This resume is made with CVwizard.com.Work experienceSeptember 2012 - presentAdjunct Faculty: Environmental Remote Sensing & GIS, Data miningOpen University of Cyprus (Environmental Convervation & Managemet)September 2011 - January 2012Research Scientist: Remote Sensing & Digital Terrain AnalysisSultan Qaboos University (Remote Sensing Center)October 2004 - October 2014Lecturer: Remote Sensing & GIS in Applied GeologyUniversity of Patras (Geology Department)March 2001 - September 2004Adjunct Faculty: Photo-Interpretation & Remote SensingUniversity of Western Attica (Department of Topography)EducationSeptember 1995 - November 2000PhD:Remote Sensing & Terrain Pattern RecognitionNational Technical University of AthensSeptember 1982 - March 1987Diploma in GeologyUniversity o f AthensSeptember 1988 - September 1989Diploma in Remote SensingUniversity College of LondonResearch statementModeling of multi-temporal environmental data sets (for example Land surface temperature, Precipitation, NDVI, Elevation, Landcover, etc.) is key issue in climatic change studies, in geosciences (volcanic and earthquake hazards, etc.), in agriculture and food security studies, in terrain analysis, in meteorology, in clmatic change assessment , etc. These are the scientific fields, I work with, develop new methods as well as added value data products.The quantification of TERRAIN knowledge is a key factor in an attempt to characterize the landscape, assess it's sensitivity to natural hazards, support environmental analysis and planning in a changing world. Topography is the manifestation of diverse dynamic processes that shape our planet (examples being landslides, volcanoes, water table variability, flooding, glaciers melting, urban heat islands/heat waves, aridity periods, wild fires, etc.).Processes monitoring requires regularly repeated acquisition of accurate, moderate to high resolution data for earth's relief (digital elevation models-DEMs), landcover, and biophysics (land surface temperature-LST, soil moisture, etc.). Quantification of processes also requires a terrain partition framework, which transforms the digital representation of the landscape to elementary objects. Physical processes are scale dependent and define various continuous or discontinuous terrain partition frameworks. A unified terrain partition framework is impossible to achieve; instead various process dependent object partition schemes are established. Three examples follow:• The modeling of CORINE landcover/landuse partition framework (Miliaresis 2006) that allowed the mapping of susceptibility of Corine classes to natural and environmental hazards.• Biomass estimate form active remote sensing sensors and the modeling of the effect of DTM and DSM mis-registration as a function of tree canopy density (Miliaresis and Delikaraoglou 2009).• The automated segmentation of urban landscape to elementary objects from LiDAR high resolution DEMS providing a city partition framework suitable for biophysical parameters integration (Miliaresis & Kokkas,2007).Currently BIOPHYSICAL DATA SETS are computed from the satellite-based remotely sensed images with high temporal resolution at a moderate resolution scale, allowing the day and night monitoring of earth’s surface. An example being the acquisitions from MODerate-resolution Imaging Spectroradiometer (MODIS) on board the two EOS satellites, Terra and Aqua. So thermal imagery products are available on regular and frequent basis for both land and oceans. Data availability stimulates the analysis of the long time series of multi-temporal images in an attempt to closely monitor regions and provide information about the spatial and temporal changes in temperature. Towards this end, new methods allowed the monitoring of terrain spatio-temporal thermal signature under a completely new framework:
This resume is made with CVwizard.com.• Thermal invariant regions mapping in both space and time from MODIS LST imagery, Miliaresis (2009).• A method for elevation, latitude, and longitude decorrelation stretch of multi-temporal LST imagery Miliaresis (2012; 2013; 2014) and revealing thermal anomalies across vast (continental scale) regions and allowing the monitoring of terrain spatio-temporal thermal signature under a completely new spatial framework.• In addition, diurnal temperature range (DTR) is a meteorological term that relates to the variation in temperature that occurs between day-time (maximum) and night-time (minimum) daily temperatures. Daily temperature oscillation is greatest in the planetary boundary layer, controlled by many factors including: latitude, distance from the sea, land cover, elevation, climatic zone, humidity, soil moisture, atmospheric circulation, clear skies, the intensity of solar radiation. The day and night temperature oscillations are also controlled by thermal inertia (a measure of the subsurface's ability to store heat during the day and re-emit it during the night). Although spatial modeling of DTR from meteorological stations can provide reliable estimates (Hill, 2013), meteorological stations are often too sparse to make reliable estimates by interpolation. MODIS near diurnal LST oscillations (day minus night LST) were computed per pixel for the 01:30 (night) and 13:30 (day) local crossing time passes of the Aqua satellite for the year 2008 in Greece (Miliaresis and Tsatsaris, 2011) and the temporal and the spatial characteristics were mapped. These estimates do not represent the diurnal temperature range since 01:30 local time LST is not minimum while, 13:30 local time LST is not maximum. Two more thermal images for the 10:30 (day) and the 22:30 (night) local crossing time passes of Terra satellite are acquired daily. Miliaresis (2014) applied a new technique to all four daily MODIS LST acquisitions from both the Aqua and the Terra satellites, in order to produce a new multi-temporal LST image sequence that enhance and isolate the day and night LST variations in Greece. The enhanced imagery identified regions with different day/night LST oscillation variability, allowing environmental terrain characterization in an attempt to support agriculture productivity and land cover studies in the context of emerging climatic change.