Technological advances in hyperspectral remote sensing have been widely applied in heavy metal soil contamination studies, as they are able to provide assessments in a rapid and cost-effective way. The present work investigates the potential role of combining field and laboratory spectroradiometry with geochemical data of lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd) in quantifying and modelling heavy metal soil contamination (HMSC) for a floodplain site located in Wales, United Kingdom. The study objectives were to: (i) collect field- and lab-based spectra from contaminated soils by using ASD FieldSpec^® 3, where the spectrum varies between 350 and 2500 nm; (ii) build field- and lab-based spectral libraries; (iii) conduct geochemical analyses of Pb, Zn, Cu and Cd using atomic absorption spectrometer; (iv) identify the specific spectral regions associated to the modelling of HMSC; and (v) develop and validate heavy metal prediction models (HMPM) for the aforementioned contaminants, by considering their spectral features and concentrations in the soil. Herein, the field- and lab-based spectral features derived from 85 soil samples were used successfully to develop two spectral libraries, which along with the concentrations of Pb, Zn, Cu and Cd were combined to build eight HMPMs using stepwise multiple linear regression. The results showed, for the first time, the feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and field spectroradiometry. The generated models help for mapping heavy metal concentrations over a huge area by using space-borne hyperspectral sensors. The results further demonstrated the feasibility of combining geochemistry analyses with filed spectroradiometric data to generate models that can predict heavy metal concentrations.

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets.

Selecting the appropriate vegetation index for accurate biomass estimation is a prerequisite before and during the ecosystem management project. This study, aims to compare Vegetation Indices (VIs) that are combining both Visible and Near Infrared OLI bands (VNIR-VIs), Visible and Short Wave Infrared OLI bands and also NIR and Short Wave Infrared OLI bands (SWIR-VIs) in order to accurately model the Aboveground Biomass (AGB) of three widely-located study sites over the arid steppe lands in Algeria. The Simple Linear Model (SLM) and Support Vector Machine (SVM) were utilised as statistical learning techniques on data; firstly, from each study site separately, and secondly, from all study sites (pooled data). In all study sites, SVM improves R² with a mean of 4.5% and decreases the Root Mean Squared Error (RMSE) and Percentage of Error (PE), respectively, with 15.50 (kg DM ha⁻¹) and 1.33% on average. In all cases, the SWIR-VIs outperforms the VNIR-VIs with an improvement rate of 40% of R² and an average reduction of 362.36 kg DM ha⁻¹ and 25% of RMSE and PE, respectively. The principal main improvement was found to involve the pooled data-based model utilising normalised difference VI form, which combines OLI₂(0.452–0.512 μm) with OLI₇(2.107–2.294 μm), (R² = 0.840, p < 0.0005).

One of the ports most affected by the phenomenon of silting in Algeria is the port of Cap Djinet. In order to consider the monitoring of this port over a period of 8 years between 2015 and 2023, the twin Sentinel-2 A/B satellites were used. After image pre-processing, the bathymetry was estimated using the algorithm developed by Stumpf and extraction of the coastline was conducted using the normalized difference water index (NWDI). As part of this work, four bathymetric surveys were conducted in situ in four different periods, the results of which then correlated with those of the Stumpf model applied to satellite images. The results of the regression analysis are consistent and show good correlation coefficients (R²) between 0.67 and 0.80 and root-mean-square error (RMSE) values between 0.87 m and 1.32 m, except for images captured under turbid water conditions. The maximum accretion surface is 2.12 ha to the north of the port, estimated for the period from 2018 to 2019, and 1.78 ha to the south for 2015 to 2016. The maximum erosion surface is 2.37 ha to the north of the port for the period from 2017 to 2018 and 1.16 ha to the south from 2017 to 2018. Therefore, the amplitude of accretion and erosion is greater to the north of the port than to the south. The present study demonstrates the ability of Sentinel-2 satellites to monitor silting and coastline changes in the vicinity of this port, which could help to address the effects of climate change and human activities in order to protect the marine ecosystem.