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[n° ou bulletin]
est un bulletin de ISPRS Journal of photogrammetry and remote sensing / International society for photogrammetry and remote sensing (1980 -) (1990 -) ![]()
[n° ou bulletin]
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Exemplaires(1)
Code-barres | Cote | Support | Localisation | Section | Disponibilité |
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081-2012031 | SL | Revue | Centre de documentation | Revues en salle | Disponible |
Dépouillements
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Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance / G. Wei in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)
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[article]
Titre : Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance Type de document : Article/Communication Auteurs : G. Wei, Auteur ; S. Shalei, Auteur ; Z. Bo, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 1 - 9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] couvert végétal
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] feuille (végétation)
[Termes IGN] Lidar
[Termes IGN] longueur d'onde
[Termes IGN] Oryza (genre)
[Termes IGN] réflectance végétaleRésumé : (Auteur) A new multi-wavelength canopy LiDAR (MWCL) system intended for the remote sensing of vegetation reflection was designed and its measurement performance was investigated. The system operates with four lasers of different wavelengths chosen according to nitrogen stresses that induce changes in the optical properties and spectral reflectance of rice leaves. The optical design and instrumentation are described in this paper as well as a discussion on system calibration. The MWCL system was demonstrated to possess a high capability of recording the physiology of the canopy, which is not possible when solely employing a traditional single-wavelength LiDAR. Numéro de notice : A2012-192 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.02.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.02.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31639
in ISPRS Journal of photogrammetry and remote sensing > vol 69 (April 2012) . - pp 1 - 9[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2012031 SL Revue Centre de documentation Revues en salle Disponible Satellite SAR geocoding with refined RPC model / L. Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)
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[article]
Titre : Satellite SAR geocoding with refined RPC model Type de document : Article/Communication Auteurs : L. Zhang, Auteur ; Timo Balz, Auteur ; M. Liao, Auteur Année de publication : 2012 Article en page(s) : pp 37 - 49 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] appariement d'images
[Termes IGN] erreur corrélée au temps
[Termes IGN] géoréférencement indirect
[Termes IGN] image radar moirée
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle par fonctions rationnelles
[Termes IGN] propagation du signalRésumé : (Auteur) Recent studies have proved that the Rational Polynomial Camera (RPC) model is able to act as a reliable replacement of the rigorous Range-Doppler (RD) model for the geometric processing of satellite SAR datasets. But its capability in absolute geolocation of SAR images has not been evaluated quantitatively. Therefore, in this article the problems of error analysis and refinement of SAR RPC model are primarily investigated to improve the absolute accuracy of SAR geolocation. Range propagation delay and azimuth timing error are identified as two major error sources for SAR geolocation. An approach based on SAR image simulation and real-to-simulated image matching is developed to estimate and correct these two errors. Afterwards a refined RPC model can be built from the error-corrected RD model and then used in satellite SAR geocoding. Three experiments with different settings are designed and conducted to comprehensively evaluate the accuracies of SAR geolocation with both ordinary and refined RPC models. All the experimental results demonstrate that with RPC model refinement the absolute location accuracies of geocoded SAR images can be improved significantly, particularly in Easting direction. In another experiment the computation efficiencies of SAR geocoding with both RD and RPC models are compared quantitatively. The results show that by using the RPC model such efficiency can be remarkably improved by at least 16 times. In addition the problem of DEM data selection for SAR image simulation in RPC model refinement is studied by a comparative experiment. The results reveal that the best choice should be using the proper DEM datasets of spatial resolution comparable to that of the SAR images. Numéro de notice : A2012-193 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.02.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.02.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31640
in ISPRS Journal of photogrammetry and remote sensing > vol 69 (April 2012) . - pp 37 - 49[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2012031 SL Revue Centre de documentation Revues en salle Disponible Robust hyperspectral vision-based classification for multi-season weed mapping / Y. Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)
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[article]
Titre : Robust hyperspectral vision-based classification for multi-season weed mapping Type de document : Article/Communication Auteurs : Y. Zhang, Auteur ; D. Slaughter, Auteur ; E. Staab, Auteur Année de publication : 2012 Article en page(s) : pp 65 - 73 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification bayesienne
[Termes IGN] cultures
[Termes IGN] herbe
