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Urban land cover/use mapping and change detection analysis using multi-temporal Landsat OLI with Lidar-DEM and derived TPI / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)
[article]
Titre : Urban land cover/use mapping and change detection analysis using multi-temporal Landsat OLI with Lidar-DEM and derived TPI Type de document : Article/Communication Auteurs : Clement E. Akumu, Auteur ; Sam Dennis, Auteur Année de publication : 2022 Article en page(s) : pp 243 - 253 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] carte d'occupation du sol
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection de changement
[Termes IGN] données multitemporelles
[Termes IGN] données topographiques
[Termes IGN] image Landsat-OLI
[Termes IGN] milieu urbain
[Termes IGN] MNS lidar
[Termes IGN] Tennessee (Etats-Unis)
[Termes IGN] utilisation du solRésumé : (auteur) The mapping and change detection of land cover and land use are essential for urban management. The aim of this study was to map and monitor the spatial and temporal change in urban land cover and land use in Davidson County, Tennessee in the periods of 2013, 2016, and 2020. The urban land cover and land use categories were classified and mapped using Random Forest algorithm. A combination of Landsat Operational Land Imager (OLI) satellite data with Light Detection and Ranging (lidar)-Digital Elevation Model (DEM) and derived Topographic Position Index (TPI) were used in the classification and monitoring of urban land cover and land use change. The urban land cover and land use types were mapped with average overall accuracies of about 87% in 2020, 85% in 2016 and 2013. The overall accuracy increased by around 8%, 9%, and 6% in 2020, 2016, and 2013 classifications respectively when lidarDEMand derived TPIwere added to Landsat OLIsatellite data in the classification relative to standalone Landsat OLI. Total change occurred in about 63% of Davidson County between 2016 and 2020 with significant net gains and losses among land cover and land use types. This information could support land use planning. Numéro de notice : A2022-286 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00042R3 Date de publication en ligne : 04/04/2022 En ligne : https://doi.org/10.14358/PERS.21-00042R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100320
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 4 (April 2022) . - pp 243 - 253[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022041 SL Revue Centre de documentation Revues en salle Disponible Classification of Eucalyptus plantation Site Index (SI) and Mean Annual Increment (MAI) prediction using DEM-based geomorphometric and climatic variables in Brazil / Aliny Aparecida Dos Reis in Geocarto international, vol 37 n° 5 ([01/03/2022])
[article]
Titre : Classification of Eucalyptus plantation Site Index (SI) and Mean Annual Increment (MAI) prediction using DEM-based geomorphometric and climatic variables in Brazil Type de document : Article/Communication Auteurs : Aliny Aparecida Dos Reis, Auteur ; Steven E. Franklin, Auteur ; Fausto Weimar Acerbi Júnior, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1256 - 1273 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Brésil
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données météorologiques
[Termes IGN] Eucalyptus (genre)
[Termes IGN] géomorphométrie
[Termes IGN] MNS SRTM
[Termes IGN] plantation forestière
[Termes IGN] rendementRésumé : (Auteur) Digital elevation model (DEM) data were used with climate data to estimate productivity in 19 Eucalyptus plantations in Minas Gerais state, Brazil. Typically, plantation and individual stand growth and productivity estimates, such as Site Index (SI) and Mean Annual Increment (MAI), are based on field measures of height, tree diameter and age. Using a Random Forest modelling approach, SI and MAI were related to: (i) DEM-based geomorphometric variables and (ii) WorldClim historical macro-climatic measures. Three operational SI classes (high, medium and low productivity) in 180 stands were mapped with an overall accuracy of 91.6%. Medium and high productivity sites were the most accurately classified. Low productivity sites had 76.5% producer’s accuracy and 92.9% user’s accuracy, and were the most extensive in the study area. Such sites are considered of high importance from a plantation management perspective since additional forestry operations are likely required to address low productivity and growth. Numéro de notice : A2022-275 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1778103 Date de publication en ligne : 19/06/2020 En ligne : https://doi.org/10.1080/10106049.2020.1778103 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100782
