This study proposes a geospatial procedure that leverages Landsat 8/9, TERRACLIMATE, and SRTM-DEM data, to spatialize drought patterns and dynamics, from a confrontation-confirmation-completion perspective of a reference (empirical) and observed (records) warmest period. First, Landsat 8/9 bi-month median image is used to develop the Improved Land Surface General Drought Index - version 3 (LSGDI3) with the Euclidean function. Next, a multi-regression computation supports the downscaling-adjustment of climate (climatic drought) and topography (topographic drought) to the previous step output. Further, the Weighted Residuals Aggregation Polynomial (WRAP) model is proposed, to produce the Land Surface-Climate-Topography Combined Drought Model (LSCTDMComb). As overall finding, the dual data reference/observed alternative is highly efficient to model drought spatial patterns at a regional scale and reflecting the global warming dynamics. For the 2014-2023 studied decade, the average decade cross-receiver operating characteristic/area under curve (cross-ROC/AUC) results are satisfying up to 0.818, sign of a balanced and robust model. Further support is provided by lower values of the root mean square error (RMSE), mean absolute error (MAE), and relative absolute error (RAE) metrics, respectively [0.0368-0.246], [0.0031-0.0233], and [0.005-0.0393]. Whereas the coefficient of determination values obtained between reference and observed warmest bi-months are, R2: [0.64-0.96], regardless of the temporal difference. Spatially, all LSCTDMComb agree on increasing risk of drought hazards with the latitudes at the Sahel-Sahara interface. These trends are similar to the patterns depicted by compared popular models, despite some caveats raised, especially the resolution discrepancies.

This paper explores the potential of downscaling Land Surface Temperature, LST, based on land features multi-interaction with a spatial regression multi-modelling. The Radiative Transfer Equation first helped to create an LST15 m layer over Landsat-OLI/TIRS. Next, a bilinear assessment of LST is conducted over elevation and hillshade, so to adjust shadow/brightness. Then, interactions are modelled on a feature-to-feature linear basis between spectral indices, SI's, representing vegetation, built-up, soil, water and shadow. A multilinear regression model is further built between combined pairs of interactions and LST15 m. The first principal Component, PC1, of all subtractions of each pair of interactions from others is stacked with individual SI's, to build another multi-regression model around LST15 m. Each of the three models is individually subtracted from LST15 m, normalized, [0-1], and their sum serves as the residuals layer. The downscaling step uses coefficients of the interactions model with PC1 over the corresponding Sentinel2-MSI 10 m SI's, and adds back the gaussian-kernel of residuals. The Normalized Urban-High Spatial Resolution-Land Surface Temperature, NU-HSR-LST10 m, is the final product, that sharpens hot/cold spots, with a highly spread of values among land features. As supporting results, directions of relations with vegetation and built-up were improved, while unexpected relations were alternatively revealed (water) or reversed (soil, shadow); determination coefficients, R-2, shows a strong correlation of NU-HSR-LST10 m to LST30 m (R-2:[0.7304-0.9844]), even stronger with a closest model (R-2:[0.85-0.99]); a variance analysis between NU-HSR-LST10 m and LST30 m is quasi-insignificant between [0.0002-0.00297]; and a root mean square error computed in a war-disturbed urban context, was lower for NU-HSR-LST10 m, [0.057-0.096], than for LST30 m,[0.106-0.151], as stability in dynamics depiction. Finally, the machine learning algorithm of random forest based on different seeds achieved overall accuracy between [0.92-1]. From these results, the downscaling process is efficient in better distinguishing contributions per land feature in diverse urban environments, while more cross-validation based on meteorological stations is still needed.

The underground world is a multi-faceted environment, caves are natural cavities shaped by water for millions of years, but their exploration for scientific and tourist purposes is late. It does not take shape until the second half of the nineteenth century. With karst explorations, we could develop knowledge of the underground world, a world to be explored and preserved. Underground tourism in Morocco remains largely under-exploited. The limestone surface in Morocco is 99,890 km2, or 14 https://www.sudoc.fr/146193989 ). The final rendering of the treatment shows the interest of 3D modeling in the exploitation of the KEF EL BAROUD cave, which represents a typical example of the exploitation of this heritage by developing an interactive visualization in the virtual space of the cave. This is the first Moroccan attempt to create a virtual 3D model of a cave.

Background The Cameroon Volcanic Line (CVL) is an oceanic-continental megastructure prone to geo-hazards, including landslide/mudslide, gully erosion and flash floods targeted in this paper. Recent geospatial practices advocated a multi-hazard analysis approach supported by artificial intelligence. This study proposes the Multi-Geoenvironmental Hazards Susceptibility (MGHS) tool, by combining Analytical Hierarchy Process (AHP) with Machine Learning (ML) over the North-Moungo perimeter (Littoral Region, Cameroon). Methods Twenty-four factors were constructed from satellite imagery, global geodatabase and fieldwork data. Multicollinearity among these factors was quantified using the tolerance coefficient (TOL) and variance inflation factor (VIF). The AHP coefficients were used to weigh the factors and produce a preliminary map per Geoenvironmental hazard through weighted linear combination (WLC). The sampling was conducted based on events records and analyst knowledge to proceed with classification using Google Earth Engine (GEE) cloud computing interface. Classification and Regression Trees (CART), Random Forest (RF) and Gradient Boosting Regression Trees (GBRT), were used as basic learners of the stacked hazard factors, whereas, Support Vector Regression (SVR), was used for a meta-learning. Results The rainfall was ranked as the highest triggering factor for all Geoenvironmental hazards according to AHP, with a coefficient of 1 , while the after-learning importance assessment was more varied. The area under receiver operating characteristic (AUROC/AUC) was always more than 0.96 , and F 1 -score is between [ 0.86–0.88 ] for basic classifiers. Landslides, gully erosion and flash floods showed different spatial distributions, confirming then their probability of co-occurrence. MGHS outputs clearly displayed two and three simultaneous occurrences. Finally, the human vulnerability assessed with population layer and SVR outputs showed that high human concentrations are also the most exposed, using the example of Nkongsamba’s extract. Conclusions Combining AHP with single learners, then a meta-learner, was efficient in modelling MGHS and related human vulnerability. Interactions among geo-environmental hazards are the next step and city councils are recommended to integrate results in the planning process.

This paper explores a spectral vector-based methodology on Landsat 8 bands of the visible wavelengths, that is deep-blue (1) to shortwave infrared (7), to improve the urban land features classification. Using two different ratio models, based on two and three bands’ combinations in the cloud environment of Google Earth Engine, the Uncertainty reducing Spectral Vector (USVr), the Onward Continuous Spectral Vector (OSVc) and the Onward Discontinuous Spectral Vector (OSVd) are proposed as new entries for the land use land cover (LULC) classification. Two different sizes of arrays are built, i.e. 42 vectors and 15 vectors corresponding to the same number of derivative bands and new pixels′ values. A decision tree is built in J.48 and applied to select the most suitable derivative bands for the analysis. Hereafter, the selected ones are stacked and submitted to five machine learning classifiers using a supervised process, namely, Classification and Regression Trees (CART), Random Forest (RF) Gradient Boosting (GBR), Support Vector Machine (SVM) and Minimum Distance (MD). This method was tested in the two cities of Bamenda and Foumban in west-Cameroon highlands, due to their good representativeness of tropical hilly urban areas’ spatial heterogeneity. The results are satisfying for 4/5 classifiers, up to 87% Overall Accuracy, OA, for 0.82 kappa coefficient, KC, in Bamenda, while combining SVM/OSVd. Whereas, in Foumban, the classifiers perform up to 85%OA and 0.78 KC for the combination SVM/USVr. Only the MD classifier has always performed below 80%OA. The process has been found better than performing classifiers directly on the multispectral (MS) image, by providing more possibilities of hidden spectral indices not yet explored, as far as we know, to detect and discriminate between LULC features, plus an accurate extraction of human settlements.

