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Land cover characterization of temperate east Asia using multi-temporal vegetation sensor data / S.H. Boles in Remote sensing of environment, vol 90 n° 4 (30/04/2004)
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Titre : Land cover characterization of temperate east Asia using multi-temporal vegetation sensor data Type de document : Article/Communication Auteurs : S.H. Boles, Auteur ; X. Xiao-Ping, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 477 - 489 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] Asie orientale
[Termes IGN] base de données d'occupation du sol
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] Enhanced vegetation index
[Termes IGN] image Aqua-MODIS
[Termes IGN] image multitemporelle
[Termes IGN] image SPOT-Végétation
[Termes IGN] image Terra-MODIS
[Termes IGN] occupation du sol
[Termes IGN] photo-identification
[Termes IGN] zone tempéréeRésumé : (Auteur) Temperate East Asia (TEA) is characterized by diverse land cover types, including forest and agricultural lands, one of the world's largest temperate grasslands, and extensive desert and barren landscapes. In this paper, we explored the potential of SPOT-4 VEGETATION (VGT) data for the classification of land cover types in TEA. An unsupervised classification was performed using multi-temporal (March November 2000) VGT-derived spectral indices (Land Surface Water Index [LSWI] and Enhanced Vegetation Index [EVI]) to generate a land cover map of TEA (called VGT-TEA). Land cover classes from VGT-TEA were aggregated to broad, general class types, and then compared and validated with classifications derived from fine-resolution (Landsat) data. VGT-TEA produced reasonable results when compared to the Landsat products. Analysis of the seasonal dynamics of LSWI and EVI allows for the identification of distinct growth patterns between different vegetation types. We suggest that LSWI seasonal curves can be used to define the growing season for temperate deciduous vegetation, including grassland types. Seasonal curves of EVI tend to have a slightly greater dynamic range than LSWI during the peak growing season and can be useful in discriminating between vegetation types. By using these two complementary spectral indices, VGT data can be used to produce timely and detailed land cover and phenology maps with limited ancillary data needed. Numéro de notice : A2004-191 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.01.016 En ligne : https://doi.org/10.1016/j.rse.2004.01.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26718
in Remote sensing of environment > vol 90 n° 4 (30/04/2004) . - pp 477 - 489[article]Classification of remotely sensed imagery stochastic gradient boosting as a refinement of classification tree analysis / R. Lawrence in Remote sensing of environment, vol 90 n° 3 (15/04/2004)
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Titre : Classification of remotely sensed imagery stochastic gradient boosting as a refinement of classification tree analysis Type de document : Article/Communication Auteurs : R. Lawrence, Auteur ; A. Bunn, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 331 - 336 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification
[Termes IGN] décomposition d'image
[Termes IGN] image hyperspectrale
[Termes IGN] image Ikonos
[Termes IGN] image Landsat-ETM+
[Termes IGN] image PROBE
[Termes IGN] précision de la classification
[Termes IGN] sylvicultureRésumé : (Auteur) Classification tree analysis (CTA) provides an effective suite of algorithms for classifying remotely sensed data, but it has the limitations of (1) not searching for optimal tree structures and (2) being adversely affected by outliers, inaccurate training data, and unbalanced data sets. Stochastic gradient boosting (SGB) is a refinement of standard CTA that attempts to minimize these limitations by (1) using classification errors to iteratively refine the trees using a random sample of the training data and (2) combining the multiple trees iteratively developed to classify the data. We compared traditional CTA results to SGB for three remote sensing based data sets, an IKONOS image from the Sierra Nevada Mountains of California, a Probe-1 hyperspectral image from the Virginia City mining district of Montana, and a series of Landsat ETM+ images from the Greater Yellowstone Ecosystem (GYE). SGB improved the overall accuracy of the IKONOS classification from 84% to 95% and the Probe-1 classification from 83% to 93%. The worst performing classes using CTA exhibited the largest increases in class accuracy using SGB. A slight decrease in overall classification accuracy resulted from the SGB analysis of the Landsat data. Numéro de notice : A2004-200 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.01.007 En ligne : https://doi.org/10.1016/j.rse.2004.01.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26727