in Geocarto international > vol 37 n° 5 [01/03/2022] . - pp 1256 - 1273[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2022051 RAB Revue Centre de documentation En réserve L003 Disponible Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 5 ([01/03/2022])
[article]
Titre : Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models Type de document : Article/Communication Auteurs : Lamin R. Mansaray, Auteur ; Fumin Wang, Auteur ; Adam Sheka Kanu, Auteur ; Lingbo Yang, Auteur Année de publication : 2022 Article en page(s) : pp 1225 - 1236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Extreme Gradient Machine
[Termes IGN] image Sentinel-SAR
[Termes IGN] jeu de données localisées
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de régression
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] polarisation
[Termes IGN] rizièreRésumé : (Auteur) Three Sentinel-1A datasets in vertical transmitted and horizontal received (VH) and vertical transmitted and vertical received (VV) polarisations, and the linear combination of VH and VV (VHVV) are evaluated for rice green leaf area index (LAI) estimation using four machine learning regression models [Support Vector Machine (SVM), k-Nearest Neighbour (k-NN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT)]. Results showed that for the entire growing season, VV outperformed VH, recording an R2 of 0.68 and an RMSE of 0.98 m2/m2 with the k-NN model. However, VHVV produced the most accurate estimates with GBDT (R2 of 0.82 and RMSE of 0.68 m2/m2), followed by that of VHVV with RF (R2 of 0.78 and RMSE of 0.90 m2/m2). Our findings have further confirmed that combining VH and VV data can achieve improved rice growth modelling, and that tree-based algorithms can better handle data dimensionality. Numéro de notice : A2022-274 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1773545 Date de publication en ligne : 05/06/2020 En ligne : https://doi.org/10.1080/10106049.2020.1773545 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100753
in Geocarto international > vol 37 n° 5 [01/03/2022] . - pp 1225 - 1236[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2022051 RAB Revue Centre de documentation En réserve L003 Disponible Land surface phenology retrieval through spectral and angular harmonization of Landsat-8, Sentinel-2 and Gaofen-1 data / Jun Lu in Remote sensing, vol 14 n° 5 (March-1 2022)
[article]
Titre : Land surface phenology retrieval through spectral and angular harmonization of Landsat-8, Sentinel-2 and Gaofen-1 data Type de document : Article/Communication Auteurs : Jun Lu, Auteur ; Tao He, Auteur ; Dan-Xia Song, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1296 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] fusion de données multisource
[Termes IGN] harmonisation des données
[Termes IGN] image Gaofen
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-MODIS
[Termes IGN] indice de végétation
[Termes IGN] phénologie
[Termes IGN] réflectance spectrale
[Termes IGN] série temporelleRésumé : (auteur) Land Surface Phenology is an important characteristic of vegetation, which can be informative of its response to climate change. However, satellite-based identification of vegetation transition dates is hindered by inconsistencies in different observation platforms, including band settings, viewing angles, and scale effects. Therefore, time-series data with high consistency are necessary for monitoring vegetation phenology. This study proposes a data harmonization approach that involves band conversion and bidirectional reflectance distribution function (BRDF) correction to create normalized reflectance from Landsat-8, Sentinel-2A, and Gaofen-1 (GF-1) satellite data, characterized by the same spectral and illumination-viewing angles as the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Nadir BRDF Adjusted Reflectance (NBAR). The harmonized data are then subjected to the spatial and temporal adaptive reflectance fusion model (STARFM) to produce time-series data with high spatio–temporal resolution. Finally, the transition date of typical vegetation was estimated using regular 30 m spatial resolution data. The results show that the data harmonization method proposed in this study assists in improving the consistency of different observations under different viewing angles. The fusion result of STARFM was improved after eliminating differences in the input data, and the accuracy of the remote-sensing-based vegetation transition date was improved by the fused time-series curve with the input of harmonized data. The root mean square error (RMSE) estimation of the vegetation transition date decreased by 9.58 days. We concluded that data harmonization eliminates the viewing-angle effect and is essential for time-series vegetation monitoring through improved data fusion. Numéro de notice : A2022-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14051296 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.3390/rs14051296 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100027
