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Relationship between landform classification and vegetation (case study: southwest of Fars province, Iran) / Marzieh Mokarram in Open geosciences, vol 8 n° 1 (January - July 2016)
[article]
Titre : Relationship between landform classification and vegetation (case study: southwest of Fars province, Iran) Type de document : Article/Communication Auteurs : Marzieh Mokarram, Auteur ; Dinesh Sathyamoorthy, Auteur Année de publication : 2016 Article en page(s) : pp 302 - 309 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification
[Termes IGN] hauteur des arbres
[Termes IGN] Iran
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression multipleRésumé : (auteur) This study is aimed at investigating the relationship between landform classification and vegetation in the southwest of Fars province, Iran. First, topographic position index (TPI) is used to perform landform classification using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) with resolution of 30 m. The classification has ten classes; high ridges, midslope ridges, upland drainage, upper slopes, open slopes, plains, valleys, local ridges, midslope drainage and streams. Visual interpretation indicates that for the local, midslope and high ridge landforms, normalized difference vegetation index (NDVI) values and tree heights are higher as compared to the other landforms. In addition, it is found that there are positive and significant correlations betweenNDVI and tree height (r = 0.923), and landform and NDVI (r = 0.640). This shows that landform classification and NDVI can be used to predict tree height in the area. High correlation of determination (R2) 0.909 is obtained for the prediction of tree height using landform classification and NDVI. Numéro de notice : A2016--067 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1515/geo-2016-0027 En ligne : https://doi.org/10.1515/geo-2016-0027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84413
in Open geosciences > vol 8 n° 1 (January - July 2016) . - pp 302 - 309[article]A spatial analysis of GEOID03 and GEOID09 in Connecticut / Kazi Arifuzzaman in Journal of applied geodesy, vol 10 n° 2 (June 2016)
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Titre : A spatial analysis of GEOID03 and GEOID09 in Connecticut Type de document : Article/Communication Auteurs : Kazi Arifuzzaman, Auteur ; Raymond J. Hintz, Auteur Année de publication : 2016 Article en page(s) : pp 95 - 102 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] analyse spatiale
[Termes IGN] Connecticut (Etats-Unis)
[Termes IGN] krigeage
[Termes IGN] North American Vertical Datum 1988
[Termes IGN] régression
[Termes IGN] United States Gravimetric Geoid 2009Résumé : (auteur) The National Geodetic Survey (NGS) recommends using a hybrid geoid model to derive orthometric heights from ellipsoid heights. The accuracy of GEOID03 and GEOID09 were assessed independently in Connecticut. The present research analyses the spatial behavior of residuals derived from the comparison of differential levelled NAVD 88 orthometric heights and GPS-derived orthometric heights (using GEOID03 & GEOID09) at 72 benchmarks in Connecticut. Both geometrical and geostatistical analyses were performed on the residuals. A planar regression model indicates a weak spatial relation for residuals derived from GEOID03. This weakness was not noted in the analysis of residuals derived from GEOID09. Results of a four-parameter regression model does not indicate any need for a correction surface. A kriging surface was created with a fitted spherical semivariogram model and suggests GEOID09 captures more spatial variability of geoid undulation than GEOID03 in Connecticut. Numéro de notice : A2016-557 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/jag-2015-0013 En ligne : http://dx.doi.org/10.1515/jag-2015-0013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81717
in Journal of applied geodesy > vol 10 n° 2 (June 2016) . - pp 95 - 102[article]Barycentre method for solving distance equations / X. Shuqiang in Survey review, vol 48 n° 348 (May 2016)
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Titre : Barycentre method for solving distance equations Type de document : Article/Communication Auteurs : X. Shuqiang, Auteur ; Y. Yuanxi, Auteur ; D. Yamin, Auteur Année de publication : 2016 Article en page(s) : pp 188 - 194 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Mathématique
[Termes IGN] algorithme de Gauss-Newton
[Termes IGN] barycentre
[Termes IGN] distance
[Termes IGN] équation
[Termes IGN] itération
