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A sustainable process for valorization of onion waste would need to entail preliminary sorting out of exhausted or suboptimal material as part of decision-making. In the present study, an approach for monitoring red onion skin (OS) phenolic composition was investigated through Visible Near-Short-Wave infrared (VNIR-SWIR) (350–2500 nm) and Fourier-Transform-Mid-Infrared (FT-MIR) (4000–600 cm−1) spectral analyses and Machine-Learning (ML) methods. Our stepwise approach consisted of: (i) chemical analyses to obtain reference values for Total Phenolic Content (TPC) and Total Monomeric Anthocyanin Content (TAC); (ii) spectroscopic analysis and creation of OS spectral libraries; (iii) generation of calibration and validation datasets; (iv) spectral exploratory analysis and regression modeling via several ML algorithms; and (v) model performance evaluation. Among all, the k-nearest neighbors model from 1st derivative VNIR-SWIR spectra at 350–2500 nm resulted promising for the prediction of TAC (R2 = 0.82, RMSE = 0.52 and RPIQ = 3.56). The 2nd derivative FT-MIR spectral fingerprint among 600–900 and 1500–1600 cm−1 proved more informative about the inherent phenolic composition of OS. Overall, the diagnostic value and predictive accuracy of our spectral data support the perspective of employing non-destructive spectroscopic tools in real-time quality control of onion waste.
Nikolaos Tziolas; Stella Ordoudi; Apostolos Tavlaridis; Konstantinos Karyotis; George Zalidis; Ioannis Mourtzinos. Rapid Assessment of Anthocyanins Content of Onion Waste through Visible-Near-Short-Wave and Mid-Infrared Spectroscopy Combined with Machine Learning Techniques. Sustainability 2021, 13, 6588 .
AMA StyleNikolaos Tziolas, Stella Ordoudi, Apostolos Tavlaridis, Konstantinos Karyotis, George Zalidis, Ioannis Mourtzinos. Rapid Assessment of Anthocyanins Content of Onion Waste through Visible-Near-Short-Wave and Mid-Infrared Spectroscopy Combined with Machine Learning Techniques. Sustainability. 2021; 13 (12):6588.
Chicago/Turabian StyleNikolaos Tziolas; Stella Ordoudi; Apostolos Tavlaridis; Konstantinos Karyotis; George Zalidis; Ioannis Mourtzinos. 2021. "Rapid Assessment of Anthocyanins Content of Onion Waste through Visible-Near-Short-Wave and Mid-Infrared Spectroscopy Combined with Machine Learning Techniques." Sustainability 13, no. 12: 6588.
Monitoring the status of the soil ecosystem to identify the spatio-temporal extent of the pressures exerted and mitigate the effects of climate change and land degradation necessitates the need for reliable and cost-effective solutions. To address this need, soil spectroscopy in the visible, near- and shortwave-infrared (VNIR–SWIR) has emerged as a viable alternative to traditional analytical approaches. To this end, large-scale soil spectral libraries coupled with advanced machine learning tools have been developed to infer the soil properties from the hyperspectral signatures. However, models developed from one region may exhibit diminished performance when applied to a new, unseen by the model, region due to the large and inherent soil variability (e.g. pedogenetical differences, diverse soil types etc.). Given an existing spectral library with labeled data and a new unlabeled region (i.e. where no soil samples are analytically measured) the question then becomes how to best develop a model which can more accurately predict the soil properties of the unlabeled region. In this paper, a machine learning technique leveraging on the capabilities of semi-supervised learning which exploits the predictors’ distribution of the unlabeled dataset and of active learning which expertly selects a small set of data from the unlabeled dataset as a spiking subset in order to develop a more robust model is proposed. The semi-supervised learning approach is the Laplacian Support Vector Regression following the manifold regularization framework. As far as the active learning component is concerned, the pool-based approach is utilized as it best matches with the aforementioned use-case scenario, which iteratively selects a subset of data from the unlabeled region to spike the calibration set. As a query strategy, a novel machine learning–based strategy is proposed herein to best identify the spiking subset at each iteration. The experimental analysis was conducted using data from the Land Use and Coverage Area Frame Survey of 2009 which covered most of the then member-states of the European Union, and in particular by focusing on the mineral cropland soil samples from 5 different countries. The statistical analysis conducted ascertained the efficacy of our approach when compared to the current state-of-the-art in soil spectroscopy.
Nikolaos L. Tsakiridis; John B. Theocharis; Andreas L. Symeonidis; George C. Zalidis. Improving the predictions of soil properties from VNIR–SWIR spectra in an unlabeled region using semi-supervised and active learning. Geoderma 2021, 387, 114830 .
AMA StyleNikolaos L. Tsakiridis, John B. Theocharis, Andreas L. Symeonidis, George C. Zalidis. Improving the predictions of soil properties from VNIR–SWIR spectra in an unlabeled region using semi-supervised and active learning. Geoderma. 2021; 387 ():114830.
