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Urban infrastructure expansion and artificial light pollution degrade coastal ecosystems, increasing natural-to-urban structural connectivity / Moisés A. Aguilera in Landscape and Urban Planning, vol 229 (January 2023)
[article]
Titre : Urban infrastructure expansion and artificial light pollution degrade coastal ecosystems, increasing natural-to-urban structural connectivity Type de document : Article/Communication Auteurs : Moisés A. Aguilera, Auteur ; Maria Gracia González, Auteur Année de publication : 2023 Article en page(s) : n° 104609 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] ArcGIS
[Termes IGN] Chili
[Termes IGN] croissance urbaine
[Termes IGN] dégradation de l'environnement
[Termes IGN] écosystème
[Termes IGN] étalement urbain
[Termes IGN] habitat (nature)
[Termes IGN] intensité lumineuse
[Termes IGN] littoral
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] paysage urbain
[Termes IGN] pollution lumineuse
[Termes IGN] urbanismeRésumé : (auteur) Urbanization is provoking habitat loss and fragmentation, driving rapid landscape transformation worldwide. Remnant habitats in urban areas can be especially prone to degradation by human activities at short time scales, and poor planning during urban expansion can erode their structural and functional connectivity. Foredunes in particular are threatened significantly by human activities, including coastal urban infrastructure expansion, by bulldozing them and/or by interrupting their continuity across the shoreline, and also by associated light pollution. However, there is still scarce quantification about how urban processes determine changes in remnant habitat extent and modify the configuration of structural connectivity in coastal urban settings. Using an expanding conurbation located in north-central Chile (∼29°S) as model system, we investigated the rate of coastal foredune loss and spatial fragmentation due to urban expansion, and the change in the type of structural connectivity, i.e. with other natural habitats vs with urban infrastructure. Based on map analyses of structural connectivity among habitats and with urban infrastructure through time, we estimated foredune habitat extent and fragmentation and their shared border with other habitats and built infrastructure during two time intervals, 2010–2015 and 2015–2020. Distribution and intensity of light pollution on present foredunes were also quantified in situ through field sampling. We found 36 % decline in foredune area and increase in their connection with urban infrastructure. Urban wetlands and parallel dunes also experienced persistent area loss and increase in connection with urban infrastructure. Light pollution was intense in the foredune-beach ecotone. Given the rapid erosion of functional and structural connectivity of natural habitats, it becomes imperious to halt the reduction of remnant habitats and ecotones, and improve natural corridors in urban settings. Numéro de notice : A2023-127 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.landurbplan.2022.104609 Date de publication en ligne : 17/10/2022 En ligne : https://doi.org/10.1016/j.landurbplan.2022.104609 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102507
in Landscape and Urban Planning > vol 229 (January 2023) . - n° 104609[article]Using Google Earth Engine to classify unique forest and agroforest classes using a mix of Sentinel 2a spectral data and topographical features: a Sri Lanka case study / W.D.K.V. Nandasena in Geocarto international, vol 38 n° inconnu ([01/01/2023])
[article]
Titre : Using Google Earth Engine to classify unique forest and agroforest classes using a mix of Sentinel 2a spectral data and topographical features: a Sri Lanka case study Type de document : Article/Communication Auteurs : W.D.K.V. Nandasena, Auteur ; Lars Brabyn, Auteur ; Silvia Serrao-Neumanna, Auteur Année de publication : 2023 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] forêt
[Termes IGN] Google Earth Engine
[Termes IGN] image Sentinel-MSI
[Termes IGN] matrice de co-occurrence
[Termes IGN] occupation du sol
[Termes IGN] Sri LankaRésumé : (auteur) Global land cover classifications may lead to the loss of important local and national nuances such as forest and agroforestry classes. These classes are important to local contexts because they contribute to sustainable land management systems. This paper demonstrates the application of Sentinel-2A satellite images, elevation data, and the Google Earth Engine platform to generate more detailed, specialist land cover classification for forestry classes important in Sri Lanka deriving ten spectral, 16 textural, and three topographical features from the input datasets. The random forest classification model discriminates vegetation types as forest, forest plantations, shrub, grassland, home garden, and cultivation with an overall accuracy of 94% and kappa value of 0.91. Results indicate the elevation feature contributes the most to discriminate forest and agroforestry classes, and red band (664.6 nm) textural metrics derived from grey-level co-occurrence matrix analysis are more useful for separating the home garden from other land cover classes. Numéro de notice : A2023-094 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1080/10106049.2021.2022010 Date de publication en ligne : 29/12/2021 En ligne : https://doi.org/10.1080/10106049.2021.2022010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99617
