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Titre : Remotely sensing the species of individual trees Type de document : Thèse/HDR Auteurs : Yifang Shi, Auteur ; Andrew K. Skidmore, Directeur de thèse ; Tiejun Wang, Directeur de thèse Editeur : Enschede [Pays Bas] : University of Twente Année de publication : 2020 Collection : ITC Dissertation num. 376 Importance : 163 p. Format : 21 x 30 cm Note générale : bibliographie
Doctor of Philosophy, Faculty of Geo-Information Science and Earth Observation, University of TwenteLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Abies alba
[Termes IGN] analyse comparative
[Termes IGN] analyse diachronique
[Termes IGN] Bavière (Allemagne)
[Termes IGN] chlorophylle
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tempérée
[Termes IGN] image hyperspectrale
[Termes IGN] image infrarouge couleur
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Leaf Mass per Area
[Termes IGN] orthoimageRésumé : (auteur) The accurate identification of tree species is critical for the management of forest ecosystems. Mapping of tree species is an important task as it can assist a wide range of environmental applications, such as biodiversity monitoring, ecosystem services assessment, invasive species detection, and sustainable forest management. Compared to the conventional approaches based on labor-intensive field measurements, remote sensing has supplied a large variety of cutting-edge techniques to accomplish forest inventory. However, individual tree species classification in natural mixed forests, as it is typical in central Europe, is still a challenging task. High spectral and structural intra-species variability and inter-species similarity, due to phenological effects, differences in tree age and openness of canopies, shadowing effects, and environment variability, restrict tree species separability. An in-depth understanding of the relationship between species-specific features and remote sensing observations for tree species classification needs further investigation. This thesis aimed to accurately map the species of individual trees using multi-source remotely sensed data, including aerial photographs, airborne LiDAR and hyperspectral data. The research in the thesis firstly evaluated the performance of geometric and radiometric metrics from airborne LiDAR data under leaf-on and leaf-off conditions for individual tree species discrimination. The results empathized the importance of intensity-related LiDAR metrics for tree species identification under both leaf-on and leaf-off conditions. Then, the thesis examined whether multi-temporal digital CIR orthophotos could be used to further increase the accuracy of airborne LiDAR-based individual tree species mapping. The results showed that the texture features generated from multi-temporal digital CIR orthophotos under different view-illumination conditions are species-specific. Combining these texture features with LiDAR metrics significantly improved the accuracy of individual tree species mapping. To explore more valuable species-specific features, the thesis consequently integrated three plant functional traits (i.e. equivalent water thickness, leaf mass per area and leaf chlorophyll) retrieved from hyperspectral data with hyperspectral derived spectral features and airborne LiDAR derived metrics for mapping five tree species. Three selected plant functional traits were accurately retrieved using radiative transfer model and further improved the accuracy of tree species classification. Eventually, the thesis focused on an important tree species silver fir, and accurately mapped individuals of this species based on one-class classifiers using integrated airborne hyperspectral and LiDAR data. The mapping results provided the references locating the areas with a high occurrence probability of silver fir trees and hence increase the efficiency in subsequent field campaigns for forest management and biodiversity monitoring. This thesis explored the potential of various remotely sensed datasets for individual tree species mapping. The methodologies and findings in this thesis can be applied in the mapping of other tree species, which enriches the knowledge of species-specific characteristics and related remotely sensed signatures. The emerging of UAVs and the upcoming hyperspectral missions such as EnMAP and HySPIRI deliver valuable datasets with multi-scale coverage and revisit observations, which can be used for mapping the diversity of tree species at stand or regional level. Note de contenu : - General introduction
- Important LiDAR metrics for discriminating tree species
- Improving LiDAR-based tree species mapping using multi-temporal CIR orthophotos
- Tree species classification using remotely sensed plant functional traits
- Mapping individual silver fir trees in a Norway spruce dominated forest