Chicago/Turabian StyleNikolaos L. Tsakiridis; John B. Theocharis; Andreas L. Symeonidis; George C. Zalidis. 2021. "Improving the predictions of soil properties from VNIR–SWIR spectra in an unlabeled region using semi-supervised and active learning." Geoderma 387, no. : 114830.
Soil properties estimation with the use of reflectance spectroscopy has met major advances over the last decades. Their non-destructive nature and their high accuracy capacity enabled a breakthrough in the efficiency of performing soil analysis against conventional laboratory techniques. As the need for rapid, low cost, and accurate soil properties’ estimations increases, micro electro mechanical systems (MEMS) have been introduced and are becoming applicable for informed decision making in various domains. This work presents the assessment of a MEMS sensor (1750–2150 nm) in estimating clay and soil organic carbon (SOC) contents. The sensor was first tested under various experimental setups (different working distances and light intensities) through its similarity assessment (Spectral Angle Mapper) to the measurements of a spectroradiometer of the full 350–2500 nm range that was used as reference. MEMS performance was evaluated over spectra measured from 102 samples in laboratory conditions. Models’ calibrations were performed using random forest (RF) and partial least squares regression (PLSR). The results provide insights that MEMS could be employed for soil properties estimation, since the RF model demonstrated solid performance over both clay (R2 = 0.85) and SOC (R2 = 0.80). These findings pave the way for supporting daily agriculture applications and land related policies through the exploration of a wider set of soil properties.
Konstantinos Karyotis; Theodora Angelopoulou; Nikolaos Tziolas; Evgenia Palaiologou; Nikiforos Samarinas; George Zalidis. Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation. Land 2021, 10, 63 .
AMA StyleKonstantinos Karyotis, Theodora Angelopoulou, Nikolaos Tziolas, Evgenia Palaiologou, Nikiforos Samarinas, George Zalidis. Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation. Land. 2021; 10 (1):63.
Chicago/Turabian StyleKonstantinos Karyotis; Theodora Angelopoulou; Nikolaos Tziolas; Evgenia Palaiologou; Nikiforos Samarinas; George Zalidis. 2021. "Evaluation of a Micro-Electro Mechanical Systems Spectral Sensor for Soil Properties Estimation." Land 10, no. 1: 63.
The agricultural sector and natural resources are heavily interdependent, comprising a coherent but complex system. The soil and water assessment tool (SWAT) is widely used in assessing these interdependencies for regional watershed management. However, long-term simulations of agricultural watersheds are considered as not realistic since they have often been performed assuming constant land use over time and are based on the coarse resolution of the existing global or national data. This work presents the first insights of the synergy among SWAT model and deep learning classification algorithms to provide annually updated and realistic model’s parameterization and simulations. The proposed hybrid modelling approach couples the physical process SWAT model with the versatility of Earth observation data-driven non-linear deep learning algorithms for land use classification (Overall Accuracy (OA) = 79.58% and Kappa = 0.79), giving a strong advantage to decision makers for efficient management planning. A validation case at an agricultural watershed located in Northern Greece is provided to demonstrate their synergistic use to estimate nitrate and sediment concentrations that load in Zazari Lake. The SWAT model has been implemented under two different simulations; one with the use of a static coarse land use map and the other with the use of the annual updated land use maps for three consecutive years (2017–2019). The results indicate that the land use changes affect the final estimations resulting to an enhanced prediction performance of 1% and 2% for sediment and nitrate, respectively, when the annual land use maps are incorporated into SWAT simulations. In this context, a hybrid approach could further contribute to addressing challenges and support a data-centric scheme for informed decision making with regard to environmental and agricultural issues on the river basin scale.
Nikiforos Samarinas; Nikolaos Tziolas; George Zalidis. Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm. ISPRS International Journal of Geo-Information 2020, 9, 576 .
AMA StyleNikiforos Samarinas, Nikolaos Tziolas, George Zalidis. Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm. ISPRS International Journal of Geo-Information. 2020; 9 (10):576.
Chicago/Turabian StyleNikiforos Samarinas; Nikolaos Tziolas; George Zalidis. 2020. "Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm." ISPRS International Journal of Geo-Information 9, no. 10: 576.
