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Super-resolution land cover mapping with indicator geostatistics / A. Boucher in Remote sensing of environment, vol 104 n° 3 (15/10/2006)
[article]
Titre : Super-resolution land cover mapping with indicator geostatistics Type de document : Article/Communication Auteurs : A. Boucher, Auteur ; P.C. Kyriakidis, Auteur Année de publication : 2006 Article en page(s) : pp 264 - 282 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte d'occupation du sol
[Termes IGN] Chine
[Termes IGN] delta
[Termes IGN] fleuve
[Termes IGN] géostatistique
[Termes IGN] image Landsat-TM
[Termes IGN] incertitude de position
[Termes IGN] krigeage
[Termes IGN] problème inverse
[Termes IGN] variogrammeRésumé : (Auteur) Many satellite images have a coarser spatial resolution than the extent of land cover patterns on the ground, leading to mixed pixels whose composite spectral response consists of responses from multiple land cover classes. Spectral unmixing procedures only determine the fractions of such classes within a coarse pixel without locating them in space. Super-resolution or sub-pixel mapping aims at providing a fine resolution map of class labels, one that displays realistic spatial structure (without artifact discontinuities) and reproduces the coarse resolution fractions. In this paper, existing approaches for super-resolution mapping are placed within an inverse problem framework, and a geostatistical method is proposed for generating alternative synthetic land cover maps at the fine (target) spatial resolution; these super-resolution realizations are consistent with all the information available. More precisely, indicator coKriging is used to approximate the probability that a pixel at the fine spatial resolution belongs to a particular class, given the coarse resolution fractions and (if available) a sparse set of class labels at some informed fine pixels. Such Kriging-derived probabilities are used in sequential indicator simulation to generate synthetic maps of class labels at the fine resolution pixels. This non-iterative and fast simulation procedure yields alternative super-resolution land cover maps that reproduce: (i) the observed coarse fractions, (ii) the fine resolution class labels that might be available, and (iii) the prior structural information encapsulated in a set of indicator variogram models at the fine resolution. A case study is provided to illustrate the proposed methodology using Landsat TM data from SE China. Numéro de notice : A2006-415 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.04.020 En ligne : https://doi.org/10.1016/j.rse.2006.04.020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28139
in Remote sensing of environment > vol 104 n° 3 (15/10/2006) . - pp 264 - 282[article]Resolution dependent errors in remote sensing of cultivated areas / M. Ozdogan in Remote sensing of environment, vol 103 n° 2 (30/07/2006)
[article]
Titre : Resolution dependent errors in remote sensing of cultivated areas Type de document : Article/Communication Auteurs : M. Ozdogan, Auteur ; Curtis E. Woodcock, Auteur Année de publication : 2006 Article en page(s) : pp 203 - 217 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse de sensibilité
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] Chine
[Termes IGN] cultures
[Termes IGN] distribution spatiale
[Termes IGN] erreur de classification
[Termes IGN] image Ikonos
[Termes IGN] limite de résolution géométrique
[Termes IGN] précision infrapixellaire
[Termes IGN] seuillage d'image
[Termes IGN] surface cultivée
[Termes IGN] variogrammeRésumé : (Auteur) Remote sensing has become a common and effective method for estimating the areal coverage of land cover classes. One class of particular interest is agriculture as area estimates of cultivated lands are important for purposes such as estimating yields or irrigation needs. The synoptic coverage of satellite imagery and the relative ease of automated analysis have led to widespread mapping of agriculture using remote sensing. The accuracy of area estimates derived from these maps is known to be related to the accuracy of the maps. However, even in the situation where the map is very accurate, errors in area estimates may occur. These errors result from the behavior of the distribution of subpixel proportions of cultivated areas, and how that behavior changes as a result of sensor spatial resolution and class definitions. The sensitivity of estimates of cultivated areas to sensor spatial resolution and to the choice of threshold used to define cultivated land is explored in six agriculturally distinct locations around the world. Using a beta model for the distribution of subpixel proportions that is parameterized using variograms, it is possible to model the distribution of subpixel proportions for any spatial resolution. When the spatial resolution is small with respect to the spatial structure of the landscape (as measured by the variogram range) use of any class definition threshold produces an estimate very close to the true area coverage. On the other hand, as the resolution becomes coarse in relation to the variogram range, the subpixel proportions are no longer concentrated at the extremes of the distribution and the difference between the estimated and the