- Synthesis: Mapping individual tree species using multi-source remotely sensed dataNuméro de notice : 17671 Affiliation des auteurs : non IGN Thématique : FORET Nature : Thèse étrangère Note de thèse : PhD thesis : : University of Twente : 2020 DOI : 10.3990/1.978903654953-0 Date de publication en ligne : 31/01/2020 En ligne : https://doi.org/10.3990/1.978903654953-0 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97985 Assessment of forest canopy vertical structure with multi - scale remote sensing : from the plot to the large area / Phil Wilkes (2016)
Titre : Assessment of forest canopy vertical structure with multi - scale remote sensing : from the plot to the large area Type de document : Thèse/HDR Auteurs : Phil Wilkes, Auteur Editeur : Enschede [Pays Bas] : University of Twente Année de publication : 2016 Collection : ITC Dissertation num. 280 Importance : 180 p. ISBN/ISSN/EAN : 978-90-365-4038-4 Note générale : bibliographie
Dissertation to obtain the Double-Badged Degree of Doctor at the University of Twente, Enschede, The Netherlands; and RMIT University, Melbourne, AustraliaLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] allométrie
[Termes IGN] canopée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] hauteur des arbres
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] ombre
[Termes IGN] placette d'échantillonnage
[Termes IGN] régression
[Termes IGN] semis de points
[Termes IGN] strate végétale
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] Victoria (Australie)Index. décimale : 33.80 Lasergrammétrie Résumé : (auteur) The attribution of forest structure forms an integral part of international monitoring and reporting obligations with regard to sustainable forest management. Furthermore, detailed information about forest structure allows land managers and forest scientists to determine a forests ability to provide ecosystems services. Currently, forest attribution is achieved using a network of forest inventory plots that are revisited periodically. This approach comprises a sparse sample, both temporally and spatially, that may not capture variance in forest structure. This is particularly true in dynamic native forests where variability in forest structure can be high. In recent years the capability of remote sensing techniques has been realised for sustainable forest management applications. Advantages of a remote sensing approach include synoptic and high temporal coverage as well as reduced costs to the end - user. Furthermore, recent advancement in active sensors, such as Light Detection and Ranging Instruments (LiDAR) have allowed for detailed three - dimensional forest measurement of structure across large areas.
This thesis presents new metrics, techniques and acquisition specifications for the attribution of forest canopy over large areas (e.g. comprising two or more forest types where forest structure maybe unknown a priori) using active and passive remote sensing. In particular, the focus is on attributes that quantify the vertical structure of forests; canopy height and canopy vertical structure. Canopy height is a commonly measured multipurpose attribute that is utilised, for example, to estimate biomass. Attribution of the canopy height profile, although less common, is important for mapping habitat suitability, biomass and fire susceptibility. Current techniques to attribute forests tend to be tailored to a particular forest type or location and therefore application of these models across large areas is unreliable. Here the aim is to develop metrics and techniques that are transferable between different forest types and applicable to forests where there is no prior knowledge of forest structure.
Here a multi - scale remote sensing approach was taken, where plot scale measurements were upscaled to attribute large areas. Initially, existing LiDAR derived metrics applicable at the plot scale were tested at three 5 km x 5 km study areas in Victoria, Australia where forests cover a broad range of structural types. Results indicate existing metrics of canopy height were applicable across the range of forest types, for example the 95 th percentile of LiDAR derived height estimated inventory measured canopy height with a RMSE of 12% (~5 m). An existing mixture modelling technique to attribute the canopy height profile was found unsuitable when applied across heterogeneously forested landscape. This was due to the inability to parameterise the model correctly without a priori knowledge of forest structure e.g. presence or absence of shade tolerant layers. For this reason a new technique was developed utilising a nonparametric regression of LiDAR derived gap probability that generalised the canopy profile. Taking the second derivative of the regression curve identified locations within the canopy that correspond with canopy strata, this therefore allowed a dynamic attribution of canopy vertical structure. Model output was validated with a crown volume modelling approach at 24 plots, where crown models were parameterised with inventory data and allometry. Results indicate this technique can estimate the number of canopy strata with a RMSE of 0. 41 strata. Furthermore, the new technique met the transferability criteria , as a universal regression coefficient was transferable between forest types with different structural attributes.