• Such efforts seek to better understand the links between human activities (food supplies, agriculture, landuse planning, etc.) and the variety of impacts of climate change. For example night temperatures are expected to increase at a faster rate than day temperatures due to less radiant heat loss because of increased cloudiness (Alward et al., 1999).• The duration of a crop growth cycle is conditioned by the daily temperatures absorbed by the plant. Therefore, an increase in daily temperature will speed up plant development by reducing the duration between sowing and harvesting (Hertel et al., 2010). Thus, crop productivity may fall with the shortening of a cycle. In addition high night temperature decreases production by decreasing the photosynthetic function (Turnbull et al., 2002).It is quite clear that the spatial modeling of biophysical data (landcover, LST, sea surface temperature-SST, biomass, tree canopy density, aerosol optical thickness & particle density, vegetation density, soil moisture, etc.) is of great importance in assessing environmental change. Thus, a variety of quantitative techniques has been developed to automate the extraction of terrain features including segmentation, representation-classification while new data analysis/data modeling techniques were derived and new applications of biophysical datasets are revealed. Key issues are the spatio-temporal monitoring of environmental changes, the integration of biophysical parameters to terrain and geographic objects in order to support environmental analysis, decision support, planning, etc. My research efforts are focused in the fields:• TERRAIN MODELING: Terrain segmentation, Terrain pattern recognition, Urban terrain object recognition, Digital terrain analysis, Terrain evaluation, Geomorphometry, Site selection, Landcover mapping, Landuse modeling, Antarctic terrain analysis Fire risk, Flood hazard.• BIOPHYSICAL GEOSCIENCES: Thermal inertia mapping, Geothermal sensing, Bio-pattern analysis, Bio-physical terrain classification, Biopattern analysis, Natural hazards, Volcanic hazard/risk monitoring, Earthquakes bio-patterns modeling.• ENVIRONMETRICS: Elevation latitude longitude decorrelation stretch, Diurnal oscillation enhancement, Selective variance reduction, Temporal frequency enhancement, Spatio-temporal processes modelling.• CLIMATIC CHANGE MONITORING: Land Surface temperature, Sea Surface Temperature, Precipitation, Aerosol, Soil Moisture, Albedo, Vegetation DensityFuture research plans:• Map "anomalies" from multi-temporal LST data (including precipitation) to recognize genetic physical processes.• Temporal frequency enhancement of biophysical data & terrain bio-pattern definition.• Accuracy assessment of biophysical data sets.• Environmetrics of man-made objects from LiDAR data and biophysical data-sets.Integrate biophysical data (diurnal LST cycle, aerosols, vegetation density, soil moisture, wind assessments) in the representation of terrain features, landcover & landuse entities and man-made objects. Support environmental decision making, planning, food security studies, landcover modeling.• Time dependent monitoring & modeling of biophysical processes in Eastern Mediterranean Countries, Arabian
This resume is made with CVwizard.com.Peninsula (Oman), Central and South Africa (Botswana) through the integration of precipitation, LST cycle, vegetation, aerosols, soil moisture data-sets.• Decomposition of the spatio-temporal variability of the geophysical component that might be evident in remotely sensed biophysical data sets (ground data will be used too for testing and calibration purposes).The underlying theoretical basis is that the earth crust is constantly subject to endogenic processes (stress from tectonic plates motion, fluid motions in earth mantle, chemical reactions, etc.) and exogenic processes (climate change, etc.) with magnitude that vary in both space and time, RESULTING occasionally to earthquakes, volcanic activity etc.Resources necessary to carry out my research:• Environmental data & imagery acquired from earth orbiting satellites (Landsat, Sentinel, MODIS etc.) and GIS databases, that are available for free (NASA, USGS, ESA).• Commercial software for programming, data processing and visualization (SPSS, Idrisi) as well as• open source software (QGIS, ILWIS, R, Octave, WinPython, MultiSpec, WhiteBox, Libre Office, etc.).