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Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data : A case study of Wuhan, Central China / Xin Huang in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)
[article]
Titre : Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data : A case study of Wuhan, Central China Type de document : Article/Communication Auteurs : Xin Huang, Auteur ; Ying Wang, Auteur Année de publication : 2019 Article en page(s) : pp 119 - 131 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbre urbain
[Termes IGN] Chine
[Termes IGN] ilot thermique urbain
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-TIRS
[Termes IGN] image ZiYuan-3
[Termes IGN] morphologie urbaine
[Termes IGN] régression multiple
[Termes IGN] température au sol
[Termes IGN] Wuhan (Chine)Résumé : (Auteur) The Urban heat island (UHI) effect is an increasingly serious problem in urban areas. Information on the driving forces of intra-urban temperature variation is crucial for ameliorating the urban thermal environment. Although prior studies have suggested that urban morphology (e.g., landscape pattern, land-use type) can significantly affect land surface temperature (LST), few studies have explored the comprehensive effect of 2D and 3D urban morphology on LST in different urban functional zones (UFZs), especially at a fine scale. Therefore, in this research, we investigated the relationship between 2D/3D urban morphology and summer daytime LST in Wuhan, a representative megacity in Central China, which is known for its extremely hot weather in summer, by adopting high-resolution remote sensing data and geographical information data. The “urban morphology” in this study consists of 2D urban morphological parameters, 3D urban morphological parameters, and UFZs. Our results show that: (1) The LST is significantly related to 2D and 3D urban morphological parameters, and the scattered distribution of buildings with high rise can facilitate the mitigation of LST. Although sky view factor (SVF) is an important measure of 3D urban geometry, its influence on LST is complicated and context-dependent. (2) Trees are the most influential factor in reducing LST, and the cooling efficiency mainly depends on their proportions. The fragmented and irregular distribution of grass/shrubs also plays a significant role in alleviating LST. (3) With respect to UFZs, the residential zone is the largest heat source, whereas the highest LST appears in commercial and industrial zones. (4) Results of the multivariate regression and variation partitioning indicate that the relative importance of 2D and 3D urban morphological parameters on LST varies among different UFZs and 2D morphology outperforms 3D morphology in LST modulation. The results are generally consistent in spring, summer and autumn. These findings can provide insights for urban planners and designers on how to mitigate the surface UHI (SUHI) effect via rational landscape design and urban management during summer daytime. Numéro de notice : A2019-456 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.04.010 Date de publication en ligne : 22/04/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.04.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92869
in ISPRS Journal of photogrammetry and remote sensing > vol 152 (June 2019) . - pp 119 - 131[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia / Enkhjargal Natsagdorj in Geocarto international, vol 34 n° 7 ([01/06/2019])
[article]
Titre : Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia Type de document : Article/Communication Auteurs : Enkhjargal Natsagdorj, Auteur ; Tsolmon Renchin, Auteur ; Philippe De Maeyer, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 722 - 734 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] données météorologiques
[Termes IGN] image Aqua-MODIS
[Termes IGN] image SPOT-Végétation
[Termes IGN] image Terra-MODIS
[Termes IGN] Mongolie
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] surface cultivée
[Termes IGN] teneur en eau de la végétation
[Termes IGN] variation temporelleRésumé : (auteur) The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r2 = 0.58) and at a 50-cm depth (r2 = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region. Numéro de notice : A2019-513 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1434686 Date de publication en ligne : 08/03/2018 En ligne : https://doi.org/10.1080/10106049.2018.1434686 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93822