Earth observation (EO) has an immense potential as being an enabling tool for mapping spatial characteristics of the topsoil layer. Recently, deep learning based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the processing of EO data. This paper aims to present a novel EO-based soil monitoring approach leveraging open-access Copernicus Sentinel data and Google Earth Engine platform. Building on key results from existing data mining approaches to extract bare soil reflectance values the current study delivers valuable insights on the synergistic use of open access optical and radar images. The proposed framework is driven by the need to eliminate the influence of ambient factors and evaluate the efficiency of a convolutional neural network (CNN) to effectively combine the complimentary information contained in the pool of both optical and radar spectral information and those form auxiliary geographical coordinates mainly for soil. We developed and calibrated our multi-input CNN model based on soil samples (calibration = 80% and validation 20%) of the LUCAS database and then applied this approach to predict soil clay content. A promising prediction performance (R2 = 0.60, ratio of performance to the interquartile range (RPIQ) = 2.02, n = 6136) was achieved by the inclusion of both types (synthetic aperture radar (SAR) and laboratory visible near infrared–short wave infrared (VNIR-SWIR) multispectral) of observations using the CNN model, demonstrating an improvement of more than 5.5% in RMSE using the multi-year median optical composite and current state-of-the-art non linear machine learning methods such as random forest (RF; R2 = 0.55, RPIQ = 1.91, n = 6136) and artificial neural network (ANN; R2 = 0.44, RPIQ = 1.71, n = 6136). Moreover, we examined post-hoc techniques to interpret the CNN model and thus acquire an understanding of the relationships between spectral information and the soil target identified by the model. Looking to the future, the proposed approach can be adopted on the forthcoming hyperspectral orbital sensors to expand the current capabilities of the EO component by estimating more soil attributes with higher predictive performance.
Nikolaos Tziolas; Nikolaos Tsakiridis; Eyal Ben-Dor; John Theocharis; George Zalidis. Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data. Remote Sensing 2020, 12, 1389 .
AMA StyleNikolaos Tziolas, Nikolaos Tsakiridis, Eyal Ben-Dor, John Theocharis, George Zalidis. Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data. Remote Sensing. 2020; 12 (9):1389.
Chicago/Turabian StyleNikolaos Tziolas; Nikolaos Tsakiridis; Eyal Ben-Dor; John Theocharis; George Zalidis. 2020. "Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data." Remote Sensing 12, no. 9: 1389.
The use of visible near-infrared and shortwave-infrared (VNIR-SWIR) diffuse reflectance spectroscopy for the estimation of soil properties is increasingly maturing with large-scale soil spectral libraries (SSLs) of laboratory spectra developed across the globe. Such an SSL is the publicly available LUCAS topsoil database with approximately 20,000 soil samples encompassing 23 countries of the European Union. A wide variety of machine learning tools have been applied to the LUCAS SSL to predict some of the soil samples’ physicochemical properties with different degrees of accuracy. In this paper, we developed and examined the use of a novel one-dimensional convolutional neural network (CNN) to simultaneously predict ten physicochemical properties of the LUCAS SSL. Leveraging on the use of multiple-input channels it uses as model inputs the absorbance spectra along with some pre-processed spectra developed using standard techniques. Moreover, it exploits the use of local spectral neighborhoods to perform an adaptive error-correction mechanism. This novel localized multi-channel 1-D CNN was applied to all the available physicochemical properties of the LUCAS SSL and was statistically compared with the current state-of-the-art where it was shown to statistically outperform its counterparts, as well as with other CNNs where it exhibited the best performance. In particular, for the mineral soil samples, the RMSE for the Clay content was 4.80% (R2 0.86), for soil organic carbon the RMSE was 10.96 g kg−1 (R2 0.86), while for total nitrogen the RMSE was 0.66 g kg−1 (R2 0.83).
Nikolaos L. Tsakiridis; Konstantinos D. Keramaris; John B. Theocharis; George C. Zalidis. Simultaneous prediction of soil properties from VNIR-SWIR spectra using a localized multi-channel 1-D convolutional neural network. Geoderma 2020, 367, 114208 .
AMA StyleNikolaos L. Tsakiridis, Konstantinos D. Keramaris, John B. Theocharis, George C. Zalidis. Simultaneous prediction of soil properties from VNIR-SWIR spectra using a localized multi-channel 1-D convolutional neural network. Geoderma. 2020; 367 ():114208.
Chicago/Turabian StyleNikolaos L. Tsakiridis; Konstantinos D. Keramaris; John B. Theocharis; George C. Zalidis. 2020. "Simultaneous prediction of soil properties from VNIR-SWIR spectra using a localized multi-channel 1-D convolutional neural network." Geoderma 367, no. : 114208.
To ensure the sustainability of the soil ecosystem, which is the basis for food production, efficient large-scale baseline predictions and trend assessments of key soil properties are necessary. In that regard, visible, near-infrared, and shortwave infrared (VNIR–SWIR) spectroscopy can provide an alternative for the expensive wet chemistry. In this paper, we examined the application of the Multiple-Kernel Learning (MKL) approach to soil spectroscopy by integrating the information from heterogeneous features. In particular, the proposed three-level MKL framework acts in the following way: at the first level, it uses multiple kernels at each spectral feature (wavelength) to maximize the information of each band. At the second level, it performs implicit feature selection at the spectral source level, enabling it to provide interpretable results. Finally, at the third level of integration it combines the complementary information contained within a pool of spectral sources, each derived from its own set of pre-processing techniques. Additionally, at this stage, the proposed approach is also capable of fusing heterogeneous sources of information, such as auxiliary predictors, which can assist the spectral predictions. The experimental analysis was conducted using the pan-European LUCAS (Land Use/Cover Area frame statistical Survey) topsoil database, with a goal to predict from the VNIR–SWIR spectra the concentration of soil organic carbon (SOC), a key indicator for agricultural productivity and environmental resilience. The particle size distribution which describes the soil texture was selected as the set of auxiliary predictors. The proposed MKL framework was compared with other state-of-the-art approaches, and the results indicated that it attains the best performance in terms of accuracy, whilst at the same time producing interpretable results.
