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Discriminating pure Tamarix species and their putative hybrids using field spectrometer / Solomon G. Tesfamichael in Geocarto international, vol 37 n° 25 ([01/12/2022])
[article]
Titre : Discriminating pure Tamarix species and their putative hybrids using field spectrometer Type de document : Article/Communication Auteurs : Solomon G. Tesfamichael, Auteur ; Solomon W. Newete, Auteur ; Elhadi Adam, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 7733 - 7752 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Afrique du sud (état)
[Termes IGN] apprentissage automatique
[Termes IGN] canopée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] espèce exotique envahissante
[Termes IGN] essence indigène
[Termes IGN] Extreme Gradient Machine
[Termes IGN] feuille (végétation)
[Termes IGN] image Landsat-8
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 6
[Termes IGN] image Worldview
[Termes IGN] spectroradiomètre
[Termes IGN] Tamarix (genre)Résumé : (auteur) South Africa is home to a native Tamarix species, while two were introduced in the early 1900s to mitigate the effects of mining on soil. The introduced species have spread to other ecosystems resulting in ecological deteriorations. The problem is compounded by hybridization of the species making identification between the native and exotic species difficult. This study investigated the potential of remote sensing in identifying native, non-native and hybrid Tamarix species recorded in South Africa. Leaf- and canopy-level classifications of the species were conducted using field spectroradiometer data that provided two inputs: original hyperspectral data and bands simulated according to Landsat-8, Sentinel-2, SPOT-6 and WorldView-3. The original hyperspectral data yielded high accuracies for leaf- and plot-level discriminations (>90%), while promising accuracies were also obtained using Landsat-8, Sentinel-2 and Worldview-3 simulations (>75%). These findings encourage for investigating the performance of actual space-borne multispectral data in classifying the species. Numéro de notice : A2022-928 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2021.1983033 Date de publication en ligne : 27/09/2021 En ligne : https://doi.org/10.1080/10106049.2021.1983033 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102661
in Geocarto international > vol 37 n° 25 [01/12/2022] . - pp 7733 - 7752[article]Fusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands / Katrin Krzepek in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol 90 n° 6 (December 2022)
[article]
Titre : Fusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands Type de document : Article/Communication Auteurs : Katrin Krzepek, Auteur ; Jacob Schmidt, Auteur ; Dorota Iwaszczuk, Auteur Année de publication : 2022 Article en page(s) : pp 561 - 575 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage non-dirigé
[Termes IGN] aquifère
[Termes IGN] Bade-Wurtemberg (Allemagne)
[Termes IGN] bande C
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] fusion d'images
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Water Index
[Termes IGN] puits de carbone
[Termes IGN] seuillage d'image
[Termes IGN] théorie de Dempster-Shafer
[Termes IGN] tourbièreRésumé : (auteur) Peatlands as natural carbon sinks have a major impact on the climate balance and should therefore be monitored and protected. The hydrology of the peatland serves as an indicator of the carbon storage capacity. Hence, we investigate the question how suitable different remote sensing data are for monitoring the size of open water surface and the water table depth (WTD) of a peatland ecosystem. Furthermore, we examine the potential of combining remote sensing data for this purpose. We use C-band synthetic aperture radar (SAR) data from Sentinel-1 and multi-spectral data from Sentinel-2. The radar backscatter σ0, the normalized difference water index (NDWI) and the modified normalized difference water index (MNDWI) are calculated and used for consideration of the WTD and the lake size. For the measurement of the lake size, we implement and investigate the methods: random forest, adaptive thresholding and an analysis according to the Dempster–Shafer theory. Correlations between WTD and the remote sensing data σ0 as well as NDWI are investigated. When looking at the individual data sets the results of our case study show that the VH polarized σ0 data produces the clearest delineation of the peatland lake. However the adaptive thresholding of the weighted fusion image of σ0-VH, σ0-VV and MNDWI, and the random forest algorithm with all three data sets as input proves to be the most suitable for determining the lake area. The correlation coefficients between σ0/NDWI and WTD vary greatly and lie in ranges of low to moderate correlation. Numéro de notice : A2022-942 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s41064-022-00216-w Date de publication en ligne : 06/09/2022 En ligne : https://doi.org/10.1007/s41064-022-00216-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102876
