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Titre : Remote sensing and GIS Type de document : Guide/Manuel Auteurs : Basudeb Bhatta, Auteur Mention d'édition : 3ème édition Editeur : Oxford, Londres, ... : Oxford University Press Année de publication : 2021 Importance : 752 p. Format : 24 x 18 cm ISBN/ISSN/EAN : 978-0-19-949664-8 Note générale : Bibliographie
additional reading material with Oxford arealLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes descripteurs IGN] acquisition d'images
[Termes descripteurs IGN] airborne multispectral scanner
[Termes descripteurs IGN] analyse spatiale
[Termes descripteurs IGN] Global Navigation Satellite System
[Termes descripteurs IGN] image hyperspectrale
[Termes descripteurs IGN] image thermique
[Termes descripteurs IGN] interféromètrie par radar à antenne synthétique
[Termes descripteurs IGN] Lidar
[Termes descripteurs IGN] modèle numérique de surface
[Termes descripteurs IGN] modèle numérique de terrain
[Termes descripteurs IGN] modélisation 3D
[Termes descripteurs IGN] orthorectification
[Termes descripteurs IGN] Passive and Active L and S band Sensor
[Termes descripteurs IGN] photographie aérienne
[Termes descripteurs IGN] Satellite Microwave Radiometer
[Termes descripteurs IGN] scène 3D
[Termes descripteurs IGN] stéréoscopie
[Termes descripteurs IGN] système d'information géographique
[Termes descripteurs IGN] traitement d'image
[Termes descripteurs IGN] visualisation 3DIndex. décimale : 35.00 Télédétection - généralités Résumé : (Editeur) Beginning with the history and basic concepts of remote sensing and GIS, the book gives an exhaustive coverage of optical, thermal, and microwave remote sensing, global navigation satellite systems (such as GPS and IRNSS), digital photogrammetry, visual image analysis, digital image processing, spatial and attribute data model, geospatial analysis, and planning, implementation, and management of GIS. It also presents the modern trends of remote sensing and GIS with an illustrated discussion on its numerous applications. Note de contenu : 1. Concept of Remote Sensing
1.1 Introduction
1.2 Distance of Remote Sensing
1.3 Definition of Remote Sensing
1.4 Remote Sensing: Art and/or Science
1.5 Data
1.6 Remote Sensing Process
1.7 Source of Energy
1.8 Interaction with Atmosphere
1.9 Interaction with Target
1.9.1 Hemispherical Absorptance, Transmittance, and Reflectan
1.10 Interaction with the Atmosphere Again
1.11 Recording of Energy by Sensor
1.12 Transmission, Reception, and Processing
1.13 Interpretation and Analysis
1.14 Applications of Remote Sensing
1.15 Advantages of Remote Sensing
1.16 Limitations of Remote Sensing
1.17 Ideal Remote Sensing System
2. Types of Remote Sensing and Sensor Characteristics
2.1 Introduction
2.2 Types of Remote Sensing
2.3 Characteristics of Images
2.4 Orbital Characteristics of Satellite
2.5 Remote Sensing Satellites
2.6 Concept of Swath
2.7 Concept of Nadir
2.8 Sensor Resolutions
2.9 Image Referencing System
2.9.1 Path
2.9.2 Row
2.9.3 Orbital Calendar
3. History of Remote Sensing and Indian Space Program
3.1 Introduction
3.2 The Early Age
3.3 The Middle Age
3.4 The Modern Age or Space Age
3.5 Indian Space Program
4. Photographic Imaging
4.1 Introduction
4.2 Camera Systems
4.3 Types of Camera
4.4 Filter
4.5 Film
4.6 Geometry of Aerial Photography
4.7 Ideal Time and Atmosphere for Aerial Remote Sensing
5. Digital Imaging
5.1 Introduction
5.2 Digital Image
5.3 Sensor
5.4 Imaging by Scanning Technique
5.5 Hyper-spectral Imaging
5.6 Imaging By Non-scanning Technique
5.7 Thermal Remote Sensing
5.8 Other Sensors
6. Microwave Remote Sensing
6.1 Introduction
6.2 Passive Microwave Remote Sensing
6.3 Active Microwave Remote Sensing
6.4 Radar Imaging
6.5 Airborne Versus Space-Borne Radars
6.6 Radar Systems
