Descripteur
Documents disponibles dans cette catégorie (984)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Generation of large-scale moderate-resolution forest height mosaic with spaceborne repeat-pass SAR interferometry and lidar / Yang Lei in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)
[article]
Titre : Generation of large-scale moderate-resolution forest height mosaic with spaceborne repeat-pass SAR interferometry and lidar Type de document : Article/Communication Auteurs : Yang Lei, Auteur ; Paul Siqueira, Auteur ; Nathan Torbick, Auteur ; Mark J. Ducey, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 770 - 787 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bande L
[Termes IGN] biomasse aérienne
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] hauteur des arbres
[Termes IGN] image ALOS-PALSAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] Maine (Etats-Unis)
[Termes IGN] New Hampshire (Etats-Unis)Résumé : (Auteur) This paper provides an overview of the scattering model, inversion approach, and validation of the application results for creating large-scale moderate-resolution (hectare-level) mosaics of forest height through using spaceborne repeat-pass SAR interferometry and lidar. By incorporating several improvements to the forest height inversion and mosaicking approach, the height estimation accuracy along with the robustness of this approach have been considerably enhanced from its originally reported accuracy of RMSE of 3-4 m at a 20-hectare aggregated pixel size to RMSE of 3-4 m on the order of 3-6 hectares. Furthermore, practical data processing schemes are provided in detail. Extensive validation results are demonstrated which include: 1) a forest height mosaic (total area of 11.6 million hectares) is generated for the U.S. states of Maine and New Hampshire using Japanese Aerospace Exploration Agency's (JAXA) ALOS-1 InSAR correlation data and a small airborne lidar strip (44 000 hectares); 2) the mosaic height estimates are further compared with the available airborne lidar data and field measurements over both flat and mountainous areas; and 3) feasibility of using modern repeat-pass InSAR satellites with short repeat interval is also examined by using JAXA's ALOS-2 data. This simple and efficient approach is a potential observational prototype with much smaller error budget for the future spaceborne repeat-pass L-band InSAR systems with small spatial baseline and moderate/large temporal baseline (such as NISAR) in combination with lidar (such as GEDI) on the application of large-scale forest height/biomass mapping. It also serves as a complementary tool to the spaceborne single-pass InSAR systems using InSAR/PolInSAR methods when full-pol data are not available and/or when the underlying topography slope causes problems for these approaches. Numéro de notice : A2019-109 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2860590 Date de publication en ligne : 17/08/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2860590 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92427
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 2 (February 2019) . - pp 770 - 787[article]Quantification of airborne lidar accuracy in coastal dunes (Fire Island, New York) / William J. Schmelz in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 2 (February 2019)
[article]
Titre : Quantification of airborne lidar accuracy in coastal dunes (Fire Island, New York) Type de document : Article/Communication Auteurs : William J. Schmelz, Auteur ; Norbert P. Psuty, Auteur Année de publication : 2019 Article en page(s) : pp 133 - 144 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse comparative
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données topographiques
[Termes IGN] dune
[Termes IGN] erreur géométrique
[Termes IGN] géomorphologie locale
[Termes IGN] New York (Etats-Unis ; état)
[Termes IGN] plage
[Termes IGN] précision du positionnement
[Termes IGN] résiduRésumé : (Auteur) To establish a basis for the utilization of lidar topography as a data source for coastal geomorphological analyses, this study generated statistical metrics of lidar error through the comparison of a June 2014 USGS collection of airborne lidar with a concurrently collected high-accuracy GPS topographical survey collected within the beach and dunes of a portion of Fire Island National Seashore. The examination of bare earth lidar error within the experiment site revealed a complex association between accuracy and environment within the coastal landscape. Accuracy was constrained to better than 50 cm RMSE in areas with vegetated dune topography and, overall, a 38.9 cm RMSE was measured. Higher accuracies were achieved in the flat, non-vegetated beach. A three-dimensional minimization of residuals between the lidar and GPS surveys reduced the total RMSE to 25.2 cm, indicating a correctable systematic offset between the surface generated from the lidar and the true ground surface. Numéro de notice : A2019-060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.2.133 Date de publication en ligne : 01/02/2019 En ligne : https://doi.org/10.14358/PERS.85.2.133 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92110
