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Exploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data / Yanan Zhou in Remote sensing, vol 14 n° 21 (November-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 21 (November-1 2022) . - n° 5571 Titre : Exploring the influencing factors in identifying soil texture classes using multitemporal Landsat-8 and Sentinel-2 data Type de document : Article/Communication Auteurs : Yanan Zhou, Auteur ; Wei Wu, Auteur ; Hongbin Liu, Auteur Année de publication : 2022 Article en page(s) : n° 5571 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] composition des sols [Termes IGN] données multitemporelles [Termes IGN] Extreme Gradient Machine [Termes IGN] Fleuve bleu (Chine) [Termes IGN] image Landsat-8 [Termes IGN] image Sentinel-MSI [Termes IGN] limon [Termes IGN] qualité du sol [Termes IGN] réflectance spectrale [Termes IGN] texture du sol Résumé : (auteur) Soil texture is a key soil property driving physical, chemical, biological, and hydrological processes in soils. The rapid development of remote sensing techniques shows great potential for mapping soil properties. This study highlights the effectiveness of multitemporal remote sensing data in identifying soil textural class by using retrieved vegetation properties as proxies of soil properties. The impacts of sensors, modeling resolutions, and modeling techniques on the accuracy of soil texture classification were explored. Multitemporal Landsat-8 and Sentinel-2 images were individually acquired at the same time periods. Three satellite-based experiments with different inputs, i.e., Landsat-8 data, Sentinel-2 data (excluding red-edge parameters), and Sentinel-2 data (including red-edge parameters) were conducted. Modeling was carried out at three spatial resolutions (10, 30, 60 m) using five machine-learning (ML) methods: random forest, support vector machine, gradient-boosting decision tree, categorical boosting, and super learner that combined the four former classifiers based on the stacking concept. In addition, a novel SHapley Addictive Explanation (SHAP) technique was introduced to explain the outputs of the ML model. The results showed that the sensors, modeling resolutions, and modeling techniques significantly affected the prediction accuracy. The models using Sentinel-2 data with red-edge parameters performed consistently best. The models usually gave better results at fine (10 m) and medium (30 m) modeling resolutions than at a coarse (60 m) resolution. The super learner provided higher accuracies than other modeling techniques and gave the highest values of overall accuracy (0.8429), kappa (0.7611), precision (0.8378), recall rate (0.8393), and F1-score (0.8398) at 30 m with Sentinel-2 data involving red-edge parameters. The SHAP technique quantified the contribution of each variable for different soil textural classes, revealing the critical roles of red-edge parameters in separating loamy soils. This study provides comprehensive insights into the effective modeling of soil properties on various scales using multitemporal optical images. Numéro de notice : A2022-856 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14215571 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.3390/rs14215571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102104 [article]

Using vertices of a triangular irregular network to calculate slope and aspect / Guanghui Hu in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 382 - 404 Titre : Using vertices of a triangular irregular network to calculate slope and aspect Type de document : Article/Communication Auteurs : Guanghui Hu, Auteur ; Chun Wang, Auteur ; Sijin Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 382 - 404 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] analyse comparative [Termes IGN] bassin hydrographique [Termes IGN] géomorphologie [Termes IGN] grille [Termes IGN] loess [Termes IGN] maillage [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle numérique de surface [Termes IGN] noeud [Termes IGN] pente [Termes IGN] point d'appui [Termes IGN] Triangulated Irregular Network Résumé : (auteur) Terrain derivative calculations from triangulated irregular network (TIN)-based digital elevation models (DEMs) have been extensively explored in geomorphometry. However, most calculation methods focus on the triangulation facets of TIN-based DEMs and ignore the vertices. In fact, these vertices are the original sampling points from the terrain surface and serve as the basis for triangulation. In this study, we argue that terrain derivative calculations using TIN-based DEMs should focus on the vertices. Employing examples with slope and aspect, we applied the TIN vertex-based method to a mathematical surface and a real topography using TIN-based DEMs with a range of sampling point densities. We performed a comparative analysis of the TIN vertex-based, TIN facet-based, and grid-based methods. Assessments on the mathematical surface showed that the TIN vertex-based method achieved the highest accuracy among the three methods. Error analysis for the real landform case indicated that the TIN vertex-based method performed slightly better than the grid-based method for slope calculation and slightly worse than the grid-based method for aspect calculation. Among the three methods, the TIN facet-based method was most sensitive to error. The TIN vertex-based method can provide a reference for the slope and aspect calculation based on point clouds. Numéro de notice : A2022-165 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1933493 Date de publication en ligne : 01/07/2021 En ligne : https://doi.org/10.1080/13658816.2021.1933493 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99788 [article]

A soil texture categorization mapping from empirical and semi-empirical modelling of target parameters of synthetic aperture radar / Shoba Periasamy in Geocarto international, vol 36 n° 5 ([15/03/2021])  