Nikolaos L. Tsakiridis; Christos G. Chadoulos; John B. Theocharis; Eyal Ben-Dor; George C. Zalidis. A three-level Multiple-Kernel Learning approach for soil spectral analysis. Neurocomputing 2020, 389, 27 -41.
AMA StyleNikolaos L. Tsakiridis, Christos G. Chadoulos, John B. Theocharis, Eyal Ben-Dor, George C. Zalidis. A three-level Multiple-Kernel Learning approach for soil spectral analysis. Neurocomputing. 2020; 389 ():27-41.
Chicago/Turabian StyleNikolaos L. Tsakiridis; Christos G. Chadoulos; John B. Theocharis; Eyal Ben-Dor; George C. Zalidis. 2020. "A three-level Multiple-Kernel Learning approach for soil spectral analysis." Neurocomputing 389, no. : 27-41.
Rapid and cost-effective soil properties estimations are considered imperative for the monitoring and recording of agricultural soil condition for the implementation of site-specific management practices. Conventional laboratory measurements are costly and time-consuming, and, therefore, cannot be considered appropriate for large datasets. This article reviews laboratory and proximal sensing spectroscopy in the visible and near infrared (VNIR)–short wave infrared (SWIR) wavelength region for soil organic carbon and soil organic matter estimation as an alternative to analytical chemistry measurements. The aim of this work is to report the progress made in the last decade on data preprocessing, calibration approaches, and system configurations used for VNIR-SWIR spectroscopy of soil organic carbon and soil organic matter estimation. We present and compare the results of over fifty selective studies and discuss the factors that affect the accuracy of spectroscopic measurements for both laboratory and in situ applications.
Theodora Angelopoulou; Athanasios Balafoutis; George Zalidis; Dionysis Bochtis. From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review. Sustainability 2020, 12, 443 .
AMA StyleTheodora Angelopoulou, Athanasios Balafoutis, George Zalidis, Dionysis Bochtis. From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review. Sustainability. 2020; 12 (2):443.
Chicago/Turabian StyleTheodora Angelopoulou; Athanasios Balafoutis; George Zalidis; Dionysis Bochtis. 2020. "From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review." Sustainability 12, no. 2: 443.
The recent efforts for obtaining vast soil spectral libraries covering a significant part of the spatial and compositional variability of soils have underscored the need for accurate and interpretable models. Herein, the application of an evolutionary Fuzzy Rule-based System (FRBS) named DECO3RUM (Differential Evolution based Cooperative and Competing learning of Compact Rule-based Models) for the prediction of soil properties from visible, near-infrared, and shortwave-infrared (VNIR–SWIR) laboratory spectral data obtained from the LUCAS topsoil database is investigated. FRBSs model the input–output relation with fuzzy logic statements, offering an enhanced interpretability degree for the experts over classical rule-based systems and other black box models. The proposed algorithm was statistically compared with other state of the art approaches and was found to outperform other global models, while being statistically similar with local approaches that offer lower interpretation capabilities.
Nikolaos L. Tsakiridis; John B. Theocharis; Panos Panagos; George C. Zalidis. An evolutionary fuzzy rule-based system applied to the prediction of soil organic carbon from soil spectral libraries. Applied Soft Computing 2019, 81, 105504 .
AMA StyleNikolaos L. Tsakiridis, John B. Theocharis, Panos Panagos, George C. Zalidis. An evolutionary fuzzy rule-based system applied to the prediction of soil organic carbon from soil spectral libraries. Applied Soft Computing. 2019; 81 ():105504.
Chicago/Turabian StyleNikolaos L. Tsakiridis; John B. Theocharis; Panos Panagos; George C. Zalidis. 2019. "An evolutionary fuzzy rule-based system applied to the prediction of soil organic carbon from soil spectral libraries." Applied Soft Computing 81, no. : 105504.
In this paper, the use of a novel evolutionary fuzzy rule-based system (FRBS) for the prediction of Soil Organic Carbon from visible, near-infrared, and short-wave infrared (VNIR/SWIR) spectra and the textural information as additional predictor is examined. Compared to other techniques, the proposed model generates a compact set of rules with a high interpretation degree, mapping local input to local output regions. This is achieved through an evolutionary learning procedure which is applied to establish linguistic rules and assist in the interpretation of the association between spectra and the target property. The rule base may be also decomposed into texture-specific sets of rules, allowing a more detailed analysis on a per textural class basis. These intrinsic properties enable the development of spectral prototype signatures and sparse feature utilization histograms at different levels of aggregation, i.e. per textural class and/or output region. The proposed model is applied to the LUCAS topsoil database comprised of roughly 18,000 mineral samples across 23 European Union member-states. We first demonstrate the enhanced interpretation capabilities of our fuzzy approach, which can assist in the extraction of fruitful knowledge governing the association between soil properties and VNIR/SWIR spectra. The model is then compared with other contemporary approaches, namely PLS, SVM, and Cubist. The results indicate that our approach produced compact and interpretable results with fair prediction accuracies (equivalent with the best approach).
