Pléiades potentialities :
|
|
- Robyn Brianna Lester
- 5 years ago
- Views:
Transcription
1 GT2 Risque et Aide humanitaire Pléiades potentialities : Assessment of clearing levels for operational management of forest fires in the Maures massif Marechal D., Thierion V., Kabar B., Ayral P.-A., Salze D., Sauvagargues- Lesage S.
2 Expectations and needs Dedicated tool for automatic update of DFCI tracks clearing levels Application on the whole region of intervention (Maures Massif): quickness, automation and efficiency Need for consensus between operational actors (firemen and foresters)
3 Assessment of clearing levels in the DFCI tracks areas PHASE 1 : Are Pleiades products well adapted to operational management of clearing operations? PHASE 2 : How Pleiades products combinated with OTB enable operational management of clearing operations?
4 Assessment of clearing levels in the DFCI tracks areas PHASE 1 : Are Pleiades products well adapted to operational management of clearing operations? PHASE 2 : How Pleiades products combinated with OTB enable operational management of clearing operations?
5 Scientific outcomes of the first phase ( ) Good clearing Intermed. clearing Bad clearing
6 Scientific outcomes of the first phase ( ) First approach Tree / Ground QuickBird Panchromatic PELICAN fusion Second approach Multi-layer «Pixel» method QuickBird fusion «Object» method QuickBird fusion + Spatial analysis
7 Scientific outcomes of the first phase ( ) First approach Tree / Ground QuickBird Panchromatic PELICAN fusion Second approach " Multi-layer " «Pixel» method QuickBird fusion «Object» method QuickBird fusion 81% Bare soil Herbaceous layer Shrub layer Tree layer 96% PIXEL OBJECT
8 Scientific outcomes of the first phase ( ) Spatial Analysis : From ecological approach to operational use Tree neighbouring + Shrub layer density Spatial analysis Bad Intermed. Good
9 PHASE 1 : Are Pleiades products well adapted to operational management of clearing operations? A priori, spatial and spectral resolution allows an precise assessment of ecological types and a fortiori of the clearing levels. Situation in 2008 Quickbird DFCI / non-dfci 4 ecological types 3 clearing levels
10 2009 PHASE 1 : Are Pleiades products well adapted to operational management of clearing operations? A priori, spatial and spectral resolution allows an precise assessment of ecological types and a fortiori of the clearing levels. PHASE 2 : How Pleiades products combinated with OTB enable operational management of clearing operations? 1. Methodological validation 2. Multi-temporal clearing levels evaluation between 2006 and Specifications and prototype of dedicated tool
11 PHASE 2 : How Pleiades products combinated with OTB enable operational management of clearing operations? 1. Methodological validation EMA Students (Desbois, Dutault, Recordet, 2009) 2. Multi-temporal clearing levels evaluation between 2006 and 2008 EMA Students (Abrial, Champion et Morello, 2008 et 2009) 3. Specifications and prototype of dedicated tool Master degree internship financed by the CNES (Kabar, 2009)
12 Methodological validations Remote sensing methods validation 3 study sites (Le Laïre, Barral et Rayol) Object approach
13 2006 and 2008 images Multi-temporal analysis Rayol
14 2006 and 2008 images Multi-temporal analysis Le Laïre
15 PHASE 2 : How Pleiades products combinated with OTB enable operational management of clearing operations? 1. Methodological validation EMA Students (Desbois, Dutault, Recordet, 2009) 2. Multi-temporal clearing levels evaluation between 2006 and 2008 EMA Students (Abrial, Champion et Morello, 2008 et 2009) 3. Specifications and prototype of dedicated tool Master degree internship financed by the CNES (Kabar, 2009)
16 Pleiades simulated imagery (Quickbird ) Orfeo Toolbox ORFEO Motivate future imagery exploitation Team «Risques industriels et naturels» (LGEI) Issues: Methods Tool specifications SDIS 83 and SIVOM Management of operational clearing Do Pléiades products associated with OTB allow an operational management of clearing? 1. Analysis of existing methods (ENVI, Definiens, ArcGIS) 2. OTB implementation 3. Enhancement
17 Issue OTB Results Perspectives Why OTB?: Free and Flexible Numerous applications for dedicated tools
18 Issue OTB Results Perspectives OTB Workflow Technological Consensus OTB Object approach
19 Issue OTB Results Perspectives DFCI extraction (a) Quickbird RGB, Rayol zone. (b) Intensity-ARVI with «Mean Shift» segmentation; δs = 3, δr = 35, min size region = 8000, scale= 1. (c) Labeled image «forest» and «DFCI». (a) (b) (c). Good discrimination. Few errors of classifications. Not directly usable for multi-layer classification Buffer zone around DFCI tracks