in Geocarto international > vol 34 n° 7 [01/06/2019] . - pp 722 - 734[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2019071 RAB Livre Centre de documentation En réserve L003 Disponible Mise en oeuvre d'outils open source pour le suivi opérationnel de l'occupation des sols et de la déforestation à partir des données Sentinel radar optique : études de cas en Guyane et au Togo / Cédric Lardeux in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)
[article]
Titre : Mise en oeuvre d'outils open source pour le suivi opérationnel de l'occupation des sols et de la déforestation à partir des données Sentinel radar optique : études de cas en Guyane et au Togo Type de document : Article/Communication Auteurs : Cédric Lardeux, Auteur ; Anoumou Kemavo, Auteur ; Maxence Rageade, Auteur ; Mathieu Rahm, Auteur ; Pierre-Louis Frison , Auteur ; Jean-Paul Rudant , Auteur Année de publication : 2019 Article en page(s) : pp 59 - 70 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] déboisement
[Termes IGN] Guyane (département français)
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Orfeo Tool Box
[Termes IGN] Python (langage de programmation)
[Termes IGN] QGIS
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétation
[Termes IGN] télédétection en hyperfréquence
[Termes IGN] TogoRésumé : (auteur) Remote sensing is a particularly suitable tool for monitoring land use but also particularly suited for deforestation monito-ring. By launching the Setinel-1 and Sentinel-2 satellites in the Copernicus program, the community now has free data with important time-revisits allowing the greatest number of people to effectively monitor land cover of a study area. This paper presents a land use monitoring method based on the combination of two approaches computed using Open Source tools ((MIS, Orfeo ToolBox, python). First, we focus on land use monitoring at an annual time scale using Sentinel-1 and Sentinel-2 data, and then, by the use f entienl-1 data, we detect changes in the forest cover due to deforestation at bi-monthly time scale. The results obtained show a very good synergy of these approaches allowing the complementary of optical and radar data. In order to make accessible the proposed method, all the used open source tools are available on this link http ://remotesensing4all.net/index.php/2018/09/11/ kit-use-des-donnees-radar- sentinel-1-in-de-latelier-radar-du-FOSS4G-en-2018-2/. Numéro de notice : A2019-348 Affiliation des auteurs : UPEM-LASTIG+Ext (2016-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.52638/rfpt.2019.467 En ligne : https://doi.org/10.52638/rfpt.2019.467 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93385
in Revue Française de Photogrammétrie et de Télédétection > n° 219-220 (juin - octobre 2019) . - pp 59 - 70[article]A new stochastic simulation algorithm for image-based classification : Feature-space indicator simulation / Qing Wang in ISPRS Journal of photogrammetry and remote sensing, vol 152 (June 2019)
[article]
Titre : A new stochastic simulation algorithm for image-based classification : Feature-space indicator simulation Type de document : Article/Communication Auteurs : Qing Wang, Auteur ; Hua Sun, Auteur ; Ruopu Li, Auteur ; Guangxing Wang, Auteur Année de publication : 2019 Article en page(s) : pp 145 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] forêt
[Termes IGN] géostatistique
[Termes IGN] image Landsat-OLI
[Termes IGN] image SPOT 5
[Termes IGN] Mongolie intérieure (Chine)
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] utilisation du sol
[Termes IGN] variogrammeRésumé : (Auteur) Traditional parametric methods for classification of land use and land cover (LULC) types using remote sensing imagery assume a global distribution model and fail to consider local variation of categorical variables. Differently, non-parametric methods do not make any statistical assumptions but are typically sensitive to the sample sizes of training sample data that usually require a high cost to collect in the field. Geostatistical classifiers, such as indicator kriging and simulation, are local variability-based methods that exhibit great potential for image-based classification of LULC types. However, variogram models required are highly sensitive to the spatial configuration of training samples as well as sample size given a study area. Moreover, when a large number of spectral variables are considered into kriging systems, modeling the variograms and cross-variograms would be problematic. To circumvent these issues, this study extended the geostatistical methods from a 2-dimensional geographic space to a m-dimensional image feature space to derive feature-space indicator variograms (FSIVs). Moreover, a novel stochastic simulation classification algorithm, Feature-Space Indicator Simulation (FSIS), was proposed and examined for classification