A5064145516- Remote Sensing in Agriculture
- Soil Moisture and Remote Sensing
- Remote-Sensing Image Classification
- Plant Water Relations and Carbon Dynamics
- Climate change and permafrost
- Remote Sensing and Land Use
- Fire effects on ecosystems
- Land Use and Ecosystem Services
- Remote Sensing and LiDAR Applications
- Climate variability and models
- Soil Geostatistics and Mapping
- Meteorological Phenomena and Simulations
- Landslides and related hazards
- Atmospheric and Environmental Gas Dynamics
- Precipitation Measurement and Analysis
- Soil and Unsaturated Flow
- Hydrology and Watershed Management Studies
- Flood Risk Assessment and Management
- Cryospheric studies and observations
- Urban Heat Island Mitigation
- Caching and Content Delivery
- Geochemistry and Geologic Mapping
- Advanced Surface Polishing Techniques
- Advanced machining processes and optimization
- Software-Defined Networks and 5G
Harokopio University of Athens
2020-2025
Hellenic Open University
2022-2024
Hellenic Civil Aviation Authority
2017-2022
Technical University of Crete
2017-2021
National Agricultural Research Foundation
1995-2020
Aberystwyth University
2012-2020
Intracom Telecom (Greece)
2015-2018
Indian Institute of Technology BHU
2016
Banaras Hindu University
2016
Agricultural University of Athens
1992-2015
Abstract. In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by European Space Agency, to serve centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together collected and freely shared multitude of organisations, harmonises them terms units sampling rates, applies advanced quality control, stores database. Users can retrieve from this database through an online...
Land use/land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases human and physical environment. Earth observation (EO) technology provides an informative source data covering entire globe in spatial spectral resolution appropriate better easier classify land than traditional or conventional methods. The use high imagery from EO sensors has increased remarkably over past decades, as more platforms are placed orbit new applications emerge different...
This study aimed at evaluating the synergistic use of Sentinel-1 and Sentinel-2 data combined with Support Vector Machines (SVMs) machine learning classifier for mapping land cover (LULC) emphasis on wetlands. In this context, added value spectral information derived from Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) Grey Level Co-occurrence Matrix (GLCM) to classification accuracy was also evaluated. As a case study, National Park Koronia Volvi Lakes (NPKV) located in...
Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis post-fire assessment, including estimation of burnt areas. In this study the potential for area mapping combined use Artificial Neural Network (ANN) Spectral Angle Mapper (SAM) classifiers Landsat TM satellite imagery was evaluated in a Mediterranean setting. As case one most catastrophic forest fires, which occurred near capital Greece during...
Analysis of Earth observation (EO) data, often combined with geographical information systems (GIS), allows monitoring land cover dynamics over different ecosystems, including protected or conservation sites. The aim this study is to use contemporary technologies such as EO and GIS in synergy fragmentation analysis, quantify the changes landscape Rajaji National Park (RNP) during period 19 years (1990–2009). Several statistics principal component analysis (PCA) spatial metrics are used...
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 central Greece covering period 9 years (2001–2009). Herein, we examined synergistic operation among Hyperion hyperspectral satellite imagery with Support Vector Machines, FRAGSTATS® spatial analysis programme Principal Component Analysis (PCA) for this purpose. The change showed that notable...
This study explored the capability of Support Vector Machines (SVMs) and regularised kernel Fisher’s discriminant analysis (rkFDA) machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability both techniques was evaluated using a case riverine flood event 2010 heterogeneous Mediterranean region, for which imagery acquired shortly after available. For two classifiers, linear non-linear (kernel) versions were utilised their implementation....
Human activities and climate change constitute the contemporary catalyst for natural processes their impacts, i.e., geo-environmental hazards. Globally, catastrophic phenomena hazards, such as drought, soil erosion, quantitative qualitative degradation of groundwater, frost, flooding, sea level rise, etc., are intensified by anthropogenic factors. Thus, they present rapid increase in intensity, frequency occurrence, spatial density, significant spread areas occurrence. The impact these is...
Flood extent delineation techniques have benefited from the increasing availability of remote sensing imagery, classification and introduction geomorphic descriptors derived Digital Elevation Models (DEM). On other hand, high-performing Machine Learning (ML) methods allowed for development accurate flood maps by integrating several predictor variables into supervised or unsupervised algorithms. Among others, Random Forest (RF) is a powerful widely applied ML classifier, providing predictions...
Being able to quantify land cover changes due mining and reclamation at a watershed scale is of critical importance in managing assessing their potential impacts the Earth system. In this study, remote sensing-based methodology proposed for quantifying impact surface activity from local scale. The method based on Support Vector Machines (SVMs) classifier combined with multi-temporal change detection Landsat TM imagery. performance technique was evaluated selected open sites located island...