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Data assimilation of growing stock volume using a sequence of remote sensing data from different sensors / Niels Lindgren in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)  

Public [article]  inCanadian journal of remote sensing > vol 48 n° 2 (April 2022) . - pp Titre : Data assimilation of growing stock volume using a sequence of remote sensing data from different sensors Titre original : Assimilation de données de volume de bois à l’aide d’une séquence de données de télédétection provenant de différents capteurs Type de document : Article/Communication Auteurs : Niels Lindgren, Auteur ; Hakan Olsson, Auteur ; Kenneth Nyström, Auteur ; Mattias Nyström, Auteur ; Göran Stahl, Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Betula (genre) [Termes IGN] capital sur pied [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] étalonnage des données [Termes IGN] filtre de Kalman [Termes IGN] forêt boréale [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] Quercus (genre) [Termes IGN] Suède [Termes IGN] volume en bois Résumé : (auteur) Airborne Laser Scanning (ALS) has implied a disruptive transformation of how data are gathered for forest management planning in Nordic countries. We show in this study that the accuracy of ALS predictions of growing stock volume can be maintained and even improved over time if they are forecasted and assimilated with more frequent but less accurate remote sensing data sources like satellite images, digital photogrammetry, and InSAR. We obtained these results by introducing important methodological adaptations to data assimilation compared to previous forestry studies in Sweden. On a test site in the southwest of Sweden (58°27′N, 13°39′E), we evaluated the performance of the extended Kalman filter and a proposed modified filter that accounts for error correlations. We also applied classical calibration to the remote sensing predictions. We evaluated the developed methods using a dataset with nine different acquisitions of remotely sensed data from a mix of sensors over four years, starting and ending with ALS-based predictions of growing stock volume. The results showed that the modified filter and the calibrated predictions performed better than the standard extended Kalman filter and that at the endpoint the prediction based on data assimilation implied an improved accuracy (25.0% RMSE), compared to a new ALS-based prediction (27.5% RMSE). Numéro de notice : A2022-144 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/07038992.2021.1988542 Date de publication en ligne : 17/10/2021 En ligne : https://doi.org/10.1080/07038992.2021.1988542 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99985 [article]

The integration of multi-source remotely sensed data with hierarchically based classification approaches in support of the classification of wetlands / Aaron Judah in Canadian journal of remote sensing, vol 48 n° 2 (April 2022)  

Public [article]  inCanadian journal of remote sensing > vol 48 n° 2 (April 2022) . - pp 158 - 181 Titre : The integration of multi-source remotely sensed data with hierarchically based classification approaches in support of the classification of wetlands Type de document : Article/Communication Auteurs : Aaron Judah, Auteur ; Baoxin Hu, Auteur Année de publication : 2022 Article en page(s) : pp 158 - 181 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] classification bayesienne [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-TM [Termes IGN] image Sentinel-SAR [Termes IGN] intégration de données [Termes IGN] modèle numérique de terrain [Termes IGN] précision de la classification [Termes IGN] tourbière [Termes IGN] utilisation du sol [Termes IGN] zone humide Résumé : (auteur) Methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from remotely sensed data using advanced classification algorithms through two hierarchical approaches. The data utilized included multispectral optical and thermal data (Landsat-5, and Landsat-8), radar imagery (Sentinel-1), and a digital elevation model. Goals were to determine the best way to combine imagery to classify wetlands through hierarchically based classification approaches to produce more accurate and efficient maps compared to standard classification. Algorithms used were Random Forest (RF), and Naïve Bayes. A hierarchically based RF classification methodology produced the most accurate classification result (91.94%). The hierarchically based approaches also improved classification accuracies for low-quality data, as defined through feature analysis, when compared to a nonhierarchical classifier. The hierarchical approaches also produced a significant increase in classification accuracy for the Naïve Bayes classifier versus the standard approach (∼12% increase) while not significantly increasing computation time – comparable in accuracy to the RF tests for around 20% the computational effort. Preselection of spectral bands, polarizations and other input parameters (Normalized Difference Vegetation Index, Normalized Difference Water Index, albedo, slope, etc.) using log-normal or RF variable importance analysis was very effective at identifying low-quality features and features which were of higher quality. Numéro de notice : A2022-372 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1080/07038992.2021.1967732 Date de publication en ligne : 13/11/2021 En ligne : https://doi.org/10.1080/07038992.2021.1967732 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100614 [article]