Browsing by Author "Salim Lamine"

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Earth Observation for MONITORING AND MODELING LAND USE
(Elsevier, 2024) George P. Petropoulos; Daniela Fernanda Da Silva Fuzzo; Dimitris Triantakonstantis; João Alberto Fischer Filho; Prashant K. Srivastava; Salim Lamine
Earth Observation for Monitoring and Modeling Land Use presents a practical guide and theoretical overview of the latest techniques and Earth observation technologies applied to land use and land cover change through qualitative assessment of Earth observation technologies. The book's chapters include detailed case studies, Earth observation datasets, and detailed applications of the technologies covered that are presented in a way that each chapter is a self-contained guide on a specific application of Earth observation technologies to land use problems, ensuring all technical and background information is provided on each subject without the need for cross-referencing or searching for other sources. The book spatializes the understanding of monitoring land cover and use, and quantifies the challenges faced, allowing analysis of the dynamics of the territory in terms of occupation processes, land use, and its transformations. It focuses on practical applications of using remote sensing and modeling that support new research in relation to monitoring of land use and spectral modelling, elucidating the importance of advanced methodologies in the coverage and use mappings of the Earth. © 2025 Elsevier Ltd. All rights are reserved.
Heavy metal soil contamination detection using combined geochemistry and field spectroradiometry in the United Kingdom
(MDPI AG, 2019) Salim Lamine; George P. Petropoulos; Paul A. Brewer; Nour-El-Islam Bachari; Prashant K. Srivastava; Kiril Manevski; Chariton Kalaitzidis; Mark G. Macklin
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. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Large scale operational soil moisture mapping from passive MW radiometry: SMOS product evaluation in Europe & USA
(Elsevier B.V., 2019) Khidir Abdalla Kwal Deng; Salim Lamine; Andrew Pavlides; George P. Petropoulos; Yansong Bao; Prashant K. Srivastava; Yuanhong Guan
Earth Observation (EO) allows deriving from a range of sensors, often globally, operational estimates of surface soil moisture (SSM) at range of spatiotemporal resolutions. Yet, an evaluation of the accuracy of those products in a variety of environmental conditions has been often limited. In this study, the accuracy of the SMOS SSM global operational product across 2 continents (USA, and Europe) and a range of land use/cover types is investigated. SMOS predictions were compared against near concurrent in-situ SSM measurements from the FLUXNET observational network. In total, 7 experimental sites were used to assess the accuracy of SMOS derived soil moisture for 2 complete years of observations (2010–2011). The accuracy of the SMOS SSM product is investigated in different seasons for the seasonal cycle as well as different continents and land use/cover types. Results showed a generally reasonable agreement between the SMOS product and the in-situ soil moisture measurements in the 0–5 cm soil moisture layer. Root Mean Square Error (RMSE) in most cases was close to 0.1 m3 m−3 (minimum 0.067 m3 m−3). With a few exceptions, Pearson's correlation coefficient was found up to approx. 55%. Grassland, shrublands and woody savanna land cover types attained a satisfactory agreement between satellite derived and in-situ measurements but needleleaf forests had lower correlation. Better agreement was found for the grassland sites in both continents. Seasonally, summer and autumn underperformed spring and winter. Our study results provide supportive evidence of the potential value of this operational product for meso-scale studies in a range of practical applications, helping to address key challenges present nowadays linked to food and water security. © 2019 Elsevier B.V.