of LULC types in Duolun County located in Inner Mongolia and in Huang-Feng-Qiao (HFQ) forest farm, Hunan of China. In Duolun, six LULC types were involved and in HFQ a complicated forest landscape consisting of nine forest types plus water, built-up area, and agricultural/bare soil, was classified. The classification results of FSIS were compared with another feature-space geostatistical classifier – feature-space indicator kriging (FSIK), a traditional parametric method – maximum likelihood (ML), a widely used nonparametric method – support vector machine (SVM), and a recently popular method – random forest (RF). The results showed that compared with ML, SVM and RF, in both study areas FSIS statistically significantly increased the accuracy of the classifications by 10.0–29.9% for percentage correct and 19.0–47.6% for Kappa statistic. Compared with FSIK, FSIS also improved the classification accuracy but the accuracy increases were relatively smaller with the percentages correct of 3.5% and 7.6% and the Kappa values of 4.6% and 8.6% for Duolun and HFQ, respectively. Moreover, FSIS led to the spatial uncertainties of the classification estimates as the quality measure of the estimates. In addition, the results also demonstrated that FSIVs were sensitive to the within-class heterogeneity but not very much to the size of training samples. Overall, FSIS exhibited the greater potential to improve the classification accuracy of LULC and forest types using remote sensing image. Numéro de notice : A2019-457 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.04.011 Date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.04.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92871
in ISPRS Journal of photogrammetry and remote sensing > vol 152 (June 2019) . - pp 145 - 165[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment / Eduarda M.O. Silveira in International journal of applied Earth observation and geoinformation, vol 78 (June 2019)
[article]
Titre : Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment Type de document : Article/Communication Auteurs : Eduarda M.O. Silveira, Auteur ; Sérgio Henrique G. Silva, Auteur ; Fausto Weimar Acerbi, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 175 - 188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] biomasse aérienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] distribution spatiale
[Termes IGN] forêt équatoriale
[Termes IGN] image Landsat-TM
[Termes IGN] Minas Gerais (Brésil)
[Termes IGN] montagneRésumé : (Auteur) The Brazilian Atlantic Forest is a highly heterogeneous biome of global ecological significance with high levels of terrestrial carbon stocks and aboveground biomass (AGB). Accurate maps of AGB are required for monitoring, reporting, and modelling of forest resources and carbon stocks. Previous research has linked plot-level AGB with environmental and remotely sensed data using pixel-based approaches. However, few studies focused on investigating possible improvements via object-based image analysis (OBIA) including terrain related data to predict AGB in topographically variable and mountainous regions, such as Atlantic forest in Minas Gerais, Brazil. OBIA is expected to reduce known uncertainties related to the positional discrepancy between the image and field data and forest heterogeneity, while terrain derivatives are strong predictors in forest ecosystems driving forest biomass variability. In this research, we compare an object-based approach to a pixel-based method for modeling, mapping and quantifying AGB in the Rio Doce basin, within the Brazilian Atlantic Forest biome. We trained a random forest (RF) machine learning algorithm using environmental, terrain, and Landsat Thematic Mapper (TM) remotely sensed imagery. We aimed to: (i) increase the precision of the AGB estimates; (ii) identify optimal variables that fit the best model, with the lowest root mean square error (RMSE, Mg/ha); (iii) produce an accurate map of the AGB for the study area, and subsequently (iv) describing the AGB spatial distribution as a function of the selected variables. The RF object-based model notably improved the AGB prediction by reducing the mean absolute error (MAE) from 28.64 to 20.95%, and RMSE from 33.43 to 20.08 Mg/ha, and increasing the R² (from 0.57 to 0.86) by using a combination of selected remote sensing, environmental, and terrain variables. Object-based modelling is a promising alternative to common pixel-based approaches to reduce AGB variability in topographically diverse and heterogeneous environments. Investigation of mapped outcomes revealed a decreasing AGB from west towards the east region of the Rio Doce Basin. Over the entire study area, we map a total of 195,799,533 Mg of AGB, ranging from 25.52 to 238 Mg/ha, following seasonal precipitation patterns and anthropogenic disturbance effects. This study provided reliable AGB estimates for the Rio Doce basin, one of the most important watercourses of the globally important Brazilian Atlantic Forest. In conclusion, we highlight that OBIA is a better solution to map forest AGB than the pixel-based traditional method, increasing the precision of AGB estimates in a heterogeneous and mountain tropical environment. Numéro de notice : A2019-230 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.02.004 Date de publication en ligne : 15/02/2019 En ligne : https://doi.org/10.1016/j.jag.2019.02.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92748
in International journal of applied Earth observation and geoinformation > vol 78 (June 2019) . - pp 175 - 188[article]Polarimétrie radar complète et partielle pour le suivi des surfaces terrestres / Pierre-Louis Frison in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkUsing Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece / D.D. 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Yepez-Rincon in Geocarto international, vol 34 n° 2 ([01/02/2019])PermalinkTree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 ([01/02/2019])PermalinkAdvanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure / Maged Marghany (2019)PermalinkPermalinkAilanthus altissima mapping from multi-temporal very high resolution satellite images / Cristina Tarantino in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkAnalyse de la déformation récente dans le Grand Tunis par interférométrie radar SAR / Anis Chaabani (2019)PermalinkAnalysis and modelling of remote sensing reflectance during anoxic crisis in the Thau lagoon using satellite images / Manchun Lei (2019)PermalinkApport des mesures du radar à synthèse d'ouverture de Sentinel-1 pour l'étude des propriétés du manteau neigeux / Gaëlle Veyssière (2019)PermalinkApports de l'imagerie satellitaire pour caractériser les évolutions morphologiques de l'embouchure du Tage / Anne Jaouen (2019)PermalinkApports des techniques photogrammétriques à l'étude du dynamisme des structures volcaniques du piton de la Fournaise / Allan Derrien (2019)PermalinkAssessment 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)PermalinkPermalinkBridging the gap: toward a French MS-NFI for territories / Jean-Pierre Renaud (2019)PermalinkCaractérisation des déplacements liés aux coulées de lave au Piton de la Fournaise à partir de données InSAR / Alexis Hrysiewicz (2019)PermalinkCartographie des déformations sur le site de colocalisation de Grasse par méthode INSAR / Isabelle Delprat (2019)PermalinkChallenges in grassland mowing event detection with multimodal Sentinel images / Anatol Garioud (2019)PermalinkClassification du type et de la concentration de la banquise, à partir d’images Sentinel-1 SAR, grâce à des réseaux de neurones convolutifs / Hugo Boulze (2019)PermalinkClimate variability and climate change impacts on land surface, hydrological processes and water management / Yongqiang Zhang (2019)PermalinkConstruction of bulk temperature/salinity from surface temperature and atlas profiles for monitoring water volume variations in the Caspian Sea / Ayoub Moradi (2019)PermalinkPermalinkPermalinkDiscriminating ship from radio frequency interference based on noncircularity and non-gaussianity in sentinel-1 SAR imagery / Xiangguang Leng in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkEarth observation, remote sensing and geoscientific ground investigations for archaeological and heritage research / Deodato Tapete (2019)PermalinkEvaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)PermalinkEvaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands / Fabio Castaldi in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkÉvaluation de la dégradation des forêts primaires par télédétection dans un espace de front pionnier consolidé d’Amazonie orientale (Paragominas) / Ali Fadhil Hasan (2019)PermalinkEvaluation of time-series SAR and optical images for the study of winter land-use / Julien Denize (2019)PermalinkExploitation de séries temporelles d'images multi-sources pour la cartographie des surfaces en eau / Filsa Bioresita (2019)PermalinkPermalinkGéomatique webmapping en open source / David Collado (2019)PermalinkPermalinkGlobal observations of ocean surface winds and waves using spaceborne synthetic aperture radar measurements / Huimin Li (2019)PermalinkPermalinkImproving the spatial bias correction algorithm in SMOS image reconstruction processor : validation of soil moisture retrievals with in situ data / Ali Khazaal in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkIndividual tree detection and crown delineation with 3D information from multi-view satellite Images / Changlin Xiao in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)PermalinkPermalinkJoint analysis of SAR and optical satellite images time series for grassland event detection / Anatol Garioud (2019)PermalinkPermalinkPermalinkMéthodes d'exploitation de données historiques pour la production de cartes d'occupation des sols à partir d'images de télédétection et en absence de données de référence de la période à cartographier / Benjamin Tardy (2019)PermalinkMonitoring crops water needs at high spatio-temporal resolution by synergy of optical / thermal and radar observations / Abdelhakim Amazirh (2019)PermalinkPermalinkA multi-faceted CNN architecture for automatic classification of mobile LiDAR data and an algorithm to reproduce point cloud samples for enhanced training / Bhavesh Kumar in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkMultimodal scene understanding: algorithms, applications and deep learning, ch. 11. Decision fusion of remote-sensing data for land cover classification / Arnaud Le Bris (2019)PermalinkMultitemporal SAR images denoising and change detection : applications to Sentinel-1 data / Weiying Zhao (2019)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkRetrieving relevant land cover and land use data to study urban climate change / Bénédicte Bucher (2019)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkSemantic aware quality evaluation of 3D building models : Modeling and simulation / Oussama Ennafii (2019)PermalinkSensitivity of urban material classification to spatial and spectral configurations from visible to short-wave infrared / Arnaud Le Bris (2019)PermalinkPermalinkA spatiotemporal calculus for reasoning about land-use trajectories / Adeline Marinho Maciel in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)PermalinkPermalinkThe necessary yet complex evaluation of 3D city models: a semantic approach / Oussama Ennafii (2019)PermalinkTime-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series / Vivien Sainte Fare Garnot (2019)PermalinkToward global soil moisture monitoring with sentinel-1 : harnessing assets and overcoming obstacles / Bernhard Bauer-Marschallinger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkTraitement d'images multispectrales et spatialisation des données pour la caractérisation de la matière organique des phases solides naturelles / Kevin Jacq (2019)PermalinkUnderstanding of atmospheric systems with efficient numerical methods for observation and prediction / Lei-Ming Ma (2019)PermalinkUrban deformation monitoring using persistent scatterer Interferometry and SAR tomography / Michele Crosetto (2019)PermalinkUrban morpho-types classification from SPOT-6/7 imagery and Sentinel-2 time series / Arnaud Le Bris (2019)PermalinkUtilisation de données Sentinel-2 et SPOT 6/7 pour la classification de l’occupation du sol / Olivier Stocker (2019)PermalinkVariational learning of mixture wishart model for PolSAR image classification / Qian Wu in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkPermalinkAtmospheric artifacts correction with a covariance-weighted linear model over mountainous regions / Zhongbo Hu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkAutomatic building rooftop extraction from aerial images via hierarchical RGB-D priors / Shibiao Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkLong-term land deformation monitoring using quasi-persistent scatterer (Q-PS) technique observed by sentinel-1A : case study Kelok Sembilan / Pakhrur Razi in Advances in Remote Sensing, vol 7 n° 4 (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)PermalinkPolarimetric radar vegetation index for biomass estimation in desert fringe ecosystems / Jisung Geba Chang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkPotential of Sentinel-1 data for monitoring temperate mixed forest phenology / Pierre-Louis Frison in Remote sensing, vol 10 n° 12 (December 2018)PermalinkScene classification based on multiscale convolutional neural network / Yanfei Liu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkSuper-resolution of Sentinel-2 images : Learning a globally applicable deep neural network / Charis Lanaras in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkUrban impervious surface estimation from remote sensing and social data / Yan Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 12 (December 2018)PermalinkApplication of Landsat-8 and ASTER satellite remote sensing data for porphyry copper exploration: a case study from Shahr-e-Babak, Kerman, south of Iran / Morteza Safari in Geocarto international, vol 33 n° 11 (November 2018)PermalinkChange detection based on stacked generalization system with segmentation constraint / Kun Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)PermalinkIndividual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images / Fabien Hubert Wagner in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part B (November 2018)PermalinkMulti-scale object detection in remote sensing imagery with convolutional neural networks / Zhipeng Deng in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkPan-sharpening via deep metric learning / Yinghui Xing in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkSemantic labeling in very high resolution images via a self-cascaded convolutional neural network / Yoncheng Liu in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 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)PermalinkCartographie des forêts humides dans la région d’El Kala (Algérie) à l’aide des outils d’observation de la Terre / Asma Kahli in Revue d'écologie, vol 73 n° 4 (octobre - décembre 2018)PermalinkEstimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery / Lin Chen in Forests, vol 9 n° 10 (October 2018)PermalinkMulti‐scale observations of atmospheric moisture variability in relation to heavy precipitating systems in the northwestern Mediterranean during HyMeX IOP12 / Samiro Khodayar in Quarterly Journal of the Royal Meteorological Society, vol 144 n° 717 (October 2018 Part B)PermalinkNovel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000 / Umut Gunes Sefercik in Geocarto international, vol 33 n° 10 (October 2018)PermalinkObject-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier / Huanxue Zhang in Geocarto international, vol 33 n° 10 (October 2018)PermalinkPredicting tree diameter distributions from airborne laser scanning, SPOT 5 satellite, and field sample data in the perm region, Russia / Jussi Peuhkurinen in Forests, vol 9 n° 10 (October 2018)PermalinkStand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel- and object-based tree growth model and annual Landsat time series / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)PermalinkUnmixing polarimetric radar images based on land cover type identified by higher resolution optical data before target decomposition: application to forest and bare soil / Sébastien Giordano in IEEE Transactions on geoscience and remote sensing, vol 56 n° 10 (October 2018)PermalinkAssessment of Nigeriasat-1 satellite data for urban land use/land cover analysis using object-based image analysis in Abuja, Nigeria / Christopher Ifechukwude Chima in Geocarto international, vol 33 n° 9 (September 2018)PermalinkEffects of a large-scale late spring frost on a beech (Fagus sylvatica L.) dominated Mediterranean mountain forest derived from the spatio-temporal variations of NDVI / Angelo Nolè in Annals of Forest Science, vol 75 n° 3 (September 2018)PermalinkEstimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)PermalinkImprovement of countrywide vegetation mapping over Japan and comparison to existing maps / Ram C. Sharma in Advances in Remote Sensing, vol 7 n° 3 (September 2018)PermalinkIntegration of ZY3-02 satellite laser altimetry data and stereo images for high-accuracy mapping / Guoyuan Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 9 (September 2018)PermalinkInvestigation of the success of monitoring slow motion landslides using Persistent Scatterer Interferometry and GNSS methods / K.O. Hastaoglu in Survey review, vol 50 n° 363 (September 2018)PermalinkSynergetic use of Sentinel-1 and Sentinel-2 for assessments of heathland conservation status / Johannes Schmidt in Remote sensing in ecology and conservation, vol 4 n° 3 (September 2018)PermalinkThe 2015 Mw 6.4 Pishan earthquake, China: geodetic modelling inferred from Sentinel-1A TOPS interferometry / Yongsheng Li in Survey review, vol 50 n° 363 (September 2018)PermalinkAn improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data / Li Zhuo in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)PermalinkComparison of high-density LiDAR and satellite photogrammetry for forest inventory / Grant D. Pearse in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)PermalinkA generic remote sensing approach to derive operational essential biodiversity variables (EBVs) for conservation planning / Samuel Alleaume in Methods in ecology and evolution, vol 9 n° 8 (August 2018)PermalinkICARE-VEG: A 3D physics-based atmospheric correction method for tree shadows in urban areas / Karine R.M. Adeline in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 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)PermalinkMapping