20 Issue OTB Results Perspectives Multi-layer classification Mean Shift clustering (a) Pansharpened image (b) Clustered image B-V-R-PIR (pansharpened)-arvi with: δs = 2, δr = 30, min region size = 1, scale = 1. (c) Focus on non-clustered image. (d) Focus on clustered image
21 Issue OTB Results Perspectives Multi-layer classification Combination: R-PIR-PAN-ARVI-IC Extent : 4245,6m x 3220,2m SVM classification Kappa: 97% Bare soil Tree layer Shrub layer Herbaceous layer Non-DFCI
22 Issue OTB Results Perspectives Multi-layer classification Local control Reference: Google Earth (2006) Bare soil Tree layer Shrub layer Herbaceous layer Non-DFCI Some errors of confusion: optimisation of training sample delineation, features combination
23 Issue OTB Results Perspectives Results:. Similar quality of classification compared to previous methods. Still some imprecisions. No spatial analysis Futur works:. Real object oriented classification (use of texture, geometry and neighbouring). Correlation between spectral signatures and ecological layer. Operational validation (with operational actors). Performing spatial analysis in OTB? Dedicated tool specifications
24 Issue OTB Results Perspectives Apply OTB algorithms to the whole region of interventions First tool building (OTB) Quantitative analysis of the classification quality Spatial analysis integration Spatial analysis validation 2010 Internship Scale Multi-temporal analysis (3 images) Septembre 2009 classification Automatisation with OTB (Test) Link with clearing technics efficiency
25 Issue OTB Results Perspectives To an operational use Price, delay and programmation (1 /year) Concertation with local and regional administrations for imagery requests Zones of interventions monitoring during 3 or 4 years: ¼ of the territory of interest every year (?) Assessment of financial interests (field work vs imagery)
26 Thanks Pierre-Alain Ayral : pierre-alain.ayral@mines-ales.fr Boris Kabar : boris.kabar@mines-ales.fr Denis Maréchal : denis.marechal@mines-ales.fr David Salze : david.salze@mines-ales.fr Vincent Thierion : vincent.thierion@mines-ales.fr
27 Operational scale of validation? 1 (few m 2 ) 2 (several m 2 ) Scale of validation Remote sensing efficiency 3 (ha) Technological needs
large area By Juan Felipe Villegas E Scientific Colloquium Forest information technology
A comparison of three different Land use classification methods based on high resolution satellite images to find an appropriate methodology to be applied on a large area By Juan Felipe Villegas E Scientific
More informationDISTINGUISHING URBAN BUILT-UP AND BARE SOIL FEATURES FROM LANDSAT 8 OLI IMAGERY USING DIFFERENT DEVELOPED BAND INDICES
DISTINGUISHING URBAN BUILT-UP AND BARE SOIL FEATURES FROM LANDSAT 8 OLI IMAGERY USING DIFFERENT DEVELOPED BAND INDICES Mark Daryl C. Janiola (1), Jigg L. Pelayo (1), John Louis J. Gacad (1) (1) Central
More informationAdvanced Techniques in Urban Remote Sensing
Advanced Techniques in Urban Remote Sensing Manfred Ehlers Institute for Geoinformatics and Remote Sensing (IGF) University of Osnabrueck, Germany mehlers@igf.uni-osnabrueck.de Contents Urban Remote Sensing:
More informationDetecting Land Cover Changes by extracting features and using SVM supervised classification
Detecting Land Cover Changes by extracting features and using SVM supervised classification ABSTRACT Mohammad Mahdi Mohebali MSc (RS & GIS) Shahid Beheshti Student mo.mohebali@gmail.com Ali Akbar Matkan,
More informationORFEO program : Methodological part Report
ORFEO program : Methodological part Report - 2010 Introduction CNES Paris December 2010 2 Goals of the methodological part Goal: make the development of new algorithms and their validation easier Respond
More informationLand Cover Analysis to Determine Areas of Clear-cut and Forest Cover in Olney, Montana. Geob 373 Remote Sensing. Dr Andreas Varhola, Kathry De Rego
1 Land Cover Analysis to Determine Areas of Clear-cut and Forest Cover in Olney, Montana Geob 373 Remote Sensing Dr Andreas Varhola, Kathry De Rego Zhu an Lim (14292149) L2B 17 Apr 2016 2 Abstract Montana
More informationCHARACTERISTICS OF REMOTELY SENSED IMAGERY. Spatial Resolution
CHARACTERISTICS OF REMOTELY SENSED IMAGERY Spatial Resolution There are a number of ways in which images can differ. One set of important differences relate to the various resolutions that images express.
