George Miliaresis CV

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George Miliaresis CV

  1. 1. Dr George Ch. Miliaresis Email: miliaresis.g@gmail.com Skype: george.miliaresis Address: 38, Tripoleos Str., Athens 104-42, Greece Telephone: +30- 6971.633.732 Societies ✔ Remote Sensing & Photogrammetry Society (RSPS), AM 1821, (AFRSPSoc) ✔ The Imaging & GeoSpatial Society (ASPRS) Member, ID 36758 ✔ Int. Society for Mathematical Geology (IAMG) AM 1713, (life membership) ✔ American Geophysical Union (AGU) Member Web page: http://miliaresis.tripod.com Distinctions/Awards ✔ Editorial board of the Remote Sensing Letters, published by the Remote Sensing & Photogrammetry Society, Taylor & Francis, 2010-present ✔ Boeing Award 2015: Best Scientific paper (ASPRS) in Image analysis & interpretation. Miliaresis G., 2014. Daily Temperature Oscillation Enhancement of Multi-temporal LST Imagery. Photogrammetric Engineering & Remote Sensing 80(5):423-428. ✔ Keynote speaker: Int. Symposium on Terrain Analysis & Digital Terrain Modeling, Nanjing, China, 23-25/11/2006 [ 1 / 14 ]
  2. 2. Web profiles: Research Gate + OrcID + Citations + Google scholar + Scopus Research Interests: Elevation, LST, etc. dependent terrain quantification to assist interpretation of a) geologic spatio-temporal processes, b) spatial modeling of geographic phenomena, c) environmental monitoring & climatic change studies: ✔ GEOSCIENCES: Thermal mapping, Natural hazards, Climatic pattern recognition, Environmental monitoring, Agricultural site selection. ✔ IMAGE ANALYSIS: Elevation latitude & longitude de-correlation stretch, Selective variance reduction, Temporal frequency enhancement, Spatio-temporal modeling. ✔ TERRAIN ANALYSIS: Terrain segmentation, Terrain pattern recognition, Terrain classification, Geomorphometric mapping, Terrain bio-patterns, Terrain Evaluation. Professional appointments 2012 - 2015, Environmental Conservation & Management, Faculty of Pure & Applied Sciences, Open University of Cyprus, Methodology & Techniques in Environmental Research (Remote Sensing, GIS, Data Analysis, Modeling, Research Methodology), [Scientific coordinator & Adjunct Faculty] ← Athens Branch. 2011 – 2012, Remote Sensing & GIS Research Center, Sultan Qaboos University, Remote Sensing & Data Modeling, [Research Scientist, Grade B] ← Muscat, Oman 2004 – 2010, Geology Dpt., University of Patras, Greece, Remote Sensing & GIS [Lecturer] 2001 – 2004, Topography Dpt., Technological Institute of Athens, Greece, Remote Sensing & Photo-Intepretation [Adjunct Faculty] Education ✔ PhD (in Remote Sensing & Terrain Pattern Recognition), National Technical University of Athens (2000) DOI: 10.6084/m9.figshare.1004889. & http://www.didaktorika.gr/eadd/handle/10442/13563 ✔ Diploma in Remote Sensing, University College of London (1990). ✔ Diploma in Geology, National University of Athens, Dpt. of Geology (1988). [ 2 / 14 ]
  3. 3. Teaching Links: References, Stumbler + Flight simulation, Presentations Web maps: see the web page http://miliaresis.tripod.com Data repositories: Greece Cyprus Oman Botswana SW USA ( DOI: 10.13140/RG.2.1.3970.2169/1 ) Undergraduate Courses [2000-2004] Photo-interpretation + Book-1 [2000-2004] Remote Sensing + Book-2 [2004-2010] Remote Sensing [2004-2010] GIS + Book-3 [2004-2010] Hydrology + Floods + WhiteBox Postgraduate Courses [2006-2010] Remotely Sensing+ Decision making [2011-2012] Terrain Bio-pattern + Global datasets [2012-2015] Techniques in Environmental Research, [2015-2016] Environmental Image Analysis Techniques Supplements • GIS: [QGIS + SAGA + Grass] • Data analysis: [The R + Octave] • Remote Sensing: [ MultiSpec + Monteverdi + Ilwis] Software: IDRISI & ARCGIS, MatLab & SPSS, ERDAS-IMAGINE & ENVI, Matlab, as well as OS software: Grass, Octave, R, MultiSpec, QGIS, Saga, Whitebox, etc. [ 3 / 14 ]