true area has greater sensitivity to the selected threshold used to define classes. Thus, for the cases examined here, both the resolution and the class definition threshold have a strong influence on area estimates. The spatial resolutions where errors can be large depend on landscape spatial structure, which can be quantified using variograms. The net effect is that for the same spatial resolution, some places will exhibit much larger errors in area estimates than others. For the site in the Anhui province of China, where agricultural fields are very small (0.07 ha on the average), area estimates are highly sensitive to class definition thresholds even at the relatively fine resolution of 45 m. Conversely, in California (USA) spatial resolutions as coarse as 500 m can be used to reliably estimate cultivated areas. Results also suggest that the proportion of the total area that is cultivated significantly influences the accuracy of area estimates. When the area proportion is low, the class definition threshold must also be low to achieve an accurate area estimate. Conversely, in areas dominated by agriculture, a very stringent class definition of cultivated lands is required for accurate area estimates. While explored in the context of estimation of cultivated areas, the findings presented here are generic to the problem of area estimation using remote sensing. Copyright Elsevier Numéro de notice : A2006-321 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.04.004 En ligne : https://doi.org/10.1016/j.rse.2006.04.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28045
in Remote sensing of environment > vol 103 n° 2 (30/07/2006) . - pp 203 - 217[article]Quantifying spatial heterogeneity at the landscape scale using variogram models / S. Garrigues in Remote sensing of environment, vol 103 n° 1 (15 July 2006)
[article]
Titre : Quantifying spatial heterogeneity at the landscape scale using variogram models Type de document : Article/Communication Auteurs : S. Garrigues, Auteur ; Denis Allard, Auteur ; F. Baret, Auteur ; M. Weiss, Auteur Année de publication : 2006 Article en page(s) : pp 81 - 96 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] couvert végétal
[Termes IGN] erreur systématique
[Termes IGN] hétérogénéité spatiale
[Termes IGN] image à basse résolution
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] pixel
[Termes IGN] utilisation du sol
[Termes IGN] variogrammeRésumé : (Auteur) The monitoring of earth surface dynamic processes at a global scale requires high temporal frequency remote sensing observations which are provided up to now by moderate spatial resolution sensors. However, the spatial heterogeneity within the moderate spatial resolution pixel biases non-linear estimation processes of land surface variables from remote sensing data. To limit its influence on the description of land surface processes, corrections based on the quantification of the intra-pixel heterogeneity may be applied to non-linear estimation processes. A complementary strategy is to define the proper pixel size to capture the spatial variability of the data and minimize the intra-pixel variability.
This work provides a methodology to characterize and quantify the spatial heterogeneity of landscape vegetation cover from the modeling of the variogram of high spatial resolution NDVI data. NDVI variograms for 18 landscapes extracted from the VALERI database show that the land use is the main factor of spatial variability as quantified by the variogram sill. Crop sites are more heterogeneous than natural vegetation and forest sites at the landscape level. The integral range summarizes all structural parameters of the variogram into a single characteristic area. Its square root quantifies the mean length scale (i.e. spatial scale) of the data, which varies between 216 and 1060 m over the 18 landscapes considered. The integral range is also used as a yardstick to judge if the size of an image is large enough to measure properly the length scales of the data with the variogram. We propose that it must be smaller than 5% of the image surface. The theoretical dispersion variance, computed from the variogram model, quantifies the spatial heterogeneity within a moderate resolution pixel. It increases rapidly with pixel size until this size is larger than the mean length scale of the data. Finally based on the analysis of 18 landscapes, the sufficient pixel size to capture the major part of the spatial variability of the vegetation cover at the landscape scale is estimated to be less than 100 m. Since for all the heterogeneous landscapes the loss of NDVI spatial variability was small at this spatial resolution, the bias generated by the intra-pixel spatial heterogeneity on non-linear estimation processes will be reduced. Copyright ElsevierNuméro de notice : A2006-283 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2006.03.013 En ligne : https://doi.org/10.1016/j.rse.2006.03.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28010
in Remote sensing of environment > vol 103 n° 1 (15 July 2006) . - pp 81 - 96[article]Urban land-use classification using variogram-based analysis with an aerial photograph / S.S. Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 7 (July 2006)
[article]