More informationOPTICAL AND SAR DATA INTEGRATION FOR AUTOMATIC CHANGE PATTERN DETECTION
OPTICAL AND SAR DATA INTEGRATION FOR AUTOMATIC CHANGE PATTERN DETECTION B. Mishra, J. Susaki Department of Civil and Earth Resources Engineering, Kyoto University; Email address: mishra.bhogendra.46c@st.kyoto-u.ac.jp;
More informationIMPROVEMENT IN THE DETECTION OF LAND COVER CLASSES USING THE WORLDVIEW-2 IMAGERY
IMPROVEMENT IN THE DETECTION OF LAND COVER CLASSES USING THE WORLDVIEW-2 IMAGERY Ahmed Elsharkawy 1,2, Mohamed Elhabiby 1,3 & Naser El-Sheimy 1,4 1 Dept. of Geomatics Engineering, University of Calgary
More informationAUTOMATIC DETECTION OF HEDGES AND ORCHARDS USING VERY HIGH SPATIAL RESOLUTION IMAGERY
AUTOMATIC DETECTION OF HEDGES AND ORCHARDS USING VERY HIGH SPATIAL RESOLUTION IMAGERY Selim Aksoy Department of Computer Engineering, Bilkent University, Bilkent, 06800, Ankara, Turkey saksoy@cs.bilkent.edu.tr
More informationRemote sensing in archaeology from optical to lidar. Krištof Oštir ModeLTER Scientific Research Centre of the Slovenian Academy of Sciences and Arts
Remote sensing in archaeology from optical to lidar Krištof Oštir ModeLTER Scientific Research Centre of the Slovenian Academy of Sciences and Arts Introduction Optical remote sensing Systems Search for
More informationImage interpretation. Aliens create Indian Head with an ipod? Badlands Guardian (CBC) This feature can be found 300 KMs SE of Calgary.
Image interpretation Aliens create Indian Head with an ipod? Badlands Guardian (CBC) This feature can be found 300 KMs SE of Calgary. 50 1 N 110 7 W Milestones in the History of Remote Sensing 19 th century
More informationUrban Classification of Metro Manila for Seismic Risk Assessment using Satellite Images
Urban Classification of Metro Manila for Seismic Risk Assessment using Satellite Images Fumio YAMAZAKI/ yamazaki@edm.bosai.go.jp Hajime MITOMI/ mitomi@edm.bosai.go.jp Yalkun YUSUF/ yalkun@edm.bosai.go.jp
More informationTextural analysis of coca plantations using 1-meter-resolution remotely-sensed data
UNODC Workshop, 25-28 November, Bogota, Colombia 1 Textural analysis of coca plantations using 1-meter-resolution remotely-sensed data Workshop on Measurement of Cultivation and Production of Coca Leaves
More informationLANDSAT-SPOT DIGITAL IMAGES INTEGRATION USING GEOSTATISTICAL COSIMULATION TECHNIQUES
LANDSAT-SPOT DIGITAL IMAGES INTEGRATION USING GEOSTATISTICAL COSIMULATION TECHNIQUES J. Delgado a,*, A. Soares b, J. Carvalho b a Cartographical, Geodetical and Photogrammetric Engineering Dept., University
More informationClassification in Image processing: A Survey
Classification in Image processing: A Survey Rashmi R V, Sheela Sridhar Department of computer science and Engineering, B.N.M.I.T, Bangalore-560070 Department of computer science and Engineering, B.N.M.I.T,
More informationEvaluating the Effects of Shadow Detection on QuickBird Image Classification and Spectroradiometric Restoration
Remote Sens. 2013, 5, 4450-4469; doi:10.3390/rs5094450 Article OPEN ACCESS Remote Sensing ISSN 2072-4292 www.mdpi.com/journal/remotesensing Evaluating the Effects of Shadow Detection on QuickBird Image
More informationIceTrendr - Polygon. 1 contact: Peder Nelson Anne Nolin Polygon Attribution Instructions
INTRODUCTION We want to describe the process that caused a change on the landscape (in the entire area of the polygon outlined in red in the KML on Google Earth), and we want to record as much as possible
More informationDIGITALGLOBE ATMOSPHERIC COMPENSATION
See a better world. DIGITALGLOBE BEFORE ACOMP PROCESSING AFTER ACOMP PROCESSING Summary KOBE, JAPAN High-quality imagery gives you answers and confidence when you face critical problems. Guided by our
More informationLAND USE MAP PRODUCTION BY FUSION OF MULTISPECTRAL CLASSIFICATION OF LANDSAT IMAGES AND TEXTURE ANALYSIS OF HIGH RESOLUTION IMAGES
LAND USE MAP PRODUCTION BY FUSION OF MULTISPECTRAL CLASSIFICATION OF LANDSAT IMAGES AND TEXTURE ANALYSIS OF HIGH RESOLUTION IMAGES Xavier OTAZU, Roman ARBIOL Institut Cartogràfic de Catalunya, Spain xotazu@icc.es,
More informationGE 113 REMOTE SENSING
GE 113 REMOTE SENSING Topic 8. Image Classification and Accuracy Assessment Lecturer: Engr. Jojene R. Santillan jrsantillan@carsu.edu.ph Division of Geodetic Engineering College of Engineering and Information