Nikolaos L. Tsakiridis; John B. Theocharis; Eyal Ben-Dor; George C. Zalidis. Using interpretable fuzzy rule-based models for the estimation of soil organic carbon from VNIR/SWIR spectra and soil texture. Chemometrics and Intelligent Laboratory Systems 2019, 189, 39 -55.
AMA StyleNikolaos L. Tsakiridis, John B. Theocharis, Eyal Ben-Dor, George C. Zalidis. Using interpretable fuzzy rule-based models for the estimation of soil organic carbon from VNIR/SWIR spectra and soil texture. Chemometrics and Intelligent Laboratory Systems. 2019; 189 ():39-55.
Chicago/Turabian StyleNikolaos L. Tsakiridis; John B. Theocharis; Eyal Ben-Dor; George C. Zalidis. 2019. "Using interpretable fuzzy rule-based models for the estimation of soil organic carbon from VNIR/SWIR spectra and soil texture." Chemometrics and Intelligent Laboratory Systems 189, no. : 39-55.
Towards the need for sustainable development, remote sensing (RS) techniques in the Visible-Near Infrared–Shortwave Infrared (VNIR–SWIR, 400–2500 nm) region could assist in a more direct, cost-effective and rapid manner to estimate important indicators for soil monitoring purposes. Soil reflectance spectroscopy has been applied in various domains apart from laboratory conditions, e.g., sensors mounted on satellites, aircrafts and Unmanned Aerial Systems. The aim of this review is to illustrate the research made for soil organic carbon estimation, with the use of RS techniques, reporting the methodology and results of each study. It also aims to provide a comprehensive introduction in soil spectroscopy for those who are less conversant with the subject. In total, 28 journal articles were selected and further analysed. It was observed that prediction accuracy reduces from Unmanned Aerial Systems (UASs) to satellite platforms, though advances in machine learning techniques could further assist in the generation of better calibration models. There are some challenges concerning atmospheric, radiometric and geometric corrections, vegetation cover, soil moisture and roughness that still need to be addressed. The advantages and disadvantages of each approach are highlighted and future considerations are also discussed at the end.
Theodora Angelopoulou; Nikolaos Tziolas; Athanasios Balafoutis; George Zalidis; Dionysis Bochtis. Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review. Remote Sensing 2019, 11, 676 .
AMA StyleTheodora Angelopoulou, Nikolaos Tziolas, Athanasios Balafoutis, George Zalidis, Dionysis Bochtis. Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review. Remote Sensing. 2019; 11 (6):676.
Chicago/Turabian StyleTheodora Angelopoulou; Nikolaos Tziolas; Athanasios Balafoutis; George Zalidis; Dionysis Bochtis. 2019. "Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review." Remote Sensing 11, no. 6: 676.
A systematic study of the effect of nitrogen levels in the cultivation medium of Chlorella vulgaris microalgae grown in photobioreactor (PBR) on biomass productivity, biochemical and elemental composition, fatty acid profile, heating value (HHV), and composition of the algae-derived fast pyrolysis (bio-oil) is presented in this work. A relatively high biomass productivity and cell concentration (1.5 g of dry biomass per liter of cultivation medium and 120 × 106 cells/ml, respectively) were achieved after 30 h of cultivation under N-rich medium. On the other hand, the highest lipid content (ca. 36 wt.% on dry biomass) was obtained under N-depletion cultivation conditions. The medium and low N levels favored also the increased concentration of the saturated and mono-unsaturated C16:0 and C18:1(n-9) fatty acids (FA) in the lipid/oil fraction, thus providing a raw lipid feedstock that can be more efficiently converted to high-quality biodiesel or green diesel (via hydrotreatment). In terms of overall lipid productivity, taking in consideration both the biomass concentration in the medium and the content of lipids on dry biomass, the most effective system was the N-rich one. The thermal (non-catalytic) pyrolysis of Chlorella vulgaris microalgae produced a highly complex bio-oil composition, including fatty acids, phenolics, ethers, ketones, etc., as well as aromatics, alkanes, and nitrogen compounds (pyrroles and amides), originating from the lipid, protein, and carbohydrate fractions of the microalgae. However, the catalytic fast pyrolysis using a highly acidic ZSM-5 zeolite, afforded a bio-oil enriched in mono-aromatics (BTX), reducing at the same time significantly oxygenated compounds such as phenolics, acids, ethers, and ketones. These effects were even more pronounced in the catalytic fast pyrolysis of Chlorella vulgaris residual biomass (after extraction of lipids), thus showing for the first time the potential of transforming this low value by-product towards high added value platform chemicals.
