriculture durable et prospère dans la région méditerranéenne
Mapping soil suitability using phenological information derived from MODIS time series data in a semi-arid region: A case study of Khouribga, Morocco
No full textTo address the increasing global demand for food, it is crucial to implement sustainable agricultural practices, which include effective soil management techniques for enhancing productivity and environmental conditions. In this regard, a study was conducted to assess the efficacy of utilizing phenological metrics derived from satellite data in order to map and identify suitable agricultural soil within a semi-arid region. Two distinct methodologies were compared: one based on physicochemical soil parameters and the other utilizing the phenological response of vegetation through the application of the Normalized Difference Vegetation Index (NDVI) Modis-time series. The study findings indicated that the NDVI-based approach successfully identified a specific class of soil suitability for agriculture (referred to as S1) that could not be effectively mapped using the multi-criteria analysis (MCAD) method relying on soil physicochemical parameters. This S1 class of soil suitability accounted for approximately 5 % of the total study area. These outcomes suggest that phenological-based approaches offer greater potential for spatio-temporal monitoring of soil suitability status compared to MCAD, which heavily relies on discrete observations and necessitates frequent updates of soil parameters. The approach developed to map the soil-suitability is a valuable tool for sustainable agricultural development, and it can play an effective role in ensuring food security and conducting a land agriculture assessment
Performance Assessment of Individual and Ensemble Learning Models for Gully Erosion Susceptibility Mapping in a Mountainous and Semi-Arid Region
No full textHigh-accuracy gully erosion susceptibility maps play a crucial role in erosion vulnerability assessment and risk management. The principal purpose of the present research is to evaluate the predictive power of individual machine learning models such as random forest (RF), decision tree (DT), and support vector machine (SVM), and ensemble machine learning approaches such as stacking, voting, bagging, and boosting with k-fold cross validation resampling techniques for modeling gully erosion susceptibility in the Oued El Abid watershed in the Moroccan High Atlas. A dataset comprising 200 gully points, identified through field observations and high-resolution Google Earth imagery, was used, alongside 21 gully erosion conditioning factors selected based on their importance, information gain, and multi-collinearity analysis. The exploratory results indicate that all derived gully erosion susceptibility maps had a good accuracy for both individual and ensemble models. Based on the receiver operating characteristic (ROC), the RF and the SVM models had better predictive performances, with AUC = 0.82, than the DT model. However, ensemble models significantly outperformed individual models. Among the ensembles, the RF-DT-SVM stacking model achieved the highest predictive accuracy, with an AUC value of 0.86, highlighting its robustness and superior predictive capability. The prioritization results also confirmed the RF-DT-SVM ensemble model as the best. These findings highlight the superiority of ensemble learning models over individual ones and underscore their potential for application in similar geo-environmental contexts
Modelling Susceptibility to Water Erosion in the Moroccan High Atlas Using Machine Learning Model: The Case of the Upstream Tassaoute Watershed
No full textWater erosion is one of the most widespread land degradation processes in arid and semi-arid mountainous regions, causing significant soil loss and severely impacting natural resources. This study aims to assess water erosion susceptibility in the Upper Tassaoute watershed (High Atlas, Morocco) using two machine learning models: Random Forest (RF) and Support Vector Machine (SVM). An inventory of approximately 200 eroded sites, established through the integration of field observations and satellite imagery, was used for model training (70%) and validation (30%). Twenty environmental conditioning factors were selected, encompassing topographic, geological, climatic, soil, and vegetation variables. The performance of both models was evaluated using the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC), showing satisfactory predictive accuracy for both RF and SVM. The analysis of variable importance revealed that NDVI, slope, curvature, soil properties, and lithology were among the most influential factors. The results confirm the effectiveness of machine learning approaches for mapping water erosion vulnerability and provide a robust scientific basis to support sustainable land management strategies in sensitive mountainous environments
Head-cut gully erosion susceptibility mapping in semi-arid region using machine learning methods: insight from the high atlas, Morocco
No full textGully erosion has been identified in recent decades as a global threat to people and
property. This problem also affects the socioeconomic stability of societies and
therefore limits their sustainable development, as it impacts a nonrenewable
resource on a human scale, namely, soil. The focus of this study is to evaluate the
prediction performance of four machine learning (ML) models: Logistic Regression
(LR), classification and regression tree (CART), Linear Discriminate Analysis (LDA), and
the k-Nearest Neighbors (kNN), which are novel approaches in gully erosion modeling
research, particularly in semi-arid regions with a mountainous character. 204 samples
of erosion areas and 204 samples of non-erosion areas were collected through field
surveys and high-resolution satellite images, and 17 significant factors were
considered. The dataset cells of samples (70% for training and 30% for testing)
were randomly prepared to assess the robustness of the different models. The
functional relevance between soil erosion and effective factors was computed
using the ML models. The ML models were evaluated using different metrics,
including accuracy, the kappa coefficient. kNN is the ideal model for this study.