in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science > vol 90 n° 6 (December 2022) . - pp 561 - 575[article]Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])
[article]
Titre : Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling Type de document : Article/Communication Auteurs : Saeid Janizadeh, Auteur Année de publication : 2022 Article en page(s) : pp 8273 - 8292 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] ArcGIS
[Termes IGN] bassin hydrographique
[Termes IGN] cartographie des risques
[Termes IGN] classification par arbre de décision
[Termes IGN] colinéarité
[Termes IGN] estimation bayesienne
[Termes IGN] Extreme Gradient Machine
[Termes IGN] inondation
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation spatiale
[Termes IGN] optimisation (mathématiques)
[Termes IGN] TéhéranRésumé : (auteur) The purpose of this investigation is to develop an optimal model to flood susceptibility mapping in the Kan watershed, Tehran, Iran. Therefore, in this study, three Bayesian optimization hyper-parameter algorithms including Upper confidence bound (UCB), Probability of improvement (PI) and Expected improvement (EI) in order to Extreme Gradient Boosting (XGB) machine learning model optimization and Extreme randomize tree (ERT) model for modeling flood hazard were used. In order to perform flood susceptibility mapping, 118 historic flood locations were identified and analyzed using 17 geo-environmental explanatory variables to predict flooding susceptibility. Flood locations data were divided into 70% for training and 30% for testing of models developed. The receiver operating characteristic (ROC) curve parameters were used to evaluate the performance of the models. The evaluation results based on the criterion area under curve (AUC) in the testing stage showed that the ERT and XGB models have efficiencies of 91.37% and 91.95%, respectively. The evaluation of the efficiency of Bayesian hyperparameters optimization methods on the XGB model also showed that these methods increase the efficiency of the XGB model, so that the model efficiency using these methods EI-XGB, POI-XGB and UCB-XGB based on the AUC in the testing stage were 95.89%, 96.87% and 96.38%, respectively. The results of the relative importance of the five models shows that the variables of elevation and distance from the river are the significant compared to other variables in predicting flood hazard in the Kan watershed. Numéro de notice : A2022-931 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2021.1996641 Date de publication en ligne : 29/10/2021 En ligne : https://doi.org/10.1080/10106049.2021.1996641 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102666
in Geocarto international > vol 37 n° 25 [01/12/2022] . - pp 8273 - 8292[article]Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) / Vahid Nasiri in Arabian Journal of Geosciences, vol 15 n° 24 (December 2022)
[article]
Titre : Integration of radar and optical Sentinel images for land use mapping in a complex landscape (case study: Arasbaran Protected Area) Type de document : Article/Communication Auteurs : Vahid Nasiri, Auteur ; Arnaud Le Bris , Auteur ; Ali Asghar Darvishsefat, Auteur ; Fardin Moradi, Auteur Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : n° 1759 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] aire protégée
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SARRésumé : (auteur) Considering the importance of accurate and up-to-date land use/cover (LULC) maps and in a situation of fast LULC changes, an accurate mapping of complex landscapes requires real-time high-resolution remote sensed data and powerful classification algorithms. The new ESA Copernicus satellites Sentinel-1 (S-1) and Sentinel-2 (S-2) have contributed to the effective monitoring of the Earth’s surface. This paper aims at assessing the potential of mono-temporal S-1 and S-2 satellite images and three common classification algorithms including maximum likelihood (ML), support vector machine (SVM), and random forest (RF) for LULC classification. The research methodology consists of a sequence of tasks including data collection and preprocessing, the extraction of texture and spectral features, the definition of several feature set configurations, classification, and accuracy assessment. Based on the results, using S-1 data alone leads to quite poor