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Titre : Remote Sensing Type de document : Monographie Auteurs : Andrew Hammond, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 Importance : 140 p. ISBN/ISSN/EAN : 978-1-83880-978-2 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] Amérique du sud
[Termes IGN] analyse d'image orientée objet
[Termes IGN] biomasse
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données spatiotemporelles
[Termes IGN] Enhanced vegetation index
[Termes IGN] géostatistique
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-MODIS
[Termes IGN] incendie de forêt
[Termes IGN] Inde
[Termes IGN] mésosphère
[Termes IGN] précision stéréoscopique
[Termes IGN] sciences naturelles
[Termes IGN] segmentation d'image
[Termes IGN] série temporelle
[Termes IGN] stratosphère
[Termes IGN] système d'information géographique
[Termes IGN] température au sol
[Termes IGN] troposphèreIndex. décimale : 35.00 Télédétection - généralités Résumé : (Editeur) This Edited Volume is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Remote Sensing. The book comprises single chapters authored by various researchers and edited by an expert active in this research area. All chapters are complete in themselves but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on this field of study, and open new possible research paths for further novel developments. Note de contenu : 1. Lidar Observations in South America. Part I - Mesosphere and Stratosphere
2. Lidar Observations in South America. Part II - Troposphere
3. Application of Remote Sensing in Natural Sciences
4. Assessment of Ecological Disturbance Caused by Flood and Fire in Assam Forests, India, Using MODIS Time Series Data of 2001-2011
5. Delineation of Open-Pit Mining Boundaries on Multispectral Imagery
6. Stereoscopic Precision of the Large Format Digital Cameras
7. Remote Sensing Applications in Disease MappingNuméro de notice : 26799 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.87829 Date de publication en ligne : 08/12/2021 En ligne : https://doi.org/10.5772/intechopen.87829 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100066 Remote sensing and GIS / Basudeb Bhatta (2021)
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 IGN] acquisition d'images
[Termes IGN] airborne multispectral scanner
[Termes IGN] analyse spatiale
[Termes IGN] Global Navigation Satellite System
[Termes IGN] image hyperspectrale
[Termes IGN] image thermique
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] Lidar
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] modélisation 3D
[Termes IGN] orthorectification
[Termes IGN] Passive and Active L and S band Sensor
[Termes IGN] photographie aérienne
[Termes IGN] Satellite Microwave Radiometer
[Termes IGN] scène 3D
[Termes IGN] stéréoscopie
[Termes IGN] système d'information géographique
[Termes IGN] traitement d'image
[Termes 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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Titre : Soil erosion : current challenges and future perspectives in a changing world Type de document : Monographie Auteurs : António Vieira, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 Importance : 152 p. ISBN/ISSN/EAN : 978-1-83962-300-4 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] acquisition d'images
[Termes IGN] Algérie
[Termes IGN] Bénin
[Termes IGN] changement d'occupation du sol
[Termes IGN] couvert végétal
[Termes IGN] érosion côtière
[Termes IGN] état du sol
[Termes IGN] Ethiopie
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] indice de végétation
[Termes IGN] Indonésie
[Termes IGN] modèle RUSLE
[Termes IGN] montagne
[Termes IGN] occupation du sol
[Termes IGN] orthoimage
[Termes IGN] photogrammétrie aérienne
[Termes IGN] Pix4D
[Termes IGN] protection des sols
[Termes IGN] risque naturel
[Termes IGN] Rwanda
[Termes IGN] système d'information géographiqueRésumé : (Editeur) Soil erosion is a major environmental issue with a worldwide impact and direct and indirect effects on soil productivity and consequently on human survival. Although a natural process, soil erosion has increased significantly due to human intervention, especially in the last centuries, through diverse activities such as intensive agriculture, overgrazing, urban sprawl, deforestation, and industrial and mining activities. Presently, soil erosion and degradation promoted by human action have reached extreme levels, necessitating urgent measures to promote soil conservation and rehabilitation. This book presents perspectives on soil erosion occurring in different parts of the world as well as some successful initiatives and strategies for soil conservation and rehabilitation. Note de contenu :
1. RGB Spectral Indices for the Analysis of Soil Protection by Vegetation Cover against Erosive Processes / Henry Antonio Pacheco Gil and Argenis de Jesús Montilla Pacheco
2. Spatial Estimation of Soil Erosion Risk Using RUSLE/GIS Techniques and Practices Conservation Suggested for Reducing Soil Erosion in Wadi Mina Catchment (Northwest, Algeria) / Ahmed Benchettouh, Sihem Jebari and Lakhdar Kouri
3. Remote Sensing and GIS-Based Soil Loss Estimation Using RUSLE in Bahir Dar Zuria District, Ethiopia / Nurhussen Ahmed Mohammed and Desale Kidane Asmamaw
4. Determination of the Most Priority Conservation Areas Based on Population Pressure and Erosion Hazard Levels in Lesti Sub-Watershed, Malang Regency, Indonesia / Andi Setyo Pambudi
5. The Impacts of Soil Degradation Effects on Phytodiversity and Vegetation Structure on Atacora Mountain Chain in Benin (West Africa) / Farris Okou, Achille Assogbadjo and Brice Augustin Sinsin
6. Erosion Control Success Stories and Challenges in the Context of Sustainable Landscape Management, Rwanda Experience / Jules Rutebuka