  4. 4. Research Statement Modeling of multi-temporal environmental data sets (for example Land surface temperature, Precipitation, NDVI, Elevation, Landcover, etc.) is key issue in climatic change studies, in geoscience (volcanic and earthquake hazards, etc.), in agriculture and food security studies, in terrain analysis, in meteorology, etc. These are the scientific fields, I work with and I develop new methods as well as added value data products. More details hereunder: 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. For example 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: • 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- [ 4 / 14 ]
  5. 5. 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: 1. 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. 2. BIOPHYSICAL GEOSCIENCES: Thermal inertia mapping, Geothermal sensing, Bio-pattern analysis, Bio-physical terrain classification, Biopattern analysis, Natural hazards, Volcanic hazard/risk monitoring, Earthquakes biopattern modeling [ 5 / 14 ]
  6. 6. 3. ENVIRONMETRICS: Elevation latitude longitude decorrelation stretch, Diurnal oscillation enhancement, Selective variance reduction, Temporal frequency enhancement, Spatio-temporal processes modelling 4. CLIMATIC CHANGE MONITORING: Land Surface temperature, Sea Surface Temperature, Precipitation, Aerosol, Soil Moisture, Albedo, Vegetation Density Future research plans: 1. Map "anomalies" from multi-temporal data (including precipitation) in an attempt to recognise 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 high resolution terrain data (LiDAR) and biophysical data-sets. 2. Integrate multidimensional 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 -AND SUPPORT- environmental decision making, planning, food security studies, landcover/landuse modeling., etc. 3. Time dependent monitoring & modeling of biophysical processes in Eastern Mediterranean Countries, Arabian Peninsula (Oman), Central and South Africa (Botswana) through the integration of precipitation, LST cycle, vegetation, aerosols, soil moisture data-sets. 4. To decompose 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., etc,) with magnitude that vary in both space and time, RESULTING occasionally to earthquakes, volcanic activity etc., etc. Resources necessary to carry out my research proposal • 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 ( MatLab, SPSS, Idrisi) as well as open source software (QGIS, ILWIS, R, Octave, MultiSpec, WhiteBox, Libre Office, etc.). • Occasionally field work is required for model and data evaluation. [ 6 / 14 ]
  7. 7. P u b l i c a t i o n s A1. Journals 1. Miliaresis G., 2016. An Unstandardized Selective Variance Reduction Script for Elevation, Latitude & Longitude Decorrelation Stretch of Multi-temporal LST Imagery. Modeling Earth Systems & Environment, vol. 2, no 1 (Article 41), 1- 13 p. DOI: 10.1007/s40808-016-0103-0 2. 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 3. Vassilis Aschonitis, 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. 4. 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 5. 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 6. 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 7. 