Ioannis-Dimosthenis Adamakis; Polykarpos A. Lazaridis; Evangelia Terzopoulou; Stylianos Torofias; Maria Valari; Photeini Kalaitzi; Vasilis Rousonikolos; Dimitris Gkoutzikostas; Anastasios Zouboulis; Georgios Zalidis; Konstantinos S. Triantafyllidis. Cultivation, characterization, and properties of Chlorella vulgaris microalgae with different lipid contents and effect on fast pyrolysis oil composition. Environmental Science and Pollution Research 2018, 25, 23018 -23032.
AMA StyleIoannis-Dimosthenis Adamakis, Polykarpos A. Lazaridis, Evangelia Terzopoulou, Stylianos Torofias, Maria Valari, Photeini Kalaitzi, Vasilis Rousonikolos, Dimitris Gkoutzikostas, Anastasios Zouboulis, Georgios Zalidis, Konstantinos S. Triantafyllidis. Cultivation, characterization, and properties of Chlorella vulgaris microalgae with different lipid contents and effect on fast pyrolysis oil composition. Environmental Science and Pollution Research. 2018; 25 (23):23018-23032.
Chicago/Turabian StyleIoannis-Dimosthenis Adamakis; Polykarpos A. Lazaridis; Evangelia Terzopoulou; Stylianos Torofias; Maria Valari; Photeini Kalaitzi; Vasilis Rousonikolos; Dimitris Gkoutzikostas; Anastasios Zouboulis; Georgios Zalidis; Konstantinos S. Triantafyllidis. 2018. "Cultivation, characterization, and properties of Chlorella vulgaris microalgae with different lipid contents and effect on fast pyrolysis oil composition." Environmental Science and Pollution Research 25, no. 23: 23018-23032.
Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products.
Dimitrios A. Kasampalis; Thomas K. Alexandridis; Chetan Deva; Andrew Challinor; Dimitrios Moshou; Georgios Zalidis. Contribution of Remote Sensing on Crop Models: A Review. Journal of Imaging 2018, 4, 52 .
AMA StyleDimitrios A. Kasampalis, Thomas K. Alexandridis, Chetan Deva, Andrew Challinor, Dimitrios Moshou, Georgios Zalidis. Contribution of Remote Sensing on Crop Models: A Review. Journal of Imaging. 2018; 4 (4):52.
Chicago/Turabian StyleDimitrios A. Kasampalis; Thomas K. Alexandridis; Chetan Deva; Andrew Challinor; Dimitrios Moshou; Georgios Zalidis. 2018. "Contribution of Remote Sensing on Crop Models: A Review." Journal of Imaging 4, no. 4: 52.
Thomas K. Alexandridis; Agamemnon Andrianopoulos; George Galanis; Eleni Kalopesa; Agathoklis Dimitrakos; Fotios Katsogiannos; George Zalidis. An Integrated Approach to Promote Precision Farming as a Measure Toward Reduced-Input Agriculture in Northern Greece Using a Spatial Decision Support System. Comprehensive Geographic Information Systems 2018, 315 -352.
AMA StyleThomas K. Alexandridis, Agamemnon Andrianopoulos, George Galanis, Eleni Kalopesa, Agathoklis Dimitrakos, Fotios Katsogiannos, George Zalidis. An Integrated Approach to Promote Precision Farming as a Measure Toward Reduced-Input Agriculture in Northern Greece Using a Spatial Decision Support System. Comprehensive Geographic Information Systems. 2018; ():315-352.
Chicago/Turabian StyleThomas K. Alexandridis; Agamemnon Andrianopoulos; George Galanis; Eleni Kalopesa; Agathoklis Dimitrakos; Fotios Katsogiannos; George Zalidis. 2018. "An Integrated Approach to Promote Precision Farming as a Measure Toward Reduced-Input Agriculture in Northern Greece Using a Spatial Decision Support System." Comprehensive Geographic Information Systems , no. : 315-352.
This study aimed to investigate the potency of soil reflectance spectroscopy in the visible and near infrared (Vis-NIR) spectral regions in estimating soil heavy metal pollution in the western coastal front of Thessaloniki (N. Greece) and how the protocol used for chemical analyses can affect the models’ performance. For this purpose, 49 topsoil samples were collected and the concentrations of Cd, Cr, Cu, and Pb were determined by two different analytical methods, i.e., ISO 11466 based on the technique of atomic absorbance spectrometry (AAS) and ISO 14869-1 using the technique of inductively coupled plasma-atomic emission spectrometry (ICP-AES). The spectral signatures were applied for modeling the metal concentrations by using the partial least squares regression (PLSR) method. To eliminate the “noise” of data and enhance the models’ accuracy, four spectral pre-treatment methods were used. The overall results showed that there is heavy metal pollution in the soils of specific areas in the studied region and that the use of different chemical analytical methods can affect the performance of examined prediction models. Better prediction models were created for the cases of Pb, Cu, and Cr concentrations, which were estimated by the application of ISO 14869-1, while for the case of Cd better prediction models were obtained, by the application of ISO 11466. These results may indicate that soil reflectance spectroscopy can measure the total heavy metal content in soil samples.
