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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)  

Public [article]  inScience of the total environment > vol 853 (December 2022) . - n° 158374 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] 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 eaux Résumé : (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 [article]

Climate envelope analyses suggests significant rearrangements in the distribution ranges of Central European tree species / Gàbor Illés in Annals of Forest Science, vol 79 n° 1 (2022)  

Public [article]  inAnnals of Forest Science > vol 79 n° 1 (2022) . - n° 35 Titre : Climate envelope analyses suggests significant rearrangements in the distribution ranges of Central European tree species Type de document : Article/Communication Auteurs : Gàbor Illés, Auteur ; Norbert Móricz, Auteur Année de publication : 2022 Article en page(s) : n° 35 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] adaptation (biologie) [Termes IGN] bioclimatologie [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] Europe centrale [Termes IGN] Fagus sylvatica [Termes IGN] gestion forestière durable [Termes IGN] INSPIRE [Termes IGN] modèle dynamique [Termes IGN] modélisation de la forêt [Termes IGN] Picea abies [Termes IGN] Quercus cerris [Termes IGN] Quercus pubescens [Termes IGN] Quercus sessiliflora [Termes IGN] répartition géographique [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Key message: Climate envelope analysis of nine tree species shows that Fagus sylvatica L. and Picea abies H. Karst could lose 58% and 40% of their current distribution range. Quercus pubescens Willd and Quercus cerris L. may win areas equal with 47% and 43% of their current ranges. The ratio of poorly predictable areas increases by 105% in southern and south-eastern Europe. Context: Climate change requires adaptive forest management implementations. To achieve climate neutrality, we have to maintain and expand forest areas. Impact assessments have great importance. Aims: The study estimates the potential climate envelopes of nine European tree species for a past period (1961–1990) and for three future periods (2011–2040, 2041–2070, 2071–2100) under two emission scenarios (RCP4.5 and RCP8.5) based on the current species distribution. Methods: Climate envelopes were estimated simultaneously using the random forest method. Multi-resolution segmentation was used to determine the climatic characteristics of each species and their combinations. Models were limited to the geographical area within which the climatic conditions correspond to the climatic range of the training areas. Results: Results showed remarkable changes in the extent of geographic areas of all the investigated species’ climate envelopes. Many of the tree species of Central Europe could lose significant portions of their distribution range. Adhering to the shift in climate, these tree species shift further north as well as towards higher altitudes. Conclusion: European forests face remarkable changes, and the results support climate envelope modelling as an important tool that provides guidelines for climate adaptation to identify threatened areas or to select source and destination areas for reproductive material. Numéro de notice : A2022-631 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s13595-022-01154-8 Date de publication en ligne : 09/08/2022 En ligne : https://doi.org/10.1186/s13595-022-01154-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101395 [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)  

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]

Features predisposing forest to bark beetle outbreaks and their dynamics during drought / M. Müller in Forest ecology and management, vol 523 (1 November 2022)  

