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The effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events / Sidgley Camargo de Andrade in International journal of geographical information science IJGIS, vol 36 n° 6 (June 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 6 (June 2022) . - pp 1140 - 1165 Titre : The effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events Type de document : Article/Communication Auteurs : Sidgley Camargo de Andrade, Auteur ; João Porto de Albuquerque, Auteur ; Camilo Restrepo-Estrada, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1140 - 1165 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] auto-régression [Termes IGN] distribution spatiale [Termes IGN] données issues des réseaux sociaux [Termes IGN] données socio-économiques [Termes IGN] hétérogénéité spatiale [Termes IGN] mobilité urbaine [Termes IGN] modélisation spatio-temporelle [Termes IGN] pluie [Termes IGN] précipitation [Termes IGN] Sao Paulo [Termes IGN] Twitter Résumé : (auteur) Although it is acknowledged that urban inequalities can lead to biases in the production of social media data, there is a lack of studies which make an assessment of the effects of intra-urban movements in real-world urban analytics applications, based on social media. This study investigates the spatial heterogeneity of social media with regard to the regular intra-urban movements of residents by means of a case study of rainfall-related Twitter activity in São Paulo, Brazil. We apply a spatial autoregressive model that uses population and income as covariates and intra-urban mobility flows as spatial weights to explain the spatial distribution of the social response to rainfall events in Twitter vis-à-vis rainfall radar data. Results show high spatial heterogeneity in the response of social media to rainfall events, which is linked to intra-urban inequalities. Our model performance (R2=0.80) provides evidence that urban mobility flows and socio-economic indicators are significant factors to explain the spatial heterogeneity of thematic spatiotemporal patterns extracted from social media. Therefore, urban analytics research and practice should consider not only the influence of socio-economic profile of neighborhoods but also the spatial interaction introduced by intra-urban mobility flows to account for spatial heterogeneity when using social media data. Numéro de notice : A2022-405 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1957898 Date de publication en ligne : 03/08/2021 En ligne : https://doi.org/10.1080/13658816.2021.1957898 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100717 [article]

Classification of Eucalyptus plantation Site Index (SI) and Mean Annual Increment (MAI) prediction using DEM-based geomorphometric and climatic variables in Brazil / Aliny Aparecida Dos Reis in Geocarto international, vol 37 n° 5 ([01/05/2022])  

Public [article]  inGeocarto international > vol 37 n° 5 [01/05/2022] . - pp 1256 - 1273 Titre : Classification of Eucalyptus plantation Site Index (SI) and Mean Annual Increment (MAI) prediction using DEM-based geomorphometric and climatic variables in Brazil Type de document : Article/Communication Auteurs : Aliny Aparecida Dos Reis, Auteur ; Steven E. Franklin, Auteur ; Fausto Weimar Acerbi Júnior, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1256 - 1273 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] Brésil [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données météorologiques [Termes IGN] Eucalyptus (genre) [Termes IGN] géomorphométrie [Termes IGN] MNS SRTM [Termes IGN] plantation forestière [Termes IGN] rendement Résumé : (Auteur) Digital elevation model (DEM) data were used with climate data to estimate productivity in 19 Eucalyptus plantations in Minas Gerais state, Brazil. Typically, plantation and individual stand growth and productivity estimates, such as Site Index (SI) and Mean Annual Increment (MAI), are based on field measures of height, tree diameter and age. Using a Random Forest modelling approach, SI and MAI were related to: (i) DEM-based geomorphometric variables and (ii) WorldClim historical macro-climatic measures. Three operational SI classes (high, medium and low productivity) in 180 stands were mapped with an overall accuracy of 91.6%. Medium and high productivity sites were the most accurately classified. Low productivity sites had 76.5% producer’s accuracy and 92.9% user’s accuracy, and were the most extensive in the study area. Such sites are considered of high importance from a plantation management perspective since additional forestry operations are likely required to address low productivity and growth. Numéro de notice : A2022-275 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1778103 Date de publication en ligne : 19/06/2020 En ligne : https://doi.org/10.1080/10106049.2020.1778103 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100782 [article]

Monthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning / Feng Zhao in Remote sensing of environment, vol 269 (February 2022)  