As LiDAR acquisition that cover large areas will inevitably encounter a range of forest types, parameters for attributing canopy structure that were transferable between forest types were investigated; in particular sampling frequency. To effectively assess a range of pulse densities would require repeat capture over a study area at a range of flying heights , which would be prohibitively expensive. For this reason a new technique was developed that systematically thinned point clouds. This technique differs from previous approaches by allowing simulation of multi - return instruments as well as repeat capture of the same plot. Six sites from around Australia were utilised which covered a broad range of forest types, from open savanna to tropical rainforest. For a suite of metrics, the ability of progressively less dense point clouds ( 4 – 0. 05 pl m - 2 ) to estimate canopy structure was estimated by comparison with higher density data (10 pl m - 2 ). Results indicate that canopy structure can be adequately attributed with data captured at 0.5 pl m - 2 . When pulse densities are Techniques derived at the plot scale were then applied to estimate canopy height across 2.9 million hectares of heterogeneous forest. Canopy height in the study area ranged from 0 – 70 m and comprised forest types from open woodland to tall closed canopy rainforest. LiDAR derived canopy height was used to t rain ensemble regression tree s (random forest) , where predictor datasets included synoptic passive optical imagery and other ancillary spatial datasets , such as Landsat TM and MODIS. Results suggest canopy height can be estimated with a RMSE of 30% (5.5 m) when validated with an independent inventory dataset. This is a similar error to that reported in previous studies for less complex forests and is within the European Space Agency target for canopy height estimation. However, model output did show a systematic error, where the height of short and tall forests were over and underestimated respectively. This was corrected by subtracting a model led estimate of error from the random forest output. Production of a canopy height map over a large area allowed for a consistent product that covered a broad range of forest types, derivation at a 30 m resolution allowed the identification of landscape features such as logging coupes. The presented technique utilised an open source computing framework as well as freely available predictor datasets to facilitate uptake of by land management agencies and forest scientists.Note de contenu : Chapter 1 : Introduction
1.1. General introduction
1.2. Problem statement
1.3. Research questions
1.4. Thesis structure
Chapter 2 : Metrics of canopy vertical structure suitable for large area forest attribution
2.1. Introduction
2.1.1. Canopy height
2.1.2. Canopy vertical structure
2.1.3. Aims and objectives
2.2. Materials and methods
2.2.1. Study area
2.2.2. Forest inventory data
2.2.3. Airborne laser scanning data
2.3. Data processing
2.3.1. Canopy height
2.3.2. Canopy vertical structure
2.4. Results
2.4.1. Canopy height
2.4.2. Canopy height profiles
2.5. Discussion
2.6. Conclusion
Chapter 3 : Using discrete-return ALS to quantify number of canopy strata across diverse forest types
3.1. Introduction
3.2. Attributing canopy vertical structure
3.3. Application across a diverse forested landscape
3.3.1. ALS acquisition and preprocessing
3.3.2. Pgap from ALS
3.3.3. Derivation of smoothing coefficient (α)
3.3.4. Bootstrapping simulated point clouds
3.3.5. Validation with field inventory
3.4. Results and Discussion
3.4.1. Methodology evaluation
3.4.2. Validation results
3.4.3. Canopy vertical structure as an independent metric
3.5. Conclusion
Chapter 4 : Understanding the effects of ALS pulse density for metric retrieval across diverse forest types
4.1. Introduction
4.2. Method
4.2.1. Study area and data capture
4.2.2. Data processing
4.2.3. Metrics
4.3. Results
4.3.1. Canopy height
4.3.2. Canopy cover
4.3.3. Canopy vertical structure
4.3.4. Characteristics of thinned point clouds
4.4. Discussion
4.5. Conclusion
Chapter 5 : Mapping forest canopy height across large areas by upscaling ALS estimates with freely available satellite data
5.1. Introduction
5.2. Materials and methods
5.3. Results
5.3.1. Canopy height estimation
5.3.2. Validation with inventory data
5.3.3. Training and validation of random forest using smaller geographic areas
5.3.4. Simulating disparate ALS capture for training a random forest
5.4. Discussion
5.5. Conclusions
Chapter 6 : Summary and synthesis
6.1. Summary of results
6.2. Identifying trends in large area forest structure
6.3. Remote sensing in sustainable forest management: a future perspectiveNuméro de notice : 17249 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD thesis : Remote sensing : Twente : 2016 Organisme de stage : RMIT DOI : sans En ligne : http://www.itc.nl/library/papers_2016/phd/wilkes.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81928 
Titre : Classification and change detection in multi - epoch airborne laser scanning point clouds Type de document : Thèse/HDR Auteurs : Sudan Xu, Auteur Editeur : Enschede [Pays Bas] : University of Twente Année de publication : 2015 Collection : ITC Dissertation num. 266 Importance : 121 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-90-365-3835-0 Note générale : bibliographie
Enschede, University of Twente, Faculty of Geo-Information and Earth Observation (ITC)Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données multitemporelles
[Termes IGN] semis de pointsRésumé : (auteur) Detailed change detection in buildings using airborne laser scanning data (ALS data) has become possible with the availability of multi-temporal ALS data sets. In this thesis we present a methodology for building change detection in urban scenes, which is composed of two main parts: the classification of point clouds of an urban scene and the detection of changes in buildings. A classification methodology is put forward to solve the problem of how to detect buildings in point clouds and how to distinguish the building roofs, building roof elements, and building walls. The change detection methodology is not only used to detect changes but also aims to interpret the type of change that occurred to a building. The two methodologies are suitable for application to raw ALS laser points. They do not require the ALS data to be organised in Digital Surface Models.
