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Mapping and assessing of urban impervious areas using multiple endmember spectral mixture analysis: a case study in the city of Tampa, Florida / Fenqing Weng in Geocarto international, vol 28 n° 7-8 (November - December 2013)
[article]
Titre : Mapping and assessing of urban impervious areas using multiple endmember spectral mixture analysis: a case study in the city of Tampa, Florida Type de document : Article/Communication Auteurs : Fenqing Weng, Auteur ; Ruiliang Pu, Auteur Année de publication : 2013 Article en page(s) : pp 594 - 615 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] analyse de mélange spectral d’extrémités multiples
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] carte d'occupation du sol
[Termes IGN] Floride (Etats-Unis)
[Termes IGN] image à moyenne résolution
[Termes IGN] image Landsat-TM
[Termes IGN] occupation du sol
[Termes IGN] surface imperméable
[Termes IGN] zone urbaineRésumé : (Auteur) The advance in remote sensing technology makes people easily assess urban growth. In this study, a multiple endmember spectral mixture analysis (MESMA) was examined in a sub-pixel analysis of Landsat Thematic Mapper (TM) imagery to map three physical components of urban land cover (LC): impervious surface, vegetation and soil, and compared with a traditional spectral mixture analysis (SMA) in mapping the physical components. A comparative analysis of the impervious surface areas (ISA) mapped with MESMA and SMA indicates that MESMA produced more accurately results of mapping urban physical components than those by SMA. With the multiyear Landsat TM data, we quantified sub-pixel percentage of ISA and the percentage of ISA changes to assess urban growth in Tampa, FL during the past 20 years. The experimental results demonstrate that MESMA approach is effective in mapping and monitoring urban land use/LC changes using moderate-resolution multispectral imagery at a sub-pixel level. Numéro de notice : A2013-698 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2013.764355 Date de publication en ligne : 05/04/2013 En ligne : https://doi.org/10.1080/10106049.2013.764355 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32834
in Geocarto international > vol 28 n° 7-8 (November - December 2013) . - pp 594 - 615[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2013041 RAB Revue Centre de documentation En réserve L003 Disponible Markov land cover change modeling using pairs of time-series satellite images / Priyakant Sinha in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)
[article]
Titre : Markov land cover change modeling using pairs of time-series satellite images Type de document : Article/Communication Auteurs : Priyakant Sinha, Auteur ; Lalit Kumar, Auteur Année de publication : 2013 Article en page(s) : pp 1037 - 1051 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] automate cellulaire
[Termes IGN] chaîne de Markov
[Termes IGN] flore locale
[Termes IGN] image Landsat-MSS
[Termes IGN] image Landsat-TM
[Termes IGN] image multitemporelle
[Termes IGN] Nouvelle-Galles du Sud
[Termes IGN] occupation du sol
[Termes IGN] prédictionRésumé : (Auteur) Models of change processes created with the Markov chain model (MCM) can be used in the interpolation of temporal data and in short-term change projections. However, there are two major issues associated with the use of Markov models for land-cover change projections: the stationarity of change and the impact of neighboring cells on the change areas. This study addressed these two issues using an investigation of five time-series land-cover datasets generated between 1972 and 2009 for the Liverpool region of NSW, Australia. Four short- term transition matrices were computed, and the results were used to predict land-cover distributions for the near future. The issue of neighborhood effects was addressed by incorporating spatial components in a Cellular Automata (CA)-based MCM, and the results were compared with those derived from a normal MCM. Given the marginal improvements in the simulation achieved with CA-MCM rather than MCM, and because of the ability of CA-MCM to incorporate spatial variants, CA-MCM was determined to be the more suitable method for predicting land-cover changes for the year 2019. The land-cover projection indicated that future land-cover changes will likely continue to affect the natural vegetation, which will in turn likely decrease through the continued conversion of natural to agricultural lands over the years. Numéro de notice : A2013-598 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.11.1037 En ligne : https://doi.org/10.14358/PERS.79.11.1037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32734
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 11 (November 2013) . - pp 1037 - 1051[article]Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey / Ugur Alganci in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)
