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Evaluation of growth models for mixed forests used in Swedish and Finnish decision support systems / Jorge Aldea in Forest ecology and management, vol 529 (February-1 2023)  

Public [article]  inForest ecology and management > vol 529 (February-1 2023) . - n° 120721 Titre : Evaluation of growth models for mixed forests used in Swedish and Finnish decision support systems Type de document : Article/Communication Auteurs : Jorge Aldea, Auteur ; Simone Bianchi, Auteur ; Urban Nilsson, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 120721 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Betula (genre) [Termes IGN] Finlande [Termes IGN] forêt boréale [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle de croissance végétale [Termes IGN] modèle de simulation [Termes IGN] peuplement mélangé [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] Suède [Termes IGN] système d'aide à la décision [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Interest in mixed forests is increasing since they could provide higher benefits and positive externalities compared to monocultures, although their management is more complex and silvicultural prescriptions for them are still scarce. Growth simulations are a powerful tool for developing useful guidelines for mixed stands. Heureka and Motti are two decision support systems commonly used for forest management in Sweden and Finland respectively. They were developed mostly with data from pure stands, so how they would perform in mixed stands is currently uncertain. We compiled a large and updated common database of well-replicated experimental research sites and monitoring networks composed by 218 and 1,160 plot-level observations of mixed stands from Sweden and Finland, respectively. We aimed to evaluated the accuracy of Heureka and Motti basal area growth models in those mixed-species stands and to detect any bias in their short-term predictions. Basal area growth simulations (excluding mortality models) were compared to observed stand-level values in a period-wise process with update of the start values in each period. The residual plots were visually examined for different stand mixtures: Norway spruce (Picea abies Karst.)-birch (Betula spp), Scots pine (Pinus sylvestris L.)-birch and Scots pine-Norway spruce. We observed that the basal area growth models in both decision support systems performed quite well for all mixtures regardless of the proportion of species. Motti simulations overestimated growth in Scots pine-Norway spruce mixtures by 0.063 m2·ha−1·year−1 which may be acceptable for practical use. Therefore, we corroborated that both decision support systems can be currently utilized for short-term forest growth simulation of mixed boreal forests. Numéro de notice : A2023-107 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120721 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120721 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102441 [article]

A model-based scenario analysis of the impact of forest management and environmental change on the understorey of temperate forests in Europe / Bingbin Wen in Forest ecology and management, vol 522 (October-15 2022)  

Public [article]  inForest ecology and management > vol 522 (October-15 2022) . - n° 120465 Titre : A model-based scenario analysis of the impact of forest management and environmental change on the understorey of temperate forests in Europe Type de document : Article/Communication Auteurs : Bingbin Wen, Auteur ; Haben Blondeel, Auteur ; Dries Landuyt, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120465 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de sensibilité [Termes IGN] azote [Termes IGN] biodiversité [Termes IGN] changement climatique [Termes IGN] dynamique de la végétation [Termes IGN] Europe centrale [Termes IGN] forêt tempérée [Termes IGN] gestion forestière durable [Termes IGN] impact sur l'environnement [Termes IGN] modèle de simulation [Termes IGN] sous-étage [Termes IGN] système d'aide à la décision [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) The temperate forest understorey is rich in terms of vascular plant diversity and plays a vital functional role. Given the sensitivity of this forest layer to forest management and global environmental change and the limited knowledge on its long-term dynamics, there is a need for decision support systems that can guide temperate forest managers to optimize their management in terms of understorey outcomes. In this study, using understorey resurvey data collected from across temperate Europe, we developed Generalized Additive Models (GAM) to predict four understorey properties based on forest management and environmental change data, and implemented this model in a web-based tool as a prototype understorey Decision Support System (DSS). Using seventy-two combined climate change, nitrogen(N) deposition and forest management scenarios, applied to two case study regions in Europe, we predicted temperate forest understorey biodiversity dynamics between 2020 and 2050. A sensitivity analysis subsequently allowed to quantify the relative importance of canopy opening, N deposition and climate change on understorey dynamics. Our study showed that, regardless of regions, understorey richness and the proportion of forest specialists generally decreased among most scenarios, but the proportion of woody species and the understorey vegetation total cover increased. Climate warming, N deposition, and increases in canopy openness all influenced understorey dynamics. Climate warming will shift composition towards a selection of forest generalists and woody species, but a less open canopy could mitigate this shift by increasing the proportion of forest specialists. The case studies also showed that these responses can be context-dependent, especially in terms of responses to N deposition. Numéro de notice : A2022-710 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120465 Date de publication en ligne : 19/08/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101587 [article]

