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Surface 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])
[article]
Titre : Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data Type de document : Article/Communication Auteurs : Xiaofang Sun, Auteur ; Bai Li, Auteur ; Zhengping Du, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1549 - 1564 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] carbone
[Termes IGN] carte de la végétation
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données ICEsat
[Termes IGN] données lidar
[Termes IGN] données multisources
[Termes IGN] Geoscience Laser Altimeter System
[Termes IGN] image Terra-MODIS
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Kiangsi (Chine)
[Termes IGN] krigeage
[Termes IGN] modèle numérique de surface
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression des moindres carrés partielsRésumé : (auteur) An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. In this study, six methods, including partial least squares regression, regression kriging, k-nearest neighbour, support vector machines, random forest and high accuracy surface modelling (HASM), were used to simulate forest AGB. Forest AGB was mapped by combining Geoscience Laser Altimeter System data, optical imagery and field inventory data. The Normalized Difference Vegetation Index (NDVI) and Wide Dynamic Range Vegetation Index (WDRVI0.2) of September and October, which had a stronger correlation with forest AGB than that of the peak growing season, were selected as predictor variables, along with tree cover percentage and three GLAS-derived parameters. The results of the different methods were evaluated. The HASM model had the best modelling accuracy (small MAE, RMSE, NRMSE, RMSV and NMSE and large R2). A forest AGB map of the study area was generated using the optimal model. Numéro de notice : A2021-555 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1655799 Date de publication en ligne : 28/08/2019 En ligne : https://doi.org/10.1080/10106049.2019.1655799 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98108
in Geocarto international > vol 36 n° 14 [01/08/2021] . - pp 1549 - 1564[article]Unsupervised representation high-resolution remote sensing image scene classification via contrastive learning convolutional neural network / Fengpeng Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 8 (August 2021)
[article]
Titre : Unsupervised representation high-resolution remote sensing image scene classification via contrastive learning convolutional neural network Type de document : Article/Communication Auteurs : Fengpeng Li, Auteur ; Jiabao Li, Auteur ; Wei Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 577 - 591 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] grande échelle
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] moyenne échelle
[Termes IGN] petite échelle
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) Inspired by the outstanding achievement of deep learning, supervised deep learning representation methods for high-spatial-resolution remote sensing image scene classification obtained state-of-the-art performance. However, supervised deep learning representation methods need a considerable amount of labeled data to capture class-specific features, limiting the application of deep learning-based methods while there are a few labeled training samples. An unsupervised deep learning representation, high-resolution remote sensing image scene classification method is proposed in this work to address this issue. The proposed method, called contrastive learning, narrows the distance between positive views: color channels belonging to the same images widens the gaps between negative view pairs consisting of color channels from different images to obtain class-specific data representations of the input data without any supervised information. The classifier uses extracted features by the convolutional neural network (CNN)-based feature extractor with labeled information of training data to set space of each category and then, using linear regression, makes predictions in the testing procedure. Comparing with existing unsupervised deep learning representation high-resolution remote sensing image scene classification methods, contrastive learning CNN achieves state-of-the-art performance on three different scale benchmark data sets: small scale RSSCN7 data set, midscale aerial image data set, and large-scale NWPU-RESISC45 data set. Numéro de notice : A2021-670 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.8.577 Date de publication en ligne : 01/08/2021 En ligne : https://doi.org/10.14358/PERS.87.8.577 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98806
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 8 (August 2021) . - pp 577 - 591[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021081 SL Revue Centre de documentation Revues en salle Disponible Geographical and temporal huff model calibration using taxi trajectory data / Shuhui Gong in Geoinformatica, vol 25 n° 3 (July 2021)
[article]
Titre : Geographical and temporal huff model calibration using taxi trajectory data Type de document : Article/Communication Auteurs : Shuhui Gong, Auteur ; John Cartlidge, Auteur ; Ruibin Bai, Auteur ; Yang Yue, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 485 - 512 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] attractivité (aménagement)
[Termes IGN] étalonnage de modèle
[Termes IGN] New York (Etats-Unis ; ville)
[Termes IGN] régression des moindres carrés partiels
[Termes IGN] régression géographiquement pondérée
[Termes IGN] Shenzhen
[Termes IGN] trajectoire (véhicule non spatial)Résumé : (auteur) The Huff model is designed to estimate the probability of shopping centre patronage based on a shopping centre’s attractiveness and the cost of a customer’s travel. In this paper, we attempt to discover some general shopping trends by calibrating the Huff model in Shenzhen, China, and New York, USA, using taxi trajectory GPS data and sharing bikes GPS data. Geographical and Temporal Weighted Regression (GTWR) is used to fit the model, and calibration results are compared with Ordinary Least Squares (OLS) regression, Geographical Weighted Regression (GWR), and Temporal Weighted Regression (TWR). Results show that GTWR gives the highest performance due to significant geographical and temporal variation in the Huff model parameters of attractiveness and travel cost. To explain the geographical variation, we use residential sales’ and rental prices in Shenzhen and New York as a proxy for customers’ wealth in each region. Pearson product-moment correlation results show a medium relationship between localised sales’ and rental prices and the Huff model parameter of attractiveness: that is, customer wealth explains geographic sensitivity to shopping area attractiveness. To explain temporal variation, we use census data in both Shenzhen and New York to provide job profile distributions for each region as a proxy to estimate customers’ spare leisure time. Regression results demonstrate that there is a significant linear relationship between the length of spare time and the parameter of shopping area attractiveness. In particular, we demonstrate that wealthy customers with less spare time are more sensitive to a shopping centre’s attractiveness. We also discover