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Combination of Sentinel-1 and Sentinel-2 data for tree species classification in a Central European biosphere reserve / Michael Lechner in Remote sensing, vol 14 n° 11 (June-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 11 (June-1 2022) . - n° 2687 Titre : Combination of Sentinel-1 and Sentinel-2 data for tree species classification in a Central European biosphere reserve Type de document : Article/Communication Auteurs : Michael Lechner, Auteur ; Alena Dostalova, Auteur ; Markus Hollaus, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2687 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse comparative [Termes IGN] analyse harmonique [Termes IGN] Autriche [Termes IGN] biosphère [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] espèce végétale [Termes IGN] feuillu [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] nébulosité [Termes IGN] phénologie [Termes IGN] Pinophyta [Termes IGN] rapport signal sur bruit [Termes IGN] réserve forestière Résumé : (auteur) Microwave and optical imaging methods react differently to different land surface parameters and, thus, provide highly complementary information. However, the contribution of individual features from these two domains of the electromagnetic spectrum for tree species classification is still unclear. For large-scale forest assessments, it is moreover important to better understand the domain-specific limitations of the two sensor families, such as the impact of cloudiness and low signal-to-noise-ratio, respectively. In this study, seven deciduous and five coniferous tree species of the Austrian Biosphere Reserve Wienerwald (105,000 ha) were classified using Breiman’s random forest classifier, labeled with help of forest enterprise data. In nine test cases, variations of Sentinel-1 and Sentinel-2 imagery were passed to the classifier to evaluate their respective contributions. By solely using a high number of Sentinel-2 scenes well spread over the growing season, an overall accuracy of 83.2% was achieved. With ample Sentinel-2 scenes available, the additional use of Sentinel-1 data improved the results by 0.5 percentage points. This changed when only a single Sentinel-2 scene was supposedly available. In this case, the full set of Sentinel-1-derived features increased the overall accuracy on average by 4.7 percentage points. The same level of accuracy could be obtained using three Sentinel-2 scenes spread over the vegetation period. On the other hand, the sole use of Sentinel-1 including phenological indicators and additional features derived from the time series did not yield satisfactory overall classification accuracies (55.7%), as only coniferous species were well separated. Numéro de notice : A2022-540 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs14112687 Date de publication en ligne : 03/06/2022 En ligne : https://doi.org/10.3390/rs14112687 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101103 [article]

