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Predicting surface fuel models and fuel metrics using Lidar and CIR imagery in a dense, mountainous forest / Marek Jakubowksi in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 1 (January 2013)
[article]
Titre : Predicting surface fuel models and fuel metrics using Lidar and CIR imagery in a dense, mountainous forest Type de document : Article/Communication Auteurs : Marek Jakubowksi, Auteur ; Quinhua Guo, Auteur ; Brandon Collins, Auteur ; Scott Stephens, Auteur ; Maggi Kelly, Auteur Année de publication : 2013 Article en page(s) : pp 37 - 49 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] biomasse (combustible)
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt
[Termes IGN] image multibande
[Termes IGN] image optique
[Termes IGN] interpolation inversement proportionnelle à la distance
[Termes IGN] lutte contre l'incendie
[Termes IGN] montagne
[Termes IGN] PinophytaRésumé : (Auteur) We compared the ability of several classification and regression algorithms to predict forest stand structure metrics and standard surface fuel models. Our study area spans a dense, topographically complex Sierra Nevada mixed-conifer forest. We used clustering, regression trees, and support vector machine algorithms to analyze high density (average 9 pulses/m2), discrete return, small-footprint lidar data, along with multispectral imagery. Stand structure metric predictions generally decreased with increased canopy penetration. For example, from the top of canopy, we predicted canopy height (r2 ! 0.87), canopy cover (r2 ! 0.83), basal area (r2 ! 0.82), shrub cover (r2 ! 0.62), shrub height (r2 ! 0.59), combined fuel loads (r2 ! 0.48), and fuel bed depth (r2 ! 0.35). While the general fuel types were predicted accurately, specific surface fuel model predictions were poor (76 percent and "50 percent correct classification, respectively) using all algorithms. These fuel components are critical inputs for wildfire behavior modeling, which ultimately support forest management decisions. This comprehensive examination of the relative utility of lidar and optical imagery will be useful for forest science and management. Numéro de notice : A2013-004 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.79.1.37 En ligne : http://kellylab.berkeley.edu/storage/papers/2013-Jakubowski-etal-PERS.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32142
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 1 (January 2013) . - pp 37 - 49[article]Phenology-based crop classification algorithm and its implications on agricultural water use assessments in California's central valley / L. Zhong in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 8 (August 2012)
[article]
Titre : Phenology-based crop classification algorithm and its implications on agricultural water use assessments in California's central valley Type de document : Article/Communication Auteurs : L. Zhong, Auteur ; P. Gong, Auteur ; Gregory S. Biging, Auteur Année de publication : 2012 Article en page(s) : pp 799 - 813 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] carte agricole
[Termes IGN] classification par arbre de décision
[Termes IGN] cultures
[Termes IGN] Enhanced vegetation index
[Termes IGN] évapotranspiration
[Termes IGN] fusion d'images
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] image Terra-MODIS
[Termes IGN] phénologie
[Termes IGN] segmentation d'imageRésumé : (Auteur) The overarching goal of this study was to map specific crop types in the Central Valley, California and estimate the effect of classification uncertainty on the calculation of crop evapotranspiration (ETc). A phenology-based classification (PBC) approach was developed to identify crop types based on phenological and spectral metrics derived from the time series of Landsat TM/ETM_ imagery. Phenological metrics, calculated by fitting asymmetric double sigmoid functions to temporal profiles of enhanced vegetation index (EVI), were capable of separating crop types with distinct crop calendars. An innovative method was used to compute spectral metrics to represent crops' spectral characteristics at certain phenological stages instead of any specific imaging date. Crop mapping using these metrics showed a stable performance without influences of low-quality data and inter-annual differences in imaging dates. The requirement for ground reference data by the PBC approach was low because classification algorithms were mostly built according to the knowledge on crop calendars and agricultural practices. Techniques including image segmentation, data fusion with MODIS imagery, and decision tree were incorporated to make the approach effective and efficient. Though moderate accuracy (~65.0 percent) was achieved, ETc calculated by the Food and Agriculture Organization (FAO) 56 method showed that the estimate of water use was not likely to be significantly affected by the classification error in PBC. All these advantages imply the strength of the PBC approach in the regular crop mapping of the Central Valley. Numéro de notice : A2012-428 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.78.8.799 En ligne : https://doi.org/10.14358/PERS.78.8.799 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31874
in Photogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 8 (August 2012) . - pp 799 - 813[article]Robust hyperspectral vision-based classification for multi-season weed mapping / Y. Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)
[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 Mapping transit-based access: integrating GIS, routes and schedules / T. Lei in International journal of geographical information science IJGIS, vol 24 n°1-2 (january 2010)
[article]
Titre : Mapping transit-based access: integrating GIS, routes and schedules Type de document : Article/Communication Auteurs : T. Lei, Auteur ; R.L. Church, Auteur Année de publication : 2010 Article en page(s) : pp 283 - 304 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] accessibilité
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] réseau routier