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 2 (February 2019) . - pp 133 - 144[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2019021 SL Revue Centre de documentation Revues en salle Disponible Assessment of different vegetation parameters for parameterizing the coupled water cloud model and advanced integral equation model for soil moisture retrieval using time series Sentinel-1A data / Long Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)
[article]
Titre : Assessment of different vegetation parameters for parameterizing the coupled water cloud model and advanced integral equation model for soil moisture retrieval using time series Sentinel-1A data Type de document : Article/Communication Auteurs : Long Wang, Auteur ; Binbin He, Auteur ; Xiaojing Bai, Auteur ; Minfeng Xing, Auteur Année de publication : 2019 Article en page(s) : pp 43 - 54 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Enhanced vegetation index
[Termes IGN] étalonnage de modèle
[Termes IGN] humidité du sol
[Termes IGN] image Sentinel-SAR
[Termes IGN] image Terra-MODIS
[Termes IGN] indice foliaire
[Termes IGN] Iowa (Etats-Unis)
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de rétrodiffusion
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] série temporelleRésumé : (auteur) Soil moisture is an important state variable of the land surface ecosystem. In this paper, the water cloud model (WCM) and advanced integral equation model (AIEM) are coupled to retrieve soil moisture using time series Sentinel-1A data and moderate resolution imaging spectroradiometer (MODIS) data. Normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR), are cross-combined to initialize the calibrated model. The calibration results show the following: (1) Vegetation parameters have a great influence on model calibration; and (2) The combination of (NDVI, LAI) is recommended to calibrate the coupled model, the RMSE, R2 is 0.739 dB, and 0.716 for the observed and estimated backscattering coefficients. The soil moisture inversion results show that: (1) the accuracy of model calibration and soil moisture inversion are inconsistent; and (2) The normalized vegetation parameters, such as NDVI, EVI and FPAR, are suitable for WCM to describe vegetation characteristics, and NDVI is the optimum. When V2 is the NDVI, the average bias, MAE, RMSE, ubRMSE and R2 are –0.007 m3/m3, 0.074 m3/m3, 0.087 m³/m³, 0.087 m3/m3 and 0.750, respectively. Numéro de notice : A2019-029 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.1.43 Date de publication en ligne : 01/01/2019 En ligne : https://doi.org/10.14358/PERS.85.1.43 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91965
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 1 (January 2019) . - pp 43 - 54[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2019011 SL Revue Centre de documentation Revues en salle Disponible Combining potentially incompatible community datasets when harmonizing forest inventories in subarctic Alaska, USA / Robert J. Smith in Journal of vegetation science, vol 30 n° 1 (January 2019)
[article]
Titre : Combining potentially incompatible community datasets when harmonizing forest inventories in subarctic Alaska, USA Type de document : Article/Communication Auteurs : Robert J. Smith, Auteur ; Andrew N. Gray, Auteur Année de publication : 2019 Article en page(s) : pp 18 - 29 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Alaska (Etats-Unis)
[Termes IGN] forêt boréale
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] qualité des données
[Termes IGN] variabilité
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Aims : Plant responses to disturbances and environmental variation can manifest in communities as compositional nestedness (i.e., one community is a subset of another) and/or turnover (two communities represent different compositional gradient spaces). Yet, different sampling designs can artificially give an illusion of such compositional differences among two datasets, making it problematic to harmonize them in multi‐species analysis. We test the prediction that sampling differences which increase beta‐diversity components (nestedness and turnover) among two vegetation datasets will decrease their exchangeability.
Location : Boreal forests of Tanana River region, interior Alaska, USA.
Methods : We develop novel methods for comparing compositional variation among two datasets in nonmetric multidimensional scaling (NMDS) ordination. Resampled NMDS establishes internal sampling variability for each dataset independently, and reciprocal NMDS determines external exchangeability when the two are mutually exchanged. We first compare simulated data with specified beta‐diversity differences, then evaluate two forest inventories based on local vs regional sampling designs in Alaska's boreal forests.
Results : As simulated species turnover and nestedness increased, internal sampling variability remained essentially constant, but external exchangeability progressively declined. Species turnover (not nestedness) had the larger negative effect on exchangeability. Among the boreal forest inventories, internal sampling variability was relatively similar, and exchangeability was weakly moderate, but the regional inventory exhibited much better fit to broad‐scale environment. Species turnover (not nestedness) contributed the majority of beta‐diversity differences among the two forest inventories, suggesting that strong environmental gradients were unequally represented.