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 8. Miliaresis G., 2014. Global LST Anomaly Mapping from MODIS Night Imagery. Malaysian Journal of Remote Sensing & GIS, 3(1), 1-9. DOI: 10.6084/m9.figshare.1004819 9. Miliaresis G., 2013. Terrain analysis for active tectonic zone characterization, a new application for MODIS night LST (MYD11C3) dataset. International Journal of Geographical Information Science, 27(7):1417-1432 p., doi: 10.1080/13658816.2012.685172. 10. 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. 11. 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. 12. 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 13. 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 14. 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: [ 7 / 14 ]
  8. 8. 10.2747/1548-1603.48.2.210 15. 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: 10.1080/01431161.2010.503667 16. 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 17. 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 18. 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 19. 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. DOI: 10.6084/m9.figshare.1004831 20. 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 21. 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-x 22. 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: 10.1007/s10661-008-0237-2 23. 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:10.1080/01431160802562271 24. Miliaresis 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/rs1020036 25. 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 26. Miliaresis 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. 27. 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. 28. 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 29. 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 30. Miliaresis 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.2004.12.001 31. Miliaresis G, Sabatakakis N, Koukis G, 2005.Terrain pattern recognition & [ 8 / 14 ]
  9. 9. spatial decision for regional slope stability studies. Natural Resources Research, 14(2):91-100. DOI: 10.1007/s11053-005-6951-3 32. 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. 33. Miliaresis G., Kokkas N. 2004. Segmentation and terrain modeling of extra-terrestrial chasmata. Journal of Spatial Sciences, 49(2): 89-99 34. 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 35. 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 36. 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 37. Miliaresis, G.Ch., and D.P. Argialas, 2000. Extraction & Delineation of Alluvial Fans from DΕΜs & Landsat TM Images. Photogrammetric Engineering & Remote Sensing, 66(9):1093-1101. doi: 0099-1112/00/6609-109 38. 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)00025-4 A2. Chapters in edited volumes 1. 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. 2. 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.) 3. 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 A3. Software (published) A. Clustering-PY, 2016. Clustering& Classification Pythonscripts to support SVR case studies. SourceForge Project: clustering-PY. https://sourceforge.net/p/clustering-py/ GNU GPL v.3 B. Selective Variance Reduction 2016. SourceForge Project: selective-variance-reduction. Octave Implementation. http://selective-variance-reduction.sourceforge.net GNU GPL v.3 C. Test_sites_datasets 2016. Data sets to test the scripts of Environmental Image Analysis course [ 9 / 14 ]