• Occasionally field work is required for model and data evaluation.Teaching statementUndergraduate Courses[2000-2004] Photo-interpretation, Remote Sensing, Digital image processing[2004-2010] Remote Sensing, GIS, Hydrology, Flood hazard mapping, Wild fires monitoring, Digital Elevation ModelsPostgraduate Courses• [2006-2010] Remotely Sensing, , Terrain modelling• [2011-2012] Terrain Bio-pattern + Global datasets• [2012-2019]: Environmental Remote Sensing, GIS, Data miningScientific publicationsSOFTWARE & DataMiliaresis G., 2018. Clusters post-processing. A Git-Hub project. Under GNU GLP v.3. , https://github.com/miliaresis/SVR.CLUSTERS [Visualization of feature space with 2-d plots per cluster & statistics - linear regression of clusters etc. etc. ]Miliaresis G., 2018. Dimension Reduction of multi-dimensional elevation data for DEMs optimization and evaluation. Git-Hub project. Under GNU GPL v.3. https://github.com/miliaresis/SVR.DEM [2.d DEM optimization method presents an alternative to DEM comparison by elevation differences modeling. In it's general version (3.d, 4.d etc., etc.) is not limited to only 2 DEMs but can handle 3 DEMs, 4 DEMs etc. etc.]Miliaresis G., 2016. Selective Variance Reduction, (Win-Python script). GitHub project. Under GNU GPL v.3. https://github.com/miliaresis/SVRMiliaresis G., 2016. GeoLogic Shell (Delphi ) - Terrain pattern recognition. Github, Under GNU GPL v.3. https://github.com/miliaresis/GeoLogic_ShellLandsat imagery: Image enhancement & Spectral OptimizationMiliaresis G., 2018. Selectivethematic information content enhancement of LANDSAT ETM Imagery. Remote Sensing of Earth Systems Sciences,1(3-4):53-62 https://doi.org/10.1007/s41976-018-0005-1DEMs: Evaluation & Accuracy assessmentMiliaresis G., 2019. Dimensionreduction of multi-temporal elevation data. Applied Geomatics. DOI: 10.1007/s12518-019-00271-wMiliaresis G., Paraschou Ch.V., 2011. An evaluation of the accuracy of the ASTER GDEM and the role of stack number: A case study of Nisiros Island, Greece. Remote Sensing Letters 2(2):127-135. DOI:10.1080/01431161.2010.503667Miliaresis G., Delikaraoglou D., 2009. Effects of Percent Tree Canopy Density and DEM Mis-registration to SRTM/NED Vegetation Height Estimates. Remote Sensing. 1(2):36-49, DOI:10.3390/rs1020036Miliaresis G., 2008. The Landcover Impact on the Aspect/Slope Accuracy Dependence of the SRTM-1 Elevation Data for the Humboldt Range. Sensors, 8(5):3134-3149. DOI: 10.3390/s8053134.Miliaresis G., 2007. An upland object based modeling of the vertical accuracy of the SRTM-1 elevation dataset. Journal
This resume is made with CVwizard.com.of Spatial Sciences, 52(1):13-29. DOI: 10.1080/14498596.2007.9635097Miliaresis G., Paraschou Ch., 2005. Vertical accuracy of the SRTM DTED Level 1 of Crete. Int. J. of Applied Earth Observation & GeoInformation 7(1):49-59. DOI: 10.1016/j.jag.2004.12.001Precipitation: modeling it’s effect to LST & terrain standardization• Miliaresis G. 2017. Iterative Selective Spatial Variance Reduction of MYD11A2 LST Data. Earth Science Informatics 10(1): 15-27. DOI: 10.1007/s12145-016-0271-5• Miliaresis G., 2016. Revealing the precipitation dependency of regional in time and in space thermal anomaly peaks in SW USA. Modeling Earth Systems & Environment, vol. 2, no 1 (article 34), 1-10 p. DOI: 10.1007/s40808-016-0093-y• Miliaresis G. 2016. Spatial decorrelation stretch of annual (2003-2014) Daymet precipitation summaries on a 1-km grid for California, Nevada, Arizona and Utah. Environmental Monitoring & Assessment, 188 (article 361) 1-21 DOI: 10.1007/s10661-016-5365-5.DTR modeling• Miliaresis G., 2014. Daily Temperature Oscillation Enhancement of Multi-temporal LST Imagery. Photogrammetric Engineering & Remote Sensing 80(5)423-428 DOI: 10.14358/PERS.80.5.423• Miliaresis G., 2012. Elevation, latitude and longitude decorrelation stretch of multi-temporal near-diurnal LST imagery. International Journal of Remote Sensing, 33(19):6020-6034, DOI: 10.1080/01431161.2012.676690.• Miliaresis G., Tsatsaris A., 2011. Mapping the spatial and temporal pattern of day-night temperature difference in Greece from MODIS imagery. GIScience & Remote Sensing, 48(2):210-224, DOI: 10.2747/1548Selective variance reduction• Miliaresis G., 2016. An Unstandardized Selective Variance Reduction Script for Elevation, Latitude & Longitude Decorrelation Stretch of Multi-temporal LST Imagery. Modeling Earth Systems & Environment, 2, (1) (Article 41), 1-13 p. DOI: 10.1007/s40808-016-0103-0• Miliaresis G., 2012. Elevation, latitude/longitude decorrelation stretch of multi-temporal LST imagery. Photogrammetric Engineering & Remote Sensing, 78(2):151-160. DOI: 10.14358/PERS.78.2.151SVR: Tectonic terrains characterization• Miliaresis G., 2013. Terrain analysis for active tectonic zone characterization, a new application for MODIS night LST (MYD11C3) dataset. Int. J. of Geographical Information Science, 27(7):1417-1432 p. DOI: 10.1080/13658816.2012.685172• Miliaresis, G., 2012. Selective variance reduction of multi-temporal LST imagery in the East Africa Rift System. Earth Science Informatics 5(1):1-12. DOI: 10.1007/s12145-011-0091-6SVR: Environmental analysis• Miliaresis G., 2016. NDVI signatures of regional in time and in space thermal anomalies in SW USA. Spatial Information Research, 24(3), 267-277. DOI: 10.1007/s41324-016-0028-8• Miliaresis G., 2014, Spatiotemporal patterns of land surface temperature of Antarctica from MODIS Monthly LST data (MYD11C3). Journal of Spatial Science, 59(1)157-166. DOI: 10.1080/14498596.2013.857382• Miliaresis G., 2014. Global LST Anomaly Mapping from MODIS Night Imagery .Malaysian J. of Remote Sensing & GIS, 3(1), 1-9• Miliaresis G., 2013. Thermal anomaly mapping from night MODIS imagery of USA, a tool for environmental assessment. Environmental Monitoring & Assessment 185(2):1601-1612, DOI: 10.1007/s10661-012-2654-5.