Operational evapotranspiration estimates from SEVIRI in support of sustainable water management
(Elsevier B.V., 2016) George P. Petropoulos; Gareth Ireland; Salim Lamine; Hywel M. Griffiths; Nicolas Ghilain; Vasileios Anagnostopoulos; Matthew R. North; Prashant K. Srivastava; Hro Georgopoulou
This study aimed at evaluating the accuracy of the evapotranspiration (ET) operational estimates from the Meteosat Second Generation (MSG) Spinning Enhanced Visible Infra-Red Imager (SEVIRI) at a range of selected ecosystems in Europe. For this purpose in-situ eddy covariance measurements were used, acquired from 7 selected experimental sites belonging to the CarboEurope ground observational network over 2 full years of observations (2010–2011). Appraisal of ET accuracy was also investigated with respect to land cover, season and each site(s) degree of heterogeneity, the latter being expressed by the fractional vegetation cover (FVC) operational product of SEVIRI. Results indicated a close agreement between the operational product's ET estimates and the tower based in-situ ET measurements for all days of comparison, showing a satisfactory correlation (r of 0.709) with accuracies often comparable to previous analogous studies. For all land cover types, the grassland and cropland sites exhibited the closest agreement (r from 0.705 to 0.759). In terms of seasons the strongest correlations were observed during the summer and autumn (r of 0.714 & 0.685 respectively), and with FVC the highest correlation of 0.735 was observed for the class FVC 0.75-1 when compared against the observed values for the complete monitoring period. Our findings support the potential value of the SEVIRI ET product for regional to mesoscale studies and corroborate its credibility for usage in many practical applications. The latter is of particular importance for water limiting environments, such as those found in the Mediterranean basin, as accurate information on ET rates can provide tremendous support in sustainable water resource management as well as policy and decision making in those areas. © 2016 Elsevier B.V.
Operational soil moisture from ASCAT in support of water resources management
(MDPI AG, 2019) Khidir Abdalla Kwal Deng; Salim Lamine; Andrew Pavlides; George P. Petropoulos; Prashant K. Srivastava; Yansong Bao; Dionissios Hristopulos; Vasileios Anagnostopoulos
This study provides the results of an extensive investigation of the Advanced Scaterometter (ASCAT) surface soil moisture global operational product accuracy across three continents (United States of America (USA), Europe, and Australia). ASCAT predictions of surface soil moisture were compared against near concurrent in situ measurements from the FLUXNET observational network. A total of nine experimental sites were used to assess the accuracy of ASCAT Surface Soil Moisture (ASCAT SSM) predictions for two complete years of observations (2010, 2011). Results showed a generally reasonable agreement between the ASCAT product and the in situ soil moisture measurements in the 0-5 cm soil moisture layer. The Root Mean Square Error (RMSE) was below 0.135 m3 m-3 at all of the sites. With a few exceptions, Pearson's correlation coefficient was above 45%. Grassland, shrublands, and woody savanna land cover types exhibited satisfactory agreement in all the sites analyzed (RMSE ranging from 0.05 to 0.13 m3 m-3). Seasonal performance was tested, but no definite conclusion can be made with statistical significance at this time, as the seasonal results varied from continent to continent and from year to year. However, the satellite and in situ measurements for Needleleaf forests were practically uncorrelated (R = -0.11 and -0.04). ASCAT predictions overestimated the observed values at all of the sites in Australia. A positive bias of approximately 0.05 m3 m-3 was found with respect to the observed values that were in the range 0-0.3 m3 m-3. Better agreement was observed for the grassland sites in most cases (RMSE ranging from 0.09 to 0.10 m3 m-3 and R from 0.46 to 0.90). Our results provide supportive evidence regarding the potential value of the ASCAT global operational product for meso-scale studies and the relevant practical applications. A key contribution of this study is a comprehensive evaluation of ASCAT product soil moisture estimates at different sites around the globe. These sites represent a variety of climatic, environmental, biome, and topographical conditions. © 2019 by the authors.