  10. 10. SourceForge Project: https://sourceforge.net/p/test-sites-datasets/ GNU GPL v.3 D. GeoLogic Shell http://sourceforge.net/p/geologic-shell GNU GPL v.3. Part ofthe publications DOI:10.1016/S0098-3004(01)00032-2 and DOI:10.1016/j.cageo.2006.11.012 from the Int. Ass. for Mathematical Geology (Links to programcode for Computers & Geosciences) A4. Editorials, Review articles, Book reviews 1. Grohmann C., & Miliaresis G., 2013. EDITORIAL for the special issue:Digital terrain analysis and modelling / Geological applications of digital terrain analysis. Int. Journal of Geographical Information Science, 27(7):1403-1404. DOI: 10.1080/13658816.2013.772617 2. Miliaresis G., 2013. Book Review in Photogrammetric Engineering & Remote Sensing, vol. 79, no 8, pp. 685) Jakob Van Zyl and Yunjin Kim, 2012. Synthetic Aperture Radar Polarimetry. JPL Space Science & Technology Series. John Wiley & Sons, New Jersey, 288 p.] DOI: 10.6084/m9.figshare.1004842 3. Miliaresis G., 2012. Monitoring of Environmental Processes and Natural Resources Using Satellites. Horizons, Sultan Qaboos University, Issue 253, Nov. 10th,, pp. 5. DOI: 10.6084/m9.figshare.1004841 4. Miliaresis G., 2011. Book Review in Photogrammetric Engineering & Remote Sensing, published in May 2011, p. 453 [Timothy L. Nyerges & Piotr Jankowski 2009. Regional & Urban GIS: A decision support approach. Guilford Press, ISBN 978-1-60623-906-3] DOI: 10.6084/m9.figshare.1004843 A5. Conferences 1. Tsekme Eleni and George Ch. Miliaresis, 2015. Terrain, landcover & landuse evaluation of Cyprus from modern multi- temporal biophysical imagery. 3Rd International Conference on Remote Sensing & Geoinformation of the Environment, 16-19 March 2015, Pafos, Cyprus. 2. Partsinevellos P., Miliaresis, G., 2014. Ship extraction and categorization from ASTER VNIR imagery. Proc. SPIE 9229, 2nd Int. Conference on Remote Sensing & Geoinformation of Environment, (RSCy2014), 92291Y (7-10 April , Pafos, Cyprus); 5 pages, doi:10.1117/12.2069202 3. 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 (RSCy2013), 879501 (Paphos-Cyprus, August 14, 2013); 9 pages, DOI: 10.1117/12.2041580 4. 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 p. DOI: 10.6084/m9.figshare.1004880 5. Partsinevelos P. Miliaresis G., 2009. Spatiotemporal filtering of multi-temporal images: application on MODIS sea surface temperature imagery. 5th Int. Workshop on the Analysis of Multi-temporal Remote Sensing Images. Mystic, Connecticut, 28-30/07, 377-384. DOI: http://dx.doi.org/10.6084/m9.figshare.10 04805 6. 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 the Sustainable [ 10 / 14 ]
  11. 11. Development, International Centre for Integrated Mountain Development (ICIMOD), 14 - 25 Jan., 8 p. DOI: 10.6084/m9.figshare.1004651 7. Miliaresis G., 2007. Delineation & Representation of Linear Megadunes from CSI-SRTM DEM. GeoComputation, Session 1A: Remote Sensing. National University of Ireland, Maynooth, 3-5 Sept., 5 p. DOI: 10.6084/m9.figshare.1004647 8. 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. DOI: 10.6084/m9.figshare.1004645 9. 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, 2007, Leuven, Belgium, 6 p. DOI: 10.1109/multitemp.2007.4293071 10. Miliaresis G. 2006. Terrain modelling for specific geomorphologic processing (keynote speaker presentation). Int. Symposium on Terrain Analysis and Digital Terrain Modelling, Nanjing, China, 23-25 November 2006, 10 p. DOI: 10.6084/m9.figshare.1004644 11. Miliaresis G. and Kokkas N., 2006. Geomorphometric segmentation applied to the city modeling problem. Int. Symposium on Terrain Analysis & Digital Terrain Modelling, China (Nanjing), 23-25 