More informationChapter 1. Introduction
Chapter 1 Introduction One of the major achievements of mankind is to record the data of what we observe in the form of photography which is dated to 1826. Man has always tried to reach greater heights
More informationMichigan Technological University. Characterization of Unpaved Road Condition Through the Use of Remote Sensing
Michigan Technological University Characterization of Unpaved Road Condition Through the Use of Remote Sensing Deliverable 6-A: A Demonstration Mission Planning System for use in Remote Sensing the Phenomena
More informationLand Remote Sensing Lab 4: Classication and Change Detection Assigned: October 15, 2017 Due: October 27, Classication
Name: Land Remote Sensing Lab 4: Classication and Change Detection Assigned: October 15, 2017 Due: October 27, 2017 In this lab, you will generate several gures. Please sensibly name these images, save
More informationAn Introduction to Geomatics. Prepared by: Dr. Maher A. El-Hallaq خاص بطلبة مساق مقدمة في علم. Associate Professor of Surveying IUG
An Introduction to Geomatics خاص بطلبة مساق مقدمة في علم الجيوماتكس Prepared by: Dr. Maher A. El-Hallaq Associate Professor of Surveying IUG 1 Airborne Imagery Dr. Maher A. El-Hallaq Associate Professor
More informationIncreasing the potential of Razaksat images for map-updating in the Tropics
IOP Conference Series: Earth and Environmental Science OPEN ACCESS Increasing the potential of Razaksat images for map-updating in the Tropics To cite this article: C Pohl and M Hashim 2014 IOP Conf. Ser.:
More informationANALYSIS OF SPOT-6 DATA FUSION USING GRAM-SCHMIDT SPECTRAL SHARPENING ON RURAL AREAS
International Journal of Remote Sensing and Earth Sciences Vol.10 No.2 December 2013: 84-89 ANALYSIS OF SPOT-6 DATA FUSION USING GRAM-SCHMIDT SPECTRAL SHARPENING ON RURAL AREAS Danang Surya Candra Indonesian
More informationDETECTION, CONFIRMATION AND VALIDATION OF CHANGES ON SATELLITE IMAGE SERIES. APLICATION TO LANDSAT 7
DETECTION, CONFIRMATION AND VALIDATION OF CHANGES ON SATELLITE IMAGE SERIES. APLICATION TO LANDSAT 7 Lucas Martínez, Mar Joaniquet, Vicenç Palà and Roman Arbiol Remote Sensing Department. Institut Cartografic
More informationUsing Imagery for Intelligence Analysis. Jim Michel Renee Bernstein
Using Imagery for Intelligence Analysis Jim Michel Renee Bernstein Deriving Value from GIS and Imagery Capabilities Evolved Along Separate but Parallel Paths GIS Imagery brings value Imagery Contextual
More informationRemote Sensing. Odyssey 7 Jun 2012 Benjamin Post
Remote Sensing Odyssey 7 Jun 2012 Benjamin Post Definitions Applications Physics Image Processing Classifiers Ancillary Data Data Sources Related Concepts Outline Big Picture Definitions Remote Sensing
More informationWetlands Investigation Utilizing GIS and Remote Sensing Technology for Lucas County, Ohio: a hybrid analysis.
Wetlands Investigation Utilizing GIS and Remote Sensing Technology for Lucas County, Ohio: a hybrid analysis. Update on current wetlands research in GISAG Nathan Torbick Spring 2003 Component One Remote
More informationEXAMPLES OF OBJECT-ORIENTED CLASSIFICATION PERFORMED ON HIGH-RESOLUTION SATELLITE IMAGES
EXAMPLES OF OBJECT-ORIENTED CLASSIFICATION... 349 Stanisław Lewiński, Karol Zaremski EXAMPLES OF OBJECT-ORIENTED CLASSIFICATION PERFORMED ON HIGH-RESOLUTION SATELLITE IMAGES Abstract: Information about
More informationMonitoring the vegetation success of a rehabilitated mine site using multispectral UAV imagery. Tim Whiteside & Renée Bartolo, eriss
Monitoring the vegetation success of a rehabilitated mine site using multispectral UAV imagery Tim Whiteside & Renée Bartolo, eriss About the Supervising Scientist Main roles Working to protect the environment
More informationOptimizing Multiresolution Segmentation for Extracting Plastic Greenhouses from WorldView 3 Imagery
Optimizing Multiresolution Segmentation for Extracting Plastic Greenhouses from WorldView 3 Imagery Manuel A. Aguilar, Antonio Novelli, Abderrahim Nemmaoui, Fernando J. Aguilar, Andrés García Lorca, Óscar
More informationREMOTE SENSING. Topic 10 Fundamentals of Digital Multispectral Remote Sensing MULTISPECTRAL SCANNERS MULTISPECTRAL SCANNERS