[Termes IGN] système d'information géographique
[Termes IGN] transport collectifRésumé : (Auteur) Accessibility is a concept that is not entirely easy to define. Gould (1969) once stated that it is a 'slippery notion ... one of those common terms that everyone uses until faced with the problem of defining and measuring it'. Considerable research over the last 40 years has been devoted to defining and measuring accessibility, ranging from access to jobs within an hour's travel time to the ease at which given places can be reached. This article is concerned with the measurement of access provided by transit. It includes a review of past work on measuring accessibility in general and with respect to transit services in particular. From this overview of the literature, it can be seen that current methods fall short in measuring transit service access in several meaningful aspects. Based on this review and critique, we propose new refinements that can be used to help overcome some of these shortcomings. As a part of this, we define an extended GIS data structure to handle temporal elements of transit service. To demonstrate the value of these new measures, examples are presented with respect to mapping accessibility of transit services in Santa Barbara, California. Finally, we show how these measures can be used to develop a framework for supporting transit service analysis and planning. Copyright Taylor & Francis Numéro de notice : A2010-118 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810902835404 En ligne : http://dx.doi.org/10.1080/13658810902835404 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30314
in International journal of geographical information science IJGIS > vol 24 n°1-2 (january 2010) . - pp 283 - 304[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 079-2010012 RAB Revue Centre de documentation En réserve L003 Disponible 079-2010011 RAB Revue Centre de documentation En réserve L003 Disponible Developing collaborative classifiers using an Expert-based Model / Giorgos Mountrakis in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 7 (July 2009)
[article]
Titre : Developing collaborative classifiers using an Expert-based Model Type de document : Article/Communication Auteurs : Giorgos Mountrakis, Auteur ; R. Watts, Auteur ; L. Luo, Auteur ; Jing Wang, Auteur Année de publication : 2009 Article en page(s) : pp 831 - 843 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur
[Termes IGN] classification à base de connaissances
[Termes IGN] image Landsat
[Termes IGN] Las Vegas
[Termes IGN] mise à l'échelle
[Termes IGN] précision de la classification
[Termes IGN] surface imperméable
[Termes IGN] système expertRésumé : (Auteur) This paper presents a hierarchical, multi-stage adaptive strategy for image classification. We iteratively apply various classification methods (e.g., decision trees, neural networks), identify regions of parametric and geographic space where accuracy is low, and in these regions, test and apply alternate methods repeating the process until the entire image is classified. Currently, classifiers are evaluated through human input using an expert-based system; therefore, this paper acts as the proof of concept for collaborative classifiers. Because we decompose the problem into smaller, more manageable sub-tasks, our classification exhibits increased flexibility compared to existing methods since classification methods are tailored to the idiosyncrasies of specific regions. A major benefit of our approach is its scalability and collaborative support since selected low-accuracy classifiers can be easily replaced with others without affecting classification accuracy in high accuracy areas. At each stage, we develop spatially explicit accuracy metrics that provide straightforward assessment of results by non-experts and point to areas that need algorithmic improvement or ancillary data. Our approach is demonstrated in the task of detecting impervious surface areas, an important indicator for human-induced alterations to the environment, using a 2001 Landsat scene from Las Vegas, Nevada. Copyright ASPRS Numéro de notice : A2009-263 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.75.7.831 En ligne : https://doi.org/10.14358/PERS.75.7.831 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29893
in Photogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 7 (July 2009) . - pp 831 - 843[article]Comparative analysis of SRTM-NED vegetation canopy height to LIDAR-derived vegetation canopy metrics / L. Kenyi in International Journal of Remote Sensing IJRS, vol 30 n°11-12 (June 2009)PermalinkFinding shortest paths on real road networks: the case for A* / W. Zeng in International journal of geographical information science IJGIS, vol 23 n°3-4 (march - april 2009)PermalinkArcGIS seabed characterization toolbox developed for investigating benthic habitats / M. Erdey-Heydorn in Marine geodesy, vol 31 n° 4 (December 2008)PermalinkAutomated conflation of digital gazetteer data / J.T. Hastings in International journal of geographical information science IJGIS, vol 22 n° 10 (october 2008)PermalinkCharacterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations / S. Sadro in Remote sensing of environment, vol 110 n° 2 (28/09/2007)PermalinkUpdating for two recent earthquakes in California / C Pearson in SaLIS Surveying and land information science, vol 67 n° 3 (October 2007)PermalinkSpatial resolution and algorithm choice as modifiers of downslope flow computed from digital elevation models / K. Clarke in Cartography and Geographic Information Science, vol 34 n° 3 (July 2007)PermalinkMonitoring cross-border trails using airborne digital multispectral imagery and interactive image analysis techniques / L. Cao in Geocarto international, vol 22 n° 2 (June - August 2007)PermalinkComputing coastal ocean surface curreants from infrared and ocean color satellite imagery / R.I. Crocker in IEEE Transactions on geoscience and remote sensing, vol 45 n° 2 (February 2007)PermalinkAutomatically conflating road vector data with orthoimagery / C.C. Chen in Geoinformatica, vol 10 n° 4 (December 2006)Permalink