Nov. 2006, 12 p. DOI: 10.6084/m9.figshare.1004643 12. Miliaresis G., 2006. Geometric and landcover signatures of local authorities in Peloponnesus. Int. Conf. on Energy, Environment, Ecosystems & Sustainable Development, WSEAS, Athens, July 11-13, 128-133. DOI: 10.6084/m9.figshare.1004646 13. Blasco F., Bellan M.F.,Barbaroussi B., Miliaresis G., 2004. Ground truth by the use of orthophotos in IKONOS image processing. Int. Archieves of Photogrammetry, Remote Sensing & GIS , XXXV,417-421. DOI: 10.6084/m9.figshare.1004638 14. Kokkas N., Miliaresis G., 2004. Geomorphometric Mapping of Grand Canyon from the 1o DEM. Int. Arc. of Photogrammetry, Remote Sensing & GIS,XXXV, 406-411. DOI: 10.6084/m9.figshare.1004791 15. Rodopoulos J., & Miliaresis G., 2004. Geomorphometric description of cluter maps. ASPRS Annual Conf., Anchorage, Alaska, May, 5-9, 399-403. DOI: 10.6084/m9.figshare.1004642 16. 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 DOI: 10.6084/m9.figshare.1004636 17. 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 DOI: 10.6084/m9.figshare.1004639 18. Miliaresis, G. & Paraschou Ch., 2002. The Globe DEM Parameterization of the mountain features of Minor Asia. ASPRS Annual Conf.,April 19-26, Washington DC, 8 p. DOI: 10.6084/m9.figshare.1004635 19. Miliaresis G.Ch., 2002. Characterizing landscape dynamics by general & specific geomorphometric techniques. WEGENER Int. Ass. of Geodesy, Athens 12-14/6, 12 p. DOI: 10.6084/m9.figshare.1004637 20. Miliaresis G., Paraschou C. 2001. A feature based accuracy evaluation of GTOPO30. Remote Sensing & Photogrammetry Society Conf., 12-14/09, London, 203-213 DOI: 10.6084/m9.figshare.1004672 21. Miliaresis, G.Ch., 2001. Automated Segmentation of Bajadas from 15-minute- [ 11 / 14 ]
  12. 12. DEMs and Landsat TM imagery. ASPRS Annual Conf., St. Louis, Missouri, April, 23- 27, 8 p. DOI: 10.6084/m9.figshare.1004673 22. Miliaresis, G. Ch., 2000. Landscape Characterization of Zargos Ranges. 26th Conf. of the Remote Sensing Society, University of Leicester, 12-14th of September. 8 p. DOI: 10.6084/m9.figshare.1004671 23. Miliaresis, G. Ch., 2000. The DEM to Mountain Transformation of Zagros Ranges. 5thInt. Conf. onGeocomputation, UniversityofGreenwich, 23-25th of August, 8 p. DOI: 10.6084/m9.figshare.1004669 24. Argialas, D.P.,andG.CH.Miliaresis,2000. PhysiographicRegionInterpretation: FormalizationWith RuleBasedStructures and ObjectHierarchies. Int.Archivesof Photogrammetry& RemoteSensing,July 19-23th, 2000,Amsterdam,TheNetherlands, Vol. XXXIII,Part B4,91-98 DOI: 10.6084/m9.figshare.1004670 25. Miliaresis,G.Ch., 1999. Automated SegmentationofAlluvial FanstoRegions of Highto Intermediate FloodHazardfrom Landsat ThematicMaperImagery. 2nd Int. Symposium OperationalizationofRemote Sensing,ITCEnschede, 16-20August.6 p. DOI: 10.6084/m9.figshare.1004664 26. Miliaresis,G.Ch. and D.P.Argialas,1999. Fuzzy Pattern Recognition of Compressional MountainRangesinIran.Proceedings,5th Int. Conf.of the Ass.forMathematical Geology, Trondheim,August6,227-232. DOI: 10.6084/m9.figshare.1004667 27. Miliaresis,G.Ch., 1999. ARegionGrowing Algorithmforthe SegmentationofAlluvial FansfromDEMs. 