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Evaluation of the MODIS LAI at coniferous forest site in Finland / Y. Wang in Remote sensing of environment, vol 91 n° 1 (15/05/2004)
[article]
Titre : Evaluation of the MODIS LAI at coniferous forest site in Finland Type de document : Article/Communication Auteurs : Y. Wang, Auteur ; Curtis E. Woodcock, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 114 - 127 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] bande infrarouge
[Termes IGN] Finlande
[Termes IGN] forêt
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Terra-MODIS
[Termes IGN] indice foliaire
[Termes IGN] Leaf Area Index
[Termes IGN] Pinophyta
[Termes IGN] segmentation d'imageRésumé : (Auteur) Leaf area index (LAI) collected in a needle-leaf forest site near Ruokolahti, Finland, during a field campaign in June 14-21, 2000, WA a, used to validate Moderate Resolution Imaging Spectroradionieter (MODIS) LAI algorithm. The field LAI data was first related to 30-m resolution Enhanced Thermal Mapper Plus (ETM+) images using empirical methods to create a high-resolution LAI map. The analysis of empirical approaches indicates that preliminary segmentation of the image followed by empirical modeling with the resulting patches, was an effective approach to developing an LAI validation surface. Comparison of the aggregated high-resolution LAI map and corresponding MODIS LAI retrievals suggests satisfactory behavior of the MODIS LAI algorithm although variation in MODIS LAI product is higher than expected. The MODIS algorithm, adjusted to high resolution, generally overestimates the LAI due to the influence of the understory vegetation. This indicates the need for improvements in the algorithm. An improved correlation between field measurements and the reduced simple ratio (RSR) suggests that the shortwave infrared (SWIR) band may provide valuable information for needle-leaf forests. Numéro de notice : A2004-238 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.02.007 En ligne : https://doi.org/10.1016/j.rse.2004.02.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26765
in Remote sensing of environment > vol 91 n° 1 (15/05/2004) . - pp 114 - 127[article]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)
[article]
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)
[article]
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]Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modelling and validation in the context of precision agriculture / D. Haboudane in Remote sensing of environment, vol 90 n° 3 (15/04/2004)
[article]
Titre : Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modelling and validation in the context of precision agriculture Type de document : Article/Communication Auteurs : D. Haboudane, Auteur ; J.R. Miller, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 337 - 352 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture de précision
[Termes IGN] analyse comparative
[Termes IGN] blé (céréale)
[Termes IGN] chlorophylle
[Termes IGN] cultures
[Termes IGN] données de terrain
[Termes IGN] Glycine max
[Termes IGN] Green Leaf Area Index
[Termes IGN] image CASI
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] maïs (céréale)
[Termes IGN] modèle de transfert radiatif
[Termes IGN] prévision
[Termes IGN] réflectance végétaleRésumé : (Auteur) A growing number of studies have focused on evaluating spectral indices in terms of their sensitivity to vegetation biophysical parameters, as well as to external factors affecting canopy reflectance. In this context, leaf and canopy radiative transfer models are valuable for modeling and understanding the behavior of such indices. In the present work, PROSPECT and SAILH models have been used to simulate a wide range of crop canopy reflectances in an attempt to study the sensitivity of a set of vegetation indices to green leaf area index (LAI), and to modify some of them in order to enhance their responsivity to LAI variations. The aim of the paper was to present a method for minimizing the effect of leaf chlorophyll content on the prediction of green LAI, and to develop new algorithms that adequately predict the LAI of crop canopies. Analyses based on both simulated and real hyperspectral data were carried out to compare performances of existing vegetation indices (Normalized Difference Vegetation Index [NDVI], Renormalized Difference Vegetation Index [RDVI], Modified Simple Ratio [MSR], Soil-Adjusted Vegetation Index [SAVI], Soil and Atmospherically Resistant Vegetation Index [SARVI], MSAVI, Triangular Vegetation Index [TVI], and Modified Chlorophyll Absorption Ratio Index [MCARI]) and to design new ones (MTVII, MCARII, MTV12, and MCAR12) that are both less sensitive to chlorophyll content variations and linearly related to green LAI. Thorough analyses showed that the above existing vegetation indices were either sensitive to chlorophyll concentration changes or affected by saturation at high LAI levels. Conversely, two of the spectral indices developed as a part of this study, a modified triangular vegetation index (MTV12) and a modified chlorophyll absorption ratio index (MCAR12), proved to be the best predictors of green LAI. Related predictive algorithms were tested on CASI (Compact Airborne Spectrographic Imager) hyperspectral images and, then, validated using ground truth measurements. The latter were collected simultaneously with image acquisition for different crop types (soybean, corn, and wheat), at different growth stages, and under various fertilization treatments. Prediction power analysis of proposed algorithms based on MCAR12 and MTV12 resulted in agreements between modeled and ground measurement of non-destructive LAI, with coefficients of determination (r) being 0.98 for soybean, 0.89 for com, and 0.74 for wheat. The corresponding RMSE for LAI were estimated at 0.28, 0.46, and 0.85, respectively. Numéro de notice : A2004-201 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.12.013 En ligne : https://doi.org/10.1016/j.rse.2003.12.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26728
in Remote sensing of environment > vol 90 n° 3 (15/04/2004) . - pp 337 - 352[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)
[article]
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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