in Remote sensing > vol 14 n° 5 (March-1 2022) . - n° 1296[article]Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 3 ([01/02/2022])
[article]
Titre : Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models Type de document : Article/Communication Auteurs : Lamin R. Mansaray, Auteur ; Adam Sheka Kanu, Auteur ; Lingbo Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 828 - 840 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Extreme Gradient Machine
[Termes IGN] Green Leaf Area Index
[Termes IGN] image Gaofen
[Termes IGN] image HJ-1A
[Termes IGN] image HJ-1B
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] indice foliaire
[Termes IGN] modèle de régression
[Termes IGN] rizièreRésumé : (auteur) Optical satellite imagery has been widely used to monitor leaf area index (LAI). However, most studies have focussed on single- or dual-source data, thus making little use of a growing repository of freely available optical imagery. Hence this study has evaluated the feasibility of quad-source optical satellite imagery involving Landsat-8, Sentinel-2A, China’s environment satellite constellation (HJ-1 A and B) and Gaofen-1 (GF-1) in modelling rice green LAI over a test site located in southeast China at two growing seasons. With the application of machine learning regression models including Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbour (k-NN) and Gradient Boosting Decision Tree (GBDT), results indicated that regression models based on an ensemble of decision trees (RF and GBDT) were more suitable for modelling rice green LAI. The current study has demonstrated the feasibility of quad-source optical imagery in modelling rice green LAI and this is relevant for cloudy areas. Numéro de notice : A2022-346 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1745299 Date de publication en ligne : 03/04/2020 En ligne : https://doi.org/10.1080/10106049.2020.1745299 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100530
in Geocarto international > vol 37 n° 3 [01/02/2022] . - pp 828 - 840[article]Mapping abundance distributions of allergenic tree species in urbanized landscapes: A nation-wide study for Belgium using forest inventory and citizen science data / Sébastien Dujardin in Landscape and Urban Planning, vol 218 (February 2022)PermalinkPlanning of commercial thinnings using machine learning and airborne Lidar data / Tauri Arumäe in Forests, vol 13 n° 2 (February 2022)PermalinkSiamese Adversarial Network for image classification of heavy mineral grains / Huizhen Hao in Computers & geosciences, vol 159 (February 2022)Permalink3D modeling of urban area based on oblique UAS images - An end-to-end pipeline / Valeria-Ersilia Oniga in Remote sensing, vol 14 n° 2 (January-2 2022)PermalinkVariable selection for estimating individual tree height using genetic algorithm and random forest / Evandro Nunes Miranda in Forest ecology and management, vol 504 (January-15 2022)PermalinkAbove-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data / Fardin Moradi in Annals of forest research, vol 65 n° 1 (January - June 2022)PermalinkAn extended patch-based cellular automaton to simulate horizontal and vertical urban growth under the shared socioeconomic pathways / Yimin Chen in Computers, Environment and Urban Systems, vol 91 (January 2022)PermalinkClassification of mediterranean shrub species from UAV point clouds / Juan Pedro Carbonell-Rivera in Remote sensing, vol 14 n° 1 (January-1 2022)PermalinkCombining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China / Huijuan Zhang in Computers & geosciences, vol 158 (January 2022)PermalinkDétection des prairies de fauche et estimation des périodes de fauche par télédétection / Emma Seneschal (2022)PermalinkEstimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data / Fardin Moradi in Forests, vol 13 n° 1 (January 2022)PermalinkÉvolution rétrospective et prospective d’un massif dunaire par imagerie multispectrale et LiDAR / Iris Jeuffrard (2022)PermalinkForest fire susceptibility assessment using Google Earth engine in Gangwon-do, Republic of Korea / Yong Piao in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkA GIS-based landslide susceptibility mapping and variable importance analysis using artificial intelligent training-based methods / Pengxiang Zhao in Remote sensing, vol 14 n° 1 (January-1 2022)PermalinkIdentifying map users with eye movement data from map-based spatial tasks: user privacy concerns / Hua Liao in Cartography and Geographic Information Science, vol 49 n° 1 (January 2022)PermalinkTowards synthetic sensing for smart cities : a