ecosystem services at the regional scale: the validity of an upscaling approach / Solen Le Clec'h in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)PermalinkA method of downscaling temperature maps based on analytical hillshading for use in species distribution modelling / Ángel M. Felicísimo in Cartography and Geographic Information Science, Vol 45 n° 4 (July 2018)PermalinkMulti-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data / Siddhartha Khare in Geocarto international, vol 33 n° 7 (July 2018)PermalinkSoil moisture estimation in Ferlo region (Senegal) using radar (ENVISAT/ASAR) and optical (SPOT/VEGETATION) data / Gayane Faye in The Egyptian Journal of Remote Sensing and Space Science, Vol. 21 suppl.1 (juillet 2018)PermalinkAssessment of Sentinel-1A data for rice crop classification using random forests and support vector machines / Nguyen-Thanh Son in Geocarto international, vol 33 n° 6 (June 2018)PermalinkClassification à très large échelle d’images satellites à très haute résolution spatiale par réseaux de neurones convolutifs / Tristan Postadjian in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkFusion tardive d’images SPOT 6/7 et de données multitemporelles Sentinel-2 pour la détection de la tache urbaine / Cyril Wendl in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkMapping rubber trees based on phenological analysis of Landsat time series data-sets / Janatul Aziera binti Abd Razak in Geocarto international, vol 33 n° 6 (June 2018)PermalinkModeling of inland flood vulnerability zones through remote sensing and GIS techniques in the highland region of Papua New Guinea / Porejane Harley in Applied geomatics, vol 10 n° 2 (June 2018)PermalinkSpatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data / Michalis A. Savelonas in Computer Vision and image understanding, vol 171 (June 2018)PermalinkUncertainties in tree cover maps of Sub-Saharan Africa and their implications for measuring progress towards CBD Aichi Targets / Dorit Gross in Remote sensing in ecology and conservation, vol 4 n° 2 (June 2018)Permalink3D reconstruction from multi-view VHR-satellite images in MicMac / Ewelina Rupnik in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)PermalinkAn object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery / Luis Angel Ruiz in Geocarto international, vol 33 n° 5 (May 2018)PermalinkComparison of total water vapour content in the Arctic derived from GNSS, AIRS, MODIS and SCIAMACHY / Dunya Alraddawi in Atmospheric measurement techniques, vol 11 n° 5 (May 2018)PermalinkImproving the analysis of biogeochemical patterns associated with internal waves in the strait of Gibraltar using remote sensing images / Gabriel Navarro in Estuarine, Coastal and Shelf Science, vol 204 (May 2018)PermalinkA new scheme for urban impervious surface classification from SAR images / Hongsheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)PermalinkCartographie des défoliations du massif forestier du Pays des étangs en Lorraine : Apports potentiels de la télédétection / Thierry Bélouard in Revue forestière française, vol 70 n° 5 (2018)PermalinkGeneric rule-sets for automated detection of urban tree species from very high-resolution satellite data / Razieh Shojanoori in Geocarto international, vol 33 n° 4 (April 2018)PermalinkMapping spatial variability of foliar nitrogen in coffee (Coffea arabica L.) plantations with multispectral Sentinel-2 MSI data / Abel Chemura in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkTowards automatic SAR-optical stereogrammetry over urban areas using very high resolution imagery / Chunping Qiu in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkAccuracy assessment of different digital surface models / Ugur Alganci in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)PermalinkActive tectonics of the onshore Hengchun Fault using UAS DSM combined with ALOS PS-InSAR time series (Southern Taiwan) / Benoit Deffontaines in Natural Hazards and Earth System Sciences, vol 18 n° 3 ([01/03/2018])PermalinkGenerative street addresses from satellite imagery / İlke Demir in ISPRS International journal of geo-information, vol 7 n° 3 (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)PermalinkMapping tree cover with Sentinel-2 data using the Support Vector Machine (SVM) / Anna Mirończuk in Geoinformation issues, Vol 9 n° 1 (2017)PermalinkRemote estimation of canopy leaf area index and chlorophyll content in Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data / Xiaojun Xu in Annals of Forest Science, vol 75 n° 1 (March 2018)PermalinkSensitivity analysis of pansharpening in hyperspectral change detection / Seyd Teymoor Seydi in Applied