Theodora Angelopoulou; Agathoklis Dimitrakos; Evangelia Terzopoulou; George Zalidis; John Theocharis; Trajce Stafilov; Anastasios Zouboulis. Reflectance Spectroscopy (Vis-NIR) for Assessing Soil Heavy Metals Concentrations Determined by two Different Analytical Protocols, Based on ISO 11466 and ISO 14869-1. Water, Air, & Soil Pollution 2017, 228, 436 .
AMA StyleTheodora Angelopoulou, Agathoklis Dimitrakos, Evangelia Terzopoulou, George Zalidis, John Theocharis, Trajce Stafilov, Anastasios Zouboulis. Reflectance Spectroscopy (Vis-NIR) for Assessing Soil Heavy Metals Concentrations Determined by two Different Analytical Protocols, Based on ISO 11466 and ISO 14869-1. Water, Air, & Soil Pollution. 2017; 228 (11):436.
Chicago/Turabian StyleTheodora Angelopoulou; Agathoklis Dimitrakos; Evangelia Terzopoulou; George Zalidis; John Theocharis; Trajce Stafilov; Anastasios Zouboulis. 2017. "Reflectance Spectroscopy (Vis-NIR) for Assessing Soil Heavy Metals Concentrations Determined by two Different Analytical Protocols, Based on ISO 11466 and ISO 14869-1." Water, Air, & Soil Pollution 228, no. 11: 436.
Nikolaos Tsakiridis; Nikolaos Tziolas; Agathoklis Dimitrakos; Georgios Galanis; Eleftheria Ntonou; Anastasia Tsirika; Evangelia Terzopoulou; Eleni Kalopesa; George C. Zalidis. Predicting soil properties for sustainable agriculture using vis-NIR spectroscopy: a case study in northern Greece. Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017) 2017, 1 .
AMA StyleNikolaos Tsakiridis, Nikolaos Tziolas, Agathoklis Dimitrakos, Georgios Galanis, Eleftheria Ntonou, Anastasia Tsirika, Evangelia Terzopoulou, Eleni Kalopesa, George C. Zalidis. Predicting soil properties for sustainable agriculture using vis-NIR spectroscopy: a case study in northern Greece. Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017). 2017; ():1.
Chicago/Turabian StyleNikolaos Tsakiridis; Nikolaos Tziolas; Agathoklis Dimitrakos; Georgios Galanis; Eleftheria Ntonou; Anastasia Tsirika; Evangelia Terzopoulou; Eleni Kalopesa; George C. Zalidis. 2017. "Predicting soil properties for sustainable agriculture using vis-NIR spectroscopy: a case study in northern Greece." Fifth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2017) , no. : 1.
In this paper a novel Genetic Fuzzy Rule-based Classification System, named DECO3R (Differential Evolution based Cooperative and Competing learning of Compact FRBCS), is proposed. DECO3R follows the genetic cooperative - competitive learning (GCCL) approach and uses Differential Evolution as its learning algorithm. In this frame, every chromosome encodes a single fuzzy rule. The proposed AdaBoost-based Fuzzy Token Competition (FTC) method is employed to deal with the cooperation - competition problem, an integral part to all GCCL algorithms. DECO3R learns clear, precise and predictive rules where the fuzzy sets in the premise part are consecutive. The experimental component analysis demonstrates that DE as a learning algorithm outperforms a simple Genetic Algorithm. Additionally, the novel FTC method exceeds the performance of other similar techniques. The experimental comparative analysis highlights the robust performance of DECO3R compared to other rule learning algorithms, both in terms of accuracy and of structural complexity.
Nikolaos Tsakiridis; John B. Theocharis; George C. Zalidis. DECO3R: A Differential Evolution-based algorithm for generating compact Fuzzy Rule-based Classification Systems. Knowledge-Based Systems 2016, 105, 160 -174.
AMA StyleNikolaos Tsakiridis, John B. Theocharis, George C. Zalidis. DECO3R: A Differential Evolution-based algorithm for generating compact Fuzzy Rule-based Classification Systems. Knowledge-Based Systems. 2016; 105 ():160-174.
Chicago/Turabian StyleNikolaos Tsakiridis; John B. Theocharis; George C. Zalidis. 2016. "DECO3R: A Differential Evolution-based algorithm for generating compact Fuzzy Rule-based Classification Systems." Knowledge-Based Systems 105, no. : 160-174.
It is widely recognized that the organic micropollutants, coming from the intensive agricultural use of land, are the major thread against surface and ground water. However, they are an environmental engineering challenge in order to encounter the pollution by the use of constructed wetlands. The aim of this work is the study of the potential transport and dissipation of the herbicide terbuthylazine (TER) and its major hydroxy and dealkylated metabolites at the vertical profile of a constructed wetland sediment substrate, planted with Typha latifolia L., in order to determine the processes and study the possible remediation mechanisms for wetland ecosystems contaminated by the aforementioned substances. The results show that the dissipation of TER exhibits a gradient behavior through depth of the sediment substrate of wetlands and its major degradation products follow the effect of biotic and abiotic mechanisms of degradation in the bioreactor substrate. Moreover, the greater recovery of the herbicide appears in the sediments substrate with zeolite content.