machine/deep learning-based approach / Faraz Malik Awan (2022)PermalinkTowards urban flood susceptibility mapping using data-driven models in Berlin, Germany / Omar Seleem in Geomatics, Natural Hazards and Risk, vol 13 (2022)PermalinkEarly detection of spruce vitality loss with hyperspectral data: Results of an experimental study in Bavaria, Germany / Kathrin Einzmann in Remote sensing of environment, vol 266 (December 2021)PermalinkEstimation of individual tree stem biomass in an uneven-aged structured coniferous forest using multispectral LiDAR data / Nikos Georgopoulos in Remote sensing, vol 13 n° 23 (December-1 2021)PermalinkBagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation / Hamid Jafarzadeh in Remote sensing, vol 13 n° 21 (November-1 2021)PermalinkA comparison of a gradient boosting decision tree, random forests, and artificial neural networks to model urban land use changes: the case of the Seoul metropolitan area / Myung-Jin Jun in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)PermalinkDiffuse attenuation coefficient (Kd) from ICESat-2 ATLAS spaceborne Lidar using random-forest regression / Forrest Corcoran in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)PermalinkMulti-sensor aboveground biomass estimation in the broadleaved hyrcanian forest of Iran / Ghasem Ronoud in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])PermalinkUsing LiDAR and Random Forest to improve deer habitat models in a managed forest landscape / Colin S. Shanley in Forest ecology and management, vol 499 (November-1 2021)PermalinkDeep-learning-based burned area mapping using the synergy of Sentinel-1&2 data / Qi Zhang in Remote sensing of environment, vol 264 (October 2021)PermalinkSpatial interpolation of mobile positioning data for population statistics / Anto Aasa in Journal of location-based services, vol 15 n° 4 ([01/10/2021])PermalinkClassification of tree species in a heterogeneous urban environment using object-based ensemble analysis and World View-2 satellite imagery / Simbarashe Jombo in Applied geomatics, vol 13 n° 3 (September 2021)PermalinkTwo hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])PermalinkUnsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, vol 36 n° 15 ([15/08/2021])PermalinkInvestigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)PermalinkRandom forests with bagging and genetic algorithms coupled with least trimmed squares regression for soil moisture deficit using SMOS satellite soil moisture / Pashrant K. Srivastava in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)PermalinkSurface modelling of forest aboveground biomass based on remote sensing and forest inventory data / Xiaofang Sun in Geocarto international, vol 36 n° 14 ([01/08/2021])PermalinkDEM- and GIS-based analysis of soil erosion depth using machine learning / Kieu Anh Nguyen in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)PermalinkEstimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data / Yueting Wang in Ecological indicators, vol 126 (July 2021)PermalinkImplementing a mass valuation application on interoperable land valuation data model designed as an extension of the national GDI / Arif Cagdas Aydinoglu in Survey review, Vol 53 n° 379 (July 2021)PermalinkUsing machine learning to map Western Australian landscapes for mineral exploration / Thomas Albrecht in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)PermalinkFast weakly supervised detection of railway-related infrastructures in lidar acquisitions / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)PermalinkForest cover mapping and Pinus species classification using very high-resolution satellite images and random forest / Laura Alonso-Martinez in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)PermalinkApplication of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery / Sikdar M. M. Rasel in Geocarto international, vol 36 n° 10 ([01/06/2021])PermalinkCloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 / Dimitris Poursanidis in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)PermalinkA combined drought monitoring index based on multi-sensor remote sensing data and machine learning / Hongzhu Han in Geocarto international, vol 36 n° 10 ([01/06/2021])PermalinkEvaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkFractional vegetation cover estimation algorithm for FY-3B reflectance data based on random forest regression method / Duanyang Liu in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkRapid ecosystem change at the southern limit of the Canadian Arctic, Torngat Mountains National Park / Emma L. Davis in Remote sensing, vol 13 n° 11 (June-1 2021)PermalinkAutomatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning / Malarvizhi Arulraj in Remote sensing of environment, vol 257 (May 2021)Permalink