geomatics, vol 10 n° 1 (March 2018)PermalinkUnderstanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery / Pablo J. Zarco-Tejada in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)PermalinkAnalyse de l'incertitude et de la précision thématique de classifications GEOBIA d'une image WorldView-2 / François Messner in Revue Française de Photogrammétrie et de Télédétection, n° 216 (février 2018)PermalinkEstimating forest standing biomass in savanna woodlands as an indicator of forest productivity using the new generation WorldView-2 sensor / Timothy Dube in Geocarto international, vol 33 n° 2 (February 2018)PermalinkEstimation of forest aboveground biomass from HJ1B imagery using a canopy reflectance model and a forest growth model / Xinyun Wang in Geocarto international, vol 33 n° 2 (February 2018)PermalinkNouvelle méthode en cascade pour la classification hiérarchique multi-temporelle ou multi-capteur d'images satellitaires haute résolution / Ihsen Hedhli in Revue Française de Photogrammétrie et de Télédétection, n° 216 (février 2018)PermalinkAdapting an existing semi-automatized image processing chain to enable Sentinel-2 data classification. / Hiyam Elbadri (2018)PermalinkPermalinkAutomated delineation of wildfire areas using Sentinel-2 satellite imagery / Mira Weirather in GI Forum, vol 2018 n° 1 ([01/01/2018])PermalinkCartographie des déformations de surface sur l’île de Taiwan par interférométrie RADAR Sentinel-1 / Miloud Fekaouni (2018)PermalinkCartographier l'occupation du sol à grande échelle : optimisation de la photo-interprétation par segmentation d'image / Maxime Vitter (2018)PermalinkPermalinkClassification à très haute résolution (THR) spatiale et fusion d'occupation des sols (OCS) / Tristan Postadjian (2018)PermalinkClassification à très large échelle d'images satellite à très haute résolution spatiale par réseaux de neurones convolutifs / Tristan Postadjian (2018)PermalinkA comparative analysis of the NDVIg and NDVI3g in monitoring vegetation phenology changes in the Northern Hemisphere / Qing Chang in Geocarto international, vol 33 n° 1 (January 2018)PermalinkConception d’une méthode radar de suivi bimensuel des déforestations et d’une méthode optique de classification d’occupation des sols / Luc Baudoux (2018)PermalinkContribution actuelle de la calotte Antarctique à la variation du niveau marin / Clémence Chupin (2018)PermalinkCrop-rotation structured classification using multi-source sentinel images and LPIS for crop type mapping / Simon Bailly (2018)PermalinkDecision fusion of SPOT6 and multitemporal Sentinel2 images for urban area detection / Cyril Wendl (2018)PermalinkDétection de changement par imagerie radar sur les zones naturelles et agricoles en milieu tropical / Jérôme Lebreton (2018)PermalinkEstimation of surface roughness over bare agricultural soil from Sentinel-1 data / Mohammad Choker (2018)PermalinkEtude préalable à l'installation d'un coin radar sur le site de co-localisation de Calern / Guillaume Schmidt (2018)PermalinkEvaluation des performances des modèles numérique d’élévation issus de l’imagerie tri-stéréo Pléiades pour le suivi de l’évolution morphologique des dunes littorales / Mannaïg L'haridon (2018)PermalinkExploring image fusion of ALOS/PALSAR data and LANDSAT data to differentiate forest area / Saygin Abdikan in Geocarto international, vol 33 n° 1 (January 2018)PermalinkExploring the impact of seasonality on urban land-cover mapping using multi-season sentinel-1A and GF-1 WFV images in a subtropical monsoon-climate region / Tao Zhou in ISPRS International journal of geo-information, vol 7 n° 1 (January 2018)PermalinkPermalinkFusion tardive d’images SPOT-6/7 et de données multitemporelles Sentinel-2 pour la détection de la tache urbaine / Cyril Wendl (2018)PermalinkGeometric multi-wavelet total variation for SAR image time series analysis / Abdourrahmane M. Atto (2018)PermalinkA hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning / Rasmus M. Houborg in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)PermalinkUn inventaire forestier multisource pour la gestion des territoires / Dinesh Babu Irulappa-Pillai-Vijayakumar (2018)PermalinkLearning multiscale deep features for high-resolution satellite image scene classification / Qingshan Liu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)PermalinkMapping grassland management intensity using Sentinel-2 satellite data / Marijke Elisabeth Bekkema in GI Forum, vol 2018 n° 1 ([01/01/2018])PermalinkMise en évidence de l’activité récente des failles du bassin de Naryn (Kyrgyzstan) à partir de données photogrammétriques Pléiades et drone : un nouvel apport pour l’aléa sismique / Aurélie Médard (2018)Permalink