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Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules / Yongjiu Feng in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)
[article]
Titre : Modeling dynamic urban land-use change with geographical cellular automata and generalized pattern search-optimized rules Type de document : Article/Communication Auteurs : Yongjiu Feng, Auteur Année de publication : 2017 Article en page(s) : pp 1198 - 1219 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] algorithme génétique
[Termes IGN] analyse diachronique
[Termes IGN] automate cellulaire
[Termes IGN] base de règles
[Termes IGN] changement d'utilisation du sol
[Termes IGN] données GPS
[Termes IGN] jointure spatiale
[Termes IGN] Kiangsou (Chine)
[Termes IGN] modèle de simulation
[Termes IGN] prédiction
[Termes IGN] régression logistique
[Termes IGN] simulation
[Termes IGN] zone urbaineRésumé : (auteur) A novel generalized pattern search (GPS)-based cellular automata (GPS-CA) model was developed to simulate urban land-use change in a GIS environment. The model is built on a fitness function that computes the difference between the observed results produced from remote-sensing images and the simulated results produced by a general CA model. GPS optimization incorporating genetic algorithms (GAs) searches for the minimum difference, i.e. the smallest accumulated residuals, in fitting the CA transition rules. The CA coefficients captured by the GPS method have clear physical meanings that are closely associated with the dynamic mechanisms of land-use change. The GPS-CA model was applied to simulate urban land-use change in Kunshan City in the Yangtze River Delta from 2000 to 2015. The results show that the GPS method had a smaller root mean squared error (0.2821) than a logistic regression (LR) method (0.5256) in fitting the CA transition rules. The GPS-CA model thus outperformed the LR-CA model, with an overall accuracy improvement of 4.7%. As a result, the GPS-CA model should be a superior tool for modeling land-use change as well as predicting future scenarios in response to different conditions to support the sustainable urban development. Numéro de notice : A2017-244 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1287368 En ligne : http://dx.doi.org/10.1080/13658816.2017.1287368 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85180
in International journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 1198 - 1219[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2017031 RAB Revue Centre de documentation En réserve L003 Disponible Panda∗: A generic and scalable framework for predictive spatio-temporal queries / Abdeltawab M. Hendawi in Geoinformatica, vol 21 n° 2 (April - June 2017)
[article]
Titre : Panda∗: A generic and scalable framework for predictive spatio-temporal queries Type de document : Article/Communication Auteurs : Abdeltawab M. Hendawi, Auteur ; Mohamed Ali, Auteur ; Mohamed F. Mokbel, Auteur Année de publication : 2017 Article en page(s) : pp 175 - 208 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] environnement de développement
[Termes IGN] espace euclidien
[Termes IGN] gestion de trafic
[Termes IGN] objet mobile
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] prédiction
[Termes IGN] requête spatiotemporelleRésumé : (Auteur) Predictive spatio-temporal queries are crucial in many applications. Traffic management is an example application, where predictive spatial queries are issued to anticipate jammed areas in advance. Also, location-aware advertising is another example application that targets customers expected to be in the vicinity of a shopping mall in the near future. In this paper, we introduce Panda∗, a generic framework for supporting spatial predictive queries over moving objects in Euclidean spaces. Panda∗ distinguishes itself from previous work in spatial predictive query processing by the following features: (1) Panda∗ is generic in terms of supporting commonly-used types of queries, (e.g., predictive range, KNN, aggregate queries) over stationary points of interests as well as moving objects. (2) Panda∗ employees a prediction function that provides accurate prediction even under the absence or the scarcity of the objects’ historical trajectories. (3) Panda∗ is customizable in the sense that it isolates the prediction calculation from query processing. Hence, it enables the injection and integration of user defined prediction functions within its query processing framework. (4) Panda∗ deals with uncertainties and variabilities in the expected travel time from source to destination in response to incomplete information and/or dynamic changes in the underlying Euclidean space. (5) Panda∗ provides a controllable parameter that trades low latency responses for computational resources. Experimental analysis proves the scalability of Panda∗ in evaluating a massive volume of predictive queries over large numbers of moving objects. Numéro de notice : A2017-068 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-016-0284-8 En ligne : http://dx.doi.org/10.1007/s10707-016-0284-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84295
in Geoinformatica > vol 21 n° 2 (April - June 2017) . - pp 175 - 208[article]Quantifying early-seral forest composition with remote sensing / Rayma A Cooley in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 11 (November 2016)
[article]
Titre : Quantifying early-seral forest composition with remote sensing Type de document : Article/Communication Auteurs : Rayma A Cooley, Auteur ; Peter T. Wolter, Auteur ; Brian R. Sturtevant, Auteur Année de publication : 2016 Article en page(s) : pp 853 - 863 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] données de terrain
[Termes IGN] incendie de forêt
[Termes IGN] Populus tremula
[Termes IGN] prédiction
[Termes IGN] surface terrière
[Termes IGN] télédétection
[Termes IGN] teneur en carbone
[Termes IGN] troncRésumé : (Auteur) Spatially explicit modeling of recovering forest structure within two years following wildfire disturbance has not been attempted, yet such knowledge is critical for determining successional pathways. We used remote sensing and field data, along with digital climate and terrain data, to model and map early-seral aspen structure and vegetation species richness following wildfire. Richness was the strongest model (rmse = 2.47 species, Adj. R2 = 0.60), followed by aspen stem diameter, basal area (ba), height, density, and percent cover (Adj. R2 range = 0.22 to 0.53). Effects of pre-fire aspen ba and fire severity on post-fire aspen structure and richness were analyzed. Post-fire recovery attributes were not significantly related to fire severity, while all but percent cover and richness were sensitive to pre-fire aspen ba (Adj. R2 range = 0.12 to 0.33, p Numéro de notice : A2016-945 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.11.853 En ligne : http://dx.doi.org/10.14358/PERS.82.11.853 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83437
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 11 (November 2016) . - pp 853 - 863[article]Propagating uncertainty through individual tree volume model predictions to large-area volume estimates / Ronald E. McRoberts in Annals of Forest Science, vol 73 n° 3 (September 2016)
[article]
Titre : Propagating uncertainty through individual tree volume model predictions to large-area volume estimates Type de document : Article/Communication Auteurs : Ronald E. McRoberts, Auteur ; James A. Westfall, Auteur Année de publication : 2016 Article en page(s) : pp 625 – 633 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] diamètre des arbres
[Termes IGN] hauteur des arbres
[Termes IGN] incertitude des données
[Termes IGN] modèle de simulation
[Termes IGN] prédiction
[Termes IGN] propagation d'erreur
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Key message : The effects on large-area volume estimates of uncertainty in individual tree volume model predictions were negligible when using simple random sampling estimators for large-area estimation, but non-negligible when using stratified estimators which reduced the effects of sampling variability.