Titre : Urban land-use classification using variogram-based analysis with an aerial photograph Type de document : Article/Communication Auteurs : S.S. Wu, Auteur ; B. Xu, Auteur ; L. Wang, Auteur Année de publication : 2006 Article en page(s) : pp 813 - 822 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse texturale
[Termes IGN] bati
[Termes IGN] classification dirigée
[Termes IGN] classification spectrale
[Termes IGN] Kappa de Cohen
[Termes IGN] milieu urbain
[Termes IGN] photographie aérienne
[Termes IGN] photographie infrarouge couleur
[Termes IGN] utilisation du sol
[Termes IGN] variogramme
[Termes IGN] zone urbaineRésumé : (Auteur) In this study, a variogram-based texture analysis was tested for classifying detailed urban land-use classes, such as mobile home, single-family house, multi-family house, industrial, and commercial from a digital color infrared aerial photograph. Spectral classification was first carried out to separate the building class from non-building classes. Then, a building-presence binary image was generated so that building pixels were assigned a value of "1 " and non-building pixels were assigned a value of "0. " Multiple texture bands were further generated employing a variogram-based texture analysis and used for land-use classification. The generation of the building presence binary image allowed us not only to fully explore the capability of variogram-based analysis on spatial pattern detection, but also to prevent the variogram-based analysis from being disturbed by the natural fluctuation of spectral signals. The result from using a mosaic test image was considered satisfactory with a kappa coefficient of 0.72. Copyright ASPRS Numéro de notice : A2006-264 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.72.7.813 En ligne : https://doi.org/10.14358/PERS.72.7.813 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27991
in Photogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 7 (July 2006) . - pp 813 - 822[article]Relating SAR image texture to the biomass of regenerating tropical forests / T.M. Kuplich in International Journal of Remote Sensing IJRS, vol 26 n° 21 (November 2005)
[article]
Titre : Relating SAR image texture to the biomass of regenerating tropical forests Type de document : Article/Communication Auteurs : T.M. Kuplich, Auteur ; P.J. Curran, Auteur ; P.M. Atkinson, Auteur Année de publication : 2005 Article en page(s) : pp 4829 - 4854 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande L
[Termes IGN] canopée
[Termes IGN] forêt tropicale
[Termes IGN] image JERS
[Termes IGN] image radar
[Termes IGN] Manaus
[Termes IGN] masse végétale
[Termes IGN] niveau de gris (image)
[Termes IGN] teneur en carbone
[Termes IGN] texture d'image
[Termes IGN] variogrammeRésumé : (Auteur) An accurate global carbon budget requires information on terrestrial carbon sink strength. Regenerating tropical forests are known to be important terrestrial carbon sinks but information on their location, extent and biomass (from which carbon content can be estimated) is incomplete. The use of remotely sensed data in optical wavelengths has been of limited use due to both the weak relationship between optical radiation and forest biomass and near-constant cloud cover in the tropics. L-band Synthetic Aperture Radar (SAR) backscatter, however, is related positively to biomass (but only up to an asymptote of around 40-90T ha-1) and can be obtained independently of cloud cover. Both canopy structure and biomass change over time as pioneer species are replaced by early and late regenerating species. These structural changes are related to an increase in (i) tree height, (ii) tree species richness and (iii) canopy thickness and influence the roughness of the canopy surface and consequently SAR image texture. Therefore, we investigated the degree to which textural information could be used to increase the correlation between image tone (backscatter) and biomass. Field data were used to estimate the biomass of 37 regenerating forests plots in Brazilian Amazonia. Texture measures derived from local statistics, the grey level co-occurrence matrix (GLCM) and the variogram were evaluated using simulated images on the basis of their ability to identify significant differences in image texture independently of image contrast. The selected texture measures were applied to L-band JERS-1 (Japanese Earth Resources Satellite) SAR images and the correlation between backscatter and biomass was determined for regenerating tropical forests. A strong correlation was found for the texture measures and biomass. The ra2 (adjusted coefficient of determination), measuring the correlation between backscatter and biomass, increased from 0.74 to 0.82 with the addition of GLCM-derived contrast. The addition of image texture (GLCM-derived contrast) to image tone (backscatter) potentially increases the accuracy with which JERS-1 SAR data can be used to estimate biomass in tropical forests. Numéro de notice : A2005-469 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500239107 En ligne : https://doi.org/10.1080/01431160500239107 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27605
in International Journal of Remote Sensing IJRS > vol 26 n° 21 (November 2005) . - pp 4829 - 4854[article]Réservation
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