• Miliaresis G. 2009. Biophysical Terrain Analysis. In: Environmental Cost Management [Randi Taylor Mancuso, Editor, ISBN:978-1-60741-815-3. ]. Nova Science Publishers, New York, Chapter 7, pp. 255-273.Volcanic terrains: thermal response• Miliaresis G. and K.ST. Seymour, 2011. Mapping the spatial & temporal SST variations in Red Sea, revealing a probable regional geothermal anomaly from Pathfinder V5 data. Int. J. of Remote Sensing, 32(07):1825-1842. DOI: 10.1080/01431161003631568• Zouzias D., Miliaresis G., Seymour, K.ST. 2011. Probable regional geothermal field reconnaissance in the Aegean Region from modern multi-temporal night LST imagery. Environmental Earth Sciences, 62(4):717-723. DOI: 10.1007/s12665-010-0560-0• Miliaresis G., 2009. Regional thermal and terrain modeling of the Afar Depression from multi-temporal night LST data. Int. J. of Remote Sensing, 30(9):2429–2446, DOI: 10.1080/01431160802562271Agricultural terrain analysisBabič M,M Huber,E Bielecka,M Soycan,W Przegon,L Gigović,S Drobnjak,D Sekulović, I.Pogarčić,G. Miliaresis,M. Mikoš, M. Komac, 2019.New Method of Visibility Network and Statistical Pattern Network Recognition Usage in Terrain Surfaces. Materials and Geoenvironment. DOI: https://doi.org/10.2478/rmzmag-2019-0006Mokarram
This resume is made with CVwizard.com.M., Shaygan M., Miliaresis G. 2018. Balancing soil parameters &farmers budget by feature selection & ordered weightedaveraging. Geographia Technica, 13(1): 73-84. DOI:10.21163/GT_2018.131.08Demertzi K, Papamichail D, Aschonitis V, Miliaresis G., 2016. Hydroclimatic analysis of Greece using multi-parametric clustering of monthly precipitation and reference crop evapotranspiration. European Water 55:141-155 p.Aschonitis Vassilis , George Miliaresis, Kleoniki Demertzi & D. Papamichail, 2016. Terrain segmentation of Greece using the spatial and seasonal variation of reference crop evapotranspiration. Advances in Meteorology, Article ID 3092671, 14 pages, DOI: 10.1155/2016/3092671.Demertzi K, Papamichail D, Aschonitis V, Miliaresis G. 2014. Spatial and seasonal patterns of precipitation in greece: the terrain segmentation approach. Global NEST Journal, 16(5), 988-997.Spatial objects: thermal modeling• Miliaresis G., Partsinevelos P., 2010. Terrain Segmentation of Egypt from Multi-temporal Night LST Imagery and Elevation Data. Remote Sensing, 2(9):2083-2096. DOI: 10.3390/rs2092083• Tsatsaris A. & Miliaresis G. 2011. Spatial correlation of Tuberculosis (TB) incidents to the MODIS LST biophysical signature of African countries. Int. Journal of Environmental Protection, 1(1), 49-57.• Miliaresis G. , Tsatsaris A., 2010. Thermal terrain modeling of spatial objects, a tool for environmental and climatic change assessment. Environmental Monitoring & Assessment, 164(1-4):561-572 DOI: 10.1007/s10661-009-0913-xWild fires & landcover modeling• Partsinevelos P., Nikolakaki N., Psillakis P., Miliaresis G. and Xanthakis M. 2015. Landcover change modeling through visualization and classification enhancement of multi-temporal imagery. Global NEST Journal, 17(2),271-280• Miliaresis G., 2009. The terrain signatures of administrative units: a tool for environmental assessment. Environmental Monitoring & Assessment, 150(1-4):386-396. DOI: 10.1007/s10661-008-0237-2•Miliaresis G. 2008. Monitoring/Impact of Wild Fires of the August 2007 in the Mountain Region of Ilia Prefecture from Web Spatial Databases. GI & Earth Observation for Sustainable Development, Integrated Mount Development, http://pfigshare-u-files.s3.amazonaws.com/1471228/2008_MNT_GIS.PDFLandslides: irregular terrain segmentation framework• Konstantopoulos K., Miliaresis G., 2018. Unsupervised landslide risk dependent terrain segmentation on the basis of historical landslide data and geomorphometrical indicators. SDRP Journal of Earth Sciences & Environmental Studies 3(2), 386-394, DOI: 10.25177/JESES.3.2.2• Miliaresis G. and Basoukos K., 2007. Landslides susceptibility of barren class objects from modern imagery. Conference on Environmental Management, Engineering, Planning and Economics, Skiathos, June 24-28, 2145-2150.• Miliaresis G, Sabatakakis N, Koukis G, 2005.Terrain pattern recognition & spatial decision for regional slope stability studies. Natural Resources Research, 14(2):91-100. DOI:10.1007/s11053-005-6951-3Volcanic geomorphometry• Zouzias D., Miliaresis G., Seymour, K.ST., 2011, Interpretation of Nisyros Volcanic Terrain using Land Surface Parameters Generated from the ASTER Global DEM. Journal of Volcanology & Geothermal Research, 200(3-4):159-170. DOI: 10.1016/j.jvolgeores.2010.12.012• Miliaresis G. , Ventura G., Vilardo G., 2009. Terrain modeling of the complex volcanic terrain of Ischia Island (Italy). Canadian Journal of Remote Sensing. 35(4):385-398. DOI: 10.5589/m09-027Terrain evaluation• Grohmann C., & Miliaresis G., 2013. Digital terrain analysis and modeling / Geological applications of digital terrain analysis. Int. Journal of Geographical Information Science, 27(7):1403-1404. DOI:10.1080/13658816.2013.77261• Miliaresis G., 2008. Quantification of Terrain Processes. Lecture Notes in Geoinformation & Chartography (LNG&C),XIV,13-28. [DOI: 10.1007/978-3-540-77800-4_2] (In:Advances in digital terrain analysis, Springer, Editors: Qiming Zhou, Brian Lees,Guo-an Tang, ISBN 978-3-540-77799-1, 462 p.)City modeling• Miliaresis G., Kokkas N. 2007. Segmentation & Object Based Classification for the Extraction of the Building Class from LIDAR DEMs. Computers & Geosciences, 33(8):1076-1087. DOI:10.1016/j.cageo.2006.11.012Extra-terrestrial landscapes• Miliaresis G., Kokkas N. 2004. Segmentation and terrain modeling of extra-terrestrial chasmata. J. of Spatial Sciences, 49(2): 89-99. DOI: 10.1080/14498596.2004.9635024• Kokkas N., Miliaresis G., 2004. Geomorphometric Mapping of Grand Canyon from the 1o DEM. Int. Arc. of Photogrammetry, Remote Sensing & GIS,XXXV, 406-411.