[Termes IGN] identification de plantes
[Termes IGN] image hyperspectrale
[Termes IGN] photogrammétrie métrologique
[Termes IGN] réflectance végétale
[Termes IGN] système expert
[Termes IGN] variation saisonnièreRésumé : (Auteur) This study investigated the robustness of hyperspectral image-based plant recognition to seasonal variability in a natural farming environment in the context of automated in-row weed control. A machine vision system was developed and equipped with a CCD camera integrated with a line-imaging spectrograph for close-range weed sensing and mapping. Three canonical Bayesian classifiers were developed using canopy reflectance (400–795 nm) collected over three seasons for tomato and weeds. The performance of the three season-specific classifiers was tested by changing environmental conditions, resulting in an increase in total error rate of up to 36%. Global calibration across the complete span of the three seasons produced overall classification accuracies of 85.0%, 90.0% and 92.7%, respectively, for 2005, 2006 and 2008. To improve the stability of global classifier over multiple seasons, a multiclassifier system was constructed with three canonical Bayesian classifiers optimized for the three seasons individually. This system was tested on a data set simulating an upcoming season with field conditions similar to that in 2005. The system increased the total discrimination accuracy to 95.8% for the tested season under simulation. This method provided an innovative direction for achieving robust plant recognition over multiple seasons by integrating expert knowledge from historical data that most closely matched the new field environment. Numéro de notice : A2012-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.02.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.02.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31641
in ISPRS Journal of photogrammetry and remote sensing > vol 69 (April 2012) . - pp 65 - 73[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2012031 SL Revue Centre de documentation Revues en salle Disponible A method for extracting burned areas from Landsat TM/ETM+ images by soft aggregation of multiple Spectral Indices and a region growing algorithm / D. Stroppiana in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)
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[article]
Titre : A method for extracting burned areas from Landsat TM/ETM+ images by soft aggregation of multiple Spectral Indices and a region growing algorithm Type de document : Article/Communication Auteurs : D. Stroppiana, Auteur ; Gloria Bordogna, Auteur ; P. Carrara, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 88 - 102 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multicritère
[Termes IGN] cartographie des risques
[Termes IGN] Europe du sud
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] incendie de forêt
[Termes IGN] sous ensemble flou
[Termes IGN] surveillance de la végétationRésumé : (Auteur) Since fire is a major threat to forests and wooded areas in the Mediterranean environment of Southern Europe, systematic regional fire monitoring is a necessity. Satellite data constitute a unique cost-effective source of information on the occurrence of fire events and on the extent of the area burned. Our objective is to develop a (semi-)automated algorithm for mapping burned areas from medium spatial resolution (30 m) satellite data. In this article we present a multi-criteria approach based on Spectral Indices, soft computing techniques and a region growing algorithm; theoretically this approach relies on the convergence of partial evidence of burning provided by the indices. Our proposal features several innovative aspects: it is flexible in adapting to a variable number of indices and to missing data; it exploits positive and negative evidence (bipolar information) and it offers different criteria for aggregating partial evidence in order to derive the layers of candidate seeds and candidate region growing boundaries. The study was conducted on a set of Landsat TM images, acquired for the year 2003 over Southern Europe and pre-processed with the LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System) processing chain for deriving surface spectral reflectance ?i in the TM bands. The proposed method was applied to show its flexibility and the sensitivity of the accuracy of the resulting burned area maps to different aggregation criteria and thresholds for seed selection. Validation performed over an entire independent Landsat TM image shows the commission and omission errors to be below 21% and 3%, respectively. Numéro de notice : A2012-195 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31642
in ISPRS Journal of photogrammetry and remote sensing > vol 69 (April 2012) . - pp 88 - 102[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2012031 SL Revue Centre de documentation Revues en salle Disponible Using multi-frequency radar and discrete-return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest / O. Tsui in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)
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Titre : Using multi-frequency radar and discrete-return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest Type de document : Article/Communication Auteurs : O. Tsui, Auteur ; Nicholas C. Coops, Auteur ; Michael A. Wulder, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 121 - 133 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] biomasse
[Termes IGN] Colombie-Britannique (Canada)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tempérée
[Termes IGN] image radar
[Termes IGN] Pinophyta