[Termes IGN] méthode des moindres carrésRésumé : (auteur) When iteratively solving the distance equations, the Newton’s method has quadratic convergence but it requires the second-order derivatives. The Gauss–Newton’s method needs no information about the second-order derivatives but it may fail without the line search strategy. A simple method called barycentre method is proposed to locally solving the distance equations without the Hessian matrix, the matrix inversion and the line search strategy. The geometrical meaning of the non-linear least-squares solution of the distance equations is revealed that it is the barycentre of a particle system composed of the observational weights at the endpoints of observed distance vectors. By the geometrical meaning of the non-linear least squares, the authors structure an iterative equation to solve the distance equation, and then the convergence and the computational complexity of the method proposed is discussed. It shows that the barycentre method is a conservatively steepest decent method to guarantee the convergence and this method has good performances for solving well-conditioned problems. Ultimately, the method proposed is applied to well-condition and ill-posed positioning equations and the main results are verified. Numéro de notice : A2016-275 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1179/1752270615Y.0000000020 En ligne : http://doi.org/10.1179/1752270615Y.0000000020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80824
in Survey review > vol 48 n° 348 (May 2016) . - pp 188 - 194[article]Forest above ground biomass inversion by fusing GLAS with optical remote sensing data / Xiaohuan Xi in ISPRS International journal of geo-information, vol 5 n° 4 (April 2016)
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Titre : Forest above ground biomass inversion by fusing GLAS with optical remote sensing data Type de document : Article/Communication Auteurs : Xiaohuan Xi, Auteur ; Tingting Han, Auteur ; Cheng Wang, Auteur ; et al., Auteur Année de publication : 2016 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] classification par réseau neuronal
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] données ICEsat
[Termes IGN] forêt
[Termes IGN] hauteur de la végétation
[Termes IGN] image Landsat-TM
[Termes IGN] image optique
[Termes IGN] image Terra-MODIS
[Termes IGN] Leaf Area Index
[Termes IGN] MNS ASTER
[Termes IGN] régression
[Termes IGN] Yunnan (Chine)Résumé : (auteur) Forest biomass is an important parameter for quantifying and understanding biological and physical processes on the Earth’s surface. Rapid, reliable, and objective estimations of forest biomass are essential to terrestrial ecosystem research. The Geoscience Laser Altimeter System (GLAS) produced substantial scientific data for detecting the vegetation structure at the footprint level. This study combined GLAS data with MODIS/BRDF (Bidirectional Reflectance Distribution Function) and ASTER GDEM data to estimate forest aboveground biomass (AGB) in Xishuangbanna, Yunnan Province, China. The GLAS waveform characteristic parameters were extracted using the wavelet method. The ASTER DEM was used to compute the terrain index for reducing the topographic influence on the GLAS canopy height estimation. A neural network method was applied to assimilate the MODIS BRDF data with the canopy heights for estimating continuous forest heights. Forest leaf area indices (LAIs) were derived from Landsat TM imagery. A series of biomass estimation models were developed and validated using regression analyses between field-estimated biomass, canopy height, and LAI. The GLAS-derived canopy heights in Xishuangbanna correlated well with the field-estimated AGB (R2 = 0.61, RMSE = 52.79 Mg/ha). Combining the GLAS estimated canopy heights and LAI yielded a stronger correlation with the field-estimated AGB (R2 = 0.73, RMSE = 38.20 Mg/ha), which indicates that the accuracy of the estimated biomass in complex terrains can be improved significantly by integrating GLAS and optical remote sensing data. Numéro de notice : A2016-820 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi5040045 En ligne : https://doi.org/10.3390/ijgi5040045 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82625
in ISPRS International journal of geo-information > vol 5 n° 4 (April 2016)[article]Robust locally weighted regression techniques for ground surface points filtering in mobile laser scanning three dimensional point cloud data / Abdul Nurunnabi in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)
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Titre : Robust locally weighted regression techniques for ground surface points filtering in mobile laser scanning three dimensional point cloud data Type de document : Article/Communication Auteurs : Abdul Nurunnabi, Auteur ; Geoff West, Auteur ; David Belton, Auteur Année de publication : 2016 Article en page(s) : pp 2181 - 2193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] ajustement de paramètres