The value of the AUC from ROC considering the testing datasets of KNN is 0.93; the
remaining models are associated to ideal AUC and are similar to kNN in terms of
values. The AUC values from ROC of GLM, LDA, and CART for testing datasets are
0.90, 0.91, and 0.84, respectively. The value of accuracy considering the validation
datasets of LDA, CART, KNN, and GLM are 0.85, 0.82, 0.89, 0.84 respectively. The
values of Kappa of LDA, CART, and GLM for testing datasets are 0.70, 0.65, and 0.68,
respectively. ML models, in particular KNN, GLM, and LDA, have achieved outstanding
results in terms of creating soil erosion susceptibility maps. The maps created with the
most reliable models could be a useful tool for sustainable management, watershed
conservation and prevention of soil and water losses.info:eu-repo/semantics/publishedVersio
Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions
Get PDFIncreasing agricultural production is a major concern that aims to increase income, reduce hunger, and improve other measures of well-being. Recently, the prediction of soil-suitability has become a primary topic of rising concern among academics, policymakers, and socio-economic analysts to assess dynamics of the agricultural production. This work aims to use physico-chemical and remotely sensed phenological parameters to produce soil-suitability maps (SSM) based on Machine Learning (ML) Algorithms in a semi-arid and arid region. Towards this goal an inventory of 238 suitability points has been carried out in addition to14 physico-chemical and 4 phenological parameters that have been used as inputs of machine-learning approaches which are five MLA prediction, namely RF, XgbTree, ANN, KNN and SVM. The results showed that phenological parameters were found to be the most influential in soil-suitability prediction. The validation of the Receiver Operating Characteristics (ROC) curve approach indicates an area under the curve and an AUC of more than 0.82 for all models. The best results were obtained using the XgbTree with an AUC = 0.97 in comparison to other MLA. Our findings demonstrate an excellent ability for ML models to predict the soil-suitability using physico-chemical and phenological parameters. The approach developed to map the soil-suitability is a valuable tool for sustainable agricultural development, and it can play an effective role in ensuring food security and conducting a land agriculture assessment
Author Correction: Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk
Get PDFCorrection to: Nature Genetics, published online 13 March 2023. In the version of the article initially published, the sample sizes in the main text and Supplementary Tables 1 and 2 were incorrect. In the abstract, the last paragraph of the Introduction, the first paragraph of the Results, the top box in Figure 1a and the Supplementary Information, the total sample size has been corrected from 580,869 to 588,452 participants and the size of the European cohort from 468,062 to 475,645. Some of the effect sizes in Supplementary Table 14 (columns W, Z, AC, AF) had the wrong sign. There was also an error in Supplementary Table 3 where the sample size instead of the variant count was shown for EXCEED. The errors do not affect the conclusions of the study. Additionally, two acknowledgments for use of INTERVAL pQTL and Lung eQTL consortium data were omitted from the Supplementary Information. These errors have been corrected in the Supplementary Information and HTML and PDF versions of the article