Deep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques / Wang Yue in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 9 (September 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 9 (September 2022) . - pp 593 - 601 Titre : Deep learning–based monitoring sustainable decision support system for energy building to smart cities with remote sensing techniques Type de document : Article/Communication Auteurs : Wang Yue, Auteur ; Changgang Yu, Auteur ; A. Antonidoss, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 593 - 601 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage profond [Termes IGN] bâtiment [Termes IGN] capteur (télédétection) [Termes IGN] économie d'énergie [Termes IGN] internet des objets [Termes IGN] performance énergétique [Termes IGN] réseau neuronal artificiel [Termes IGN] système d'aide à la décision [Termes IGN] ville durable [Termes IGN] ville intelligente Résumé : (auteur) In modern society, energy conservation is an important consideration for sustainability. The availability of energy-efficient infrastructures and utilities depend on the sustainability of smart cities. The big streaming data generated and collected by smart building devices and systems contain useful information that needs to be used to make timely action and better decisions. The ultimate objective of these procedures is to enhance the city's sustainability and livability. The replacement of decades-old infrastructures, such as underground wiring, steam pipes, transportation tunnels, and high-speed Internet installation, is already a major problem for major urban regions. There are still certain regions in big cities where broadband wireless service is not available. The decision support system is recently acquiring increasing attention in the smart city context. In this article, a deep learning–based sustainable decision support system (DLSDSS) has been proposed for energy building in smart cities. This study proposes the integration of the Internet of Things into smart buildings for energy management, utilizing deep learning methods for sensor information decision making. Building a socially advanced environment aims to enhance city services and urban administration for residents in smart cities using remote sensing techniques. The proposed deep learning methods classify buildings based on energy efficiency. Data gathered from the sensor network to plan smart cities' development include a deep learning algorithm's structural assembly of data. The deep learning algorithm provides decision makers with a model for the big data stream. The numerical results show that the proposed method reduces energy consumption and enhances sensor data accuracy by 97.67% with better decision making in planning smart infrastructures and services. The experimental outcome of the DLSDSS enhances accuracy (97.67%), time complexity (98.7%), data distribution rate (97.1%), energy consumption rate (98.2%), load shedding ratio (95.8%), and energy efficiency (95.4%). Numéro de notice : A2022-812 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00010R2 Date de publication en ligne : 01/09/2022 En ligne : https://doi.org/10.14358/PERS.22-00010R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101972 [article]

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Flood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 88 (July 2021) . - n° 101628 Titre : Flood depth mapping in street photos with image processing and deep neural networks Type de document : Article/Communication Auteurs : Bahareh Alizadeh Kharazi, Auteur ; Amir H. Behzadan, Auteur Année de publication : 2021 Article en page(s) : n° 101628 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] Canada [Termes IGN] centre urbain [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] crue [Termes IGN] détection de contours [Termes IGN] Etats-Unis [Termes IGN] image Streetview [Termes IGN] inondation [Termes IGN] profondeur [Termes IGN] signalisation routière [Termes IGN] système d'aide à la décision [Termes IGN] traitement d'image [Termes IGN] transformation de Hough [Termes IGN] zone urbaine Résumé : (auteur) Many parts of the world experience severe episodes of flooding every year. In addition to the high cost of mitigation and damage to property, floods make roads impassable and hamper community evacuation, movement of goods and services, and rescue missions. Knowing the depth of floodwater is critical to the success of response and recovery operations that follow. However, flood mapping especially in urban areas using traditional methods such as remote sensing and digital elevation models (DEMs) yields large errors due to reshaped surface topography and microtopographic variations combined with vegetation bias. This paper presents a deep neural network approach to detect submerged stop signs in photos taken from flooded roads and intersections, coupled with Canny edge detection and probabilistic Hough transform to calculate pole length and estimate floodwater depth. Additionally, a tilt correction technique is implemented to address the problem of sideways tilt in visual analysis of submerged stop signs. An in-house dataset, named BluPix 2020.1 consisting of paired web-mined photos of submerged stop signs across 10 FEMA regions (for U.S. locations) and Canada is used to evaluate the models. Overall, pole length is estimated with an RMSE of 17.43 and 8.61 in. in pre- and post-flood photos, respectively, leading to a mean absolute error of 12.63 in. in floodwater depth estimation. Findings of this research are sought to equip jurisdictions, local governments, and citizens in flood-prone regions with a simple, reliable, and scalable solution that can provide (near-) real time estimation of floodwater depth in their surroundings. Numéro de notice : A2021-358 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101628 Date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97620 [article]