[Termes IGN] algorithme de filtrage
[Termes IGN] analyse comparative
[Termes IGN] analyse de données
[Termes IGN] extraction de points
[Termes IGN] régression
[Termes IGN] segmentation d'image
[Termes IGN] semis de points
[Termes IGN] télémétrie laser mobile
[Termes IGN] zone urbaineRésumé : (Auteur) This paper introduces robust algorithms for extracting the ground points in laser scanning 3-D point cloud data. Global polynomial functions have been used for filtering algorithms for point cloud data; however, it is not suitable as it may lead to bias for the filtering algorithms and can cause misclassification errors when many different objects are present. In this paper, robust statistical approaches are coupled with locally weighted 2-D regression that fits without any predefined global function for the variables of interest. Algorithms are performed iteratively on 2-D profiles: x - z and y - z. The z (elevation) values are robustly down weighted based on the residuals for the fitted points. The new set of down-weighted z values, along with the corresponding x (or y) values, is used to get a new fit for the lower surface level. The process of fitting and down weighting continues until the difference between two consecutive fits is insignificant. The final fit is the required ground level, and the ground surface points are those that fall within the ground level and the level after adding some threshold value with the ground level for z values. Experimental results are compared with the recently proposed segmentation method through simulated and real mobile laser scanning point clouds from urban areas that include many objects that appear in road scenes such as short walls, large buildings, electric poles, signposts, and cars. Results show that the proposed robust methods efficiently extract ground surface points with better than 97% accuracy. Numéro de notice : A2016-840 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2496972 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2496972 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82884
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 4 (April 2016) . - pp 2181 - 2193[article]Assessing the contribution of woody materials to forest angular gap fraction and effective leaf area index using terrestrial laser scanning data / Guang Zheng in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)PermalinkNoise simulation and correction in synthetic airborne TIR Data for mineral quantification / Christoph Hecker in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)PermalinkPrivacy and spatial pattern preservation in masked GPS trajectory data / Dara E. Seidl in International journal of geographical information science IJGIS, vol 30 n° 3-4 (March - April 2016)PermalinkRegional scale rain-forest height mapping using regression-kriging of spaceborne and airborne Lidar data: application on French Guiana / Ibrahim Fayad in Remote sensing, vol 8 n° 3 (March 2016)PermalinkLa ville à l’échelle de l’Europe : apports du couplage et de l’expertise de bases de données issues de l’imagerie satellitale / Anne Bretagnolle in Revue internationale de géomatique, vol 26 n° 1 (janvier - mars 2016)PermalinkValidation of medium-scale historical maps of southern Latvia for evaluation of impact of continuous forest cover on the present-day mean stand area and tree species richness / Anda Fescenko in Baltic forestry, vol 22 n° 1 ([01/02/2016])PermalinkAssessment of forest canopy vertical structure with multi - scale remote sensing : from the plot to the large area / Phil Wilkes (2016)PermalinkConvex programming approach to robust estimation of a multivariate Gaussian model / Samuel Balmand (2016)PermalinkEléments de géodésie et de la théorie des moindres carrés / Abdelmajid Ben Hadj Salem (février 2016)PermalinkEstimating over- and understorey canopy density of temperate mixed stands by airborne LiDAR data / Hooman Latifi in Forestry, an international journal of forest research, vol 89 n° 1 (January 2016)PermalinkEstimation of forest biomass using multivariate relevance vector regression / Alireza Sharifi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)PermalinkPermalinkPermalinkInvestigating efficacy of robust M-estimation of deformation from observation differences / Krzysztof Nowel in Survey review, vol 48 n° 346 (January 2016)PermalinkMéthodes numériques pour les problèmes inverses / Michel Kern (2016)PermalinkOn estimation of the diagonal elements of a sparse precision matrix / Samuel Balmand in Electronic Journal of Statistics, vol 10 n° 1 (January 2016)PermalinkPermalinkPermalinkPermalinkCombining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment / Tal Rapaport in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)Permalink