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SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images / Hao Wu in Computers, Environment and Urban Systems, vol 100 (March 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 100 (March 2023) . - n° 101921 Titre : SALT: A multifeature ensemble learning framework for mapping urban functional zones from VGI data and VHR images Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Wenting Luo, Auteur ; Anqi Lin, Auteur ; Fanghua Hao, Auteur ; Ana-Maria Olteanu-Raimond , Auteur ; Lanfa Liu, Auteur ; Yan Li, Auteur Année de publication : 2023 Projets : 1-Pas de projet / Article en page(s) : n° 101921 Note générale : Bibliographie This work was supported by the National Natural Science Foundation of China [42201468, 42071358], Postdoctoral Innovation Talents Support Program of China [BX20220128], China Postdoctoral Science Foundation [2022M721283] and Fundamental Research Funds for the Central Universities [CCNU22QN018]. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse multicritère [Termes IGN] apprentissage automatique [Termes IGN] boosting adapté [Termes IGN] cartographie urbaine [Termes IGN] Chine [Termes IGN] détection du bâti [Termes IGN] données localisées des bénévoles [Termes IGN] image à très haute résolution [Termes IGN] morphologie urbaine [Termes IGN] OpenStreetMap [Termes IGN] point d'intérêt [Termes IGN] représentation spatiale [Termes IGN] zone urbaine Résumé : (auteur) Urban functional zone mapping is essential for providing deeper insights into urban morphology and improving urban planning. The emergence of Volunteered Geographic Information (VGI), which provides abundant semantic data, offers a great opportunity to enrich land use information extracted from remote sensing (RS) images. Taking advantage of very-high-resolution (VHR) images and VGI data, this work proposed a SATL multifeature ensemble learning framework for mapping urban functional zones that integrated 65 features from the shapes of building objects, attributes of points of interest (POIs) tags, locations of cellphone users and textures of VHR images. The dimensionality of SALT features was reduced by the autoencoder, and the compressed features were applied to train the ensemble learning model composed of multiple classifiers for optimizing the urban functional zone classification. The effectiveness of the proposed framework was tested in an urbanized region of Nanchang City. The results indicated that the SALT features considering population dynamics and building shapes are comprehensive and feasible for urban functional zone mapping. The autoencoder has been proven efficient for dimension reduction of the original SALT features as it significantly improves the classification of urban functional zones. Moreover, the ensemble learning outperforms other machine learning models in terms of the accuracy and robustness when dealing with multi-classification tasks. Numéro de notice : A2023-125 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101921 Date de publication en ligne : 06/12/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102504 [article]

The potential of combining satellite and airborne remote sensing data for habitat classification and monitoring in forest landscapes / Anna Iglseder in International journal of applied Earth observation and geoinformation, vol 117 (March 2023)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 117 (March 2023) . - n° 103131 Titre : The potential of combining satellite and airborne remote sensing data for habitat classification and monitoring in forest landscapes Type de document : Article/Communication Auteurs : Anna Iglseder, Auteur ; Markus Immitzer, Auteur ; Alena Dostalova, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 103131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données Copernicus [Termes IGN] données lidar [Termes IGN] habitat (nature) [Termes IGN] habitat forestier [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] modèle numérique de surface [Termes IGN] paysage forestier [Termes IGN] protection de la biodiversité [Termes IGN] site Natura 2000 [Termes IGN] Vienne (capitale Autriche) Résumé : (auteur) Mapping and monitoring of habitats are requirements for protecting biodiversity. In this study, we investigated the benefit of combining airborne (laser scanning, image-based point clouds) and satellite-based (Sentinel 1 and 2) data for habitat classification. We used a two level random forest 10-fold leave-location-out cross-validation workflow to model Natura 2000 forest and grassland habitat types on a 10 m pixel scale at two study sites in Vienna, Austria. We showed that models using combined airborne and satellite-based remote sensing data perform significantly better for forests than airborne or satellite-based data alone. For frequently occurring classes, we reached class accuracies with F1-scores from 0.60 to 0.87. We identified clear difficulties of correctly assigning rare classes with model-based classification. Finally, we demonstrated the potential of the workflow to identify errors in reference data and point to the opportunities for integration in habitat mapping and monitoring. Numéro de notice : A2023-128 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103131 Date de publication en ligne : 12/01/2023 En ligne : https://doi.org/10.1016/j.jag.2022.103131 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102512 [article]

A GIS-based method for modeling methane emissions from paddy fields by fusing multiple sources of data / Linhua Ma in Science of the total environment, vol 859 n° 1 (February 2023)  