Prevention of erosion in mountain basins: A spatial-based tool to support payments for forest ecosystem services / Sandro Sacchelli in Journal of forest science, vol 67 n° 6 (July 2021)  

Public [article]  inJournal of forest science > vol 67 n° 6 (July 2021) . - pp 258 - 271 Titre : Prevention of erosion in mountain basins: A spatial-based tool to support payments for forest ecosystem services Type de document : Article/Communication Auteurs : Sandro Sacchelli, Auteur ; Costanza Borghi, Auteur ; Gianluca Grilli, Auteur Année de publication : 2021 Article en page(s) : pp 258 - 271 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] bassin hydrographique [Termes IGN] érosion hydrique [Termes IGN] géomorphologie locale [Termes IGN] gestion forestière [Termes IGN] réseau neuronal artificiel [Termes IGN] ruissellement [Termes IGN] service écosystémique [Termes IGN] système d'aide à la décision [Termes IGN] Toscane (Italie) Résumé : (auteur) This paper presents a spatial-based decision support system (DSS) to assist public and private forest managers in the analysis of potential feasibility in payments for forest ecosystem services (PES) for the prevention of soil erosion. The model quantifies the maximum willingness to pay (WTP) of managers of a reservoir to prevent soil loss. The minimum willingness to accept (WTA) of forest owners for the activation of a private market is also computed. The comparison of WTP and WTA identifies the forest area where PES are ideally feasible with additional potential for compensation to enable the schemes. The DSS highlights forest idiosyncrasies as well as local socio-economic and geomorphological characteristics influencing PES suitability at a geographic level. The potential applications and future improvements of the model are also discussed. Numéro de notice : A2021-450 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.17221/5/2021-JFS Date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.17221/5/2021-JFS Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97867 [article]

Mitigating urban visual pollution through a multistakeholder spatial decision support system to optimize locational potential of billboards / Khydija Wakil in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)  

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A web-based spatial decision support system for monitoring the risk of water contamination in private wells / Yu Lan in Annals of GIS, vol 26 n° 3 (July 2020)  

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A cyber-enabled spatial decision support system to inventory mangroves in Mozambique: coupling scientific workflows and cloud computing / Wenwu Tang in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

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Vers un modèle unifié de données entreposées et de données ouvertes liées. Concepts et expérimentations / Franck Ravat in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 22 n° 2 (mars - avril 2017)  

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OLAP de documents, modélisation et mise en oeuvre / Omar Khrouf in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 21 n° 1 (janvier - février 2016)  

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Vol 43 n° 1 - January 2016 - GeoVisual analytics: Interactivity, dynamics, and scale (Bulletin de Cartography and Geographic Information Science) / Gennady Adrienko  

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Un système décisionnel pour l’analyse de la qualité des eaux de rivières / Sandro Bimonte in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 20 n° 3 (mai - juin 2015)  

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Systèmes d'information avancés, Volume 2. Décision et système d'information / Maryse Salles (2015)

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Integrating a raster geographical information system with multi-objective land allocation optimization for conservation reserve design / WeiWei Dai in Transactions in GIS, vol 18 n° 6 (December 2014)  

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Panorama de l'intelligence artificielle, ses bases méthodologiques, ses développements, 1. Représentation des connaissances et formalisation des raisonnements / Pierre Marquis (2014)

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