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Application of time series Landsat images to examining land-use/land-cover dynamic change / Dong Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 7 (July 2012)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 7 (July 2012) . - pp 747 - 755 Titre : Application of time series Landsat images to examining land-use/land-cover dynamic change Type de document : Article/Communication Auteurs : Dong Lu, Auteur ; S. Hetrick, Auteur ; E. Moran, Auteur ; G. Li, Auteur Année de publication : 2012 Article en page(s) : pp 747 - 755 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] analyse diachronique [Termes IGN] classification ascendante hiérarchique [Termes IGN] détection de changement [Termes IGN] image Landsat [Termes IGN] Mato Grosso [Termes IGN] occupation du sol [Termes IGN] série temporelle [Termes IGN] surface imperméable [Termes IGN] utilisation du sol Résumé : (Auteur) A hierarchical-based classification method was designed to develop time series land-use/land-cover datasets from Landsat images between 1977 and 2008 in Lucas do Rio Verde, Mato Grosso, Brazil. A post-classification comparison approach was used to examine land-use/land-cover change trajectories, which emphasis is on the conversions from vegetation or agropasture to impervious surface area, from vegetation to agropasture, and from agropasture to regenerating vegetation. Results of this research indicated that increase in impervious surface area mainly resulted from the loss of cerrado in the initial decade of the study period and from loss of agricultural lands in the last two decades. Increase in agropasture was mainly at the expense of losing cerrado in the first two decades and relatively evenly from the loss of primary forest and cerrado in the last decade. When impervious surface area was less than approximately 40 km2 before 1999, impervious surface area was negatively related to cerrado and forest, and positively related to agropasture areas, but after impervious surface area reached 40 km2 in 1999, no obvious relationship exists between them. Numéro de notice : A2012-360 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358%2Fpers.78.7.747 En ligne : https://doi.org/10.14358%2Fpers.78.7.747 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31806 [article]

A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region / Dong Lu ; E. Moran ; et al. in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 70 (June 2012) . - pp 26 - 38 Titre : A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region Type de document : Article/Communication Auteurs : Dong Lu, Auteur ; E. Moran, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 26 - 38 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse comparative [Termes IGN] bande C [Termes IGN] bande L [Termes IGN] classification dirigée [Termes IGN] image ALOS-PALSAR [Termes IGN] image radar [Termes IGN] image Radarsat [Termes IGN] occupation du sol [Termes IGN] zone tropicale humide Résumé : (Auteur) This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms – maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better land-cover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification. Numéro de notice : A2012-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.010 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31733 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2012041	SL	Revue	Centre de documentation	Revues en salle	Disponible

Land-cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data / Dong Lu in International Journal of Remote Sensing IJRS, vol 28 n°23-24 (December 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°23-24 (December 2007) . - pp 5447 - 5459 Titre : Land-cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data Type de document : Article/Communication Auteurs : Dong Lu, Auteur ; M. Batistella, Auteur ; E. Moran, Auteur Année de publication : 2007 Article en page(s) : pp 5447 - 5459 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] Amazonie [Termes IGN] analyse texturale [Termes IGN] Brésil [Termes IGN] classification par maximum de vraisemblance [Termes IGN] fusion d'images [Termes IGN] image Landsat-ETM+ [Termes IGN] image multibande [Termes IGN] image optique [Termes IGN] image panchromatique [Termes IGN] image radar [Termes IGN] image Radarsat [Termes IGN] niveau de gris (image) [Termes IGN] occupation du sol [Termes IGN] transformation en ondelettes [Termes IGN] zone tropicale humide Résumé : (Auteur) Land-cover classification with remotely sensed data in moist tropical regions is a challenge due to the complex biophysical conditions. This paper explores techniques to improve land-cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM+) and Radarsat data. A wavelet-merging technique was used to integrate Landsat ETM+ multispectral and panchromatic data or Radarsat data. Grey-level co-occurrence matrix (GLCM) textures based on Landsat ETM+ panchromatic or Radarsat data and different sizes of moving windows were examined. A maximum-likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land-cover classification accuracies in Amazonian environments. The incorporation of data fusion and textures increases classification accuracy by approximately 5.8-6.9% compared to Landsat ETM+ data, but data fusion of Landsat ETM+ multispectral and panchromatic data or Radarsat data cannot effectively improve land-cover classification accuracies. Copyright Taylor & Francis Numéro de notice : A2007-538 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701227596 En ligne : https://doi.org/10.1080/01431160701227596 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28901 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07131	RAB	Revue	Centre de documentation	En réserve L003	Disponible