7. Ground-truth Data and Global Positioning System
7.1 Introduction
7.2 Requirements of Ground-Truth Data
7.3 Instruments for Ground Truthing
7.4 Parameters of Ground Truthing
7.5 Factors of Spectral Measurement
7.6 Global Navigation Satellite System
8. Photogrammetry
8.1 Introduction
8.2 Development of Photogrammetry
8.3 Classification of Photogrammetry
8.4 Photogrammetric Process
8.5 Acquisition of Imagery and its Support Data
8.6 Orientation and Triangulation
8.7 Stereo Model Compilation
8.8 Stereoscopic 3D Viewing
8.9 Stereoscopic Measurement
8.10 DTM/DEM Generation
8.11 Contour Map Generation
8.12 Orthorectification
8.13 3D Feature Extraction
8.14 3D Scene Modelling
8.15 Photogrammetry and LiDAR
8.16 Radargrammetry and Radar Interferometry
8.17 Limitations of Photogrammetry
9. Visual Image Interpretation
9.1 Introduction
9.2 Information Extraction by Human and Computer
9.3 Remote Sensing Data Products
9.4 Border or Marginal Information
9.5 Image Interpretation
9.6 Elements of Visual Image Interpretation
9.7 Interpretation Keys
9.8 Generation of Thematic Maps
9.9 Thermal Image Interpretation
9.10 Radar Image Interpretation
10. Digital Image Processing
10.1 Introduction
10.2 Categorization of Image Processing
10.3 Image Processing Systems
10.4 Digital Image
10.5 Media for Digital Data Recording, Storage, and Distribution
10.6 Data Formats of Digital Image
10.7 Header Information
10.8 Display of Digital Image
10.9 Pre-processing
10.10 Image Enhancement
10.11 Image Transformation
10.12 Image Classification
11. Data Integration, Analysis, and Presentation
11.1 Introduction
11.2 Multi-approach of Remote Sensing
11.3 Integration with Ground Truth and Other Ancillary Data
11.4 Integration of Transformed Data
11.5 Integration with GIS
11.6 Process of Remote Sensing Data Analysis
11.7 The Level of Detail
11.8 Limitations of Remote Sensing Data Analysis
11.9 Presentation
12. Applications of Remote Sensing
12.1 Introduction
12.2 Land Cover and Land Use
12.3 Agriculture
12.4 Forestry
12.5 Geology
12.6 Geomorphology
12.7 Urban Applications
12.8 Hydrology
12.9 Mapping
12.10 Oceans and Coastal Monitoring
12.11 Monitoring of Atmospheric Constituents
PART II Geographic Information Systems and Geospatial Analysis
13. Concept of Geographic Information Systems
13.1 Introduction
13.2 Definitions of GIS
13.3 Key Components of GIS
13.4 GIS-An Integration of Spatial and Attribute Information
13.5 GIS-Three Views of Information System
13.6 GIS and Related Terms
13.7 GIS-A Knowledge Hub
13.8 GIS-A Set of Interrelated Subsystems
13.9 GIS-An Information Infrastructure
13.10 Origin of GIS
14. Functions and Advantages of GIS
14.1 Introduction
14.2 Functions of GIS
14.3 Application Areas of GIS
14.4 Advantages of GIS
14.5 Functional Requirements of GIS
14.6 Limitations of GIS
15. Spatial Data Model
15.1 Introduction
15.2 Spatial, Thematic, and Temporal Dimensions of Geographic Data
15.3 Spatial Entity and Object
15.4 Spatial Data Model
15.5 Raster Data Model
15.6 Vector Data Model
15.7 Raster versus Vector
15.8 Object-Oriented Data Model
15.9 File Formats of Spatial Data
16. Attribute Data Management and Metadata Concept
16.1 Introduction
16.2 Concept of Database and DBMS
16.3 Advantages of DBMS
16.4 Functions of DBMS
16.5 File and Data Access
16.6 Data Models
16.7 Database Models
16.8 Data Models in GIS
16.9 Concept of SQL
16.10 Concept of Metadata
17. Process of GIS
17.1 Introduction
17.2 Data Capture
17.3 Data Sources
17.4 Data Encoding Methods
17.5 Linking of Spatial and Attribute Data
17.6 Organizing Data for Analysis
18. Geospatial Analysis
18.1 Introduction
18.2 Geospatial Data Analysis
18.3 Integration and Modelling of Spatial Data
18.4 Geospatial Data Analysis Methods
18.5 Database Query
18.6 Geospatial Measurements
18.7 Overlay Operations