REMOTE SENSING Topic 10 Fundamentals of Digital Multispectral Remote Sensing Chapter 5: Lillesand and Keifer Chapter 6: Avery and Berlin MULTISPECTRAL SCANNERS Record EMR in a number of discrete portions
More informationLet it snow -operational snow cover product from Sentinel-2
Let it snow -operational snow cover product from Sentinel-2 and Landsat-8 data Manuel Grizonnet CNES Toulouse, France Co-authors: S. GASCOIN (CNRS), O. HAGOLLE, C. L HELGUEN, T. KLEMPKA Let It Snow in
More informationCostal region of northern Peru, the pacific equatorial dry forest there is recognised for its unique endemic biodiversity
S.Baena@kew.org http://www.kew.org/gis/ Costal region of northern Peru, the pacific equatorial dry forest there is recognised for its unique endemic biodiversity Highly threatened ecosystem affected by
More informationIntroduction to Remote Sensing Fundamentals of Satellite Remote Sensing. Mads Olander Rasmussen
Introduction to Remote Sensing Fundamentals of Satellite Remote Sensing Mads Olander Rasmussen (mora@dhi-gras.com) 01. Introduction to Remote Sensing DHI What is remote sensing? the art, science, and technology
More informationAUTOMATED STAND DELINEATION AND FIRE FUELS MAPPING
AUTOMATED STAND DELINEATION AND FIRE FUELS MAPPING Jennifer Stefanacci, Director of Geospatial Services Parallel, Incorporated USGS Rocky Mountain Geographic Science Center Denver, CO 80225 jlstefanacci@usgs.gov
More informationWhat is Remote Sensing? Contents. Image Fusion in Remote Sensing. 1. Optical imagery in remote sensing. Electromagnetic Spectrum
Contents Image Fusion in Remote Sensing Optical imagery in remote sensing Image fusion in remote sensing New development on image fusion Linhai Jing Applications Feb. 17, 2011 2 1. Optical imagery in remote
More informationMapping Open Water Bodies with Optical Remote Sensing
Mapping Open Water Bodies with Optical Remote Sensing M. O Donnell 1,2 and E. Podest 1 1.Jet Propulsion Laboratory, California Institute of Technology 2 Alliance Gertz-Ressler High School, Los Angeles,
More informationImage Fusion. Pan Sharpening. Pan Sharpening. Pan Sharpening: ENVI. Multi-spectral and PAN. Magsud Mehdiyev Geoinfomatics Center, AIT
1 Image Fusion Sensor Merging Magsud Mehdiyev Geoinfomatics Center, AIT Image Fusion is a combination of two or more different images to form a new image by using certain algorithms. ( Pohl et al 1998)
More informationCLASSIFICATION OF VEGETATION AREA FROM SATELLITE IMAGES USING IMAGE PROCESSING TECHNIQUES ABSTRACT
CLASSIFICATION OF VEGETATION AREA FROM SATELLITE IMAGES USING IMAGE PROCESSING TECHNIQUES Arpita Pandya Research Scholar, Computer Science, Rai University, Ahmedabad Dr. Priya R. Swaminarayan Professor
More informationWhat can we check with VHR Pan and HR multispectral imagery?
2008 CwRS Campaign Kick-off meeting, Ispra, 03-04 April 2008 1 What can we check with VHR Pan and HR multispectral imagery? Pavel MILENOV GeoCAP, Agriculture Unit, JRC 2008 CwRS Campaign Kick-off meeting,
More informationIKONOS High Resolution Multispectral Scanner Sensor Characteristics
High Spatial Resolution and Hyperspectral Scanners IKONOS High Resolution Multispectral Scanner Sensor Characteristics Launch Date View Angle Orbit 24 September 1999 Vandenberg Air Force Base, California,
More informationUsing Freely Available. Remote Sensing to Create a More Powerful GIS
Using Freely Available Government Data and Remote Sensing to Create a More Powerful GIS All rights reserved. ENVI, E3De, IAS, and IDL are trademarks of Exelis, Inc. All other marks are the property of
More informationUSE OF DIGITAL AERIAL IMAGES TO DETECT DAMAGES DUE TO EARTHQUAKES
USE OF DIGITAL AERIAL IMAGES TO DETECT DAMAGES DUE TO EARTHQUAKES Fumio Yamazaki 1, Daisuke Suzuki 2 and Yoshihisa Maruyama 3 ABSTRACT : 1 Professor, Department of Urban Environment Systems, Chiba University,
More informationGEOG432: Remote sensing Lab 3 Unsupervised classification
GEOG432: Remote sensing Lab 3 Unsupervised classification Goal: This lab involves identifying land cover types by using agorithms to identify pixels with similar Digital Numbers (DN) and spectral signatures
More informationREMOTE SENSING INTERPRETATION