Preface
(Elsevier, 2023) Salim Lamine; Prashant K. Srivastava; Ahmed Kayad; Francisco Muñoz-Arriola; Prem Chandra Pandey
[No abstract available]
Quantifying land use/land cover spatio-temporal landscape pattern dynamics from Hyperion using SVMs classifier and FRAGSTATS®
(Taylor and Francis Ltd., 2018) Salim Lamine; George P. Petropoulos; Sudhir Kumar Singh; Szilárd Szabó; Nour El Islam Bachari; Prashant K. Srivastava; Swati Suman
This study aims to quantify the landscape spatio-temporal dynamics including Land Use/Land Cover (LULC) changes occurred in a typical Mediterranean ecosystem of high ecological and cultural significance in central Greece covering a period of 9 years (2001–2009). Herein, we examined the synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, the FRAGSTATS® landscape spatial analysis programme and Principal Component Analysis (PCA) for this purpose. The change analysis showed that notable changes reported in the experimental region during the studied period, particularly for certain LULC classes. The analysis of accuracy indices suggested that all the three classification techniques are performing satisfactorily with overall accuracy of 86.62, 91.67 and 89.26% in years 2001, 2004 and 2009, respectively. Results evidenced the requirement for taking measures to conserve this forest-dominated natural ecosystem from human-induced pressures and/or natural hazards occurred in the area. To our knowledge, this is the first study of its kind, demonstrating the Hyperion capability in quantifying LULC changes with landscape metrics using FRAGSTATS® programme and PCA for understanding the land surface fragmentation characteristics and their changes. The suggested approach is robust and flexible enough to be expanded further to other regions. Findings of this research can be of special importance in the context of the launch of spaceborne hyperspectral sensors that are already planned to be placed in orbit as the NASA’s HyspIRI sensor and EnMAP. © 2017 Informa UK Limited, trading as Taylor & Francis Group.
Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation
(Elsevier, 2023) Salim Lamine; Prashant K. Srivastava; Ahmed Kayad; Francisco Muñoz-Arriola; Prem Chandra Pandey
Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation compiles the latest applications of remote sensing in agriculture using spaceborne, airborne and drones’ geospatial data. The book presents case studies, new algorithms and the latest methods surrounding crop sown area estimation, determining crop health status, assessment of vegetation dynamics, crop diseases identification, crop yield estimation, soil properties, drone image analysis for crop damage assessment, and other issues in precision agriculture. This book is ideal for those seeking to explore and implement remote sensing in an effective and efficient manner with its compendium of scientifically and technologically sound information. © 2023 Elsevier Ltd. All rights reserved.
Spectroradiometry as a tool for monitoring soil contamination by heavy metals in a floodplain site
(Elsevier, 2020) Salim Lamine; Manish Kumar Pandey; George P. Petropoulos; Paul A. Brewer; Prashant K. Srivastava; Kiril Manevski; Leonidas Toulios; Nour-El-Islam Bachari; Mark G. Macklin
Soil contamination by heavy metals is common in floodplains throughout the world. Apart from other assessment techniques available, hyperspectral remote sensing is widely used as it offers a lucrative and fast assessment. The current work explores the possibility of on-field and laboratory spectroradiometry investigations together with geochemical data of lead (Pb), zinc (Zn), copper (Cu), and cadmium (Cd) in quantifying and modeling heavy metal soil contamination (HMSC) for a floodplain situated in Wales, United Kingdom. The goal of the study was to (1) gather on-field- as well as lab-based spectra from contaminated soils using analytical spectral devices FieldSpec3, in the spectrum range of 350-2500 nm; (2) construct spectral libraries of on-field- as well as lab-based readings; (3) carry out geochemical analyses of Pb, Zn, Cu, and Cd with the help of an atomic absorption spectrometer; (4) recognize the explicit spectral areas accompanying the modeling of HMSC; and (5) develop and validate heavy metal prediction models (HMPMs) through the spectral features of the contaminants and their concentrations in the soil. Two spectral libraries were developed from the on-field- and lab-based spectral features, which were derived from 85 soil samples. These spectral libraries along with the concentrations of Pb, Zn, Cu, and Cd were joint to construct eight HMPMs by stepwise multiple linear regression. The output provided, for the first time, the viability to predict HMSC in a highly contaminated floodplain site through the combination of geochemical analyses and field spectroradiometry. The resultant model offered support for mapping heavy metal concentrations over a huge area using space-borne hyperspectral sensors. © 2020 Elsevier Ltd All rights reserved.