in Remote sensing of environment > vol 90 n° 3 (15/04/2004) . - pp 331 - 336[article]Classifying land development in high-resolution panchromatic satellite images using straight-line statistics / C. Unsalan in IEEE Transactions on geoscience and remote sensing, vol 42 n° 4 (April 2004)
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Titre : Classifying land development in high-resolution panchromatic satellite images using straight-line statistics Type de document : Article/Communication Auteurs : C. Unsalan, Auteur ; K.L. Boyer, Auteur Année de publication : 2004 Article en page(s) : pp 907 - 919 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aménagement du territoire
[Termes IGN] classificateur non paramétrique
[Termes IGN] classificateur paramétrique
[Termes IGN] détection de contours
[Termes IGN] image à haute résolution
[Termes IGN] image panchromatique
[Termes IGN] méthode robuste
[Termes IGN] objet géographique linéaire
[Termes IGN] périphérie urbaine
[Termes IGN] zone rurale
[Termes IGN] zone urbaineRésumé : (Auteur) We introduce a set of measures based on straight lines to assess land development levels in high-resolution (1 m) panchromatic satellite images. Most urban areas locally (such as in a 400 x 400 M2 area) exhibit a preponderance of straight-line features, generally appearing in fairly simple quasi-periodic organizations. Wilderness and rural areas produce line structures in more random spatial arrangements. We use this observation to perform an initial triage on the image to restrict the attention of subsequent more computationally intensive analyses. Statistical measures based on straight lines guide the analysis. We base these measures on length, contrast, orientation, periodicity, and location. On these, we trained and tested parametric and nonparametric classifiers. These tests were for a two-class problem (urban versus rural). However, because our ultimate goal is to extract residential regions, we then extended these ideas to address the detection of suburban regions. To do so, some use of spatial coherence is required; suburban regions are especially difficult to detect. Therefore, we introduce a decision system to perform suburban region classification via an overlapping voting method for consensus discovery. Our data were taken from regions all around the world, which underscores the robustness of our approach. Based on extensive testing, we can report very promising results in distinguishing developed areas. Numéro de notice : A2004-188 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.818835 En ligne : https://doi.org/10.1109/TGRS.2003.818835 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26715
in IEEE Transactions on geoscience and remote sensing > vol 42 n° 4 (April 2004) . - pp 907 - 919[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-04041 RAB Revue Centre de documentation En réserve L003 Disponible Mapping residential density patterns using multi- temporal Landsat data and decision-tree classifier / S. Mccauley in International Journal of Remote Sensing IJRS, vol 25 n° 6 (March 2004)
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Titre : Mapping residential density patterns using multi- temporal Landsat data and decision-tree classifier Type de document : Article/Communication Auteurs : S. Mccauley, Auteur ; S.J. Goetz, Auteur Année de publication : 2004 Article en page(s) : pp 1077 - 1094 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse discriminante
[Termes IGN] classification par arbre de décision
[Termes IGN] densité de population
[Termes IGN] image Landsat-TM
[Termes IGN] population urbaine
[Termes IGN] utilisation du solRésumé : (Auteur) We examined the utility of Landsat Thematic Mapper (TM) imagery for mapping residential land use in Montgomery County, Maryland, USA. The study area was chosen partly because of the availability of a unique parcel-level database of land use attributes and an associated digital map of parcel boundaries. These data were used to develop a series of land use classifications from a combination of leaf-on and leaf-off TM image derivatives and an algorithm based on 'decision tree' theory. Results suggest potential utility of the approach, particularly to state and local governments for land use mapping and planning applications, but greater accuracies are needed for broad practical application. In general, it was possible to discriminate different densities of residential development, and to separate these from commercial/industrial and agricultural areas. Difficulties arose in the discrimination of low-density residential areas due to the range of land cover types within this specific land use, and their associated spatial variability. The greater classification errors associated with these low-density developed areas were not unexpected. We found that these errors could be mitigated somewhat with techniques that consider the mode of training data selection and by incorporation of methods that account for the presence and amount of impervious surfaces (e.g. pavement and rooftops). Numéro de notice : A2004-085 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000115102 En ligne : https://doi.org/10.1080/0143116031000115102 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26612