This resume is made with CVwizard.com.Earthquake occurrence• Miliaresis 2007. Segmentation of multi-temporal earthquake imagery for the detection of geophysical related geothermal activity. 4th Int. Workshop on the Analysis of Multi-Temporal Images, July 18-20, Leuven, Belgium, 6 p. DOI: 10.1109/multitemp.2007.4293071Fluvial landforms: segmentation• Miliaresis, G.Ch., 2001. Extraction of Bajadas from DEMs & Satellite Imagery. Computers & Geosciences 27(10):1157-1167. DOI:10.1016/S0098-3004(01)00032-2• Miliaresis, G.Ch., and D.P. Argialas, 2000. Extraction & Delineation of Alluvial Fans from D??s & Landsat TM Images. Photogrammetric Engineering & Remote Sensing, 66(9):1093-1101. DOI: 0099-1112/00/6609-109Mountains & peneplains: segmentation & tectonic geomorphometry• Miliaresis G., 2006. Geomorphometric mapping of Asia Minor from Globe DEM. Geografiska Annaler 88A (3):209-221. DOI:10.1111/j.1468-0459.2006.00296.x• Miliaresis G., Illiopoulou P. 2004. Clustering of Zagros Ranges from the Globe DEM representation. Int. Journal of Applied Earth Observation & GeoInformation, 5 (1):17-28. DOI: 10.1016/j.jag.2003.08.001• Miliaresis, G. Ch., Argialas, D.P. 2002. Quantitative Representation of Mountain Objects Extracted from the GTOPO30 DEM. Int. Journal of Remote Sensing, 23(5):949-964. DOI:10.1080/01431160110070690• Miliaresis, G. Ch., 2001. Geomorphometric Mapping of Zagros Ranges at Regional Scale. Computers & Geosciences, 27(7):775-786. DOI: 10.1016/S0098-3004(00)00168-0• Miliaresis, G. Ch., and D.P. Argialas, 1999. Segmentation of Physiographic Features from the Global Digital Elevation Model/GTOPO30. Computers & Geosciences, 25(7):715-728. DOI: 10.1016/S0098-3004(99)00025-4Terrain representation• Argialas, D.P., and G.CH. Miliaresis, 2001. Human factors in the Interpretation of Physiography by Symbolic and Numerical Representations within an Expert System. In «Interpreting Remote Sensing Imagery: Human factors» by R. R. Hoffman and A. B. Markman (Eds), 304 p., ISBN:1566704138. Lewis Publishers - CRC PRESS , New York, Chapter 3, pp. 59-81. DOI: 10.1201/9781420032819.sec2• Argialas, D.P., and G.CH. Miliaresis, 2000.Physiographic Region Interpretation: Formalization With Rule Based Structures and Object Hierarchies. Int. Archives of Photogrammetry & Remote Sensing, 19-23/7, Amsterdam, Vol. XXXIII, Part B4, 91-98 http://www.isprs.org/proceedings/XXXIII/congress/part4/91_XXXIII-part4.pdfReferencesReferences available upon request.Publications in chronological orderMiliaresis G., 2019. Dimension reduction of multi-temporal elevation data. Miliaresis G., 2019. Dimension reduction of multi-temporal elevation data. Applied Geomatics. DOI: 10.1007/s12518-019-00271-wBabič M,M Huber,E Bielecka,M Soycan,W Przegon,L Gigović,S Drobnjak,D Sekulović, I.Pogarčić,G. Miliaresis,M. Mikoš, M. Komac, 2019.New Method of Visibility Network and Statistical Pattern Network Recognition Usage in Terrain Surfaces. Materials and Geoenvironment. DOI: https://doi.org/10.2478/rmzmag-2019-0006Miliaresis G., 2018. Selective thematic information content enhancement of LANDSAT ETM Imagery. Remote Sensing of Earth Systems Sciences, 1(3-4):53-62, DOI: 10.1007/s41976-018-0005-1Konstantopoulos K., Miliaresis G., 2018. Unsupervised landslide risk dependent terrain segmentation on the basis of historical landslide data and geomorphometrical indicators. SDRP Journal of Earth Sciences & Environmental Studies 3(2), 386-394, DOI: 10.25177/JESES.3.2.2Mokarram M., Shaygan M., Miliaresis G. 2018. Balancing soil parameters & farmers budget by feature selection & ordered weighted averaging. Geographia Technica, 13(1): 73-84. DOI: 10.21163/GT_2018.131.08 Miliaresis G., 2018. Clusters post-processing. A Git-Hub project. Under GNU GLP v.3. , https://github.com/miliaresis/SVR.CLUSTERS [Visualization of feature space with 2-d plots per cluster & statistics - linear regression of clusters etc. etc. ] Miliaresis G., 2018. Dimension Reduction of multi-dimensional elevation data for DEMs optimization and evaluation. Git-Hub project. Under GNU GPL v.3. https://github.com/miliaresis/SVR.DEM [2.d DEM optimization method presents an alternative to DEM comparison by elevation differences modeling. In it's general version (3.d, 4.d etc., etc.) is not limited to only 2 DEMs but can handle 3 DEMs, 4 DEMs etc. etc.]Miliaresis G. 2017. Iterative Selective Spatial Variance Reduction of MYD11A2 LST Data. Earth Science
This resume is made with CVwizard.com.Informatics 10(1): 15-27. DOI: 10.1007/s12145-016-0271-5Miliaresis G. 2016. Spatial decorrelation stretch of annual (2003-2014) Daymet precipitation summaries on a 1-km grid for California, Nevada, Arizona and Utah. Environmental Monitoring & Assessment, 188 (article 361) 1-21 DOI: 10.1007/s10661-016-5365-5.Miliaresis G., 2016. Revealing the precipitation dependency of regional in time and in space thermal anomaly peaks in SW USA. Modeling Earth Systems & Environment, vol. 2, no 1 (article 34), 1-10 p. DOI: 10.1007/s40808-016-0093-y Miliaresis G., 2016. An Unstandardized Selective Variance Reduction Script for Elevation, Latitude & Longitude Decorrelation Stretch of Multi-temporal LST Imagery. Modeling Earth Systems & Environment, 2, (1) (Article 41), 1-13 p. DOI: 10.1007/s40808-016-0103-0 Miliaresis G., 2016. NDVI signatures of regional in time and in space thermal anomalies in SW USA. Spatial Information Research, 24(3), 267-277. DOI: 10.1007/s41324-016-0028-8 