Public [article]  inScience of the total environment > vol 859 n° 1 (February 2023) . - n° 159917 Titre : A GIS-based method for modeling methane emissions from paddy fields by fusing multiple sources of data Type de document : Article/Communication Auteurs : Linhua Ma, Auteur ; Yuanlai Cui, Auteur ; Bo Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 159917 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse spatio-temporelle [Termes IGN] Chine [Termes IGN] Corée [Termes IGN] données multisources [Termes IGN] Etats-Unis [Termes IGN] humidité du sol [Termes IGN] image à haute résolution [Termes IGN] image infrarouge [Termes IGN] Italie [Termes IGN] méthane [Termes IGN] modélisation [Termes IGN] réflectance du sol [Termes IGN] rizière [Termes IGN] système d'information géographique [Termes IGN] variation saisonnière Résumé : (auteur) Quantification of regional methane (CH4) gas emission in the paddy fields is critical under climate warming. Mechanism models generally require numerous parameters while empirical models are too coarse. Based on the mechanism and structure of the widely used model CH4MOD, a GIS-based Regional CH4 Emission Calculation (GRMC) method was put forward by introducing multiple sources of remote sensing images, including MOD09A1, MOD11A2, MOD15A2H as well as local water management standards. The stress of soil moisture condition (f(water)) on CH4 emissions was quantified by calculating the redox potential (Eh) from days after flooding or falling dry. The f(water)-t curve was calculated under different exogenous organic matter addition. Combining the f(water)-t curve with local water management standards, the seasonal variation of f(water) was obtained. It was proven that f(water) was effective in reflecting the regulation role of soil moisture condition. The GRMC was tested at four Eddy Covariance (EC) sites: Nanchang (NC) in China, Twitchell (TWT) in the USA, Castellaro (CAS) in Italy and Cheorwon (CRK) in Korea and has been proven to well track the seasonal dynamics of CH4 emissions with R2 ranges of 0.738–0.848, RMSE ranges of 31.94–149.22 mg C/m2d and MBE ranges of −66.42- -14.79 mg C/m2d. The parameters obtained in Nanchang (NC) site in China were then applied to the Ganfu Plain Irrigation System (GFPIS), a typical rice planting area of China, to analyse the spatial-temporal variations of CH4 emissions. The total CH4 emissions of late rice in the GFPIS from 2001 to 2013 was in the range of 14.47–20.48 (103 t CH4-C). Ts caused spatial variation of CH4 production capacity, resulting in the spatial variability of CH4 emissions. Overall, the GRMC is effective in obtaining CH4 emissions from rice fields on a regional scale. Numéro de notice : A2023-015 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1016/j.scitotenv.2022.159917 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.1016/j.scitotenv.2022.159917 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102133 [article]

Amazon forest spectral seasonality is consistent across sensor resolutions and driven by leaf demography / Nathan B. Gonçalves in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 196 (February 2023) . - pp 93 - 104 Titre : Amazon forest spectral seasonality is consistent across sensor resolutions and driven by leaf demography Type de document : Article/Communication Auteurs : Nathan B. Gonçalves, Auteur ; Ricardo Dalagnol, Auteur ; Jin Wu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 93 - 104 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Amazonie [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] forêt tropicale [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-OLI [Termes IGN] image proche infrarouge [Termes IGN] image Terra-MODIS [Termes IGN] indice de végétation [Termes IGN] Leaf Area Index [Termes IGN] réflectance spectrale [Termes IGN] sécheresse [Termes IGN] variation saisonnière Résumé : (Auteur) Controversy surrounds the reported dry season greening of the Central Amazon forests based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS). As the solar zenith angle decreases during the dry season, it affects the sub-pixel shade content and artificially increases Near-infrared (NIR) reflectance and EVI. MODIS' coarse resolution also creates a challenge for cloud and terrain filtering. To reduce these artifacts and then validate MODIS seasonal spectral patterns we use 16 years of 1 km resolution MODIS-MAIAC (Multi-Angle Implementation of Atmospheric Correction) images, corrected to a nadir view and 45° solar zenith angle, together with an improved cloud filter. Then we show that the 30 m Landsat-8 Operational Land Imager (OLI) surface reflectance over two Landsat scenes provides independent evidence supporting the MODIS-MAIAC seasonality for EVI, NIR, and GCC (an additional important vegetation index, green chromatic coordinate). Our empirical method for controlling for sun-sensor geometry effects in Landsat scenes encompasses the use of seasonally distinct images that have similar solar zenith angles and cloud-free pixels on flat uplands having the same phase angle. We extended this validation to nine Amazon sub-basins comprising ∼546 Landsat-8 images. Our study shows that the dry-season green-up pattern observed by MODIS is corroborated by Landsat-8, and is independent of satellite data artifacts. To investigate the mechanisms driving these seasonal changes we further used Central Amazon tower-mounted RGB cameras providing a 4-year record at the Amazon Tall Tower (ATTO, 2°8′36″S, 59°0′2″W) and a 7-year record at the Manaus k34 tower (2°36′33″ S, 60°12′33″W) to obtain monthly upper canopy green leaf cover (a proxy for Leaf Area Index - LAI) and monthly leaf age class abundances (based on the age since leaf flushing, by crown). These were compared to seasonal patterns of GCC and EVI in small MODIS-MAIAC windows centered on each tower. MODIS-MAIAC GCC was positively correlated with newly flushed leaves (R2 = 0.76 and 0.44 at ATTO and k34, respectively). EVI correlated strongly with the abundance of mature leaves (R2 = 0.82 and 0.80) but was poorly correlated with LAI (R2 = 0.20 and 0.41, respectively). Therefore, seasonal spectral patterns in the Central Amazon are likely controlled by leaf age variation, not quantity of leaf area. Numéro de notice : A2023-065 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.12.001 Date de publication en ligne : 04/01/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.12.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102423 [article]

