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Introduction to multiple regression equations in datum transformations and their reversibility / Andrew Carey Ruffhead in Survey review, vol 50 n° 358 (January 2018)
[article]
Titre : Introduction to multiple regression equations in datum transformations and their reversibility Type de document : Article/Communication Auteurs : Andrew Carey Ruffhead, Auteur Année de publication : 2018 Article en page(s) : pp 82 - 90 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] régression multiple
[Termes IGN] système de référence local
[Termes IGN] système de référence mondial
[Termes IGN] transformation de coordonnéesRésumé : (auteur) This paper provides an introduction to multiple regression equations as a method of performing geodetic datum transformations. The formulae are particularly useful when there are non-linear distortions that need to be built into the transformation model. However, the equations take the form of a one-way transformation, usually a local geodetic datum to a global datum. The standard procedure for applying the equations to obtain the reverse transformation only gives approximate results relative to the original model. This paper quantifies the problem and describes three methods for computing the reverse transformation (or inverse transformation) more accurately. Numéro de notice : A2018-178 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/00396265.2016.1244143 Date de publication en ligne : 31/10/2016 En ligne : https://doi.org/10.1080/00396265.2016.1244143 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89822
in Survey review > vol 50 n° 358 (January 2018) . - pp 82 - 90[article]Area-based estimation of growing stock volume in Scots pine stands using ALS and airborne image-based point clouds / Paweł Hawryło in Forestry, an international journal of forest research, vol 90 n° 5 (December 2017)
[article]
Titre : Area-based estimation of growing stock volume in Scots pine stands using ALS and airborne image-based point clouds Type de document : Article/Communication Auteurs : Paweł Hawryło, Auteur ; Piotr Tompalski, Auteur ; Piotr Wezyk, Auteur Année de publication : 2017 Article en page(s) : pp 686 - 696 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image aérienne
[Termes IGN] image infrarouge couleur
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Pinus sylvestris
[Termes IGN] régression linéaire
[Termes IGN] régression multiple
[Termes IGN] semis de points
[Termes IGN] volume en bois
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) Recent research has shown that image-derived point clouds (IPCs) are a highly competitive alternative to airborne laser scanning (ALS) data in the context of selected forest inventory activities. However, there is still a need for investigating different kinds of aerial images used for point cloud generation. This study compares the effectiveness of IPCs derived from true colour (RGB) and colour infrared (CIR) aerial images with ALS data for growing stock volume estimation of single canopy layer Scots pine stands. A multiple linear regression method was used to create predictive models. All models predicted growing stock volume with low root mean square errors – ALS: 15.2%, IPC-CIR: 17.0% and IPC-RGB: 17.5%. The following variables for each data type were found to be the most robust: ALS – mean height of points, percentage of all returns above mean height of points, interquartile range of point heights; IPC-CIR – mean height of points, percentage of all returns above mode height of points, canopy relief ratio; IPC-RGB – mean height of points and canopy relief ratio. Our results show that for single canopy layer Scots pine dominated stands it is possible to predict growing stock volume using IPCs with a comparable accuracy as using ALS data. The comparable performance of IPC-RGB and IPC-CIR based models suggests that a mixed usage of RGB and CIR data in retrospective studies could be possible. Numéro de notice : A2017-904 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpx026 En ligne : https://doi.org/10.1093/forestry/cpx026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93205
in Forestry, an international journal of forest research > vol 90 n° 5 (December 2017) . - pp 686 - 696[article]Fusing tree‐ring and forest inventory data to infer influences on tree growth / Margaret E.K. Evans in Ecosphere, vol 8 n° 7 (July 2017)
[article]
Titre : Fusing tree‐ring and forest inventory data to infer influences on tree growth Type de document : Article/Communication Auteurs : Margaret E.K. Evans, Auteur ; Donald A. Falk, Auteur ; et al., Auteur Année de publication : 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] changement climatique
[Termes IGN] classification bayesienne
[Termes IGN] croissance des arbres
[Termes IGN] dendrochronologie
[Termes IGN] inférence statistique
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle statistique
[Termes IGN] montagne
[Termes IGN] Nouveau-Mexique (Etats-Unis)
[Termes IGN] régression multiple
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Better understanding and prediction of tree growth is important because of the many ecosystem services provided by forests and the uncertainty surrounding how forests will respond to anthropogenic climate change. With the ultimate goal of improving models of forest dynamics, here we construct a statistical model that combines complementary data sources, tree‐ring and forest inventory data. A Bayesian hierarchical model was used to gain inference on the effects of many factors on tree growth—individual tree size, climate, biophysical conditions, stand‐level competitive environment, tree‐level canopy status, and forest management treatments—using both diameter at breast height (dbh) and tree‐ring data. The model consists of two multiple regression models, one each for the two data sources, linked via a constant of proportionality between coefficients that are found in parallel in the two regressions. This model was applied to a data set of ~130 increment cores and ~500 repeat measurements of dbh at a single site in the Jemez Mountains of north‐central New Mexico, USA. The tree‐ring data