Context : Forest inventory estimates of tree volume for large areas are typically calculated by adding model predictions of volumes for individual trees at the plot level and calculating the per unit area mean over plots. The uncertainty in the model predictions is generally ignored with the result that the precision of the large-area volume estimate is optimistic.
Aims : The primary objective was to estimate the effects on large-area volume estimates of volume model prediction uncertainty due to diameter and height measurement error, parameter uncertainty, and model residual variance.
Methods : Monte Carlo simulation approaches were used because of the complexities associated with multiple sources of uncertainty, the non-linear nature of the models, and heteroskedasticity.
Results : The effects of model prediction uncertainty on large-area volume estimates of growing stock volume were negligible when using simple random sampling estimators. However, with stratified estimators that reduce the effects of sampling variability, the effects of model prediction uncertainty were not necessarily negligible. The adverse effects of parameter uncertainty and residual variance were greater than the effects of diameter and height measurement errors.
Conclusion : The uncertainty of large-area volume estimates that do not account for model prediction uncertainty should be regarded with caution.Numéro de notice : A2016-711 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s13595-015-0473-x Date de publication en ligne : 22/04/2015 En ligne : https://doi.org/10.1007/s13595-015-0473-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82089
in Annals of Forest Science > vol 73 n° 3 (September 2016) . - pp 625 – 633[article]Predicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables / Aaron E. Maxwell in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)
[article]
Titre : Predicting palustrine wetland probability using random forest machine learning and digital elevation data-derived terrain variables Type de document : Article/Communication Auteurs : Aaron E. Maxwell, Auteur ; Thimoty A. Warner, Auteur ; Michael P. Strager, Auteur Année de publication : 2016 Article en page(s) : pp 437 - 447 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données topographiques
[Termes IGN] Etats-Unis
[Termes IGN] inventaire
[Termes IGN] marais salant
[Termes IGN] modèle numérique de terrain
[Termes IGN] prédiction
[Termes IGN] surveillance écologique
[Termes IGN] Virgine OccidentaleRésumé : (Auteur) The probability of palustrine wetland occurrence in the state of West Virginia, USA, was mapped based on topographic variables and using random forests (RF) machine learning. Models were developed for both selected ecological subregions and the entire state. The models were first trained using pixels randomly selected from the United States National Wetland Inventory (NWI) dataset and were tested using a separate random subset from the NWI and a database of wetlands not found in the NWI provided by the West Virginia Division of Natural Resources (WVDNR). The models produced area under the curve (AUC) values in excess of 0.90, and as high as 0.998. Models developed in one ecological subregion of the state produced significantly different AUC values when applied to other subregions, indicating that the topographical models should be extrapolated to new physiographic regions with caution. Several previously unexplored DEM-derived terrain variables were found to be of value, including distance from water bodies, roughness, and dissection. Non-NWI wetlands were mapped with an AUC value of 0.956, indicating that the probability maps may be useful for finding potential palustrine wetlands not found in the NWI . Numéro de notice : A2016-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.6.437 En ligne : http://dx.doi.org/10.14358/PERS.82.6.437 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81348
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 6 (June 2016) . - pp 437 - 447[article]Approximating prediction uncertainty for random forest regression models / John W. Coulston in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)PermalinkProjections démographiques / Françoise de Blomac in DécryptaGéo le mag, n° 172 (décembre 2015)PermalinkThe influence of application a simplified transformation model between reference frames ECEF and ECI onto prediction accuracy of position and velocity of GLONASS satellites / Robert Krzyzek in Reports on geodesy and geoinformatics, vol 99 (December 2015)PermalinkPrediction of traffic counts using statistical and neural network models / Abul Kalam Azad in Geomatica, vol 69 n° 3 (september 2015)PermalinkComparison of methods toward multi-scale forest carbon mapping and spatial uncertainty analysis: combining national forest inventory plot data and landsat TM images / Andrew L. Fleming in European Journal of Forest Research, vol 134 n° 1 (January 2015)PermalinkRegional gold potential mapping in Kelantan (Malaysia) using probabilistic based models and GIS / Suhaimizi Yusoff in Open geosciences, vol 7 n° 1 (January 2015)PermalinkPredictive policing / Jeremy Heffner in GEO: Geoconnexion international, vol 13 n° 7 (July 2014)PermalinkA morphological approach to predicting urban expansion / Jamal Jokar Arsanjani in Transactions in GIS, vol 18 n° 2 (April 2014)PermalinkAutomated stem curve measurement using terrestrial laser scanning / Xinlian Liang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 3 (March 2014)PermalinkMarkov land cover change modeling using pairs of time-series satellite images / Priyakant Sinha in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)Permalink