customers’ sensitivities to travel distance are related to their travel mode. In particular, people riding bikes to shopping areas care much more about trip distance compared with people who take taxi. Finally, results show a divergence in behaviours between customers in New York and Shenzhen at weekends. While customers in New York prefer to shop more locally at weekends, customers in Shenzhen care less about trip distance. We provide the GTWR calibration of the Huff model as our theoretical contribution. GTWR extends the Huff model to two dimensions (time and space), so as to analyse the differences of residents’ travel behaviours in different time and locations. We also provide the discoveries of factors affecting urban travel behaviours (wealth and employment) as practical contributions that may help optimise urban transportation design. In particular, the sensitivity of residents to the attraction of shopping areas has a significant positive linear relationship with the housing price and a significant negative linear relationship with the residents’ length of spare time. Numéro de notice : A2021-973 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1007/s10707-019-00390-x Date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1007/s10707-019-00390-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100392
in Geoinformatica > vol 25 n° 3 (July 2021) . - pp 485 - 512[article]JUST: MATLAB and python software for change detection and time series analysis / Ebrahim Ghaderpour in GPS solutions, vol 25 n° 3 (July 2021)
[article]
Titre : JUST: MATLAB and python software for change detection and time series analysis Type de document : Article/Communication Auteurs : Ebrahim Ghaderpour, Auteur Année de publication : 2021 Article en page(s) : Article 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse de données
[Termes IGN] détection de changement
[Termes IGN] Matlab
[Termes IGN] méthode des moindres carrés
[Termes IGN] Python (langage de programmation)
[Termes IGN] série temporelleRésumé : (Auteur) Change detection within unequally spaced and non-stationary time series is crucial in various applications, such as environmental monitoring and satellite navigation. The jumps upon spectrum and trend (JUST) is developed to detect potential jumps within the trend component of time series segments. JUST can simultaneously estimate the trend and seasonal components of any equally or unequally spaced time series by considering the observational uncertainties or measurement errors. JUST and its modules can also be applied to monitor vegetation time series in near-real-time. Herein, the details of the open-source software package for JUST, developed in both MATLAB and Python, are presented. Numéro de notice : A2021-330 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01118-x Date de publication en ligne : 09/04/2021 En ligne : https://doi.org/10.1007/s10291-021-01118-x Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97500
in GPS solutions > vol 25 n° 3 (July 2021) . - Article 85[article]Application 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])
[article]
Titre : Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery Type de document : Article/Communication Auteurs : Sikdar M. M. Rasel, Auteur ; Hsing-Chung Chang, Auteur ; Timothy J. Ralph, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1075-1099 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] bande spectrale
[Termes IGN] biomasse
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image multibande
[Termes IGN] image Worldview
[Termes IGN] marais salé
[Termes IGN] modèle de simulation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression
[Termes IGN] variableRésumé : (Auteur) Assessing large scale plant productivity of coastal marshes is essential to understand the resilience of these systems to climate change. Two machine learning approaches, random forest (RF) and support vector machine (SVM) regression were tested to estimate biomass of a common saltmarshes species, salt couch grass (Sporobolus virginicus). Reflectance and vegetation indices derived from 8 bands of Worldview-2 multispectral data were used for four experiments to develop the biomass model. These four experiments were, Experiment-1: 8 bands of Worldview-2 image, Experiment-2: Possible combination of all bands of Worldview-2 for Normalized Difference Vegetation Index (NDVI) type vegetation indices, Experiment-3: Combination of bands and vegetation indices, Experiment-4: Selected variables derived from experiment-3 using variable selection methods. The main objectives of this study are (i) to recommend an affordable low cost data source to predict biomass of a common saltmarshes species, (ii) to suggest a variable selection method suitable for multispectral data, (iii) to assess the performance of RF and SVM for the biomass prediction model. Cross-validation of parameter optimizations for SVM showed that optimized parameter of ɛ-SVR failed to provide a reliable prediction. Hence, ν-SVR was used for the SVM model. Among the different variable selection methods, recursive feature elimination (RFE) selected a minimum number of variables (only 4) with an RMSE of 0.211 (kg/m2). Experiment-4 (only selected bands) provided the best results for both of the machine learning regression methods, RF (R2= 0.72, RMSE= 0.166 kg/m2) and SVR (R2= 0.66, RMSE = 0.200 kg/m2) to predict biomass. When a 10-fold cross validation of the RF model was compared with a 10-fold cross validation of SVR, a significant difference (p = Numéro de notice : A2021-367 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1624988 Date de publication en ligne : 11/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1624988 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97729
in Geocarto international > vol 36 n° 10 [01/06/2021] . - pp 1075-1099[article]Fractional 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)PermalinkOn the relationship between normalized difference vegetation index and land surface temperature: MODIS-based analysis in a semi-arid to arid environment / Salahuddin M. Jaber in Geocarto international, vol 36 n° 10 ([01/06/2021])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)PermalinkRetrieval of ultraviolet diffuse attenuation coefficients from ocean color using the kernel principal components analysis over ocean / Kunpeng Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkEstimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])PermalinkHigh-resolution geoid modeling using least squares modification of Stokes and Hotine formulas in Colorado / Mustafa Serkan Işık in Journal of geodesy, vol 95 n° 5 (May 2021)PermalinkIncreasing efficiency of the robust deformation analysis methods using genetic algorithm and generalised particle swarm optimisation / Mehmed Batilović in Survey review, Vol 53 n° 378 (May 2021)PermalinkNew algorithms for spherical harmonic analysis of area mean values over blocks delineated by equiangular and Gaussian grids / Rong Sun in Journal of geodesy, vol 95 n° 5 (May 2021)PermalinkCloud detection from paired CrIS water vapor and CO₂ channels using machine learning techniques / Miao Tian in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkPrecipitable water vapor fusion based on a generalized regression neural network / Bao Zhang in Journal of geodesy, vol 95 n° 4 (April 2021)Permalink