Nikolaos G. Papadopoulos; Vasilios Takavakoglou; Evagelos Gikas; Anthony Tsarbopoulos; Georgios Zalidis. Transport and dissipation study of the herbicide terbuthylazine and its major metabolites in wetland sediment substrates planted withTypha latifoliaL. DESALINATION AND WATER TREATMENT 2012, 39, 209 -214.
AMA StyleNikolaos G. Papadopoulos, Vasilios Takavakoglou, Evagelos Gikas, Anthony Tsarbopoulos, Georgios Zalidis. Transport and dissipation study of the herbicide terbuthylazine and its major metabolites in wetland sediment substrates planted withTypha latifoliaL. DESALINATION AND WATER TREATMENT. 2012; 39 (1-3):209-214.
Chicago/Turabian StyleNikolaos G. Papadopoulos; Vasilios Takavakoglou; Evagelos Gikas; Anthony Tsarbopoulos; Georgios Zalidis. 2012. "Transport and dissipation study of the herbicide terbuthylazine and its major metabolites in wetland sediment substrates planted withTypha latifoliaL." DESALINATION AND WATER TREATMENT 39, no. 1-3: 209-214.
Agricultural use is by far the largest consumer of fresh water worldwide, especially in the Mediterranean, where it has reached unsustainable levels, thus posing a serious threat to water resources. Having a good estimate of the water used in an agricultural area would help water managers create incentives for water savings at the farmer and basin level, and meet the demands of the European Water Framework Directive. This work presents an integrated methodology for estimating water use in Mediterranean agricultural areas. It is based on well established methods of estimating the actual evapotranspiration through surface energy fluxes, customized for better performance under the Mediterranean conditions: small parcel sizes, detailed crop pattern, and lack of necessary data. The methodology has been tested and validated on the agricultural plain of the river Strimonas (Greece) using a time series of Terra MODIS and Landsat 5 TM satellite images, and used to produce a seasonal water use map at a high spatial resolution. Finally, a tool has been designed to implement the methodology with a user-friendly interface, in order to facilitate its operational use.
Thomas K. Alexandridis; Ines Cherif; Yann Chemin; George N. Silleos; Eleftherios Stavrinos; George C. Zalidis. Integrated Methodology for Estimating Water Use in Mediterranean Agricultural Areas. Remote Sensing 2009, 1, 445 -465.
AMA StyleThomas K. Alexandridis, Ines Cherif, Yann Chemin, George N. Silleos, Eleftherios Stavrinos, George C. Zalidis. Integrated Methodology for Estimating Water Use in Mediterranean Agricultural Areas. Remote Sensing. 2009; 1 (3):445-465.
Chicago/Turabian StyleThomas K. Alexandridis; Ines Cherif; Yann Chemin; George N. Silleos; Eleftherios Stavrinos; George C. Zalidis. 2009. "Integrated Methodology for Estimating Water Use in Mediterranean Agricultural Areas." Remote Sensing 1, no. 3: 445-465.
This chapter presents a Boosted Genetic Fuzzy Classifier (BGFC), for land cover classification from multispectral images. The model comprises a set of fuzzy classification rules, which resemble the reasoning employed by humans. BGFC's learning algorithm is divided into two stages. During the first stage, a number of fuzzy rules are generated in an iterative fashion, incrementally covering subspaces of the feature space, as directed by a boosting algorithm. Each rule is able to select the required features, further improving the interpretability of the obtained model. The rule base generation stage is followed by a genetic tuning stage, aiming at improving the cooperation among the fuzzy rules and, subsequently, increasing the classification performance attained after the former stage. The BGFC is tested using an IKONOS multispectral VHR image, in a lake-wetland ecosystem of international importance. For effective classification, we consider advanced feature sets, containing spectral and textural feature types. The results indicate that the proposed system is able to handle multi-dimensional feature spaces, effectively exploiting information from different feature sources.
D. G. Stavrakoudis; J. B. Theocharis; G. C. Zalidis. Genetic Fuzzy Rule-Based Classifiers for Land Cover Classification from Multispectral Images. Applications of Intelligent Control to Engineering Systems 2009, 195 -221.
AMA StyleD. G. Stavrakoudis, J. B. Theocharis, G. C. Zalidis. Genetic Fuzzy Rule-Based Classifiers for Land Cover Classification from Multispectral Images. Applications of Intelligent Control to Engineering Systems. 2009; ():195-221.
Chicago/Turabian StyleD. G. Stavrakoudis; J. B. Theocharis; G. C. Zalidis. 2009. "Genetic Fuzzy Rule-Based Classifiers for Land Cover Classification from Multispectral Images." Applications of Intelligent Control to Engineering Systems , no. : 195-221.