results, even though dual polarimetric C-band and texture features increased the classification accuracy. The S-2 data outperformed the S-1 data in terms of overall and class level accuracies. A combined use of S-1 and S-2 satellite images involving extracted features from both sources led to the best result for identifying all classes. This emphasizes the critical importance of using multi-modal datasets and different features in the LULC classification. Among classification algorithms, the SVM led to the highest accuracies irrespective of the dataset. To sum it up, according to the applied methodology and results, S-1 and S-2 data can provide optimal and up-to-date information for LULC mapping using non-parametric classifiers as SVM or RF. Numéro de notice : A2022-699 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12517-022-11035-z Date de publication en ligne : 07/12/2022 En ligne : https://doi.org/10.1007/s12517-022-11035-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102253
in Arabian Journal of Geosciences > vol 15 n° 24 (December 2022) . - n° 1759[article]Accuracy of vacant housing detection models: An empirical evaluation using municipal and national census datasets / Kanta Sayuda in Transactions in GIS, vol 26 n° 7 (November 2022)
[article]
Titre : Accuracy of vacant housing detection models: An empirical evaluation using municipal and national census datasets Type de document : Article/Communication Auteurs : Kanta Sayuda, Auteur ; Euijung Hong, Auteur ; Yuki Akiyama, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3003 - 3027 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage automatique
[Termes IGN] distribution spatiale
[Termes IGN] Extreme Gradient Machine
[Termes IGN] géocodage
[Termes IGN] immobilier (secteur)
[Termes IGN] Japon
[Termes IGN] logementRésumé : (auteur) In Japan, the rise in vacant housing has created the need to develop quick, effective, and inexpensive methods to detect the spatial distribution of vacant housing at the municipal level. However, due to incomplete and inaccessible data, the change in the accuracy of the vacant housing detection model must be evaluated while accounting for the limited data. Therefore, this study compares the performance of vacant housing detection models for different data combinations (Basic Resident Register; building registration, water usage, and national census) by considering Wakayama City, Japan, as the case study setting. Three main findings emerged: (1) the contribution of the data to the accuracy varies with the combination of datasets and metrics; (2) even if specific municipal data are unavailable, it is possible to acquire a similar accuracy by combining other data; and (3) the missing value contributes to the vacant housing detection rather than the feature value itself. Numéro de notice : A2022-887 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12992 Date de publication en ligne : 31/10/2022 En ligne : https://doi.org/10.1111/tgis.12992 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102217
in Transactions in GIS > vol 26 n° 7 (November 2022) . - pp 3003 - 3027[article]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)PermalinkFeatures predisposing forest to bark beetle outbreaks and their dynamics during drought / M. Müller in Forest ecology and management, vol 523 (November-1 2022)PermalinkA deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas / Hossein Pourazar in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkLand use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information / Ozlem Akar in Geocarto international, vol 37 n° 22 ([10/10/2022])PermalinkApplication of a graph convolutional network with visual and semantic features to classify urban scenes / Yongyang Xu in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)PermalinkCanopy self-replacement in Pinus sylvestris rear-edge populations following drought-induced die-off and mortality / Jordi Margalef- Marrase in Forest ecology and management, vol 521 (October-1 2022)PermalinkComparison of layer-stacking and Dempster-Shafer theory-based methods using Sentinel-1 and Sentinel-2 data fusion in urban land cover mapping / Dang Hung Bui in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkDetecting overmature forests with airborne laser scanning (ALS) / Marc Fuhr in Remote sensing in ecology and conservation, vol 8 n° 5 (October 2022)PermalinkIdentify urban building functions with multisource data: a case study in Guangzhou, China / Yingbin Deng in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)PermalinkModelling and prediction of GNSS time series using GBDT, LSTM and SVM machine learning approaches / Wenzong Gao in Journal of geodesy, vol 96 n° 10 (October 2022)Permalink