1st Symposium on Imaging ApplicationsinGeology, Liege, 6-7/05, 189– 192. DOI: 10.6084/m9.figshare.1004663 28. Miliaresis,G. Ch.and D.P. Argialas,1999. Formalizationofthe Photo-Interpretation Processby aFuzzy Set Representationof MountainObjects.25th Conf. ofthe Remote Sensing Society, Cardiff, Sept.8 –10,745-750. DOI: 10.6084/m9.figshare.1004666 29. Miliaresis, G.Ch. and D.P. Argialas, 1998. Parametric Representation and Classification Of Mountain Objects Extracted From Moderate Resolution DEMs. Int. Conf. of the Ass. for Mathematical Geology, Ispra (Italy), October 5 – 9, 892-897. DOI: 10.6084/m9.figshare.1004661 30. Miliaresis,G.Ch. and D.P.Argialas,1998. PhysiographicFeatureExtractionFrom Moderate ResolutionDigital Elevation Data. 24th Conf.ofthe Remote Sensing Society, Greenwich, Sept. 9–11,545-551. DOI: 10.6084/m9.figshare.1004665 31. Argialas, D.P.,and G.CH.Miliaresis,1997. An ObjectOriented RepresentationModelFor The LandformsOf AnArid Climate Intermontane Basin.23rd Conf. of the RemoteSensing Society,Reading, Sept.2–4,199-205. DOI: 10.6084/m9.figshare.1004659 32. Argialas, D.P.,and G.CH.Miliaresis,1997. LandformSpatialKnowledgeAcquisition: Identification,Conceptualizationand Representation. ACSM/ASPRS,Seattle- Washington,April 7-10,733-740. DOI: 10.6084/m9.figshare.1004660 33. Argialas, D.P.,and G.CH.Miliaresis,1996. PhysiographicKnowledgeAcquisition: Identification,Conceptualizationand Representation. ACSM/ASPRS,Baltimore- Maryland, April22-25,311-320. DOI: 10.6084/m9.figshare.1004662 A6. Posters & Abstracts in International Conferences 1. Partsinevelos P.,N.Nikolakaki, P.Psillakis,G.Miliaresis, M.Xanthakis, 2014. Reducing satellite imagery classification uncertainty throughspatiotemporal reasoning. European Geosciences Union, General Assembly, [NP1.3(Uncertainty&Sensitivity AnalysisinGeosciences ):EGU2014-15356 ], Vienna,Austria, 27April –02 May.DOI: 10.6084/m9.figshare.1004812 2. Miliaresis G., Tselentis G., 2009.The spatial pattern of the temporal SST variations during the [ 12 / 14 ]
  13. 13. 2004 Indian Ocean Earthquake. Int. Workshop on Validation of Earthquake Precursors by Satellite, Terrestrial & other Observations (VESTO), case studies of the recent Asian events. Chiba University, Tokyo,March 26-28,[ Poster] DOI: 10.6084/m9.figshare.1004813 3. ZouziasDimitrios,St SeymourKaren,Miliaresis George, VamvoukakisCostas(2008). Circumstantial Evidence ofPossible HotSpotActivityOutsideRhodes, Eastern MediterraneanSea. 3rd International Conference onthe Geology ofthe Tethys (8-11January,2008, South Valley University -Aswan). DOI: 10.6084/m9.figshare.1004649 4. Veizi I.,N. Tsiougou,C.Kouki,D. Matsa, G.Tsiougos,N. Lakafosis,M. Zarras,G. Miliaresis,et al. 2007. Anthropogenetic intensity,anew indicatortomeasurecoastal man-madevolume,case study: Navpaktos -Aetolia-Acarnania,Greece.Perspectives ofGI forIntegrated Coastal Management, ECOImagine, Genoa,21- 23Nov.[ Poster] DOI: 10.6084/m9.figshare.1004814 5. MiliaresisG., SabatakakisN.,KoukisG.,2004. Terrainsegmentation& parameterizationforregionalslope stability studies. European GeoSciencesUnion,1st General assembly (Copernicus),Nice,France,25-30 April. DOI: 10.6084/m9.figshare.1004815 6. MiliaresisG.Ch.,2000. SegmentationofAlluvialAprons fromthe USGSDEMswith Spacing2-ArcSeconds. RGS-IBG, Sessionon Surface Modelling, Sussex, 4-7 Jan.DOI: 10.6084/m9.figshare.1004816 B1. Books in GREEK 1. Μηλιαρέσσης Γ. 2006, Ειδικές Εφαρμογές στο ArcGIS. Εκδόσσέις ΙΩΝ, 248 σέλ. [ISBN: 960-411-560-x]. 2. Μηλιαρέσσης Γ. 2003, Φωτοερμηνεία- Τηλεπισκόπηση. Εκδόσσέις ΙΩΝ, 243 σ. [ISBN:960-411-297-x]. 3. Μηλιαρέσσης Γ., 2003. Εργαστηριακές Ασκήσεις Φωτοερμηνείας- Τηλεπισκόπησης. Εκδόσεις ΙΩΝ, 114 σέλ. [ISBN: 960-411-321-6]. B2. Journals in GREEK 1. Μηλιαρέσσης Γ., 2003. Αξιόλόσγηση των πέριόχωσν έκπαισδέυσης στην έπιβλέπόσμένη ταξινόσμηση δόρυφόρικωσν έικόσνων. Δέλτισό της Γ.Υ.Σ., Αρ. Τέυσχόυς 151, 179-190 DOI: 10.6084/m9.figshare.1004849 2. Μηλιαρέσσης Γ.2001.