[Termes IGN] polarimétrie radarRésumé : (Auteur) Height measurements from small-footprint discrete-return LiDAR and backscatter coefficients from C- and L-band radar were used independently and in combination to estimate above-ground component and total biomass for a coniferous temperate forest, located on Vancouver Island, British Columbia, Canada. Reference biomass data were obtained from plot-level data and used for comparison against the LiDAR and radar-based biomass models. For the LiDAR-only model, height metrics such as mean first return height and percentiles (e.g., 10th and 90th) of first returns correlated best to total above-ground and stem biomass. While percent of first returns above 2 m and percentiles (75th and 90th) of first returns height metrics correlated best to crown biomass. A comparison between above-ground components and total biomass indicate that stem biomass displayed the highest relationship with the LiDAR measurements while crown biomass showed the lowest relationship with relative root mean squared error ranging from 16% to 22%, respectively. Alternatively, the radar-only models indicated that for C-band radar, a combination of HH and VV backscatter demonstrated the most significant correlation with forest biomass compared to coherence based models with a relative root mean squared error of 53%. For L-band radar, a combination of HH and HV backscatter showed the most significant correlation compared to coherence based models with a relative root mean squared error of 44%. Exploring a mixture of C- and L-band backscatter and coherence based models revealed that a combination of C-HV and L-HV coherence magnitudes provided the best radar relationship with forest biomass with a relative root mean squared error of 35%. Also for all radar-based models, L- and C-band backscatter and coherence magnitudes were poorly correlated with individual biomass components when compared to total above-ground biomass. The addition of C- and L-band backscatter and coherence variables to the LiDAR-only biomass model was also investigated. The results showed that the integration of C-band HH backscatter to the LiDAR-only model significantly improved the relationship with forest biomass by explaining an additional 8.9% and 6.5% of the variability in total aboveground and stem biomass respectively, while C-band polarimetric entropy explained an additional 17.9% of the variability in crown biomass. Improvements in the relative root mean squared errors were also observed ranging from 7.1% to 11.7%. The study suggests that for a temperate forest dominated by coniferous stands, the addition of C-band radar variables to a best LiDAR-only linear model provides improved estimates of above-ground component and total biomass. Numéro de notice : A2012-196 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.02.009 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.02.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31643
in ISPRS Journal of photogrammetry and remote sensing > vol 69 (April 2012) . - pp 121 - 133[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2012031 SL Revue Centre de documentation Revues en salle Disponible Transaction rules for updating surfaces in 3D GIS / G. Groger in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)
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Titre : Transaction rules for updating surfaces in 3D GIS Type de document : Article/Communication Auteurs : G. Groger, Auteur ; L. Plumer, Auteur Année de publication : 2012 Article en page(s) : pp 134 - 145 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] cohérence des données
[Termes IGN] contrainte d'intégrité
[Termes IGN] données localisées 3D
[Termes IGN] modèle numérique de surface
[Termes IGN] programmation par contraintes
[Termes IGN] règle
[Termes IGN] SIG 3DRésumé : (Auteur) Three-dimensional surface models representing the terrain and the outer hull of objects such as buildings and bridges support important 3D GIS applications, for example telecommunication planning and noise emission simulation. Updates of surface models often introduce errors which violate basic assumptions of users and their applications. The notion of geometric-topological consistency covers many of these assumptions. It guarantees that objects do not penetrate mutually or that objects completely cover other objects. Assuring that updates do not violate geometric-topological consistency constitutes a major challenge for 3D GIS which has not been satisfactorily met so far. This article presents a solution which is based on efficient transaction rules for updating 3D surface models. We show that these rules are safe (consistency is preserved by any rule application) and complete (any consistent surface model can be generated by successive rule applications). For both properties rigorous mathematic proofs are given. Numéro de notice : A2012-197 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31644
in ISPRS Journal of photogrammetry and remote sensing > vol 69 (April 2012) . - pp 134 - 145[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2012031 SL Revue Centre de documentation Revues en salle Disponible Potential of texture measurements of two-date dual polarization PALSAR data for the improvement of forest biomass estimation / M. Sarker in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)
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[article]
Titre : Potential of texture measurements of two-date dual polarization PALSAR data for the improvement of forest biomass estimation Type de document : Article/Communication Auteurs : M. Sarker, Auteur ; J. Nichol, Auteur ; B. Ahmad, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 146 - 166 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] biomasse