Miliaresis G., 2016. Selective Variance Reduction, (Win-Python script). GitHub project. Under GNU GPL v.3. https://github.com/miliaresis/SVRMiliaresis G., 2016. GeoLogic Shell (Delphi ) - Terrain pattern recognition. Github, Under GNU GPL v.3. https://github.com/miliaresis/GeoLogic_ShellDemertzi K, Papamichail D, Aschonitis V, Miliaresis G., 2016. Hydroclimatic analysis of Greece using multi-parametric clustering of monthly precipitation and reference crop evapotranspiration. European Water 55:141-155 p. Aschonitis Vassilis , George Miliaresis, Kleoniki Demertzi & D. Papamichail, 2016. Terrain segmentation of Greece using the spatial and seasonal variation of reference crop evapotranspiration. Advances in Meteorology, Article ID 3092671, 14 pages, DOI: 10.1155/2016/3092671.Partsinevelos P., Nikolakaki N., Psillakis P., Miliaresis G. and Xanthakis M. 2015. Landcover change modeling through visualization and classification enhancement of multi-temporal imagery. Global NEST Journal, 17(2),271-280Miliaresis G., 2014. Daily Temperature Oscillation Enhancement of Multi-temporal LST Imagery. Photogrammetric Engineering & Remote Sensing 80(5)423-428 DOI: 10.14358/PERS.80.5.423 Miliaresis G., 2014, Spatiotemporal patterns of land surface temperature of Antarctica from MODIS Monthly LST data (MYD11C3). Journal of Spatial Science, 59(1)157-166. DOI: 10.1080/14498596.2013.857382 Miliaresis G., 2014. Global LST Anomaly Mapping from MODIS Night Imagery .Malaysian J. of Remote Sensing & GIS, 3(1), 1-9 Demertzi K, Papamichail D, Aschonitis V, Miliaresis G. 2014. Spatial and seasonal patterns of precipitation in greece: the terrain segmentation approach. Global NEST Journal, 16(5), 988-997.Miliaresis G., 2013. Terrain analysis for active tectonic zone characterization, a new application for MODIS night LST (MYD11C3) dataset. Int. J. of Geographical Information Science, 27(7):1417-1432 p. DOI: 10.1080/13658816.2012.685172Miliaresis G., 2013. Thermal anomaly mapping from night MODIS imagery of USA, a tool for environmental assessment. Environmental Monitoring & Assessment 185(2):1601-1612, DOI: 10.1007/s10661-012-2654-5.Grohmann C., & Miliaresis G., 2013. Digital terrain analysis and modeling / Geological applications of digital terrain analysis. Int. Journal of Geographical Information Science, 27(7):1403-1404. DOI:10.1080/13658816.2013.77261 Skarlatos D., G. Miliaresis, A. Georgiou, 2013. Investigation of Cyprus thermal tenancy using nine year MODIS LST data and Fourier analysis [8795-47]. Proc. SPIE 8795, 1st International Conference on Remote Sensing & Geoinformation of the Environment Paphos, August 14, 2013; 9 pages, DOI: 10.1117/12.2041580 Miliaresis, G., 2012. Selective variance reduction of multi-temporal LST imagery in the East Africa Rift System. Earth Science Informatics 5(1):1-12. DOI: 10.1007/s12145-011-0091-6 Miliaresis G., 2012. Elevation, latitude and longitude decorrelation stretch of multi-temporal near-diurnal LST imagery. International Journal of Remote Sensing, 33(19):6020-6034, DOI: 10.1080/01431161.2012.676690.Miliaresis G., 2012. Elevation, latitude/longitude decorrelation stretch of multi-temporal LST imagery. Photogrammetric Engineering & Remote Sensing, 78(2):151-160. DOI: 10.14358/PERS.78.2.151 Miliaresis G., Paraschou Ch.V., 2011. An evaluation of the accuracy of the ASTER GDEM and the role of stack number: A case study of Nisiros Island, Greece. Remote Sensing Letters 2(2):127-135. DOI:10.1080/01431161.2010.503667Miliaresis G., Tsatsaris A., 2011. Mapping the spatial and temporal pattern of day-night temperature difference in Greece from MODIS imagery. GIScience & Remote Sensing, 48(2):210-224, DOI: 10.2747/1548Zouzias D., Miliaresis G., Seymour, K.ST., 2011, Interpretation of Nisyros Volcanic Terrain using Land Surface Parameters Generated from the ASTER Global DEM. Journal of Volcanology & Geothermal Research, 200(3-4):159-170. DOI: 10.1016/j.jvolgeores.2010.12.012 Miliaresis G. and K.ST. Seymour, 2011. Mapping the spatial & temporal SST variations in Red Sea, revealing a probable regional geothermal anomaly from Pathfinder V5 data. Int. J. of Remote Sensing, 32(07):1825-1842. DOI: 10.1080/01431161003631568Zouzias D., Miliaresis G., Seymour, K.ST. 2011. Probable regional geothermal field reconnaissance in the Aegean Region from modern multi-temporal night LST imagery. Environmental Earth Sciences, 62(4):717-723. DOI: 10.1007/s12665-010-0560-0Tsatsaris A. & Miliaresis G. 2011. Spatial correlation of Tuberculosis (TB) incidents to the MODIS LST biophysical signature of African countries. Int. Journal of Environmental Protection, 1(1), 49-57. Miliaresis G., Partsinevelos P., 2010. Terrain Segmentation of Egypt from Multi-temporal Night LST Imagery and Elevation Data. Remote Sensing, 2(9):2083-2096. DOI: 10.3390/rs2092083 Papasotirakopoulos S., Miliaresis G., Tsatsaris A., 2010. Thermal modelling of Africa from multi-temporal MODIS LSΤ imagery. 20th ESRI Users Conf., 1-3 Nov., Athens, 6 https://doi.org/10.6084/m9.figshare.1004880.vMiliaresis G., Delikaraoglou D., 2009. Effects of Percent Tree Canopy Density and DEM Mis-registration to SRTM/NED Vegetation
This resume is made with CVwizard.com.Height Estimates. Remote Sensing. 1(2):36-49, DOI:10.3390/rs1020036Miliaresis G., 2009. Regional thermal and terrain modeling of the Afar Depression from multi-temporal night LST data. Int. J. of Remote Sensing, 30(9):2429–2446, DOI: 10.1080/01431160802562271Miliaresis G. , Ventura G., Vilardo G., 2009. Terrain modeling of the complex volcanic terrain of Ischia Island (Italy). Canadian Journal of Remote Sensing. 35(4):385-398. DOI: 10.5589/m09-027 Miliaresis G. 2009. Biophysical Terrain Analysis. In: Environmental Cost Management [Randi Taylor Mancuso, Editor, ISBN:978-1-60741-815-3. ]. Nova Science Publishers, New York, Chapter 7, pp. 255-273. Miliaresis G., 2009. The terrain signatures of administrative units: a tool for environmental assessment. Environmental Monitoring & Assessment, 150(1-4):386-396. DOI: 10.1007/s10661-008-0237-2Miliaresis G., 2008. The Landcover Impact on the Aspect/Slope Accuracy Dependence of the SRTM-1 Elevation Data for the Humboldt Range. Sensors, 8(5):3134-3149. DOI: 10.3390/s8053134. Miliaresis G., 2008. Quantification of Terrain Processes. Lecture Notes in Geoinformation & Chartography (LNG&C),XIV,13-28. [DOI: 10.1007/978-3-540-77800-4_2] (In:Advances in digital terrain analysis, Springer, Editors: Qiming Zhou, Brian Lees,Guo-an Tang, ISBN 978-3-540-77799-1, 462 p.) Miliaresis G., Kokkas N. 2007. Segmentation & Object Based Classification for the Extraction of the Building Class from LIDAR DEMs. Computers & Geosciences, 33(8):1076-1087. DOI:10.1016/j.cageo.2006.11.012 Miliaresis G., 2007. An upland object based modeling of the vertical accuracy of the SRTM-1 elevation dataset. Journal of Spatial Sciences, 52(1):13-29. DOI: 10.1080/14498596.2007.9635097 Miliaresis 2007. Segmentation of multi-temporal earthquake imagery for the detection of geophysical related geothermal activity. 4th Int. Workshop on the Analysis of Multi-Temporal Images, July 18-20, Leuven, Belgium, 6 p. DOI: 10.1109/multitemp.2007.4293071Miliaresis G. and Basoukos K., 2007. Landslides susceptibility of barren class objects from modern imagery. Conference on Environmental Management, Engineering, Planning and Economics, Skiathos, June 24-28, 2145-2150. https://doi.org/10.6084/m9.figshare.1004645.v1 Miliaresis G., 2006. Geomorphometric mapping of Asia Minor from Globe DEM. Geografiska Annaler 88A (3):209-221. DOI:10.1111/j.1468-0459.2006.00296.xMiliaresis G. and Kokkas N., 2006. Geomorphometric segmentation applied to the city modeling problem. Int. Symposium on Terrain Analysis & Digital Terrain Modeling, China (Nanjing), 23-25 Nov. 2006, 12 p. https://doi.org/10.6084/m9.figshare.1004643.v1Miliaresis G., Paraschou Ch., 2005. Vertical accuracy of the SRTM DTED Level 1 of Crete. Int. J. of Applied Earth Observation & GeoInformation 7(1):49-59. DOI: 10.1016/j.jag.2004.12.001Miliaresis G, Sabatakakis N, Koukis G, 2005.Terrain pattern recognition & spatial decision for regional slope stability studies. Natural Resources Research, 14(2):91-100. DOI:10.1007/s11053-005-6951-3 Miliaresis G., Kokkas N. 2004. Segmentation and terrain modeling of extra-terrestrial chasmata. J. of Spatial Sciences, 49(2): 89-99. DOI: 10.1080/14498596.2004.9635024Kokkas N., Miliaresis G., 2004. Geomorphometric Mapping of Grand Canyon from the 1o DEM. Int. Arc. of Photogrammetry, Remote Sensing & GIS,XXXV, 406-411.Miliaresis G., Illiopoulou P. 2004. Clustering of Zagros Ranges from the Globe DEM representation. Int. Journal of Applied Earth Observation & GeoInformation, 5 (1):17-28. DOI: 10.1016/j.jag.2003.08.001Kokkas N., Miliaresis G., 2004. Geomorphometric Mapping of Grand Canyon from the 1o DEM. Int. Arc. of Photogrammetry, Remote Sensing & GIS,XXXV, 406-411. Rodopoulos J., & Miliaresis G., 2004. Geomorphometric description of cluter maps. ASPRS Annual Conf., Anchorage, May 5-9, 399-403. https://doi.org/10.6084/m9.figshare.1004642.v1Miliaresis G., Kokkas N., 2003. The geomophometric signature of Valles Marineris from M.O.L.A. DEM. ASPRS Annual Conf., Anchorage, May 5-9, 122-130. https://doi.org/10.6084/m9.figshare.1004636.v1Panagou Th., Miliaresis G., 2003. Evaluating the thematic information content of ASTER (VNIR) imagery in urban areas by classification techniques. Int. Archieves of Photogrammetry, Remote Sensing & Spatial Information Science, XXXIV-7/W9,263-267 https://doi.org/10.6084/m9.figshare.1004639.v1Miliaresis, G. Ch., Argialas, D.P. 2002. Quantitative Representation of Mountain Objects Extracted from the GTOPO30 DEM. Int. Journal of Remote Sensing, 23(5):949-964. DOI:10.1080/01431160110070690Miliaresis, G.Ch., 2001. Extraction of Bajadas from DEMs & Satellite Imagery. Computers & Geosciences 27(10):1157-1167. DOI:10.1016/S0098-3004(01)00032-2Argialas, D.P., and G.CH. Miliaresis, 2001. Human factors in the Interpretation of Physiography by Symbolic and Numerical Representations within an Expert System. In «Interpreting Remote Sensing Imagery: Human factors» by R. R. Hoffman and A. B. Markman (Eds), 304 p., ISBN:1566704138. Lewis Publishers - CRC PRESS , New York, Chapter 3, pp. 59-81. DOI: 10.1201/9781420032819.sec2 Miliaresis, G.Ch., and D.P. Argialas, 2000. Extraction & Delineation of Alluvial Fans from D??s & Landsat TM Images. Photogrammetric Engineering & Remote Sensing, 66(9):1093-1101. DOI: 0099-1112/00/6609-109 Argialas, D.P., and G.CH. Miliaresis, 2000.Physiographic Region Interpretation: Formalization With Rule Based Structures and Object Hierarchies. Int. Archives of Photogrammetry & Remote Sensing, 19-23/7, Amsterdam, Vol. XXXIII, Part B4, 91-98 http://www.isprs.org/proceedings/XXXIII/congress/part4/91_XXXIII-part4.pdfMiliaresis, G. Ch., and D.P. Argialas, 1999. Segmentation of Physiographic Features from the Global Digital Elevation Model/GTOPO30. Computers & Geosciences, 25(7):715-728. DOI: 10.1016/S0098-3004(99)00025-4