in Geocarto international > vol 38 n° inconnu [01/01/2023][article]Wavelet-like denoising of GNSS data through machine learning. Application to the time series of the Campi Flegrei volcanic area (Southern Italy) / Rolando Carbonari in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)
[article]
Titre : Wavelet-like denoising of GNSS data through machine learning. Application to the time series of the Campi Flegrei volcanic area (Southern Italy) Type de document : Article/Communication Auteurs : Rolando Carbonari, Auteur ; Umberto Riccardi, Auteur ; Prospero De Martino, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2187271 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] caldeira
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] déformation de la croute terrestre
[Termes IGN] données GNSS
[Termes IGN] filtrage du bruit
[Termes IGN] Naples
[Termes IGN] relief volcanique
[Termes IGN] risque naturel
[Termes IGN] série temporelle
[Termes IGN] surveillance géologique
[Termes IGN] transformation en ondelettesRésumé : (auteur) The great potential of the Global Navigation Satellite System (GNSS) in monitoring ground deformation is widely recognized. As with other geophysical data, GNSS time series can be significantly noisy, hiding elusive ground deformation signals. Several denoising techniques have been proposed to improve the signal-to-noise ratio over the years. One of the most effective denoising techniques has been proved to be multi-resolution decomposition through the discrete wavelet transform. However, wavelet analysis requires long data sets to be effective, as well as long computation times, that hinder its use as a real or near real-time monitoring tool. We propose training by a Convolutional Neural Network (CNN) to perform the equivalent of wavelet analysis to overcome these limitations. Once trained, the CNN model provides answers within seconds, making it feasible as a real-time data analysis tool. Our Machine Learning algorithm is tested on daily GNSS time series collected in the Campi Flegrei caldera (Southern Italy), which is a highly volcanic risk area. Without significant gaps, the retrieved RMSE and R2 values vary in the ranges 0.65–0.98 and 0.06–0.52 cm, respectively. These results are encouraging, as they hint at the possibility of applying this methodology in more effective real-time monitoring solutions for active volcanoes. Numéro de notice : A2023-180 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/19475705.2023.2187271 Date de publication en ligne : 10/03/2023 En ligne : https://doi.org/10.1080/19475705.2023.2187271 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102949
in Geomatics, Natural Hazards and Risk > vol 14 n° 1 (2023) . - n° 2187271[article]Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models / Saadia Sultan Wahlaa in Geocarto international, vol 37 n° 27 ([20/12/2022])
[article]
Titre : Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models Type de document : Article/Communication Auteurs : Saadia Sultan Wahlaa, Auteur ; Jamil Hasan Kazmi, Auteur ; Alireza Sharifi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] changement climatique
[Termes IGN] classification par arbre de décision
[Termes IGN] évapotranspiration
[Termes IGN] Indice de précipitations antérieures
[Termes IGN] modèle de simulation
[Termes IGN] Pakistan
[Termes IGN] prévision météorologique
[Termes IGN] sécheresseRésumé : (auteur) Droughts may inflict significant damage to agricultural and water supplies, resulting in substantial financial losses as well as the death of people and livestock. This study intends to anticipate droughts by studying the changes of an acceptable index using appropriate climatic factors. This study was divided into three phases, first being the determination of the Standardized Precipitation Evapotranspiration (SPEI) index for the Cholistan, Punjab, Pakistan area based on a dataset spanning 1980 to 2020. The indices are calculated at different monthly intervals which could to predict short-term periods for the Cholistan in Pakistan, we selected two distinctive time periods of one month (SPEI–1) and three months (SPEI–3). The second phase involved dividing the data into three sample sizes, which were used for training data from 1980 to 2010, testing data from 2011 to 2015 and validation data from 2016 to 2020. The utilization of the random forest (RF) algorithm to train and evaluate the data using a variety of climate variables e.g. potential evapotranspiration, rainfall, vapor pressure cloud cover, and mean, minimum and maximum, temperature. The final phase was to analyze the performance of the model based on statistical metrics and drought classes. Based on these considerations, statistical measures, such as the Coefficient of Determination (R2) and the Root Mean Square Error (RMSE) approach, were used to evaluate the performance of the test group throughout the testing period. The model's performance revealed the satisfactory results with R2 values of 0.80 and 0.78, for SPEI–1 and SPEI–3 situations, respectively. Following the data analysis, it was discovered that the validation period had a receiving operating curve and area under the Curve (ROC-AUC) of 0.87 for the SPEI–1 case and 0.85 for the SPEI–3 case. In this context, the results indicate that the SPEI may be useful as a prediction tool for drought prediction and the performances the RF model was suitable for both timescales. However, a more rigorous analysis with a larger dataset or a combination of datasets from different areas might be more beneficial for generalization over more extended time periods provide additional insights. Numéro de notice : A2022-934 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2093411 Date de publication en ligne : 30/06/2022 En ligne : https://doi.org/10.1080/10106049.2022.2093411 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102672