Public [article]  inGeocarto international > vol 36 n° 5 [15/03/2021] . - pp 581 - 598 Titre : A soil texture categorization mapping from empirical and semi-empirical modelling of target parameters of synthetic aperture radar Type de document : Article/Communication Auteurs : Shoba Periasamy, Auteur ; Divya Senthil, Auteur ; Ramakrishnan S Shanmugam, Auteur Année de publication : 2021 Article en page(s) : pp 581 - 598 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Argile [Termes IGN] bande C [Termes IGN] coefficient de rétrodiffusion [Termes IGN] constante diélectrique [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] indice de végétation [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] limon [Termes IGN] polarisation croisée [Termes IGN] rugosité du sol [Termes IGN] sable [Termes IGN] texture du sol Résumé : (auteur) The present study investigates the potential of synthetic aperture radar in demonstrating the relative percentage of sand, silt and clay content in the soil. The contribution of vegetation and topography in the backscattering coefficient has been significantly reduced by employing the terrain correction model, dual polarized SAR vegetation index and water cloud model. The target parameters namely ‘Soil Roughness (hrms-soil)’ and ‘Dielectric Constant’ (ε′vv−soil ) has arrived from cross-polarization ratio and modified Dubois model. The extracted target parameters are sufficiently correlated with in situ sand (R2 = 0.81) and clay measurements (R2 = 0.78). The relative percentage of silt was mapped by the novel idea of performing the correlation analysis between hrms-soil and ε′vv−soil and thus represented the percentage of silt with reasonable accuracy (R2 = 0.77). From the soil triangle formed with three estimated target parameters, we found that the clay category has shared around 35% of the total area followed by sandy loam (23%). Numéro de notice : A2021-253 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1618924 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1618924 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97276 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2021051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Estimating forest canopy cover in black locust (Robinia pseudoacacia L.) plantations on the loess plateau using random forest / Qingxia Zhao in Forests, vol 9 n° 10 (October 2018)  

Public [article]  inForests > vol 9 n° 10 (October 2018) Titre : Estimating forest canopy cover in black locust (Robinia pseudoacacia L.) plantations on the loess plateau using random forest Type de document : Article/Communication Auteurs : Qingxia Zhao, Auteur ; Fei Wang, Auteur ; Jun Zhao, Auteur ; Jingjing Zhou, Auteur ; et al., Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] canopée [Termes IGN] Chine [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] détection d'arbres [Termes IGN] Enhanced vegetation index [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] loess [Termes IGN] matrice de co-occurrence [Termes IGN] plantation forestière [Termes IGN] régression [Termes IGN] Robinia pseudoacacia [Termes IGN] Soil Adjusted Vegetation Index Résumé : (Auteur) The forest canopy is the medium for energy and mass exchange between forest ecosystems and the atmosphere. Remote sensing techniques are more efficient and appropriate for estimating forest canopy cover (CC) than traditional methods, especially at large scales. In this study, we evaluated the CC of black locust plantations on the Loess Plateau using random forest (RF) regression models. The models were established using the relationships between digital hemispherical photograph (DHP) field data and variables that were calculated from satellite images. Three types of variables were calculated from the satellite data: spectral variables calculated from a multispectral image, textural variables calculated from a panchromatic image (Tpan) with a 15 × 15 window size, and textural variables calculated from spectral variables (TB+VIs) with a 9 × 9 window size. We compared different mtry and ntree values to find the most suitable parameters for the RF models. The results indicated that the RF model of spectral variables explained 57% (root mean square error (RMSE) = 0.06) of the variability in the field CC data. The soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) were more important than other spectral variables. The RF model of Tpan obtained higher accuracy (R2 = 0.69, RMSE = 0.05) than the spectral variables, and the grey level co-occurrence matrix-based texture measure—Correlation (COR) was the most important variable for Tpan. The most accurate model was obtained from the TB+VIs (R2 = 0.79, RMSE = 0.05), which combined spectral and textural information, thus providing a significant improvement in estimating CC. This model provided an effective approach for detecting the CC of black locust plantations on the Loess Plateau. Numéro de notice : A2018-477 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100623 Date de publication en ligne : 10/10/2018 En ligne : https://doi.org/10.3390/f9100623 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91178 [article]

Simple DEM-based methods to delineate Channel networks for hydrogeomorphological mapping / K. Matsunaga in Transactions in GIS, vol 13 n° 1 (February 2009)  

Public [article]  inTransactions in GIS > vol 13 n° 1 (February 2009) . - pp 87 - 103 Titre : Simple DEM-based methods to delineate Channel networks for hydrogeomorphological mapping Type de document : Article/Communication Auteurs : K. Matsunaga, Auteur ; T. Nakaya, Auteur ; T. Sugai, Auteur Année de publication : 2009 Article en page(s) : pp 87 - 103 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] carte géomorphologique [Termes IGN] Chine [Termes IGN] géomorphologie locale [Termes IGN] loess [Termes IGN] MNS SRTM [Termes IGN] modèle numérique de surface [Termes IGN] pente [Termes IGN] plateau [Termes IGN] pondération [Termes IGN] réseau hydrographique Résumé : (Auteur) To delineate channel networks from DEMs regardless of landform type, this article proposes a new method using slope-weighted flow accumulation. To validate the method, SRTM-3, a global DEM dataset with a resolution of approximately 90 m, was used for analysis of the Loess Plateau, China. Channel networks delineated with and without slope-weighted flow accumulation were derived in both uplands and hilly lands for comparison. In the weighted flow accumulation method, the thresholds for delineating the channels were defined by detecting a turning point in the frequency distribution of the weighted flow accumulation function or by visual similarity with drainage channels extracted from topographic maps. The channel networks delineated with weighting showed closer correlation with a topographic map than the channel networks without weighting, despite the differences in thresholds. Moreover, the channel networks delineated with weighting represented the differences between landform types, while the channel networks without weighting did not. Weighting on the basis of the slope angle shows promise as a general channel delineation method which reflects the actual topography due to its hydrogeomorphological functions. Numéro de notice : A2009-546 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2009.01145.x En ligne : https://doi.org/10.1111/j.1467-9671.2009.01145.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30175 [article]

A hemispherical-directional reflectance model as a tool for understanding image distinctions between cultivated and uncultivated bare surfaces / J. Cierniewski in Remote sensing of environment, vol 90 n° 4 (30/04/2004)  

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Un exemple d'évolution rapide du littoral / R. Vincent in XYZ, n° 38 (mars - mai 1989)

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vol 13 n° 2 - 01/03/1988 - Loess: Earth surface processes and landforms (Bulletin de Earth surface processes and landforms) / K.S. Richards

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