18.8 Network Analysis
18.9 Surface Analysis
18.10 Geostatistics
18.11 Geovisualization
19. Planning, Implementation, and Management of GIS
19.1 Introduction
19.2 Planning of Project
19.3 Implementation of Project
19.4 Management of Project
19.5 Keys for Successful GIS
19.6 Reasons for Unsuccessful GIS
20. Modern Trends of GIS
20.1 Introduction
20.2 Local to Global Concept in GIS
20.3 Increase in Dimensions in GIS
20.4 Linear to Non-linear Techniques in GIS
20.5 Development in Relation between Geometry and Algebra in GIS
20.6 Development of Common Techniques in GIS
20.7 Integration of GIS and Remote Sensing
20.8 Integration of GIS and Multimedia
20.9 3D GIS
20.9.1 Virtual Reality in GIS
20.10 Integration of 3D GIS and Web GIS
20.11 4D GIS and Real-time GIS
20.12 Mobile GIS
20.12.1 Mobile mapping
20.13 Collaborative GIS (CGIS)
21. Change Detection and Geosimulation
21.1 Visual change detection
21.2 Thresholding
21.3 Image difference
21.4 Image regression
21.5 Image ratioing
21.6 Vegetation index differencing
21.7 Principal component differencing
21.8 Multi-temporal image stock classification
21.9 Post classification comparison
21.10 Change vector analysis
21.12 Cellular automata simulation
21.13 Multi-agent simulation
21.14 ANN learning in simulation
Appendix A - Concept of Map, Coordinate System, and Projection
Appendix B - Concept on Mathematical TopicsNuméro de notice : 26518 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/POSITIONNEMENT Nature : Manuel de cours DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97342 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 26518-01 35.00 Livre Centre de documentation Télédétection Disponible Stereophotogrammetry for 2-D building deformation monitoring using Kalman Filter / J.O. Odumosu in Reports on geodesy and geoinformatics, vol 110 n°1 (December 2020)
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Titre : Stereophotogrammetry for 2-D building deformation monitoring using Kalman Filter Type de document : Article/Communication Auteurs : J.O. Odumosu, Auteur ; V.C. Nnam, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1 - 7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre
[Termes descripteurs IGN] corrélation croisée normalisée
[Termes descripteurs IGN] déformation d'édifice
[Termes descripteurs IGN] filtre de Kalman
[Termes descripteurs IGN] Matlab
[Termes descripteurs IGN] modèle stéréoscopique
[Termes descripteurs IGN] Nigéria
[Termes descripteurs IGN] point d'appui
[Termes descripteurs IGN] surveillance d'ouvrage
[Termes descripteurs IGN] télémètre laser terrestre
[Termes descripteurs IGN] transformation polynomialeRésumé : (auteur) Stereo photogrammetry has been used in this study to analyse and detect movements within the Lecture theater of School of Environmental Technology of Federal University of Technology Minna via the use of Kalman filter algorithm. The essential steps for implementation of this method are herein highlighted and results obtained indicate Ins. Mov.s (velocity) ranging from ±0.0000001 m/epoch to ±0.000007 m/epoch with greater movements noticed in the horizontal direction than in the vertical direction of the building. Because the observed movements were insignificant, the building has been classified as stable. However, a longer period of observation with a bi-monthly observational interval has been recommended to enable decision on the rate of rise/sink and deformation of the building. Numéro de notice : A2020-785 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.2478/rgg-2020-0006 date de publication en ligne : 07/08/2020 En ligne : https://doi.org/10.2478/rgg-2020-0006 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96530
in Reports on geodesy and geoinformatics > vol 110 n°1 (December 2020) . - pp 1 - 7[article]