Public [article]  inRemote sensing of environment > vol 269 (February 2022) . - n° 112822 Titre : Monthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning Type de document : Article/Communication Auteurs : Feng Zhao, Auteur ; Rui Sun, Auteur ; Liheng Zhong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112822 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Californie (Etats-Unis) [Termes IGN] carte thématique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déboisement [Termes IGN] image Sentinel-SAR [Termes IGN] récolte de bois [Termes IGN] Rondonia (Brésil) [Termes IGN] série temporelle [Termes IGN] surveillance forestière Résumé : (auteur) Compared with disturbance maps produced at annual or multi-year time steps, monthly mapping of forest harvesting can provide more temporal details needed for studying the socio-economic drivers (e.g., differentiating salvage logging and slash-and-burn from other timber harvesting) of harvesting and characterizing the associated intra-annual carbon and hydrological dynamics. Frequent cloud cover limits the application of optical remote sensing in timely mapping of forest changes. The freely available Sentinel-1 synthetic aperture radar (SAR) sensor provides an unprecedented opportunity to achieve more frequent mapping of forest harvesting than ever before (i.e., at monthly interval). The unique landscape pattern of forest harvesting from Sentienl-1 data (i.e., how a harvested patch contrasts to surrounding intact forests) holds critical information for harvesting mapping but have not been fully explored. In this study, we propose a deep learning-based (i.e., U-Net) approach using the landscape pattern from Sentinel-1 data to produce monthly maps of forest harvesting in two deforestation hotspots - California, USA and Rondônia, Brazil – for as long as three years. Our results show that (1) our proposed approach is reliable (mean F1 scores (the geometric mean of user's and producer's accuracies) 0.74–0.78; mean IoU (the area of intersection over union between the prediction part and target part) 0.59–0.65) for monthly forest harvesting mapping with Sentinel-1 data, outperforming the traditional object-based approach (0.38–0.43 in IoU). The varying harvesting pattern from Sentinel-1 data can be recognized by the U-Net bottleneck block as whole entities, which is the key advantage of our proposed approach; (2) multi-temporal SAR filtering is helpful for improving the accuracies of our proposed approach (increased F1 and IoU for 0.04 and 0.06, respectively); (3) our proposed model can be trained using samples collected during a particular time period over one location and be fine-tuned using sparse local samples from a new area to achieve optimal performance, and hence can greatly reduce training data collection effort when applied to new study sites; (4) forest harvesting maps produced using our approach revealed substantial variations in monthly harvesting activities: in Rondônia, most of the forest harvest occurred in July/August (the dry season) and about 14% of the dry season harvesting were followed by fires (i.e., slash-and-burn); in California, the rates of forest harvesting were relatively stable, but abnormally high values could occur due to salvage logging after big fires. Our novel approach for mapping forest harvesting at monthly interval represents an important step towards timely monitoring of forest harvesting and assisting stakeholders in developing sustainable strategy of forest management, especially for regions with frequent cloud cover. Numéro de notice : A2022-078 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112822 Date de publication en ligne : 08/12/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112822 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99745 [article]

Variable selection for estimating individual tree height using genetic algorithm and random forest / Evandro Nunes Miranda in Forest ecology and management, vol 504 (15 January 2022)  

Public [article]  inForest ecology and management > vol 504 (15 January 2022) . - n° 119828 Titre : Variable selection for estimating individual tree height using genetic algorithm and random forest Type de document : Article/Communication Auteurs : Evandro Nunes Miranda, Auteur ; Bruno Henrique Groenner Barbosa, Auteur ; Sergio Henrique Godinho Silva, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 119828 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage automatique [Termes IGN] Brésil [Termes IGN] classification par algorithme génétique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] hauteur des arbres [Termes IGN] modélisation de la forêt [Termes IGN] optimisation (mathématiques) [Vedettes matières IGN] Foresterie Résumé : (auteur) Tree height is an important trait in forest science and is highly associated with the site quality from which the trees are measured. However, other factors, such as competition and species interaction, may yield better estimates for individual tree height when taken into account, but these variables have so far been challenging in model fitting. We propose a hybrid approach using genetic algorithms for variables selection and a machine learning algorithm (random forest) for fitting models of individual tree heights. We compare our proposed hybrid method with a mixed-effects model and random forest model using a dataset of 5,608 trees and 189 environmental variables (forest inventory-based variables, soil, topographic, climate, spectral, and geographic) from sites in southeastern Brazil. The tree height models were evaluated using the coefficient of determination, absolute bias, and root means square error (RMSE) based on the validation of dataset performance. The optimal set of variables of the proposed method include the ratio of diameter at breast height to quadratic mean diameter, distance independent competition index, dominant height, the soil silt and boron content. Our findings showed that the proposed hybrid method achieved an accuracy comparable with other methodologies in estimating the total height of the individual trees, and such a modelling approach could have broader applications in forestry and ecological science where a studied response trait has a large number of potential explanatory variables. Numéro de notice : A2022-021 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2021.119828 Date de publication en ligne : 06/11/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119828 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99216 [article]