The thesis consists of seven chapters. Chapter 1 gives the motivation of this research and an introduction to the background of the two main topics mentioned above. Research problems and questions are raised, and goals and objectives are defined on the two topics. Furthermore, the limits of the research scope of this thesis are set. Chapter 2 introduces the study area used in this thesis, the available data, including the data quality and the data organization, and some pre-processing steps of the data.
Chapter 3 describes the methodology of the classification, explaining the entities, features, classifiers and the classification strategy. We introduce a classification procedure that combines classifications of three different entities: points, planar segments, and segments obtained by mean-shift segmentation. Seven types of objects, namely, water, ground, vegetation, roof, roof element, wall and undefined object, are distinguished based on feature values of the entities. Some features were already defined in literature. Other features are defined by us. Five commonly used classifiers (rule based classification, Random Tree, AdaBoost, SVM, and ANN) are tested. The rule-based method provides over 99% accuracy for the ground and roof classes, and a minimum accuracy of 90% for the water, vegetation, wall and undefined object classes, resulting in an overall accuracy of 97%. The accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. All experimental results for the classification methodology are presented and discussed in chapter 4. These results include the evaluation of the classification accuracy, comparisons between different classifiers and comparisons between different features derived from the different entities.
Chapter 5 explains the methodology of the change detection comprising a point-based change detection method and an object-based change analysis. The detection process starts with two data sets that are classified using the classification methodology in chapter 3. Next, a point-to-plane surface difference map is generated by merging the two data sets to be compared. By applying rules to the surface difference map the change status of points is set to "changed", "unchanged", or "unknown". Rules are defined to solve the problems caused by the lack of data. "Unknown" are locations where due to lack of data in at least one of the epochs it is not possible to reliably detect changes in the structure. Points on buildings labelled as "changed" are re-classified into changes related to roofs, walls, dormers, cars, constructions above roofs and undefined objects in a second classification step. Next, all the classified changes are grouped to changed building objects. Geometric descriptions of the changed building objects, such as the location of the centre point of the change objects, the height, area and volume of the change objects, are derived from their minimum 3D bounding boxes. Performance analysis showed that 80% - 90 % of the real changes are found, of which approximately 50% are considered relevant. The results of the change detection and analysis and their accuracy are discussed in chapter 6.
Finally, chapter 7 draws the main conclusions from the test results obtained with the classification and the change detection methodology. Limitations of our methodologies are summarized and potential solutions to these limitations are suggested.Note de contenu : 1- Introduction
2- Data sets
3- Methodology for the scene classification
4- Evaluation of the scene classification
5- Methodology for the change detection
6- Evaluation of the change detection
7- Conclusion ans perspectivesNuméro de notice : 14922 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD : Geo-Information and Earth Observation : University of Twente : 2015 En ligne : http://www.itc.nl/Pub/Home/library/Academic_output/AcademicOutput.html?l=16&y=15 Format de la ressource électronique : URL sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77056 Documents numériques
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Classification and change detection ... - pdf auteurAdobe Acrobat PDF
Titre : Retrieving surface variables by integrating ground measurements and earth observation data in forest canopies : a case study in Speuldersbos forest Type de document : Thèse/HDR Auteurs : Kitsiri Weligepolage, Auteur Editeur : Enschede [Pays Bas] : University of Twente Année de publication : 2015 Collection : ITC Dissertation num. 269 Importance : 148 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-90-365-3876-3 Note générale : bibliographie
University of Twente, Faculty of Geo-Information and Earth ObservationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] aiguille
[Termes IGN] albedo
[Termes IGN] canopée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Fagus (genre)
[Termes IGN] hauteur des arbres
[Termes IGN] image AHS
[Termes IGN] image thermique
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] Pinophyta
[Termes IGN] Pseudotsuga menziesii
[Termes IGN] réflectance végétale
[Termes IGN] rugosité
[Termes IGN] température au solRésumé : (auteur) The main objective of this study is to integrate tower-based measurements with ED data for estimating spatially and temporally distributed surface variables of a forest canopy for improved quantification of surface-atmosphere interactions. This study mainly focuses on three of the most important surface variables for estimating surface fluxes, namely the aerodynamic roughness, land surface albedo and land surface temperature.
