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Deriving ground surface digital elevation models from Lidar data with geostatistics / C.D. Lloyd in International journal of geographical information science IJGIS, vol 20 n° 5 (may 2006)
[article]
Titre : Deriving ground surface digital elevation models from Lidar data with geostatistics Type de document : Article/Communication Auteurs : C.D. Lloyd, Auteur ; P.M. Atkinson, Auteur Année de publication : 2006 Article en page(s) : pp 535 - 563 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] données lidar
[Termes IGN] géostatistique
[Termes IGN] interpolation inversement proportionnelle à la distance
[Termes IGN] krigeage
[Termes IGN] lissage de données
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] surface du solRésumé : (Auteur) This paper focuses on two common problems encountered when using Light Detection And Ranging (LiDAR) data to derive digital elevation models (DEMs). Firstly, LiDAR measurements are obtained in an irregular configuration and on a point, rather than a pixel, basis. There is usually a need to interpolate from these point data to a regular grid so it is necessary to identify the approaches that make best use of the sample data to derive the most accurate DEM possible. Secondly, raw LiDAR data contain information on above-surface features such as vegetation and buildings. It is often the desire to (digitally) remove these features and predict the surface elevations beneath them, thereby obtaining a DEM that does not contain any above-surface features. This paper explores the use of geostatistical approaches for prediction in this situation. The approaches used are inverse distance weighting (IDW), ordinary kriging (OK) and kriging with a trend model (KT). It is concluded that, for the case studies presented, OK offers greater accuracy of prediction than IDW while KT demonstrates benefits over OK. The absolute differences are not large, but to make the most of the high quality LiDAR data KT seems the most appropriate technique in this case. Numéro de notice : A2006-175 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810600607337 En ligne : https://doi.org/10.1080/13658810600607337 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27902
in International journal of geographical information science IJGIS > vol 20 n° 5 (may 2006) . - pp 535 - 563[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-06051 RAB Revue Centre de documentation En réserve L003 Disponible 079-06052 RAB Revue Centre de documentation En réserve L003 Disponible The discontinuous nature of kriging interpolation for digital terrain modelling / Thomas H. Meyer in Cartography and Geographic Information Science, vol 31 n° 4 (October 2004)
[article]
Titre : The discontinuous nature of kriging interpolation for digital terrain modelling Type de document : Article/Communication Auteurs : Thomas H. Meyer, Auteur Année de publication : 2004 Article en page(s) : pp 209 - 216 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] discontinuité
[Termes IGN] estimation statistique
[Termes IGN] interpolation inversement proportionnelle à la distance
[Termes IGN] interpolation spatiale
[Termes IGN] krigeage
[Termes IGN] modèle numérique de terrain
[Termes IGN] variogrammeRésumé : (Auteur) Kriging is a widely employed method for interpolating and estimating elevations from digital elevation data. Its place of prominence is due to its elegant theoretical foundation and its convenient practical implementation. From an interpolation point of view, kriging is equivalent to a thin -plate spline and is one species among the many in the genus of weighted inverse distance methods, albeit with attractive properties. However, from a statistical point of view, kriging is a best linear unbiased estimator and, consequently, has a place of distinction among all spatial estimators because any other linear estimator that performs as well as kriging ( in the least squares sense) must be equivalent to kriging, assuming that the parmeters of the semivariogram are known. Therefore, kriging is often held to be gold standard of digital terrain model estimation. However, i prove that, when used with local support, kriging creates discontinuous digital terrain models, which is to say, surfaces with "rips" and "tears" throughout them. This results is generaly ; it is true for ordinary kriging with a trend, and other forms. A U.S. Geological Survey (USGS) digital elevation model was analyzed to characterize the distribution of the discontinuities. I show that the magnitude of the discontinuity does not depend on surface gradient but is strongly dependant on the size of the kriging neighborhood. Numéro de notice : A2004-605 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1559/1523040042742385 En ligne : https://doi.org/10.1559/1523040042742385 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27121
in Cartography and Geographic Information Science > vol 31 n° 4 (October 2004) . - pp 209 - 216[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-04041 RAB Revue Centre de documentation En réserve L003 Disponible Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape / M.L. Clarke in Remote sensing of environment, vol 91 n° 1 (15/05/2004)
[article]
Titre : Small-footprint lidar estimation of sub-canopy elevation and tree height in a tropical rain forest landscape Type de document : Article/Communication Auteurs : M.L. Clarke, Auteur ; D. Clark, Auteur ; D.A. Roberts, Auteur Année de publication : 2004 Article en page(s) : pp 68 - 89 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] corrélation linéaire
[Termes IGN] données lidar