7. Biochar: A Sustainable Approach for Improving Soil Health and Environment / Shreya Das, Samanyita Mohanty, Gayatri Sahu, Mausami Rana and Kiran PilliNuméro de notice : 26759 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.91595 Date de publication en ligne : 12/05/2021 En ligne : https://doi.org/10.5772/intechopen.91595 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99775 Spatio-temporal analysis of urbanization using GIS and remote sensing in developing countries / Yuji Murayama (2021)
Titre : Spatio-temporal analysis of urbanization using GIS and remote sensing in developing countries Type de document : Monographie Auteurs : Yuji Murayama, Éditeur scientifique ; Matamyo Simwanda, Éditeur scientifique ; Manjula Ranagalage, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 327 p. Format : 16 x 23 cm ISBN/ISSN/EAN : 978-3-0365-2540-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] Chine
[Termes IGN] croissance urbaine
[Termes IGN] ilot thermique urbain
[Termes IGN] image satellite
[Termes IGN] occupation du sol
[Termes IGN] pays en développement
[Termes IGN] planification urbaine
[Termes IGN] système d'information géographique
[Termes IGN] urbanisation
[Termes IGN] ville durableRésumé : (éditeur) Over the last two decades, many researchers have focused on developing countries' urbanization patterns and processes. In this context, the scarcity of spatial data has been an obstacle to studying urbanization quantitatively, especially in Asian and African cities. The use of remote sensing data and geographical information systems (GIS) techniques can overcome the above limitations. Data on land use and land cover, land surface temperature, population density, and energy consumption can be extracted based on remote sensing at various spatial and temporal resolutions. GIS techniques can be used to analyze urbanization patterns and predict future patterns. Thus, the link between urbanization and sustainable urban development has increasingly become a principal issue in designing and developing sustainable cities at the local, regional, and global levels. This volume shows the spatiotemporal analysis of urbanization using GIS and remote sensing in developing countries, with a special emphasis on future urban sustainability in Asia and Africa. Capturing the spatial-temporal variation of urbanization patterns will help introduce proper sustainable urban planning in developing countries, especially for Asian and African cities. Note de contenu : 1- A cellular automata model constrained by spatiotemporal heterogeneity of the urban development strategy for simulating land-use change: A case study in Nanjing City, China
2- Remote sensing-based analysis of landscape pattern evolution in industrial rural areas: A case of southern Jiangsu, China
3- Spatial-temporal dynamic analysis of land use and landscape pattern in Guangzhou, China: Exploring
the driving forces from an urban sustainability perspective
4- Quantitative influence of land-use changes and urban expansion intensity on landscape pattern in Qingdao, China: Implications for urban sustainability
5- An analysis of urban land use/land cover changes in Blantyre City, Southern Malawi (1994–2018)
6- Spatiotemporal patterns and driving forces of urban expansion in coastal areas: A study on urban agglomeration in the Pearl River Delta, China
7- Spatiotemporal patterns and drivers of the surface urban heat island in 36 major cities in China: A comparison of two different methods for delineating rural areas
8- The impacts of the expansion of urban impervious surfaces on urban heat islands in a coastal city in China
9- The impacts of landscape changes on annual mean land surface temperature in the tropical mountain city of Sri Lanka: A case study of Nuwara Eliya (1996–2017)
10- Impact of landscape structure on the variation of land surface temperature in Sub-Saharan region: A case study of Addis Ababa using Landsat data (1986–2016)
11- Analysis of life quality in a tropical mountain city using a multi-criteria geospatial technique: A case study of Kandy City, Sri Lanka
12- Spatiotemporal analysis of the nonlinear negative relationship between urbanization and habitat quality in metropolitan areas
13- Dynamic monitoring and analysis of ecological quality of Pingtan comprehensive experimental zone, a new type of Sea Island City, based on RSEI
14- Role of urban public space and the surrounding environment in promoting sustainable development from the lens of social media
15- Comparison on multi-scale urban expansion derived from nightlight imagery between China and India
16- Impact of COVID-19 induced lockdown on environmental quality in four Indian megacities using Landsat 8 OLI and TIRS-derived data and Mamdani fuzzy logic modelling approachNuméro de notice : 28675 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/URBANISME Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-2540-2 En ligne : https://doi.org/10.3390/books978-3-0365-2540-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99923 The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution / Dimitri I. Rukhovitch in Remote sensing, vol 13 n° 1 (January-1 2021)
[article]
Titre : The use of deep machine learning for the automated selection of remote sensing data for the determination of areas of arable land degradation processes distribution Type de document : Article/Communication Auteurs : Dimitri I. Rukhovitch, Auteur ; Polina V. Koroleva, Auteur ; Danila D. Rukhovitch, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 155 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dégradation des sols
[Termes IGN] distribution spatiale
[Termes IGN] érosion
[Termes IGN] image Landsat
[Termes IGN] image Sentinel-MSI
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Russie
[Termes IGN] surface cultivée
[Termes IGN] système d'information géographiqueRésumé : (auteur) Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps. Numéro de notice : A2021-074 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13010155 Date de publication en ligne : 05/01/2021 En ligne : https://doi.org/10.3390/rs13010155 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96810
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