Heavy metal soil contamination detection using combined geochemistry and field spectroradiometry in the United Kingdom

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

Shortwave infrared vegetation index-based modelling for aboveground vegetation biomass assessment in the arid steppes of Algeria

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)

Evaluation of a high-resolution wave hindcast model SWAN for the West Mediterranean basin

This study aims to present an evaluation and implementation of a high-resolution SWAN wind wave hindcast model forced by the CFSR wind fields in the west Mediterranean basin, taking into account the recent developments in wave modelling as the new source terms package ST6. For this purpose, the SWAN model was calibrated based on one-year wave observations of Azeffoune buoy (Algerian coast) and validated against eleven wave buoys measurements through the West Mediterranean basin. For the calibration process, we focused on the whitecapping dissipation coefficient C-ds and on the exponential wind wave growth and whitecapping dissipation source terms. The statistical error analysis of the calibration results led to conclude that the SWAN model calibration corrected the underestimation of the significant wave height hindcasts in the default mode and improved its accuracy in the West Mediterranean basin. The exponential wind wave growth of Komen et al (1984) and the whitecapping dissipation source terms of Janssen (1991) with C-ds = 1.0 have been thus recommended for the western Mediterranean basin. The comparison of the simulation results obtained using this calibrated parameters against eleven measurement buoys showed a high performance of the calibrated SWAN model with an average scatter index of 30% for the significant wave heights and 19% for the mean wave period. This calibrated SWAN model will constitute a practical wave hindcast model with high spatial resolution ((similar to)3 km) and high accuracy in the Algerian basin, which will allow us to proceed to a finer mesh size using the SWAN nested grid system in this area