In chapter 2, a framework is presented for estimating aerodynamic roughness parameters: the momentum roughness length (z0) and the displacement height (do) of a coniferous forest stand using remote sensing data. The specific objective of the study is to make use of high resolution Terrestrial Laser Scanning (TLS) data together with Airborne Laser Scanning (ALS) data to digitally map the upper canopy surface in order to generate high resolution digital Canopy Height Models (CHMs). The digital CHMs were subsequently used to extract surface geometric parameters of the upper canopy surface. Eventually the surface geometric parameters were used as input variables in the selected morphometric models to estimate aerodynamic roughness parameters. It was observed that the estimated values of zo and do depend very much on the selected model. Comparison of model estimated roughness parameters against the literature values for similar surface types has shown that the technique can be successfully applied to estimate forest surface roughness by tuning some of the model parameters to resemble the forest structure of the study area.
Chapter 3 describes the use of these two aerodynamic methods to estimate momentum roughness length and displacement height of Douglas fir forest using simultaneous micrometeorological and flux measurements. When the flux-gradient method was used to objectively determine zo and do, corrections for roughness sub-layer effects proved to be important. A new iterative method is employed to solve the set of equations when the corrections were made. In the absence of experimentally determined roughness sub-layer height, the corrections of Harman and Finnigan (2007) yielded the best overall estimates of aerodynamic parameters. Comparison with results of over 25 other studies has shown that the results obtained in this work fit the general trend rather well. Two quadratic relationships are proposed to predict do and ha based on the observed mean tree height. These simple relationships can be easily incorporated to large scale land surface models, provided that spatially distributed tree height information is available. The flux-variance technique is shown to be robust even when measurements are made in the roughness sub-layer. However the technique cannot be objectively used to estimate zo and do as no explicit method exists to select the exact value for coefficient C1.
A detailed investigation of stand level surface albedo variability of a patchwork forest is presented in chapter 4. The top of the canopy reflectance in the visible and near-infrared domain retrieved from airborne and satellite imageries were integrated to estimate spatially distributed surface albedo while the tower-based radiation measurements in the solar-reflective region were used to obtain the temporal variation of surface albedo over a needleleaf forest canopy. The diurnal variation of surface albedo is consistent with the previous findings for needleleaf forest canopies. The spatial mean surface albedo values estimated from remote sensing data for needleleaf (pure Douglas fir), broadleaf (pure Beech) and mixed forest classes are 0.09, 0.13 and 0.11 respectively. Both visual characteristics and descriptive statistics indicate that with increased pixel size, the spatial variability of albedo progressively decreases. The semivariogram analysis was more insightful to perceive the nature and causes of albedo spatial variability in different forest classes in relation to sensor spatial resolution.
Finally a theoretical basis for directional LST estimation from top of the atmosphere radiance measurements is presented along with a spatio-temporal analysis of remotely sensed LST and concurrently carried out ground-based radiation together with contact temperature measurements in a Douglas fir forest. For the analysis we used remotely sensed TIR data from Airborne Hyperspectral Scanner to estimate spatially distributed LST of forested area. The AHS sensor, with 10 thermal bands covering the range between 8 and 13pm of the electromagnetic spectrum is an example of the new generation of airborne sensors with multispectral thermal infrared capabilities. The data acquired from the AHS sensors provided the opportunity to retrieve the directional LST of the forest canopy with a very high spatial resolution for both nadir and oblique view angles. Also the concurrent tower-based temperature measurements provided limited ground truth for a spatio-temporal analysis of surface temperature in an area covered with Douglas fir trees. The method adopted here for concurrent determination of LST and LSE is the widely-used TES algorithm together with the MODTRAN4 preprocessor for calculating the required atmospheric contributions. AHS derived average temperature values are generally in good agreement with the tower based component temperature measured at 24 m level whereas the component temperatures (trunk) measured at 17 m are consistently higher. It may be noted that in comparison with off-nadir radiometric temperature the TES method provides average LST with RMSE around 1.9K while the corresponding value with respect to component temperature measured at 24 m is around 1.4 K.Note de contenu : 1- Introduction
2- Estimation of canopy aerodynamic roughness using morphometric methods
3- Effects of sub-layer corrections on the roughness parametrization of a Douglas fir forest
4- Effects of spatial resolution on estimating surface albedo
5- Retrieving directional temperature using multiplatform thermal data