[article]
Titre : Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey Type de document : Article/Communication Auteurs : Ugur Alganci, Auteur ; Elif Sertel, Auteur ; Mutlu Ozdogan, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 1053 - 1065 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte agricole
[Termes IGN] classification orientée objet
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification Spectral angle mapper
[Termes IGN] cultures
[Termes IGN] image Landsat-TM
[Termes IGN] image SPOT 5
[Termes IGN] occupation du sol
[Termes IGN] parcelle agricole
[Termes IGN] photo-interprétation assistée par ordinateur
[Termes IGN] TurquieRésumé : (Auteur) This research investigates the accuracy of pixel- and object-based classification techniques across varying spatial resolutions to identify crop types at parcel level and estimate the area at six test sites to find the optimum data source for the identification of crop parcels. Multi-sensor data with spatial resolutions of 2.5 m, 5 m and 10 m from SPOT5 and 30 m from Landsat-5 TM were used. Maximum Likelihood (ML), Spectral Angle Mapper (SAM), and Support Vector Machines (SVM) were used as pixel-based methods in addition to object-based image classification (OBC). Post-classification methods were applied to the output of pixel-based classification to minimize the noise effects and heterogeneity within the agricultural parcels. In addition, processing-time performance of the algorithms was evaluated for the test sites and district scale classification. OBC results provided comparatively the best performance for both parcel identification and area estimation at 10 m and finer spatial resolution levels. SVM followed OBC at 2.5 m and 5 m resolutions but accuracies decreased dramatically with coarser resolutions. ML and SAM results were worse up to 30 m resolution for both crop type identification and area estimation. In general, parcel identification efficiency was strongly correlated with spatial resolution while the classification algorithm was a more effective factor than spatial resolution for area estimation accuracy. Results also provided an opportunity to discuss the effects of image resolution and the classification algorithm independent factors such as parcel size, spatial distribution of crop types and crop patterns. Numéro de notice : A2013-599 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.11.1053 En ligne : https://doi.org/10.14358/PERS.79.11.1053 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32735
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 11 (November 2013) . - pp 1053 - 1065[article]A semi-ellipsoid-model based fuzzy classifier to map grassland in Inner Mongolia, China / Hai Lan in ISPRS Journal of photogrammetry and remote sensing, vol 85 (November 2013)
[article]
Titre : A semi-ellipsoid-model based fuzzy classifier to map grassland in Inner Mongolia, China Type de document : Article/Communication Auteurs : Hai Lan, Auteur ; Yichun Xie, Auteur Année de publication : 2013 Article en page(s) : pp 21 - 31 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification floue
[Termes IGN] classification hybride
[Termes IGN] fusion d'images
[Termes IGN] image CBERS
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] Mongolie intérieure (Chine)
[Termes IGN] prairieRésumé : (Auteur) Remote sensing techniques offer effective means for mapping plant communities. However, mapping grassland with fine vegetative classes over large areas has been challenging for either the coarse resolutions of remotely sensed images or the high costs of acquiring images with high-resolutions. An improved hybrid-fuzzy-classifier (HFC) derived from a semi-ellipsoid-model (SEM) is developed in this paper to achieve higher accuracy for classifying grasslands with Landsat images. The Xilin River Basin, Inner Mongolia, China, is chosen as the study area, because an acceptable volume of ground truthing data was previously collected by multiple research communities. The accuracy assessment is based on the comparison of the classification outcomes from four types of image sets: (1) Landsat ETM+ August 14, 2004, (2) Landsat TM August 12, 2009, (3) the fused images of ETM+ with CBERS, and (4) TM with CBERS, respectively, and by three classifiers, the proposed HFC-SEM, the tetragonal pyramid model (TPM) based HFC, and the support vector machine method. In all twelve classification experiments, the HFC-SEM classifier had the best overall accuracy statistics. This finding indicates that the medium resolution Landsat images can be used to map grassland vegetation with good vegetative detail when the proper classifier is applied. Numéro de notice : A2013-605 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.07.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.07.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32741