REMOTE SENSING INTERPRETATION Jan Clevers Centre for Geo-Information - WU Remote Sensing --> RS Sensor at a distance EARTH OBSERVATION EM energy Earth RS is a tool; one of the sources of information! 1
More informationInvestigating the impact of spatial and spectral resolution of satellite images on segmentation quality
Investigating the impact of spatial and spectral resolution of satellite images on segmentation quality Nika Mesner Krištof Oštir Investigating the impact of spatial and spectral resolution of satellite
More informationINTRODUCTION II. LITERATURE SURVEY
A Survey Paper on Buildings Extraction from ly Sensed Images 1 Jenifer Grace Giftlin.C, 2 Dr.S.Jenicka 1 Dept of Computer Applications, Sarah Tucker College, 2 Department of Computer Science and Engineering,
More informationINCORPORATION OF TEXTURE, INTENSITY, HUE, AND SATURATION FOR RANGELAND MONITORING WITH UNMANNED AIRCRAFT IMAGERY
INCORPORATION OF TEXTURE, INTENSITY, HUE, AND SATURATION FOR RANGELAND MONITORING WITH UNMANNED AIRCRAFT IMAGERY A. S. Laliberte a, A. Rango b a Jornada Experimental Range, New Mexico State University,
More informationC AssesSeg concurrent computing version of AssesSeg: a benchmark between the new and previous version
C AssesSeg concurrent computing version of AssesSeg: a benchmark between the new and previous version Antonio Novelli 1, Manuel A. Aguilar 2, Fernando J. Aguilar 2, Abderrahim Nemmaoui 2, Eufemia Tarantino
More informationF2 - Fire 2 module: Remote Sensing Data Classification
F2 - Fire 2 module: Remote Sensing Data Classification F2.1 Task_1: Supervised and Unsupervised classification examples of a Landsat 5 TM image from the Center of Portugal, year 2005 F2.1 Task_2: Burnt
More informationINFORMATION CONTENT ANALYSIS FROM VERY HIGH RESOLUTION OPTICAL SPACE IMAGERY FOR UPDATING SPATIAL DATABASE
INFORMATION CONTENT ANALYSIS FROM VERY HIGH RESOLUTION OPTICAL SPACE IMAGERY FOR UPDATING SPATIAL DATABASE M. Alkan a, * a Department of Geomatics, Faculty of Civil Engineering, Yıldız Technical University,
More informationInterpreting land surface features. SWAC module 3
Interpreting land surface features SWAC module 3 Interpreting land surface features SWAC module 3 Different kinds of image Panchromatic image True-color image False-color image EMR : NASA Echo the bat
More informationManaging and Monitoring Intertidal Oyster Reefs with Remote Sensing in Coastal South Carolina
Managing and Monitoring Intertidal Oyster Reefs with Remote Sensing in Coastal South Carolina A cooperative effort between: Coastal Services Center South Carolina Department of Natural Resources City of
More informationDigitization of Trail Network Using Remotely-Sensed Data in the CFB Suffield National Wildlife Area
Digitization of Trail Network Using Remotely-Sensed Data in the CFB Suffield National Wildlife Area Brent Smith DLE 5-5 and Mike Tulis G3 GIS Technician Department of National Defence 27 March 2007 Introduction
More informationA Hierarchical Fuzzy Classification Approach for High-Resolution Multispectral Data Over Urban Areas
1920 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 41, NO. 9, SEPTEMBER 2003 A Hierarchical Fuzzy Classification Approach for High-Resolution Multispectral Data Over Urban Areas Aaron K. Shackelford,
More informationDATA FUSION AND TEXTURE-DIRECTION ANALYSES FOR URBAN STUDIES IN VIETNAM
1 DATA FUSION AND TEXTURE-DIRECTION ANALYSES FOR URBAN STUDIES IN VIETNAM Tran Dong Binh 1, Weber Christiane 1, Serradj Aziz 1, Badariotti Dominique 2, Pham Van Cu 3 1. University of Louis Pasteur, Department
More informationTexture Analysis for Correcting and Detecting Classification Structures in Urban Land Uses i
Texture Analysis for Correcting and Detecting Classification Structures in Urban Land Uses i Metropolitan area case study Spain Bahaaeddin IZ Alhaddadª, Malcolm C. Burnsª and Josep Roca Claderaª ª Centre
More informationThe Utility and Limitations of Remote Sensing in Land Use Change Detection and Conservation Planning
The Utility and Limitations of Remote Sensing in Land Use Change Detection and Conservation Planning Steffen Mueller, PhD, Principal Economist Ken Copenhaver, CropGrower LLC Presentation to: US Environmental
More informationKeywords: Agriculture, Olive Trees, Supervised Classification, Landsat TM, QuickBird, Remote Sensing.