Εντόπισμόσς όρέινωσν αντικέιμέσνων στην φυσιόγραφικησ ένόστητα Zagros Ranges/Ιρασν. Δέλτισό της Ελληνικησς Γέωλόγικησς Εταιρέισας, ΧΧΧΙV(5) 2017-2023. DOI: 10.6084/m9.figshare.1004867 B3. Conferences in GREEK 1. Μηλιαρέσης Γ., 2005. Προσομοίωση σε συνθετικές εικόνες για την ανάδειξη και προστασία του φυσικού περιβάλλοντος των ορεινών όγκων. «Περιβάλλον & Ανάπτυξη στον Ορεινό Χώρο». 5ο διεθνές συνέδριο του Ιδρύματος Κεφαλονιάς & Ιθάκης, 17-19/6 Αργοστόλι, 8 σ. DOI: 10.6084/m9.figshare.1004875 2. Μηλιαρέσης Γ, & Παράσχου Χ., 2004. Υψομετρική ακρίβεια του ψηφιακού υψομετρικού μοντέλου SRTM. 1ο Παν. Συνέδριο Αγρ. Τοπ. Μηχ. Αθήνα, Μάιος 24-26, 9 σ. DOI: 10.6084/m9.figshare.1004872 [ 13 / 14 ]
  14. 14. 3. Μηλιαρέσης Γ., Πεκαλης Δ., Πέκαλη Β., 2004. Εντοπισμός πλοίων από δορυφορικές εικόνες Aster για την προστασία του θαλάσσιου περιβάλλοντος της Ηπείρου. 4o Συνέδριο: "Η Ολοκληρωμένη Ανάπτυξη της Ηπείρου", 23-26 Σεπτ., ΜE.KE.ΔΕ. (Μέτσοβο), 7 σ. DOI: 10.6084/m9.figshare.1004878 4. Μηλιαρέσης Γ. & Αργιαλάς Δ. 2002. Εντοπισμός Αλλουβιακών Ριπιδίων από Ψηφιακά Υψομετρικά Μοντέλα Εδάφους. 6ο Πανελλήνιο Γεωγραφικό Συνέδριο, Θεσσαλονίκη 3-6 Οκτωβρίου, 187-192. DOI: 10.6084/m9.figshare.1004873 5. Μηλιαρέσης Γ., & Αργιαλάς Δ., 2001. Παραμετρική αναπαράσταση ορεινών αντικειμένων από το μέτριας διακριτικής ικανότητας ψηφιακό υψομετρικό μοντέλο GTOPO30. 3ο Διεπιστημονικό Διαπανεπιστημιακό Συνέδριο, ΜΕ.ΚΕ.Δ.Ε., Μετσόβο, 7-10 Ιουνίου, 14 σελ. DOI: 10.6084/m9.figshare.1004870 6. Αργιαλάς Δ., & Μηλιαρέσης Γ., 2001. Τυποποίηση της φωτοερμηνευτικής γνώσης για την ερμηνεία γεωμορφών σε φυσιογραφική κλίμακα. 3ο Διεπιστημονικό Διαπανεπιστημιακό Συνέδριο, ΜΕ.ΚΕ.Δ.Ε., Μετσόβο, 7-10 Ιουνίου, 12 σ. DOI: 10.6084/m9.figshare.1004871 7. Μηλιαρέσης Γ. και Δ. Αργιαλάς, 1999. Εντοπισμός ορεινών όγκων από μέτριας διακριτικής ικανότητας υψομετρικά δεδομένα. 5ο Παν. Γεωγραφικό Συνέδριο, Αθήνα 11-13/11, 308-317. DOI: 10.6084/m9.figshare.1004869 8. Αργιαλας Δ. και Μηλιαρέσης Γ., 1999. Τυποποίηση Χωρικών Συμφραζομένων σε Βάσεις Γνώσης. 5ο Πανελλήνιο Γεωγραφικό Συνέδριο, Αθήνα 11-13/11, 352-360. DOI: 10.6084/m9.figshare.1004874 B4. Abstracts & Posters in GREEK 1. Μηλιαρέσης Γ., 2008. Βιοφυσική Ανάλυση Πεδίου από Σύγχρονα Συστήματα Τηλεπισκόπησης (προσκεκλημένος ομιλητής). ΗΜΕΡΙΔΑ του Δ.Π.Μ.Σ. Ηλεκτρονικη & Επεξεργασια Της Πληροφοριας, 27/6, Τμήμα Φυσικής [abstract] DOI: 10.6084/m9.figshare.1004877 2. Μηλιαρέσης Γ., 2007. Ετήσια σεισμικά χωρο-χρονικά πρότυπα για την ταυτοποίηση γεωφυσικής θερμικής ρύπανσης από δορυφορικές εικόνες (προσκεκλημένος ομιλητής). Δι-Ημερίδα: Τηλεπισκόπηση & εφαρμογές, ΤΕΕ, ΓΕΩΤΕΕ, ΕΕΦΤ, 22-23/2, ΕΜΠ [abstract] DOI: 10.6084/m9.figshare.1004879 3. Μηλιαρέσης Γ., 2007. Περιβαλλοντική ευαισθητοποίηση με τεχνικές τηλεπισκόπησης και γεωγραφικών συστημάτων πληροφοριών (προσκεκλημένος ομιλητής). Σεμινάριο Περιβαλλοντικής Εκπαίδευσης , Τμήμα Βιολογίας, Παν/μio Πατρών, 9/12/2007 [abstract] DOI:10.13140/2.1.4361.2966 4. Μηλιαρέσης Γ., 2007. Αξιολόγηση χωρικών αντικειμένων, εκτίμηση ζωνών επικινδυνότητας, χωροθέτηση αντιπλημμυρικών (προσκεκλημένος ομιλητής). Συντονιστικό Νομαρχιακό Όργανο, Νομαρχιακή Αυτοδιοίκηση Ηλείας, Πύργος, 8-9-2007, DOI:10.13140/2.1.5147.7283 5. Μηλιαρέσης Γ. 2006. Βιοφυσική Χαρτογράφηση από Σύγχρονα Συστήματα Τηλεπισκόπησης και Σύνθεση Περιβαλλοντικών Γεωγραφικών Βάσεων Δεδομένων (προσκεκλημένος ομιλητής). 4η Ημερίδα Μεταπτυχιακών Φοιτητών του Τμήματος Βιολογίας, 7-12-2006. Παν/μίο Πατρών. DOI: 10.6084/m9.figshare.1004876 [ 14 / 14 ]

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