in ISPRS Journal of photogrammetry and remote sensing > vol 85 (November 2013) . - pp 21 - 31[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013111 RAB Revue Centre de documentation En réserve L003 Disponible A spectral gradient difference based approach for land cover change detection / Jun Chen in ISPRS Journal of photogrammetry and remote sensing, vol 85 (November 2013)
[article]
Titre : A spectral gradient difference based approach for land cover change detection Type de document : Article/Communication Auteurs : Jun Chen, Auteur ; Miao Lu, Auteur ; Xuehong Chen, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 1 - 12 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement de formes
[Termes IGN] détection de changement
[Termes IGN] gradient
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] occupation du solRésumé : (Auteur) Change detection with remotely sensed imagery plays an important role in land cover mapping, process analysis and dynamic information services. Euclidean distance, correlation and other mathematic metrics between spectral curves have been used to calculate change magnitude in most change detection methods. However, many pseudo changes would also be detected because of inter-class spectral variance, which remains a significant challenge for operational remote sensing applications. In general, different land cover types have their own spectral curves characterized by typical spectral values and shapes. These spectral values are widely used for designing change detection algorithms. However, the shape of spectral curves has not yet been fully considered. This paper proposes to use spectral gradient difference (SGD) to quantitatively describe the spectral shapes and the differences in shape between two spectra. Change magnitude calculated in the new spectral gradient space is used to detect the change/no-change areas. Then, a chain model is employed to represent the SGD pattern both qualitatively and quantitatively. Finally, the land cover change types are determined by pattern matching with the knowledgebase of reference SGD patterns. The effectiveness of this SGD-based change detection approach was verified by a simulation experiment and a case study of Landsat data. The results indicated that the SGD-based approach was superior to the traditional methods. Numéro de notice : A2013-604 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.07.009 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.07.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32740
in ISPRS Journal of photogrammetry and remote sensing > vol 85 (November 2013) . - pp 1 - 12[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013111 RAB Revue Centre de documentation En réserve L003 Disponible La télédétection au service des études urbaines : expansion de la ville de Pondichéry entre 1973 et 2009 / Emilien Kieffer in Géomatique expert, n° 95 (01/11/2013)PermalinkApport de la télédétection à l'analyse de la dynamique de l'occupation du sol à partir d'une utilisation couplée d'un modèle de markov et d'un automate cellulaire. Cas du département de Sintra (Centre-Ouest de la Cote d'Ivoire). / Vami Hermann N'guessan Bi in Revue Française de Photogrammétrie et de Télédétection, n° 204 (Octobre 2013)PermalinkLandsat 8 donne le ton en matière d’accès libre / Françoise de Blomac in DécryptaGéo le mag, n° 149 (01/09/2013)PermalinkAnalysing spatio-temporal footprints of urbanization on environment of Surat city using satellite-derived bio-physical parameters / Richa Sharma in Geocarto international, vol 28 n° 5-6 (August - October 2013)PermalinkModelling the impacts of civil war on land use and land cover change within Kono District, Sierra Leone: a socio-geospatial approach / Sigismond A. Wilson in Geocarto international, vol 28 n° 5-6 (August - October 2013)PermalinkContribution des données ALOS et Landsat dans la cartographie et l'analyse des linéaments dans le Sahel central (Maroc occidental) / Adnane Habib in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)PermalinkEffects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm / Jaehoon Jung in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)PermalinkTélédétection, SIG et modélisation de l'érosion hydrique dans le bassin versant de l'Oued Amzaz, Rif central / Jamal Chaaouan in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)PermalinkSensitivity of spectral reflectance values to different burn and vegetation ratios: A multi-scale approach applied in a fire affected area / Magdalini Pleniou in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)PermalinkAn experimental comparison of semi-supervised learning algorithms for multispectral image classification / Enmei Tu in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 4 (April 2013)Permalink