[Termes IGN] erreur moyenne quadratique
[Termes IGN] forêt tropicale
[Termes IGN] interpolation inversement proportionnelle à la distance
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] sous-bois
[Termes IGN] télémétrie laser aéroportéRésumé : (Auteur) Meso-scale digital terrain models (DTMs) and canopy-height estimates, or digital canopy models (DCMs), are two lidar products that have immense potential for research in tropical rain forest (TRF) ecology and management. In this study, we used a small-footprint lidar sensor (airborne laser scanner, ALS) to estimate sub-canopy elevation and canopy height in an evergreen tropical rain forest. A fully automated, local-minima algorithm was developed to separate lidar ground returns from overlying vegetation returns. We then assessed inverse distance weighted (IDW) and ordinary kriging (OK) geostatistical techniques for the interpolation of a sub-canopy DTM. OK was determined to be a superior interpolation scheme because it smoothed fine-scale variance created by spurious understory heights in the ground-point dataset. The final DTM had a linear correlation of 1.00 and a root-mean-square error (RMSE) of 2.29 m when compared against 3859 well-distributed ground-survey points. In old-growth forests, RMS error on steep slopes was 0.67 m greater than on flat slopes. On flatter slopes, variation in vegetation complexity associated with land use caused highly significant differences in DTM error distribution across the landscape. The highest DTM accuracy observed in this study was 0.58-m RMSE, under flat, open-canopy areas with relatively smooth surfaces. Lidar ground retrieval was complicated by dense, multi-layered evergreen canopy in old-growth forests, causing DTM overestimation that increased RMS error to 1.95 m.
A DCM was calculated from the original lidar surface and the interpolated DTM. Individual and plot-scale heights were estimated from DCM metrics and compared to field data measured using similar spatial supports and metrics. For old-growth forest emergent trees and isolated pasture trees greater than 20 in tall, individual tree heights were underestimated and had 3.67- and 2.33-m mean absolute error (MAE), respectively. Linear-regression models explained 51% (4.15-m RMSE) and 95% (2.41-m RMSE) of the variance, respectively. It was determined that improved elevation and field-height estimation in pastures explained why individual pasture trees could be estimated more accurately than old-growth trees. Mean height of tree stems in 32 young agroforestry plantation plots (0.38 to 18.53 m tall) was estimated with a mean absolute error of 0.90 m (r 2 = 0,97; 1.08-m model RMSE) using the mean of lidar returns in the plot. As in other small-footprint lidar studies, plot mean height was underestimated; however, our plot-scale results have stronger linear models for tropical, leaf-on hardwood trees than has been previously reported for temperate-zone conifer and deciduous hardwoods.Numéro de notice : A2004-237 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.02.008 En ligne : https://doi.org/10.1016/j.rse.2004.02.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26764
in Remote sensing of environment > vol 91 n° 1 (15/05/2004) . - pp 68 - 89[article]Artificial neural networks as a method of spatial interpolation for digital elevation models / D.A. Merwin in Cartography and Geographic Information Science, vol 29 n° 2 (April 2002)
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Titre : Artificial neural networks as a method of spatial interpolation for digital elevation models Type de document : Article/Communication Auteurs : D.A. Merwin, Auteur ; R.G. Cromley, Auteur ; Daniel L. Civco, Auteur Année de publication : 2002 Article en page(s) : pp 99 - 110 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] interpolation inversement proportionnelle à la distance
[Termes IGN] interpolation spatiale
[Termes IGN] modèle numérique de surface
[Termes IGN] réseau neuronal artificiel
[Termes IGN] valeur efficaceRésumé : (Auteur) This paper examines the performance of artificial neural networks (ANNs) as a method of spatial interpolation, when presented with irregular and regular samples of elevation data. The results of the ANN interpolation are compared with results obtained by kriging. Tests of spatial bias in the systematic errors contained in each of the neural network-derived DEMs were conducted using four attributes: slope, aspect, average direction and average distance from the nearest sampled value. Based on RMS and other evaluation measures, the accuracy of estimated DEMs from regular and irregular sample distributions using neural networks is lower than the accuracy level derived from kriging. The accuracy level of the ANN interpolators also decreases as the range of elevation values in DEMs increases. As reported in the literature, ANNs are approximate interpolators, and the pattern of under-prediction and over-prediction of elevation values in this study revealed that all estimated values fell within the range of sample elevations. Neural networks cannot predict values outside the range of elevation values contained in the sample, a property shared by other interpolators such as inverse weighted distance. Numéro de notice : A2002-144 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1559/152304002782053323 En ligne : https://doi.org/10.1559/152304002782053323 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22059
in Cartography and Geographic Information Science > vol 29 n° 2 (April 2002) . - pp 99 - 110[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-02021 RAB Revue Centre de documentation En réserve L003 Disponible