in International Journal of Remote Sensing IJRS > vol 25 n° 6 (March 2004) . - pp 1077 - 1094[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-04061 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Tree model based eco-climatic vegetation classification and fuzzy mapping in diverse tropical deciduous ecosystems using multi-season NDVI / J. Krishnaswamy in International Journal of Remote Sensing IJRS, vol 25 n° 6 (March 2004)
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Titre : Tree model based eco-climatic vegetation classification and fuzzy mapping in diverse tropical deciduous ecosystems using multi-season NDVI Type de document : Article/Communication Auteurs : J. Krishnaswamy, Auteur ; M.C. Kiran, Auteur ; K.N. Ganeshaiah, Auteur Année de publication : 2004 Article en page(s) : pp 1185 - 1205 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] arbre (mathématique)
[Termes IGN] classification
[Termes IGN] classification dirigée
[Termes IGN] écosystème
[Termes IGN] Kappa de Cohen
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] végétationRésumé : (Auteur) Many vegetation classification strategies in tropical ecosystems involving conventional image processing of original satellite imagery bands require considerable prior site knowledge, statistical assumptions, and are difficult, expensive and inconsistent. In this paper we show that the intra-annual variation and rates of change in NDVI for different parts of a large forest area in combination with rules derived from a tree model can be used for detailed vegetation mapping. We used three-date NDVI data for the Biligiri Rangaswamy Temple Wildlife Sanctuary in Karnataka, southern India comprising mean NDVI, coefficient of variation (CV) and two NDVI change vectors corresponding to intraseasonal NDVI differences. A rule-based classification using a tree model was developed at two levels. The overall kappa statistic is 0.61 at level 1 classification. indicating a strong correspondence with the raster version of the available vector reference map based on ground data, even though the two maps are not strictly comparable. Several limitations of the available reference map have been highlighted by the new technique, especially the absence of ecotones. At level two the tree model map has provided detailed classification of dry deciduous forests and new classes not available in the reference map. The method in combination with reference data also provides a framework for fuzzy classification. This technique offers a relatively simple cost-effective alternative to existing classification strategies, especially for areas with diverse evergreen and deciduous vegetation elements. Numéro de notice : A2004-088 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000149989 En ligne : https://doi.org/10.1080/0143116031000149989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26615
in International Journal of Remote Sensing IJRS > vol 25 n° 6 (March 2004) . - pp 1185 - 1205[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-04061 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Hyperion, Ikonos, ALI, and ETM+ sensors in the study of African rainforests / Prasad S. Thenkabail in Remote sensing of environment, vol 90 n° 1 (15/03/2004)
PermalinkAccuracy analysis of remote sensing change detection by rule-based rationality evaluation with post-classification comparison / H. Liu in International Journal of Remote Sensing IJRS, vol 25 n° 5 (March 2004)
PermalinkAreas of fuzzy geographical entities / Cidália Costa Fonte in International journal of geographical information science IJGIS, vol 18 n° 2 (march 2004)
PermalinkAssessing the potential of space-borne C-band SAR data for spatial soil moisture information over a large area / S.A. Romshoo in Geocarto international, vol 19 n° 1 (March - May 2004)
PermalinkAutomating the analysis of remotely sensed data / C. Skelsey in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 3 (March 2004)
PermalinkA double continuous approach to visualization and analysis of categorial maps / T. Hengl in International journal of geographical information science IJGIS, vol 18 n° 2 (march 2004)
PermalinkEvaluation comparative en cartographie forestière de l'analyse de texture et de la transformée en paquets d'ondelettes par le moyen d'un classifieur / A. Hammouch in Photo interprétation, vol 40 n° 1 (Mars 2004)
PermalinkIntra-urban location and clustering of road accidents using GIS: a Belgian example / T. Steenberghen in International journal of geographical information science IJGIS, vol 18 n° 2 (march 2004)
PermalinkMesure de la connectivité du paysage à travers un maillage spatial / Jean-Christophe Foltête in Revue internationale de géomatique, vol 14 n° 1 (mars - mai 2004)
PermalinkA probability-based uncertainty model for point-in-polygon analysis in GIS / C.K. Cheung in Geoinformatica, vol 8 n° 1 (March - May 2004)
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