serve as the only source of information on how annual growth responds to climate variation, whereas both data types inform non‐climatic effects on growth. Inferences from the model included positive effects on growth of seasonal precipitation, wetness index, and height ratio, and negative effects of dbh, seasonal temperature, southerly aspect and radiation, and plot basal area. Climatic effects inferred by the model were confirmed by a dendroclimatic analysis. Combining the two data sources substantially reduced uncertainty about non‐climate fixed effects on radial increments. This demonstrates that forest inventory data measured on many trees, combined with tree‐ring data developed for a small number of trees, can be used to quantify and parse multiple influences on absolute tree growth. We highlight the kinds of research questions that can be addressed by combining the high‐resolution information on climate effects contained in tree rings with the rich tree‐ and stand‐level information found in forest inventories, including projection of tree growth under future climate scenarios, carbon accounting, and investigation of management actions aimed at increasing forest resilience. Numéro de notice : A2017-907 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1002/ecs2.1889 Date de publication en ligne : 24/07/2017 En ligne : https://doi.org/10.1002/ecs2.1889 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93405
in Ecosphere > vol 8 n° 7 (July 2017)[article]Space-time multiple regression model for grid-based population estimation in urban areas / Ko Ko Lwin in International journal of geographical information science IJGIS, vol 30 n° 7- 8 (July - August 2016)
[article]
Titre : Space-time multiple regression model for grid-based population estimation in urban areas Type de document : Article/Communication Auteurs : Ko Ko Lwin, Auteur ; Komei Sugiura, Auteur ; Koji Zettsu, Auteur Année de publication : 2016 Article en page(s) : pp 1579 - 1593 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] appareil portable
[Termes IGN] densité de population
[Termes IGN] estimation statistique
[Termes IGN] milieu urbain
[Termes IGN] modèle conceptuel de données spatio-temporelles
[Termes IGN] population
[Termes IGN] régression multiple
[Termes IGN] TwitterRésumé : (Auteur) We can collect, store, and analyze a huge amount of information about human mobility and social interaction activities due to the emergence of information and communication technologies and location-enabled mobile devices under cyber physical system frameworks. The high spatial resolution of population data on a multi-temporal scale is required by transport planners, human geographers, social scientists, and emergency management teams. In this study, we build a space-time multiple regression model to estimate grid-based (500 m × 500 m) spatial resolution at multi-temporal scale (30-min intervals) population data based on the space-time relationship among geospatially enabled person trip (PT) survey data and incorporate both mobile call (MC) and geotagged Twitter (GT) data. Since using geospatially enabled PT survey data as dependent variables enables us to acquire actual population amounts, which strongly depend on MCs and social interaction activities. Although many grids have a strong correlation between PT and MC/GT, some show fewer correlation results, especially where the grids have factories, schools, and workshops in which fewer MCs are found but a large population is presented. Although GT data are sparser than MCs, people from amusement and tourist areas can be detected by GT data. The space-time multiple regression model can also estimate the different amounts of populations based on human travel behavior that changes over space and time. According to accuracy assessments, the night-time estimated results, especially between 00:00 and 06:30, strongly correlate with national census data except in places where the grids have railway and subway stations. Numéro de notice : A2016-319 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1143099 En ligne : http://dx.doi.org/10.1080/13658816.2016.1143099 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80939
in International journal of geographical information science IJGIS > vol 30 n° 7- 8 (July - August 2016) . - pp 1579 - 1593[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2016042 RAB Revue Centre de documentation En réserve L003 Disponible 079-2016041 RAB Revue Centre de documentation En réserve L003 Disponible 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)
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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]La 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)PermalinkEstimation of forest biomass using multivariate relevance vector regression / Alireza Sharifi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)PermalinkRegional dynamics of terrestrial vegetation productivity and climate feedbacks for territory of Ukraine / Dmytro Movchan in International journal of geographical information science IJGIS, vol 29 n° 8 (August 2015)PermalinkAnalytical estimation of map readability / Lars Harrie in ISPRS International journal of geo-information, vol 4 n°2 (June 2015)PermalinkRegional vertical total electron content (VTEC) modeling together with satellite and receiver differential code biases (DCBs) using semi-parametric multivariate adaptive regression B-splines (SP-BMARS) / Murat Durmaz in Journal of geodesy, vol 89 n° 4 (April 2015)PermalinkCharacterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm / Oumer S. Ahmed in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)PermalinkEffects of LiDAR point density and landscape context on estimates of urban forest biomass / Kunwar K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)PermalinkPermalinkQuantification et cartographie de la structure forestière à partir de la texture des images Pléiades / Benoit Beguet in Revue Française de Photogrammétrie et de Télédétection, n° 208 (Octobre 2014)PermalinkCaractérisation et cartographie de la structure forestière à partir d'images satellitaires à très haute résolution spatiale / Benoit Beguet (2014)Permalink