Background – NASA’s developers recently proposed the Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) algorithms. This method uses the Landsat 8 satellite images and daily rainfall recordings for a real-time mapping of this geohazard. This study adapts the processing to face the issues of data quality and unavailability/gaps for the mapping of the recent landslide events in west-Cameroon’s highlands. Methods – The SLIP algorithm is adapted, by integrating the inverse NDVI to assess the soil bareness, the Modified Normalized Multi-Band Drought Index (MNMDI) combined with the hydrothermal index to assess soil moisture, and the slope inclination to map the recent landslide. Further, the DRIP algorithm uses the mean daily rainfall to assess the thresholds corresponding to the recent landslide events. Their probability density function (PDF) curves are superimposed and their intersections are used to propose sets of dichotomous variables before (1948-2018) and after the 28 October 2019 landslide event. In addition, a survival analysis is performed to correlate the occurrence date of the landslide with the rainfall since the first known event in Cameroon, through the Cox model. Results – From the SLIP model, the Landslide Hazard Zonation (LHZ) map gives an overall accuracy of 96% . Further, the DRIP model states that 6/9 ranges of probability are rainfall-triggered landslides at 99.99% , between June and October, while 3/9 ranges show only 4.88% of risk for the same interval. Finally, the survival probability for a known site is up to 0.68 for the best value and between 0.38 and 0.1 for the lowest value through time. Conclusions – The proposed approach is an alternative based on data (un)availability, completed by the site’s lifetime.

Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified “1 = built-up; 0 = Non-Built-up”, with an average r2=0.78. The same way, a linear regression of popular built-up spectral indices such as Normalized Difference Built-up Index, NDBI, and Urban Index, UI, or recently proposed Modified New Built-up Index, MNBI, and Normalized Difference Built-up and Surroundings Unmixing Index, NDBSUI, on one hand, by light-dark spectral indices such as, Normalized Difference Soil Index, NDSI, Bare Soil Index, BSI, and Shadow index on the other hand, stands for the natural environment noise assessment in and around the built-up, with an r2=0.75. The MLP r2 standing for the built-up information, is rounded to 0.8 and according to their rank in the process, the weights allotted are 0.2, 0.4 and 0.8 in the numerator, and inversely 0.8, 0.6 and 0.2 in the denominator, to the red, SWIR1 and SWIR2 bands respectively. Whereas, the simple linear regression r2 standing for the noise is used to weigh the brightness temperature, TB in the numerator and subtracted from the previous group. The value 0.001 multiplies the whole ratio to lower the decimals of the outputs for an easy interpretation. As results, on the floating images scaled [0-1], built-up values are ≥0.1 in Yaounde (Cameroon) and ≥0.07 in Bangui (Central African Republic). The overall accuracies are 96% in Yaounde and 98.5% in Bangui, with corresponding kappa coefficients of 0.94 and 0.97. These scores are better than those of the NDBI, UI, MNBI and NDBSUI.

The concepts of Digital Twin has been recently introduced, it refers to functional connections between a complex physical system and its high-fidelity digital replica. Digital Twin process workflow is proposed in case of Mohammed VI Bridge modeling in Morocco. The current maintenance of a road infrastructure is based on a manual inspection and a system based on traditional tools. Aging infrastructures require a new approach to maintenance in terms of inspection, bridge maintenance system, simulation and systematic evaluation. This system now exists and is called the Digital Twin. Digital Twin can be thought of as a virtual prototype in service that changes dynamically in near real time as its physical twin changes. An urban infrastructure digital twin is a virtual instance of his physical twin that is continuously updated with multisource, multisensor and multitemporal data that can be used for monitoring, simulating and forecasting any potential problem that may appear in the structure and proposing planning for repair and maintenance of health status throughout the life cycle of this infrastructure. This work presents a general vision and a justification for integrating DT technology with geospatial data. The paper examines the benefits of integrating 3D GIS data acquired by automated mobile mapping (MMS) workflows for modeling the reality of a major bridge infrastructure in Morocco. This allowed to study the future performance of this bridge structure on virtual twin structures under different environmental conditions. Cloud point data are acquired by a Mobile Mapping System on Mohammed VI Bridge and converted in BIM model by a scan to BIM process and is integrated in a GIS and BIM virtual environment and shows the efficiency of volumetric auscultation in terms of surface flatness and distortion inspection. This project provides a new bridge maintenance system using the concept of a Digital Twin. This digital model is a platform that allows to collect, organize and share the maintenance history of this important road infrastructure in Morocco.