Building a collaborative online catalogue of geoportals in Brazil / Eduardo Silverio da Silva in Boletim de Ciências Geodésicas, vol 27 n° 4 ([01/12/2021])  

Public [article]  inBoletim de Ciências Geodésicas > vol 27 n° 4 [01/12/2021] . - 16 p. Titre : Building a collaborative online catalogue of geoportals in Brazil Type de document : Article/Communication Auteurs : Eduardo Silverio da Silva, Auteur ; Silvana Philippi Camboim, Auteur Année de publication : 2021 Article en page(s) : 16 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] Brésil [Termes IGN] catalogue de données localisées [Termes IGN] géoportail [Termes IGN] infrastructure nationale des données localisées [Termes IGN] recherche d'information géographique [Termes IGN] WebSIG Résumé : (auteur) It is currently possible to account for several institutions with geographic data shared through the INDE portal, with more than half of it being from federal jurisdiction. However, there are subnational geoportals not integrated with this infrastructure, which is difficult to quantify. Therefore, the research problem of this study is finding the state of subnational geographic viewers’ availability, with the general objective of producing a Brazilian panorama and to identify factors that facilitate this availability. A research methodology based on different sources was applied in 27 states and 999 municipalities. As a result, we identified 17 regional, 82 state, and 274 municipal geoportals, with the highest concentration in the South and Southeast regions and lowest in the Northern region. In order to find factors related to geoportals availability, twenty characteristics of each municipality were collected, and Pearson coefficients were calculated, revealing significant correlations for population, economic and tax factors, and non-significant correlations for location factors. This acquired information is essential for the community and must be kept up to date. For this, an online collaborative map based on free software was created, allowing access without registration for data visualization and the registration of users for sending updates to the map. Numéro de notice : A2021-960 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1590/s1982-21702021000400026 Date de publication en ligne : 17/12/2021 En ligne : https://doi.org/10.1590/s1982-21702021000400026 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100072 [article]

The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space / Renato César Dos santos in Applied geomatics, vol 13 n° 4 (December 2021)  

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Automatic tuning of segmentation parameters for tree crown delineation with VHR imagery / Camile Sothe in Geocarto international, vol 36 n° 19 ([01/11/2021])  

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A CNN-based approach for the estimation of canopy heights and wood volume from GEDI waveforms / Ibrahim Fayad in Remote sensing of environment, vol 265 (November 2021)  

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Tidal flood area mapping in the face of climate change scenarios: case study in a tropical estuary in the Brazilian semi-arid region / Paulo Victor N. Araújo in Natural Hazards and Earth System Sciences, vol 21 n° 11 (November 2021)  

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Traditional communities and mental maps: Dialogues between local knowledge and cartography from the socioenvironmental atlas of Lençóis Maranhenses, Brazil / Benedito Souza Filho in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)  

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Automatic detection of planted trees and their heights using photogrammetric rpa point clouds / Kênia Samara Mourão Santos in Boletim de Ciências Geodésicas, vol 27 n° 3 ([01/10/2021])  

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GIS models for vulnerability of coastal erosion assessment in a tropical protected area / Luís Russo Vieira in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)  

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Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)  

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Fast unsupervised multi-scale characterization of urban landscapes based on Earth observation data / Claire Teillet in Remote sensing, vol 13 n° 12 (June-2 2021)  

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Multicriterial method of AHP analysis for the identification of coastal vulnerability regarding the rise of sea level: case study in Ilha Grande Bay, Rio de Janeiro, Brazil / Julia Caon Araujo in Natural Hazards, vol 107 n° 1 (May 2021)  

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