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Titre : Les anciennes méthodes de levé Type de document : Article/Communication Auteurs : François Mazuyer, Auteur Année de publication : 2020 Article en page(s) : pp 33 - 35 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Topographie ancienne
[Termes descripteurs IGN] bornage
[Termes descripteurs IGN] chaînage
[Termes descripteurs IGN] distancemètre
[Termes descripteurs IGN] fil à plomb
[Termes descripteurs IGN] levé topographique
[Termes descripteurs IGN] tachéomètre
[Termes descripteurs IGN] topométrieRésumé : (éditeur) Les plans anciens qu’on utilise, qu’on doit réappliquer ou interpréter aujourd’hui, sont des plans de bornage, de relevés de détails, et nous n’aborderons donc pas les méthodes de levé de précision qui n’étaient pas appliquées dans ces cas. Numéro de notice : A2020-669 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96150
in Géomètre > n° 2184 (octobre 2020) . - pp 33 - 35[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 063-2020091 SL Revue Centre de documentation Revues en salle Disponible Evaluating the accuracy of ALS-based removal estimates against actual logging data / Ville Vähä-Konka in Annals of Forest Science [en ligne], vol 77 n° 3 (September 2020)
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Titre : Evaluating the accuracy of ALS-based removal estimates against actual logging data Type de document : Article/Communication Auteurs : Ville Vähä-Konka, Auteur ; Matti Maltamo, Auteur ; Timo Pukkala, Auteur ; Kalle Kärhä, Auteur Année de publication : 2020 Article en page(s) : 11 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes descripteurs IGN] éclaircie (sylviculture)
[Termes descripteurs IGN] fiabilité des données
[Termes descripteurs IGN] Finlande
[Termes descripteurs IGN] gestion forestière
[Termes descripteurs IGN] inventaire forestier étranger (données)
[Termes descripteurs IGN] récolte de bois
[Termes descripteurs IGN] télédétection par lidar
[Termes descripteurs IGN] télémètre laser aéroporté
[Termes descripteurs IGN] volume en boisRésumé : (auteur) Key message: We examined the accuracy of the stand attribute data based on airborne laser scanning (ALS) provided by the Finnish Forest Centre. The precision of forest inventory data was compared for the first time with operative logging data measured by the harvester.
Context: Airborne laser scanning (ALS) is increasingly used together with models to predict the stand attributes of boreal forests. The information is updated by growth models. Information produced by remote sensing, model prediction, and growth simulation needs field verification. The data collected by harvesters on logging sites provide a means to evaluate and verify the accuracy of the ALS-based data.
Aims: This study investigated the accuracy of ALS-based forest inventory data provided by the Finnish Forest Centre at the stand level, using harvester data as the reference. Special interest was on timber assortment volumes where the quality reductions of sawlog are model predictions in ALS-based data and true realized reductions in the logging data.
Methods: We examined the accuracy of total volume and timber assortment volumes by comparing ALS-based data and operative logging data measured by a harvester. This was done both for clear cuttings and thinning sites. Accuracy of the identification of the dominant tree species of the stand was examined using the Kappa coefficient.
Results: In clear-felling sites, the total harvest removals based on ALS and model prediction had a RMSE% of 26.0%. In thinning, the corresponding difference in the total harvested removal was 42.4%. Compared to logged volume, ALS-based prediction overestimated sawlog removals in clear cuttings and underestimated pulpwood removals.