Imlili Sebkha is a stable and flat depression in southern Morocco that is more than 10 km long and almost 3 km wide. This region is mainly sandy, but its northern part holds permanent water pockets that contain fauna and flora despite their hypersaline water. Google Earth Engine (GEE) has revolutionized land monitoring analysis by allowing the use of satellite imagery and other datasets via cloud computing technology and server-side JavaScript programming. This work highlights the potential application of GEE in processing large amounts of satellite Earth Observation (EO) Big Data for the free, long-term, and wide spatio-temporal wet/dry permanent salt water cavities and moisture monitoring of Imlili Sebkha. Optical and radar images were used to understand the functions of Imlili Sebkha in discovering underground hydrological networks. The main objective of this work was to investigate and evaluate the complementarity of optical Landsat, Sentinel-2 data, and Sentinel-1 radar data in such a desert environment. Results show that radar images are not only well suited in studying desertic areas but also in mapping the water cavities in desert wetland zones. The sensitivity of these images to the variations in the slope of the topographic surface facilitated the geological and geomorphological analyses of desert zones and helped reveal the hydrological functions of Imlili Sebkha in discovering buried underground networks.

This study investigates the problem of detecting the extent of inundation caused by flash floods following heavy rainfall and the transport of sediments that cause the overflow of waters of the wadi on the crest of the Sakia El Hamra dam, the opening of two breaches in the body of the dam. The study area is focusing on Laayoune city in Southern Morocco in the region of Laayoune-Sakia el Hamra. The geomorphology of the area and the formation of the drainage network caused the creation of flash floods which took place from October 27 to 28, 2016, following intense and heavy rain in the region. Seven satellite images (radar and optical) taken before and after this event were processed to extract the most useful information. To achieve our objectives, this study began by preprocessing the radar images (calibration, speckle filtering, Doppler ground correction) and optics images (atmospheric correction, calibration of the radiometric and correction of geometric and topographic distortions). In this study, four spectral indices were extracted, then the change of detection approach is used on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI imageries of before and after the disaster event. Normalized Difference Water Index “NDWI,” Normalized Difference Moisture Index “NDMI,” Normalized Multi-band Drought Index “NMDI” and Albedo “Al” provide a two-dimensional spectral feature space resulted which gives a very good power of discrimination to monitor the spatiotemporal evolution of the different levels of soil moisture in the area after the floods. The application of the coregistration and segmentation methods on radar Sentinel-1 images before and after the event completes this work. The results obtained show the importance of the complementarity multisensor imagery for the dynamic mapping of floods.

This study aims to present a technique that combines multi-sensor spatial data to monitor wetland areas after a flash-flood event in a Saharan arid region. To extract the most efficient information, seven satellite images (radar and optical) taken before and after the event were used. To achieve the objectives, this study used Sentinel-1 data to discriminate water body and soil roughness, and optical data to monitor the soil moisture after the event. The proposed method combines two approaches: one based on spectral processing, and the other based on categorical processing. The first step was to extract four spectral indices and utilize change vector analysis on multispectral diachronic images from three MSI Sentinel-2 images and two Landsat-8 OLI images acquired before and after the event. The second step was performed using pattern classification techniques, namely, linear classifiers based on support vector machines (SVM) with Gaussian kernels. The results of these two approaches were fused to generate a collaborative wetland change map. The application of co-registration and supervised classification based on textural and intensity information from Radar Sentinel-1 images taken before and after the event completes this work. The results obtained demonstrate the importance of the complementarity of multi-sensor images and a multi-approach methodology to better monitor changes to a wetland area after a flash-flood disaster.