[Termes IGN] Hong-Kong
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] polarimétrie radar
[Termes IGN] polarisation
[Termes IGN] texture d'imageRésumé : (Auteur) The recently available space-borne SAR sensor, PALSAR, is more promising than its predecessor JERS-1 for biomass estimation because of its long wavelength (L-band), and its ability to provide data with different polarizations, varying incidence angles and higher spatial resolutions. This research investigates the potential of two-date dual polarization (HH and HV) SAR imagery for biomass estimation using different kinds of texture processing and different combinations of single and dual polarization ratios. The investigation is conducted in a mountainous, sub-tropical study area where biomass levels are far beyond the previously recognized saturation levels for L-band SAR images, and forest is a mixture of native and non-native species and plantations. We analyzed two-date SAR data with four steps of image processing, including raw data processing in various combinations, texture measurement parameters of HH and HV polarizations, texture measurement parameters of HH and HV together (both jointly and as a ratio), and a ratio of two-date texture parameters along with a single and two-date ratio. When the processed images were compared with ground data from 50 plots, the performance from raw data processing was low, with adjusted r2 = 0.22, but after all four processing steps, promising model accuracy (adjusted r2 = 0.90 and RMSE = 28.58 t/ha) and validation accuracy (using the Leave-One-Out-Cross-Validation) with adjusted r2 = 0.88 and RMSE = 35.69 t/ha, were achieved from the combination of single- and two-date polarization ratios of texture parameters. The strong performance achieved indicates that L-band dual-polarization (HH and HV) SAR data from PALSAR has great potential for biomass estimation, far beyond the previously reported L-band saturation point for biomass. This result is attributed to the synergy among texture processing and dual polarization on the one hand, which were able to average out random speckle noise, and the use of ratio instead of absolute quantities, due to its well known ability to reduce forest structural and terrain effects. The additional use of two-date SAR data with these processing techniques was able to add complementary information derived from biomass response in both wet and dry seasons. Thus overall, undesirable image noise and terrain effects were reduced. Numéro de notice : A2012-198 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31645
in ISPRS Journal of photogrammetry and remote sensing > vol 69 (April 2012) . - pp 146 - 166[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2012031 SL Revue Centre de documentation Revues en salle Disponible Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment / Laven Naidoo in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)
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Titre : Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment Type de document : Article/Communication Auteurs : Laven Naidoo, Auteur ; Moses Azong Cho, Auteur ; Renaud Mathieu, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 167 - 179 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Afrique du sud (état)
[Termes IGN] arbre (flore)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] hauteur des arbres
[Termes IGN] image hyperspectrale
[Termes IGN] lasergrammétrie
[Termes IGN] parc naturel national
[Termes IGN] savaneRésumé : (Auteur) The accurate classification and mapping of individual trees at species level in the savanna ecosystem can provide numerous benefits for the managerial authorities. Such benefits include the mapping of economically useful tree species, which are a key source of food production and fuel wood for the local communities, and of problematic alien invasive and bush encroaching species, which can threaten the integrity of the environment and livelihoods of the local communities. Species level mapping is particularly challenging in African savannas which are complex, heterogeneous, and open environments with high intra-species spectral variability due to differences in geology, topography, rainfall, herbivory and human impacts within relatively short distances. Savanna vegetation are also highly irregular in canopy and crown shape, height and other structural dimensions with a combination of open grassland patches and dense woody thicket – a stark contrast to the more homogeneous forest vegetation. This study classified eight common savanna tree species in the Greater Kruger National Park region, South Africa, using a combination of hyperspectral and Light Detection and Ranging (LiDAR)-derived structural parameters, in the form of seven predictor datasets, in an automated Random Forest modelling approach. The most important predictors, which were found to play an important role in the different classification models and contributed to the success of the hybrid dataset model when combined, were species tree height; NDVI; the chlorophyll b wavelength (466 nm) and a selection of raw, continuum removed and Spectral Angle Mapper (SAM) bands. It was also concluded that the hybrid predictor dataset Random Forest model yielded the highest classification accuracy and prediction success for the eight savanna tree species with an overall classification accuracy of 87.68% and KHAT value of 0.843. Numéro de notice : A2012-199 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31646
in ISPRS Journal of photogrammetry and remote sensing > vol 69 (April 2012) . - pp 167 - 179[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2012031 SL Revue Centre de documentation Revues en salle Disponible