Classification of agricultural fields by using Landsat TM and QuickBird sensors. The case study of olive trees in Lesvos island. Christos Vasilakos, University of the Aegean, Department of Environmental
More informationDIFFERENTIAL APPROACH FOR MAP REVISION FROM NEW MULTI-RESOLUTION SATELLITE IMAGERY AND EXISTING TOPOGRAPHIC DATA
DIFFERENTIAL APPROACH FOR MAP REVISION FROM NEW MULTI-RESOLUTION SATELLITE IMAGERY AND EXISTING TOPOGRAPHIC DATA Costas ARMENAKIS Centre for Topographic Information - Geomatics Canada 615 Booth Str., Ottawa,
More informationSatellite Imagery Characteristics, Uses and Delivery to GIS Systems. Wayne Middleton April 2014
Satellite Imagery Characteristics, Uses and Delivery to GIS Systems Wayne Middleton April 2014 About Geoimage Founded in Brisbane 1988 Leading Independent company Specialists in satellite imagery and geospatial
More informationForest Resources Assessment using Synthe c Aperture Radar
Forest Resources Assessment using Synthe c Aperture Radar Project Background F RA-SAR 2010 was initiated to support the Forest Resources Assessment (FRA) of the United Nations Food and Agriculture Organization
More informationCOMBINATION OF OBJECT-BASED AND PIXEL-BASED IMAGE ANALYSIS FOR CLASSIFICATION OF VHR IMAGERY OVER URBAN AREAS INTRODUCTION
COMBINATION OF OBJECT-BASED AND PIXEL-BASED IMAGE ANALYSIS FOR CLASSIFICATION OF VHR IMAGERY OVER URBAN AREAS Bahram Salehi a, PhD Candidate Yun Zhang a, Professor Ming Zhong b, Associates Professor a
More informationSatellite Data Used in Land Development
4.95 Satellite Data Used in Land Development There s been much speculation that satellite data will one day replace traditional aerial photography for photogrammetric applications. Yet even with the latest
More informationActivity Data (AD) Monitoring in the frame of REDD+ MRV
Activity Data (AD) Monitoring in the frame of REDD+ MRV Preliminary comments REDD+ is sustainable low emissions, high carbon rural development Monitoring efforts should support this effort Challenges Diversity
More informationSatellite Remote Sensing: Earth System Observations
Satellite Remote Sensing: Earth System Observations Land surface Water Atmosphere Climate Ecosystems 1 EOS (Earth Observing System) Develop an understanding of the total Earth system, and the effects of
More informationORFEO Preparatory Program progress
ORFEO Preparatory Program progress CNES Preparatory Program S. CHERCHALI ORFEO Thematic meeting May 21st, 2008 CNES Paris 1 Plan of the presentation ORFEO Preparatory Program Organization and responsibilities
More informationRaster is faster but vector is corrector
Account not required Raster is faster but vector is corrector The old GIS adage raster is faster but vector is corrector comes from the two different fundamental GIS models: vector and raster. Each of
More informationA MULTISTAGE APPROACH FOR DETECTING AND CORRECTING SHADOWS IN QUICKBIRD IMAGERY
A MULTISTAGE APPROACH FOR DETECTING AND CORRECTING SHADOWS IN QUICKBIRD IMAGERY Jindong Wu, Assistant Professor Department of Geography California State University, Fullerton 800 North State College Boulevard
More informationImage interpretation and analysis
Image interpretation and analysis Grundlagen Fernerkundung, Geo 123.1, FS 2014 Lecture 7a Rogier de Jong Michael Schaepman Why are snow, foam, and clouds white? Why are snow, foam, and clouds white? Today
More informationVALIDATION OF A SEMI-AUTOMATED CLASSIFICATION APPROACH FOR URBAN GREEN STRUCTURE
VALIDATION OF A SEMI-AUTOMATED CLASSIFICATION APPROACH FOR URBAN GREEN STRUCTURE Øivind Due Trier a, * and Einar Lieng b a Norwegian Computing Center, Gaustadalléen 23, P.O. Box 114 Blindern, NO-0314 Oslo,
More informationAutomated speed detection of moving vehicles from remote sensing images
Safety, Reliability and Risk of Structures, Infrastructures and Engineering Systems Furuta, Frangopol & Shinozuka (eds) 2010 Taylor & Francis Group, London, ISBN 978-0-415-47557-0 Automated speed detection
More informationREMOTE SENSING WITH DRONES. YNCenter Video Conference Chang Cao
REMOTE SENSING WITH DRONES YNCenter Video Conference Chang Cao 08-28-2015 28 August 2015 2 Drone remote sensing It was first utilized in military context and has been given great attention in civil use
More informationSpatial-Spectral Target Detection. Table 1: Description of symmetric geometric targets
Experiment Spatial-Spectral Target Detection Investigator: Jason Kaufman Support Crew: TBD Short Title: Objectives: Spatial-Spectral Target Detection The aim of this experiment is to detect and distinguish
More informationIntroduction of Satellite Remote Sensing
Introduction of Satellite Remote Sensing Spatial Resolution (Pixel size) Spectral Resolution (Bands) Resolutions of Remote Sensing 1. Spatial (what area and how detailed) 2. Spectral (what colors bands)
More informationCoastal areas and land development. An algorithm for monitoring informal constructions An application in coastal areas. Informal building in Greece
An algorithm for monitoring informal constructions An application in coastal areas Ch. Psaltis, Ch. Ioannidis Coastal areas and land development Coastal areas more developed than continental areas Overconcentration
More informationMETHODS FOR IMAGE FUSION QUALITY ASSESSMENT A REVIEW, COMPARISON AND ANALYSIS
METHODS FOR IMAGE FUSION QUALITY ASSESSMENT A REVIEW, COMPARISON AND ANALYSIS Yun Zhang Department of Geodesy and Geomatics Engineering University of New Brunswick Fredericton, New Brunswick, Canada Email:
More informationDigital Image Processing
Digital Image Processing 1 Patrick Olomoshola, 2 Taiwo Samuel Afolayan 1,2 Surveying & Geoinformatic Department, Faculty of Environmental Sciences, Rufus Giwa Polytechnic, Owo. Nigeria Abstract: This paper
More informationImprovements in Landsat Pathfinder Methods for Monitoring Tropical Deforestation and Their Extension to Extra-tropical Areas
Improvements in Landsat Pathfinder Methods for Monitoring Tropical Deforestation and Their Extension to Extra-tropical Areas PI: John R. G. Townshend Department of Geography (and Institute for Advanced
More informationRiparian Buffer Mapper. User Manual
() User Manual Copyright 2007 All Rights Reserved Table of Contents Introduction...- 3 - System Requirements...- 5 - Installation and Configuration...- 5 - Getting Started...- 6 - Using the Viewer...-
More informationVisualizing a Pixel. Simulate a Sensor s View from Space. In this activity, you will:
Simulate a Sensor s View from Space In this activity, you will: Measure and mark pixel boundaries Learn about spatial resolution, pixels, and satellite imagery Classify land cover types Gain exposure to
More information* Tokai University Research and Information Center
Effects of tial Resolution to Accuracies for t HRV and Classification ta Haruhisa SH Kiyonari i KASA+, uji, and Toshibumi * Tokai University Research and nformation Center 2-28-4 Tomigaya, Shi, T 151,
More informationIMAGE QUATY ASSESSMENT FOR VHR REMOTE SENSING IMAGE CLASSIFICATION
IMAGE QUATY ASSESSMENT FOR VHR REMOTE SENSING IMAGE CLASSIFICATION Zhipeng LI a,b, Li SHEN a,b Linmei WU a,b a State-province Joint Engineering Laboratory of Spatial Information Technology for High-speed
More informationUse of Remote Sensing to Characterize Impervious Cover in Stormwater Impaired Watersheds
University of Massachusetts Amherst ScholarWorks@UMass Amherst Water Resources Research Center Conferences Water Resources Research Center 4-9-2007 Use of Remote Sensing to Characterize Impervious Cover
More informationBuilding Damage Mapping of the 2006 Central Java, Indonesia Earthquake Using High-Resolution Satellite Images
4th International Workshop on Remote Sensing for Post-Disaster Response, 25-26 Sep. 2006, Cambridge, UK Building Damage Mapping of the 2006 Central Java, Indonesia Earthquake Using High-Resolution Satellite
More informationImage Change Tutorial
Image Change Tutorial In this tutorial, you will use the Image Change workflow to compare two images of an area over Indonesia that was impacted by the December 26, 2004 tsunami. The first image is a before
More informationIntroduction. Introduction. Introduction. Introduction. Introduction
Identifying habitat change and conservation threats with satellite imagery Extinction crisis Volker Radeloff Department of Forest Ecology and Management Extinction crisis Extinction crisis Conservationists
More informationComparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River
Journal of Geography and Geology; Vol. 10, No. 1; 2018 ISSN 1916-9779 E-ISSN 1916-9787 Published by Canadian Center of Science and Education Comparing of Landsat 8 and Sentinel 2A using Water Extraction
More informationIntroduction to Remote Sensing
Introduction to Remote Sensing Outline Remote Sensing Defined Resolution Electromagnetic Energy (EMR) Types Interpretation Applications Remote Sensing Defined Remote Sensing is: The art and science of
More informationAtmospheric Correction (including ATCOR)
Technical Specifications Atmospheric Correction (including ATCOR) The data obtained by optical satellite sensors with high spatial resolution has become an invaluable tool for many groups interested in
More informationAN OBJECT-ORIENTED CLASSIFICATION METHOD ON HIGH RESOLUTION SATELLITE DATA , China -
25 th ACRS 2004 Chiang Mai, Thailand 347 AN OBJECT-ORIENTED CLASSIFICATION METHOD ON HIGH RESOLUTION SATELLITE DATA Sun Xiaoxia a Zhang Jixian a Liu Zhengjun a a Chinese Academy of Surveying and Mapping,
More informationModule 11 Digital image processing
Introduction Geo-Information Science Practical Manual Module 11 Digital image processing 11. INTRODUCTION 11-1 START THE PROGRAM ERDAS IMAGINE 11-2 PART 1: DISPLAYING AN IMAGE DATA FILE 11-3 Display of
More informationEarth observation image processing with the ORFEO ToolBox
Introduction What? What s new in OTB 3.12 (February) What s new in OTB 3.14 Extra Earth observation image processing with the ORFEO ToolBox Remote sensing real image processing M. Grizonnet 1,J. Michel
More informationRegion Based Satellite Image Segmentation Using JSEG Algorithm
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 4, Issue. 5, May 2015, pg.1012
More informationRemote Sensing Instruction Laboratory
Laboratory Session 217513 Geographic Information System and Remote Sensing - 1 - Remote Sensing Instruction Laboratory Assist.Prof.Dr. Weerakaset Suanpaga Department of Civil Engineering, Faculty of Engineering
More informationLecture 13: Remotely Sensed Geospatial Data
Lecture 13: Remotely Sensed Geospatial Data A. The Electromagnetic Spectrum: The electromagnetic spectrum (Figure 1) indicates the different forms of radiation (or simply stated light) emitted by nature.
More information