Conclusion: The study provided valuable information on the accuracy of ALS-based stand attribute data. Our results showed that ALS-based data need better methods to predict the technical quality of harvested trees, to avoid systematic overestimates of sawlog volume. We also found that the ALS-based estimates do not accurately predict the volume of trees removed in actual thinnings.Numéro de notice : A2020-592 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-020-00985-7 date de publication en ligne : 27/08/2020 En ligne : https://doi.org/10.1007/s13595-020-00985-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95927
in Annals of Forest Science [en ligne] > vol 77 n° 3 (September 2020) . - 11 p.[article]History of laser scanning, part 2: the later phase of industrial and heritage applications / Adam P. Spring in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 8 (August 2020)
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Titre : History of laser scanning, part 2: the later phase of industrial and heritage applications Type de document : Article/Communication Auteurs : Adam P. Spring, Auteur Année de publication : 2020 Article en page(s) : pp 479-501 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes descripteurs IGN] balayage laser
[Termes descripteurs IGN] évolution technologique
[Termes descripteurs IGN] histoire des sciences et techniques
[Termes descripteurs IGN] instrument embarqué
[Termes descripteurs IGN] télémètre laser
[Termes descripteurs IGN] télémétrie laser terrestre
[Termes descripteurs IGN] travaux de terrainRésumé : (Auteur) The second part of this article examines the transition of midrange terrestrial laser scanning (TLS)–from applied research to applied markets. It looks at the crossover of technologies; their connection to broader developments in computing and microelectronics; and changes made based on application. The shift from initial uses in on-board guidance systems and terrain mapping to tripod-based survey for as-built documentation is a main focus. Origins of terms like digital twin are identified and, for the first time, the earliest examples of cultural heritage (CH) based midrange TLS scans are shown and explained. Part two of this history of laser scanning is a comprehensive analysis upto the year 2020. Numéro de notice : A2020-435 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.8.479 date de publication en ligne : 01/08/2020 En ligne : https://doi.org/10.14358/PERS.86.8.479 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95703
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 8 (August 2020) . - pp 479-501[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2020081 SL Revue Centre de documentation Revues en salle Disponible Unsupervised semantic and instance segmentation of forest point clouds / Di Wang in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)
PermalinkMonitoring clearcutting and subsequent rapid recovery in Mediterranean coppice forests with Landsat time series / Gherardo Chirici in Annals of Forest Science [en ligne], Vol 77 n° 2 (June 2020)
PermalinkUnder-canopy UAV laser scanning for accurate forest field measurements / Eric Hyyppä in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)
PermalinkFiltering of airborne LiDAR bathymetry based on bidirectional cloth simulation / Anxiu Yang in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)
PermalinkHow far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study / Enrico Borgogno Mondino in International Journal of Remote Sensing IJRS, vol 41 n°12 (20 - 30 March 2020)
PermalinkAssessment of salt marsh change on Assateague Island National Seashore between 1962 and 2016 / Anthony Campbell in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)
PermalinkHierarchical classification of pole‐like objects in mobile laser scanning point clouds / Rufei Liu in Photogrammetric record, vol 35 n° 169 (March 2020)
PermalinkAutomated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference / Heidar Rastiveis in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)
PermalinkPermalinkRelevés par Lidar mobile de cours d’eau et intégration des profils aux relevés bathymétriques réalisés par sondeur mono-faisceau / Guillaume Didier (2020)
PermalinkStreambank topography: an accuracy assessment of UAV-based and traditional 3D reconstructions / Benjamin U. Meinen in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)
PermalinksUAS-based remote rensing of river discharge using thermal particle image velocimetry and bathymetric lidar / Paul J. Kinzel in Remote sensing, vol 11 n° 19 (October 2019)
PermalinkQuarante ans après ! Equipements et méthodes en topographie / Paul Courbon in XYZ, n° 160 (septembre 2019)
PermalinkAutomatic extraction of accurate 3D tie points for trajectory adjustment of mobile laser scanners using aerial imagery / Zille Hussnain in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)
PermalinkTotal Vertical Uncertainty (TVU) modeling for topo-bathymetric LIDAR systems / Firat Eren in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 8 (August 2019)
PermalinkEmpirical stochastic model of detected target centroids: Influence on registration and calibration of terrestrial laser scanners / Tomislav Medic in Journal of applied geodesy, vol 13 n° 3 (July 2019)
PermalinkUncertainty assessment of optical distance measurements at micrometer level accuracy for long-range applications / Joffray Guillory in IEEE Transactions on Instrumentation and Measurement, vol 68 n° 6 (June 2019)
PermalinkAnalysis of the usability of mobile laser scanning data in snowy conditions / Mathilde Letard (2019)
PermalinkMise en place d’un système sondeur multifaisceaux dans une embarcation légère semi-rigide pour campagne de mesure bathymétrique et couplage avec un scanner terrestre, GNSS et INS / Alexandre Girard (2019)
PermalinkAnalyzing the role of pulse density and voxelization parameters on full-waveform LiDAR-derived metrics / Pablo Crespo-Peremarch in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)
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