in Geocarto international > vol 37 n° 27 [20/12/2022] . - pp[article]Automatic detection of suspected sewage discharge from coastal outfalls based on Sentinel-2 imagery / Yuxin Wang in Science of the total environment, vol 853 (December 2022)
[article]
Titre : Automatic detection of suspected sewage discharge from coastal outfalls based on Sentinel-2 imagery Type de document : Article/Communication Auteurs : Yuxin Wang, Auteur ; Xianqiang He, Auteur ; Yan Bai, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 158374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par nuées dynamiques
[Termes IGN] couleur de l'océan
[Termes IGN] détection automatique
[Termes IGN] eau usée
[Termes IGN] image Sentinel-MSI
[Termes IGN] littoral
[Termes IGN] perturbation écologique
[Termes IGN] qualité des eauxRésumé : (auteur) Terrestrial pollution has a great impact on the coastal ecological environment, and widely distributed coastal outfalls act as the final gate through which pollutants flow into rivers and oceans. Thus, effectively monitoring the water quality of coastal outfalls is the key to protecting the ecological environment. Satellite remote sensing provides an attractive way to monitor sewage discharge. Selecting the coastal areas of Zhejiang Province, China, as an example, this study proposes an innovative method for automatically detecting suspected sewage discharge from coastal outfalls based on high spatial resolution satellite imageries from Sentinel-2. According to the accumulated in situ observations, we established a training dataset of water spectra covering various optical water types from satellite-retrieved remote sensing reflectance (Rrs). Based on the clustering results from unsupervised classification and different spectral indices, a random forest (RF) classification model was established for the optical water type classification and detection of suspected sewage. The final classification covers 14 optical water types, with type 12 and type 14 corresponding to the high eutrophication water type and suspected sewage water type, respectively. The classification result of model training datasets exhibited high accuracy with only one misclassified sample. This model was evaluated by historical sewage discharge events that were verified by on-site observations and demonstrated that it could successfully recognize sewage discharge from coastal outfalls. In addition, this model has been operationally applied to automatically detect suspected sewage discharge in the coastal area of Zhejiang Province, China, and shows broad application value for coastal pollution supervision, management, and source analysis. Numéro de notice : A2022-859 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scitotenv.2022.158374 Date de publication en ligne : 28/08/2022 En ligne : https://doi.org/10.1016/j.scitotenv.2022.158374 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102135
in Science of the total environment > vol 853 (December 2022) . - n° 158374[article]Consistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows / Pramaditya Wicaksono in Geocarto international, vol 37 n° 27 ([20/12/2022])PermalinkGeospatial modelling of overlapping habitats for identification of tiger corridor networks in the Terai Arc landscape of India / Nupur Rautela in Geocarto international, vol 37 n° 27 ([20/12/2022])PermalinkBayesian inference on the initiation phase of the 2014 Iquique, Chile, earthquake / Cédric Twardzik in Earth and planetary science letters, vol 600 (15 December 2022)PermalinkInteractive effects of abiotic factors and biotic agents on Scots pine dieback: A multivariate modeling approach in southeast France / Jean Lemaire in Forest ecology and management, vol 526 (December-15 2022)PermalinkAbove ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)PermalinkAn automated approach for clipping geographic data before projection that maintains data integrity and minimizes distortion for virtually any projection method / Jim Graham in Cartographica, Vol 57 n° 4 (December 2022)PermalinkAssessment of groundwater potential using multi-criteria decision analysis and geoelectrical surveying / Marzieh Shabani in Geo-spatial Information Science, vol 25 n° 4 (December 2022)PermalinkAutomatic registration method of multi-source point clouds based on building facades matching in urban scenes / Yumin Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)PermalinkAutomatic registration of point cloud and panoramic images in urban scenes based on pole matching / Yuan Wang in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)PermalinkBayesian hyperspectral image super-resolution in the presence of spectral variability / Fei Ye in IEEE Transactions on geoscience and remote sensing, vol 60 n° 12 (December 2022)Permalink