Public [article]  inForest ecology and management > vol 523 (1 November 2022) . - n° 120480 Titre : Features predisposing forest to bark beetle outbreaks and their dynamics during drought Type de document : Article/Communication Auteurs : M. Müller, Auteur ; P.O. Olsson, Auteur ; Lars Eklundh, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse des risques [Termes IGN] canopée [Termes IGN] caractérisation [Termes IGN] changement climatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données météorologiques [Termes IGN] humidité du sol [Termes IGN] peuplement mélangé [Termes IGN] Picea abies [Termes IGN] Scolytinae [Termes IGN] sécheresse [Termes IGN] Suède [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Climate change is estimated to increase the risk of the bark beetle (Ips typographus L.) mass outbreaks in Norway Spruce (Picea abies (L.) Karst) forests. Habitats that are thermally suitable for bark beetles may expand, and an increase in the frequency and intensity of droughts can promote drought stress on host trees. Drought affects tree vigor and in unison with environmental features it influences the local predisposition risk of forest stands to bark beetle attacks. We aimed to study how various environmental features influence the risk of bark beetle attacks during a drought year and the following years with more normal weather conditions but with higher bark beetle populations. We included features representing local forest stand attributes, topography, soil type and wetness, the proximity of clear-cuts and previous bark beetle attacks, and a machine learning algorithm (random forest) was applied to study the variation of predisposition risk across a 48,600 km2 study area in SE Sweden. Forest stands with increased risk of bark beetle attack were distinguished with high accuracy both during drought and in normal weather conditions. The results show that during both study periods, spruce and mixed coniferous forests had elevated risk of attack, while forests with a mix of deciduous and coniferous trees had a lower risk. Forests with high average canopy height were strongly predisposed to bark beetle attacks. However, during the drought year risk was more similar between stands with lower and higher canopy height, suggesting that during drought periods younger trees can be predisposed to bark beetle attacks. The importance of soil moisture and position within the local landscape were highlighted as important features during the drought year. Identifying areas with increased risk, supported by information on how environmental features control the predisposition risk during drought, could aid adaptation strategies and forest management intervention efforts. We conclude that geospatial data and machine learning have the potential to further support the digitalization of the forest industry, facilitating development of methods capable to quantify importance and dynamics of environmental features controlling the risk in local context. Corresponding methods could help to direct management actions more effectively and offer information for decision-making in changing climate. Numéro de notice : A2022-731 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120480 Date de publication en ligne : 07/09/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120480 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101687 [article]

A 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])  

Public [article]  inGeocarto international > vol 37 n° 23 [15/10/2022] . - pp 6695 - 6712 Titre : A deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas Type de document : Article/Communication Auteurs : Hossein Pourazar, Auteur ; Farhad Samadzadegan, Auteur ; Farzaneh Dadrass Javan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 6695 - 6712 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] alignement des données [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] image proche infrarouge [Termes IGN] image RVB [Termes IGN] modèle numérique de surface [Termes IGN] orthophotoplan numérique [Termes IGN] zone urbaine Résumé : (auteur) In this paper, a deep convolutional neural network (CNN) is developed to classify the Unmanned Aerial Vehicle (UAV) derived multispectral imagery and normalized digital surface model (DSM) data in urban areas. For this purpose, a multi-input deep CNN (MIDCNN) architecture is designed using 11 parallel CNNs; 10 deep CNNs to extract the features from all possible triple combinations of spectral bands as well as one deep CNN dedicated to the normalized DSM data. The proposed method is compared with the traditional single-input (SI) and double-input (DI) deep CNN designations and random forest (RF) classifier, and evaluated using two independent test datasets. The results indicate that increasing the CNN layers parallelly augmented the classifier’s generalization and reduced overfitting risk. The overall accuracy and kappa value of the proposed method are 95% and 0.93, respectively, for the first test dataset, and 96% and 0.94, respectively, for the second test data set. Numéro de notice : A2022-749 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1959655 Date de publication en ligne : 04/08/2021 En ligne : https://doi.org/10.1080/10106049.2021.1959655 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101741 [article]

Land 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])  

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Application 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)  

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Comparison of methods for the automatic classification of forest habitat types in the Southern Alps : Application to ecological data from the French national forest inventory / Charlotte Labit in Biodiversity & Conservation, vol 31 n° inconnu (2022)  

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A comparative assessment of modeling groundwater vulnerability using DRASTIC method from GIS and a novel classification method using machine learning classifiers / Qasim Khan in Geocarto international, vol 37 n° 20 ([20/09/2022])  

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Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood / Amid Darabi in Geocarto international, vol 37 n° 19 ([15/09/2022])  

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Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics / Jakob Wernicke in Remote sensing of environment, vol 279 (15 September 2022)  

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Analytical method for high-precision seabed surface modelling combining B-spline functions and Fourier series / Tyler Susa in Marine geodesy, vol 45 n° 5 (September 2022)  

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Forest tree species classification based on Sentinel-2 images and auxiliary data / Haotian You in Forests, vol 13 n° 9 (september 2022)  

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Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators / Luis Izquierdo-Horna in Computers, Environment and Urban Systems, vol 96 (September 2022)  

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Mapping individual abandoned houses across cities by integrating VHR remote sensing and street view imagery / Shengyuan Zou in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)  

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