An application-oriented automated approach for co-registration of forest inventory and airborne laser scanning data / W. Dorigo in International Journal of Remote Sensing IJRS, vol 31 n° 5 (March 2010)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 31 n° 5 (March 2010) . - pp 1133 - 1153 Titre : An application-oriented automated approach for co-registration of forest inventory and airborne laser scanning data Type de document : Article/Communication Auteurs : W. Dorigo, Auteur ; Markus Hollaus, Auteur ; W. Wagner, Auteur ; Klemens Schadauer, Auteur Année de publication : 2010 Conférence : Silvilaser 2008, 8th international conference on Lidar applications in forest assessment and inventory 17/09/2008 19/09/2008 Edimbourg Royaume-Uni Proceedings Taylor&Francis Article en page(s) : pp 1133 - 1153 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Autriche [Termes IGN] canopée [Termes IGN] données laser [Termes IGN] données localisées 3D [Termes IGN] feuillu [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lasergrammétrie [Termes IGN] télémétrie laser aéroporté Résumé : (Auteur) Airborne laser scanning (ALS) data can be used for downscaling point-based forest inventory (FI) measurements to obtain spatially distributed estimates of forest parameters at a more detailed, local scale. Such downscaling algorithms usually consist of a direct coupling between selected FI parameters and ALS data collected at the field sampling locations. Thus, precise co-registration between FI and ALS data is an essential preprocessing step to obtain accurate predictive relationships. This paper presents a new, automated co-registration approach that searches iteratively for the best match between an ALS-based canopy height model and the tree positions and heights measured during the FI. While the basic principle of the algorithm applies to various types of FI sampling configurations, the co-registration approach was developed specifically to take into account the tree selection criterion posed by angle count sampling. The angle count sampling method only includes trees that at a given distance from the sample plot centre have a minimum required diameter at breast height (DBH). This tree selection criterion leads to maximum plot radii and number of inventoried trees that strongly vary from sample plot to sample plot. In the automated co-registration procedure, several criteria (e.g. the occurrence of more than one spatial cluster of minimum residuals and a predominance of deciduous trees in a sample plot) were used to detect possible uncertain solutions and to reduce post-processing efforts by an image operator. Model calibration and validation were based on national forest inventory (NFI) and ALS data from the Austrian federal state of Vorarlberg. Transferability and robustness of the approach was verified using an independent local FI. The results show that 68% of the NFI sample plots and 74% of the local FI plots could be automatically co-registered to a location at a distance of less than 5.0 m from the reference location. The maximum difference of 5.0 m used for marking a solution as correct was based on the relatively small influence that deviations of up to this value have on ALS-based predictions of biophysical forest variables at a stand level. The quality flagging criteria adopted were very successful in identifying uncertain solutions; only one out of 153 co-registered sample plots with a deviation from the reference data set greater than 5.0 m was not identified as uncertain. Applying the automatically co-registered sample plots in calibration of a growing stock model provided estimates that were clearly superior to those obtained with the original plot positions and even slightly outperformed those based on manual co-registration. As the algorithm developed will be part of an operational processing chain for Austrian NFI data, it has a high practical relevance. Copyright Taylor & Francis Numéro de notice : A2010-250 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160903380581 En ligne : https://doi.org/10.1080/01431160903380581 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30444 [article]

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080-2010031	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

3D vegetation mapping using small-footprint full-waveform airborne laser scanners / W. Wagner in International Journal of Remote Sensing IJRS, vol 29 n° 5 (March 2008)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n° 5 (March 2008) . - pp 1433 - 1452 Titre : 3D vegetation mapping using small-footprint full-waveform airborne laser scanners Type de document : Article/Communication Auteurs : W. Wagner, Auteur ; Markus Hollaus, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 1433 - 1452 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] carte de la végétation [Termes IGN] données localisées 3D [Termes IGN] lidar à retour d'onde complète [Termes IGN] modèle numérique de terrain [Termes IGN] rayonnement infrarouge [Termes IGN] semis de points [Termes IGN] signal laser [Termes IGN] télémétrie laser aéroporté Résumé : (Auteur) Small-footprint full-waveform airborne laser scanning (ALS) is a remote sensing technique capable of mapping vegetation in three dimensions with a spatial sampling of about 0.5-2 m in all directions. This is achieved by scanning the laser beam across the Earth's surface and by emitting nanosecond-long infrared pulses with a high frequency of typically 50-150 kHz. The echo signals are digitized during data acquisition for subsequent off-line waveform analysis. In addition to delivering the three-dimensional (3D) coordinates of scattering objects such as leaves or branches, full-waveform laser scanners can be calibrated for measuring the scattering properties of vegetation and terrain surfaces in a quantitative way. As a result, a number of physical observables are obtained, such as the width of the echo pulse and the backscatter cross-section, which is a measure of the electromagnetic energy intercepted and re-radiated by objects. The main aim of this study was to build up an understanding of the scattering characteristics of vegetation and the underlying terrain. It was found that vegetation typically causes a broadening of the backscattered pulse, while the backscatter cross-section is usually smaller for canopy echoes than for terrain echoes. These scattering properties allowed classification of the 3D point cloud into vegetation and non-vegetation echoes with an overall accuracy of 89.9% for a dense natural forest and 93.7% for a baroque garden area. In addition, by removing the vegetation echoes before the filtering process, the quality of the digital terrain model could be improved. Copyright Taylor & Francis Numéro de notice : A2008-082 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701736398 En ligne : https://doi.org/10.1080/01431160701736398 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29077 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-08031	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt