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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]

Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China / Huijuan Zhang in Computers & geosciences, vol 158 (January 2022)  

Public [article]  inComputers & geosciences > vol 158 (January 2022) . - n° 104966 Titre : Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China Type de document : Article/Communication Auteurs : Huijuan Zhang, Auteur ; Yingxu Song, Auteur ; Shiluo Xu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104966 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aléa [Termes IGN] apprentissage automatique [Termes IGN] base de données localisées [Termes IGN] cartographie des risques [Termes IGN] Chine [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] effondrement de terrain [Termes IGN] modèle de simulation [Termes IGN] régression géographiquement pondérée [Termes IGN] régression logistique [Termes IGN] risque naturel [Termes IGN] Trois Gorges, barrage des Résumé : (auteur) This study aims to investigate the application of a class-weighted algorithm combined with conventional machine learning model (logistic regression (LR)) and ensemble machine learning models (LightGBM and random forest (RF)) to the landslide susceptibility evaluation. Wanzhou section of the Three Gorges Reservoir area, China, frequently suffering numerous landslides, is chosen as an example. The class-weighted algorithm focuses on the class-imbalanced issue of landslide and non-landslide samples, and it can turn the class-imbalanced issue into a cost-sensitive machine learning by setting unequal weights for different classes, which contribute to improving the accuracy of landslide susceptibility evaluation. The landslide inventory database was produced by field investigation and remote sensing images derived from Google Earth. Of the 233 landslides in the inventory, 40% were used for validation, and the remaining 60% were used for training purposes. Twelve environmental parameters (elevation, slope, aspect, curvature, distance to river, NDVI, NDWI, rainfall, seismic intensity, land use, TRI, lithology) were treated as inputs of the models to produce a landslide susceptibility map (LSM). The AUC value, Balanced accuracy, and Geometric mean score were utilized to estimate the quality of models. The result shows that the weighted models (weighted logistic regression (WLR), weighted LightGBM (WLightGBM), weighted random forest (WRF) have higher AUC values, Balanced accuracy, and Geometric mean scores than those of unweighted methods, which demonstrates that the weighted models exhibit better than unweighted models, with the WRF model having the best performance. The landslide susceptibility map of the Wanzhou section displays that the high and very high landslide susceptibility zones are mainly distributed on both sides of the river. The insights from this research will be useful for ameliorating the landslide susceptibility mapping and the prevention and mitigation for the Wanzhou section. Numéro de notice : A2022-029 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.cageo.2021.104966Get rights and content Date de publication en ligne : 27/10/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104966Get rights and content Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99268 [article]

Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 36 - 50 Titre : Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning Type de document : Article/Communication Auteurs : Xin Jiang, Auteur ; Shijing Liang, Auteur ; Xinyue He, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 36 - 50 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage non-dirigé [Termes IGN] apprentissage profond [Termes IGN] cartographie des risques [Termes IGN] chaîne de traitement [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] Fleuve bleu (Chine) [Termes IGN] Google Earth Engine [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] inondation [Termes IGN] modèle numérique de surface [Termes IGN] segmentation d'image [Termes IGN] superpixel [Termes IGN] surveillance hydrologique Résumé : (auteur) Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally. Numéro de notice : A2021-560 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.05.019 Date de publication en ligne : 09/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.05.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98118 [article]

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Remote sensing method for extracting topographic information on tidal flats using spatial distribution features / Yang Lijun in Marine geodesy, vol 44 n° 5 (September 2021)  

Public [article]  inMarine geodesy > vol 44 n° 5 (September 2021) . - pp 408 - 431 Titre : Remote sensing method for extracting topographic information on tidal flats using spatial distribution features Type de document : Article/Communication Auteurs : Yang Lijun, Auteur ; Xiao Yao, Auteur ; Jie Jiang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 408 - 431 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] alluvion [Termes IGN] arpentage [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] données topographiques [Termes IGN] extraction de données [Termes IGN] Fleuve bleu (Chine) [Termes IGN] géomorphologie locale [Termes IGN] image Landsat [Termes IGN] marée océanique [Termes IGN] modèle numérique de surface [Termes IGN] positionnement cinématique en temps réel [Termes IGN] Shanghai (Chine) [Termes IGN] vase Résumé : (Auteur) A remote sensing method combining remote sensing and ground surveying is proposed to extract tidal flat topographic information via the spatial distribution characteristics of tidal flat surface features. Based on the eastern Chongming beach of the Yangtze Estuary and Landsat-5 satellite images, this study identifies the spatial distribution characteristics of tidal flat features using field-based RTK data and spectral data. The remote sensing method for extracting the geometric and physical characteristics of linear and surface geographical elements on tidal flats and the elevation assignment method are discussed. The effectiveness of this method is verified by the quality of the resultant tidal flat DEM. The results show that the use of spatial distribution features in remote sensing images can provide rich topographic information. The DEM results have an accuracy of 0.16 m, are in line with the basic topographic patterns of tidal flats, and can describe local microscale geomorphic features. This technique solves the problem of a single topographic information source in current remote sensing measurement methods and provides technical support for detecting dynamic changes in coastal zones by using remote sensing technology. Numéro de notice : A2021-577 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2021.1925791 Date de publication en ligne : 04/06/2021 En ligne : https://doi.org/10.1080/01490419.2021.1925791 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98230 [article]

An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data / Li Zhuo in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 142 (August 2018) . - pp 64 - 77 Titre : An improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data Type de document : Article/Communication Auteurs : Li Zhuo, Auteur ; Qingli Shi, Auteur ; Haiyan Tao, Auteur ; Jing Zheng, Auteur ; Qiuping Li, Auteur Année de publication : 2018 Article en page(s) : pp 64 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges temporels [Termes IGN] détection de changement [Termes IGN] Enhanced vegetation index [Termes IGN] factorisation de matrice non-négative [Termes IGN] Fleuve bleu (Chine) [Termes IGN] image DMSP-OLS [Termes IGN] image Terra-MODIS [Termes IGN] Kouangtoung (Chine) [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] surface imperméable Résumé : (Auteur) Impervious surface area (ISA) is an important indicator for monitoring the intensity of human activity and ecological environment changes. Developing effective methods for estimation of ISA at different scales has thus been pursued by many scientists. The temporal mixture analysis (TMA), which is a variant of spectral mixture analysis that makes full use of the phenological information of different land cover types, is suitable for estimating the ISA fraction at a large scale. The existing TMA-based ISA fraction estimation methods rely on the assumption that pure pixels exist for all the endmembers, which, however, is not true in the case of coarse-resolution datasets. Moreover, the existing method cannot effectively differentiate bare soil from ISA effectively, which may lead to overestimation of the ISA fraction. To address these problems, we propose a new ISA estimation method based on TMA in this study, using a Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) products, the GlobeLand30 product, and the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data. The proposed method contains four major steps. First, the MODIS NDVI time-series datasets and GlobeLand30 land cover product were used to create an NDVI temporal profile subset for the TMA model. Second, a preliminary ISA fraction map was derived on the basis of optimized endmember temporal profiles, which were generated by unmixing the selected NDVI temporal profile subset through an improved spatial-spectral preprocessing nonnegative matrix factorization algorithm (ISSPP-NMF). Then, the preliminary ISA fraction was further optimized by incorporating the EVI-adjusted night-time light index (EANTLI), which can mitigate both saturation problems and the blooming effect of the DMSP-OLS data. An effective threshold method was introduced in this step to reduce the impact of bare soil on the ISA estimation. Finally, the estimated fraction of ISA was evaluated through accuracy assessment. The proposed method was tested in two study areas, namely, Guangdong Province and the Yangtze River Delta (YRD) of China, to prove its applicability in different regions. Effectiveness of the proposed method was proven through the comparison between the proposed method with traditional TMA-based methods. The results from these analyses indicate that the proposed method outperforms the others in ISA estimation, with an overall root mean square error (RMSE) of 9.2% and a coefficient of determination (R2) of 0.8872 in Guangdong and a RMSE of 8.9% and R2 of 0.8923 in YRD. This study also proves that the ISSPP-NMF method can produce more appropriate endmembers regardless of the existence of pure pixels. The post-processing with the EANLTI procedure can effectively reduce the bare soil effect in TMA-based ISA estimation. Numéro de notice : A2018-292 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.05.016 Date de publication en ligne : 05/06/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.05.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90409 [article]

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From hierarchy to networking: the evolution of the “twenty-first-century Maritime Silk Road” container shipping system / Liehui Wang in Transport reviews, vol 38 n° 4 ([01/07/2018])  

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Land use and land cover change detection using satellite remote sensing techniques in the mountainous Three Gorges Area, China / Z. Chen in International Journal of Remote Sensing IJRS, vol 31 n° 6 (March 2010)  

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A scheme for ship detection in inhomogeneous regions based on segmentation of SAR images / F. Zhang in International Journal of Remote Sensing IJRS, vol 29 n°19-20 (October 2008)  

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Suspended sediment concentrations in the Yangtze River estuary retrieved from the CMODIS data / Z. Han in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)  

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Landslide monitoring in the Three Gorges area using D-InSAR and corner reflectors / Y. Xia in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 10 (October 2004)

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Analyse et modélisation de mouvements de versant déclenchés par le plan d'eau d'une retenue de barrage / Y. Cai (2000)

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Le barrage des trois gorges (Chine) / L. Merchez in Mappemonde, vol 1999 n° 3 tome 55 (septembre 1999)  

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Geology and geomechanics of the three gorges projects / C. Zhengquan (1999)

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