This resume is made with CVwizard.com.Supervised thesisSupervised postgraduate thesisEnvironmental Protection & Management (MSc postgraduate course). Open University of Cyprus.Eustathiou Ch. 2019. Geoinformation & spatial planning into Secondary Education in Greece for supporting environmental education tasks.Katasarelia A. 2019. Vegetation mapping before and after the wild fire of 13th of Aug. 2017 in Eastern Attica from Sentinel-2 imagery.Kountouriotis T. 2019. Spatial planning and associated legislation of wind energy farms in Cyprus.Vogiatzis N. 2019. Habitat detection-mapping of specific invasive species in Greece with GIS.Karra M. 2019. Mapping the impacts of flash flood of November 2017 in Western Attica.Zisi A. 2019. Environmental impact assessment of the wild fire of july 2018 in Eastern Attica.Ioannou J. 2018. Thematic information content of of unmanned aircraft systems (UAS) mapping products (DEMs & orthophotos) of US Geological Survey.Karatzanis D., 2018. Landslide monitoring and restoration planning in artificial dam of Aposelimi in Western Crete. https://kypseli.ouc.ac.cy/handle/11128/3636Konstantopoulos K., 2018. Unsupervised landslide risk dependent terrain segmentation on the basis of historical landslide data and geomorphometrical indicators. https://kypseli.ouc.ac.cy/handle/11128/3633Christothoulopoulou D., 2018. Parents report their environmental attitude in primary education - a case study from Greece.https://kypseli.ouc.ac.cy/handle/11128/3722Pittas A. 2016. Spatial decision making for planning industrial facilities. https://kypseli.ouc.ac.cy/handle/11128/2273Ioannou M. 2015. Monitoring of environmental energy sector accidents with passive multi-temporal thermal remote sensing data. https://kypseli.ouc.ac.cy/handle/11128/1767Dona M. 2015. GIS Planning of wind power installations in Grevena prefecture. https://kypseli.ouc.ac.cy/handle/11128/1774Papantoni A. 2015. Geographic & Geomorphometric representation of Corine landcover. https://kypseli.ouc.ac.cy/handle/11128/1874Karakatsanis P. 2015. Monitoring the biophysical impact of 2007 wild fires in Peloponnesus. https://kypseli.ouc.ac.cy/handle/11128/1871Kalogeropoulou B., 2015. Botsuana: environmental terrain analysis from moderate free/open GIS & remote sensing data. https://kypseli.ouc.ac.cy/handle/11128/1872Naoumis G., 2015. Multi-temporal SST thermal anomaly mapping and interpretation in Meditteranean Sea. https://kypseli.ouc.ac.cy/handle/11128/1880Chiotaki S., 2015. Thematic information content and comparison of ETM versus OLI (Landsat). http://hdl.handle.net/11128/1878Chachami B., 2015. Water resources planning from wells monitoring and oped/free GIS data in Korintos prefecture. https://kypseli.ouc.ac.cy/handle/11128/1890Tsekme H. 2014. Biophysical mapping of Cyprus form MODIS data & applications.https://kypseli.ouc.ac.cy/handle/11128/1637Trakos A. 2014. Environmental protection & planning of Parnitha protected forest with open/free GIS and remote sensing data. https://kypseli.ouc.ac.cy/handle/11128/1527Undergraduate thesis published in conferences• D. Skarlatos, G. Miliaresis, A. Georgiou, 2013. Investigation of Cyprus thermal tenancy using nine year MODIS LST data and Fourier analysis [8795-47]. Proc. SPIE 8795, 1st International Conference on Remote Sensing & Geoinformation of the Environment Paphos, August 14, 2013; 9 pages, DOI: 10.1117/12.2041580 [http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1724933]• Papasotirakopoulos S., Miliaresis G., Tsatsaris A., 2010. Thermal modelling of Africa from multi-temporal MODIS LSΤ imagery. 20th ESRI Users Conf., 1-3 Nov., Athens, 6 https://doi.org/10.6084/m9.figshare.1004880.v1• Miliaresis G. and Basoukos K., 2007. Landslides susceptibility of barren class objects from modern imagery. Conference on Env. Management, Engineering, Planning & Economics, Skiathos, June 24-28, 2145-2150. https://doi.org/10.6084/m9.figshare.1004645.v1• Miliaresis G. and Kokkas N., 2006. Geomorphometric segmentation applied to the city modeling problem. Int. Symposium on Terrain Analysis & Digital Terrain Modeling, China (Nanjing), 23-25 Nov. 2006, 12 p. https://doi.org/10.6084/m9.figshare.1004643.v1• Kokkas N., Miliaresis G., 2004. Geomorphometric Mapping of Grand Canyon from the 1o DEM. Int. Arc. of Photogrammetry, Remote Sensing & GIS,XXXV, 406-411.• Rodopoulos J., & Miliaresis G., 2004. Geomorphometric description of cluter maps. ASPRS Annual Conf., Anchorage, Alaska, May, 5-9, 399-403. https://doi.org/10.6084/m9.figshare.1004642.v1• https://astrogeology.usgs.gov/search/map/Research/ISPRS/ISPRS04_1812_N_Kokkas• Miliaresis G., Kokkas N., 2003. The geomophometric signature of Valles Marineris from M.O.L.A. DEM. ASPRS Annual Conf., Anchorage, Alaska, May 5-9, 122-130. https://doi.org/10.6084/m9.figshare.1004636.v1• Panagou Th., Miliaresis G., 2003. Evaluating the thematic information content of ASTER (VNIR) imagery in urban areas by classification techniques. Int. Archieves of Photogrammetry, Remote Sensing & Spatial Information Science, XXXIV-7/W9,263-267 https://doi.org/10.6084/m9.figshare.1004639.v1Electronics & Data Mining (MSc postgraduate course). Department of Physics, University of Patras.• Koytsohera E., & Dedes I., 2007. Geophysics and Remote Sensing Signals.