6- Conclusion and recommendationsNuméro de notice : 14944 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD : Geo-Information and Earth Observation : University of Twente : 2015 En ligne : https://research.utwente.nl/en/publications/retrieving-surface-variables-by-inte [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77060 Documents numériques
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14944 Retrieving surface variablesAdobe Acrobat PDF
Titre : Web-based architecture for on-demand maps - integrating meaningful generalization processing Type de document : Thèse/HDR Auteurs : Theodor Foerster, Auteur Editeur : Enschede [Pays Bas] : University of Twente Année de publication : 2010 Collection : ITC Dissertation num. 165 Importance : 193 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-90-6164-285-5 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] carte sur mesure
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] programmation par contraintes
[Termes IGN] service web géographique
[Termes IGN] web mappingRésumé : (auteur) Generating readable maps at a specific scale and for a specific use is a chal-lenge in research and practice. With the advent of the web as a platform for accessing and sharing information between users, maps also became web-accessible. The web provides the means to generate and disseminate maps for specific users (i.e. on-demand). Providing on-demand maps is considered to improve map communication. In this context, automated generalization is one solution to generate these on-demand maps.
Currently, thematic content such as physical plans becomes available on the web and displayed as maps. These maps are available as separate layers without a base map. Thereby they are in need of an on-demand base map, which might be generated regarding the thematic content and the specific user. In the case of physical planning on-demand base maps enhance the communication of the thematic content and thereby improve the communication process between the planning authorities and the public.
However, a web-based architecture to generate and disseminate on-demand base maps has not been proposed yet. Regarding the aspect of disseminating on-demand base maps on the web, the thesis investigates how to formulate user requirements towards the on-demand base map in such an environment. These user requirements are captured in so-called user profiles, which are formalized in UML and XML-models. The on-demand base map is generated according to these user profiles and according to the specific thematic content. As core of the architecture, a so-called generalization-enabled Web Map Service is presented, which consumes these user profiles and generates the base maps accordingly. The architecture is implemented as a proof-of-concept and is applied to the use case of physical planning in the Netherlands.
Besides the aim of disseminating these on-demand base maps on the web, the web is also promising to serve as a platform for web-based generalization. Such web-based processes are performed by Web Services. Establishing web-based meaningful generalization processing, the semantic interoperability of these Web Services has to be enabled, which is considered to be a challenge for research. In particular, this thesis proposes a classification of generalization operators, which is formalized in the Object Constraint Language and deployed using the Web Processing Service interface and so-called WPS profiles.
This research contributes to on-demand web mapping and to automated generalization on the web. Designing user profiles to describe generalization-specific requirements and incorporating them into a web-based architecture is a novelty for web mapping. The user profiles thereby enable the on-demand notion and are a complementary approach to the already established OGC Styled Layer Descriptor documents (for symbolization) and OGC Web Map Context (for map content definition) documents as they define user-specific issues regarding scale and the link between thematic content and the base map. Defining user profiles for the application of base maps is a novel approach in itself. Generating base maps by automated generalization has not been attempted so far. The user requirements define the thematic content as input for the generalization of the base map, which is also a novelty. The generalization-enabled WMS is the component which provides the on-demand base map in a standards-compliant way based on the specific user profile. Additionally, this research contributes to the meaningful integration of generalization processes on the web. In particular, this research contributes to the theory of automated generalization by providing a classification of generalization operators. Further, this research contributes to the issue of semantic interoperability of Web Generalization Services as it formalizes the classification using standardized data models of ISO and OGC and finally proposes the application of WPS profiles and the Object Constraint Language. Using standardized data models, this classification is extensible and comprehensible for future research. The application of WPS profiles and the Object Constraint Language is not only a novelty to generalization research and Web Generalization Services, but also contributes to the aspect of web-based geoprocessing in general.Numéro de notice : 17000 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Thèse étrangère Note de thèse : PhD thesis : : University of Twente : 2010 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78942 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 17000-01 K325 Livre LASTIG Dépôt en unité Exclu du prêt Documents numériques
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Web-based architecture for on-demand mapsAdobe Acrobat PDF