Generating Sentinel-2 all-band 10-m data by sharpening 20/60-m bands: A hierarchical fusion network / Jingan Wu in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 196 (February 2023) . - pp 16 - 31 Titre : Generating Sentinel-2 all-band 10-m data by sharpening 20/60-m bands: A hierarchical fusion network Type de document : Article/Communication Auteurs : Jingan Wu, Auteur ; Liupeng Lin, Auteur ; Chi Zhang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 16 - 31 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] affinage d'image [Termes IGN] approche hiérarchique [Termes IGN] bande spectrale [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] filtre passe-haut [Termes IGN] fusion d'images [Termes IGN] image à haute résolution [Termes IGN] image Sentinel-MSI Résumé : (Auteur) Earth observations from the Sentinel-2 mission have been extensively accepted in a variety of land services. The thirteen spectral bands of Sentinel-2, however, are collected at three spatial resolutions of 10/20/60 m, and such a difference brings difficulties to analyze multispectral imagery at a uniform resolution. To address this problem, we developed a hierarchical fusion network (HFN) to sharpen 20/60-m bands and generate Sentinel-2 all-band 10-m data. The deep learning architecture is used to learn the complex mapping between multi-resolution input and output data. Given the deficiency of previous studies in which the spatial information is inferred only from the fine-resolution bands, the proposed hierarchical fusion framework simultaneously leverages the self-similarity information from coarse-resolution bands and the spatial structure information from fine-resolution bands, to enhance the sharpening performance. Technically, the coarse-resolution bands are super-resolved by exploiting the information from themselves and then sharpened by fusing with the fine-resolution bands. Both 20-m and 60-m bands can be sharpened via the developed approach. Experimental results regarding visual comparison and quantitative assessment demonstrate that HFN outperforms the other benchmarking models, including pan-sharpening-based, model-based, geostatistical-based, and other deep-learning-based approaches, showing remarkable performance in reproducing explicit spatial details and maintaining original spectral features. Moreover, the developed model works more effectively than the other models over the heterogeneous landscape, which is usually considered a challenging application scenario. To sum up, the fusion model can sharpen Sentinel-2 20/60-m bands, and the created all-band 10-m data allows image analysis and geoscience applications to be authentically carried out at the 10-m resolution. Numéro de notice : A2023-063 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.12.017 Date de publication en ligne : 01/01/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.12.017 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102392 [article]

Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models / Xikun Hu in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)  

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PSSNet: Planarity-sensible Semantic Segmentation of large-scale urban meshes / Weixiao Gao in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)  

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L’altimétrie radar remonte les fleuves / Laurent Polidori in Géomètre, n° 2209 (janvier 2023)

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Decadal assessment of agricultural drought in the context of land use land cover change using MODIS multivariate spectral index time-series data / Thuong V. Tran in GIScience and remote sensing, vol 60 n° 1 (2023)  

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Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis / Haifa Tamiminia in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

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Detection of growth change of young forest based on UAV RGB images at single-tree level / Xiaocheng Zhou in Forests, vol 14 n° 1 (January 2023)  

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Estimating mangrove above-ground biomass at Maowei Sea, Beibu Gulf of China using machine learning algorithm with Sentinel-1 and Sentinel-2 data / Zhuomei Huang in Geocarto international, vol 38 n° inconnu ([01/01/2023])  

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Exploring the addition of airborne Lidar-DEM and derived TPI for urban land cover and land use classification and mapping / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)  

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Generation of high-resolution orthomosaics from historical aerial photographs using Structure-from-motion and Lidar data / Ji Won Suh in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)  

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A geometry-aware attention network for semantic segmentation of MLS point clouds / Jie Wan in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)  

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