Conclusions : Species turnover alters multivariate outcomes more drastically than species nestedness. Therefore, combining two vegetation datasets may be inadvisable when turnover prevails. Instead, a multi‐scale perspective, with separate but complementary forest inventory analyses, can portray local and regional variation at appropriate scales. Our method is tractable for assessing exchangeability of potentially inconsistent sampling designs, like those that are common in synthesis studies and long‐term ecological monitoring.Numéro de notice : A2019-373 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1111/jvs.12694 Date de publication en ligne : 07/11/2018 En ligne : https://doi.org/10.1111/jvs.12694 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93412
in Journal of vegetation science > vol 30 n° 1 (January 2019) . - pp 18 - 29[article]Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing / Minrui Zheng in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)
[article]
Titre : Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing Type de document : Article/Communication Auteurs : Minrui Zheng, Auteur ; Wenwu Tang, Auteur ; Xiang Zhao, Auteur Année de publication : 2019 Article en page(s) : pp 314 - 345 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] Caroline du Nord (Etats-Unis)
[Termes IGN] données spatiotemporelles
[Termes IGN] géostatistique
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle empirique
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation spatiale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système d'information foncièreRésumé : (auteur) Artificial neural networks (ANNs) have been extensively used for the spatially explicit modeling of complex geographic phenomena. However, because of the complexity of the computational process, there has been an inadequate investigation on the parameter configuration of neural networks. Most studies in the literature from GIScience rely on a trial-and-error approach to select the parameter setting for ANN-driven spatial models. Hyperparameter optimization provides support for selecting the optimal architectures of ANNs. Thus, in this study, we develop an automated hyperparameter selection approach to identify optimal neural networks for spatial modeling. Further, the use of hyperparameter optimization is challenging because hyperparameter space is often large and the associated computational demand is heavy. Therefore, we utilize high-performance computing to accelerate the model selection process. Furthermore, we involve spatial statistics approaches to improve the efficiency of hyperparameter optimization. The spatial model used in our case study is a land price evaluation model in Mecklenburg County, North Carolina, USA. Our results demonstrate that the automated selection approach improves the model-level performance compared with linear regression, and the high-performance computing and spatial statistics approaches are of great help for accelerating and enhancing the selection of optimal neural networks for spatial modeling. Numéro de notice : A2019-022 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1530355 Date de publication en ligne : 12/10/2018 En ligne : https://doi.org/10.1080/13658816.2018.1530355 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91689
in International journal of geographical information science IJGIS > Vol 33 n° 1-2 (January - February 2019) . - pp 314 - 345[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2019011 RAB Revue Centre de documentation En réserve L003 Disponible PermalinkAnalyzing the role of pulse density and voxelization parameters on full-waveform LiDAR-derived metrics / Pablo Crespo-Peremarch in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkIntegrating urban and national forest inventory data in support of rural–urban assessments / James A. Westfall in Forestry, an international journal of forest research, vol 91 n° 5 (December 2018)PermalinkA new generation of the United States National Land Cover Database : Requirements, research priorities, design, and implementation strategies / Limin Yang in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkTowards operational marker-free registration of terrestrial lidar data in forests / Jean-François Tremblay in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkA 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery / Zewei Xu in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)PermalinkOn determining orthometric heights from a corrector surface model based on leveling observations, GNSS, and a geoid model / Su-Kyung Kim in Journal of applied geodesy, vol 12 n° 4 (October 2018)PermalinkOpening GIScience : A process-based approach / Jerry Shannon in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)PermalinkSpatial mining of migration patterns from web demographics / T. Edwin Chow in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)PermalinkExploring uncertainties in terrain feature extraction across multi-scale, multi-feature, and multi-method approaches for variable terrain / Boleslo E. Romero in Cartography and Geographic Information Science, Vol 45 n° 5 (August 2018)PermalinkIncorporating crown shape information for identifying ash tree species / Haijian Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 8 (août 2018)PermalinkIntra-annual phenology for detecting understory plant invasion in urban forests / Kunwar K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)PermalinkA spatial analysis of non‐English Twitter activity in Houston, TX / Matthew Haffner in Transactions in GIS, vol 22 n° 4 (August 2018)PermalinkExploring geo-tagged photos for land cover validation with deep learning / Hanfa Xing in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)PermalinkThe political economy of spatial data infrastructures / Luis Felipe Alvarez León in International journal of cartography, vol 4 n° 2 (June 2018)PermalinkManipulating tree crown structure to promote old-growth characteristics in second-growth redwood forest canopies / Stephen C. Sillett in Forest ecology and management, vol 417 (15 May 2018)PermalinkUsing radial basis functions in airborne gravimetry for local geoid improvement / Xiaopeng Li in Journal of geodesy, vol 92 n° 5 (May 2018)PermalinkMapping forest characteristics at fine resolution across large landscapes of the southeastern united states using NAIP imagery and FIA field plot data / John Hogland in ISPRS International journal of geo-information, vol 7 n° 4 (April 2018)PermalinkEvaluation of 10-year temporal and spatial variability in structure and growth across contrasting commercial thinning treatments in spruce-fir forests of northern Maine, USA / Christian Kuehne in Annals of Forest Science, vol 75 n° 1 (March 2018)PermalinkHarmonic regression of Landsat time series for modeling attributes from national forest inventory data / Barry T. Wilson in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)Permalink