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scientifique

Climatic sensitivities derived from tree rings improve predictions of the forest vegetation simulator growth and yield model / Courtney L. Giebink in Forest ecology and management, vol 517 (1 August 2022)  

Public [article]  inForest ecology and management > vol 517 (1 August 2022) . - n° 120256 Titre : Climatic sensitivities derived from tree rings improve predictions of the forest vegetation simulator growth and yield model Type de document : Article/Communication Auteurs : Courtney L. Giebink, Auteur ; R. Justin DeRose, Auteur ; Mark Castle, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 120256 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] cerne [Termes IGN] croissance des arbres [Termes IGN] gestion forestière [Termes IGN] inventaire forestier étranger (données) [Termes IGN] modèle de croissance végétale [Termes IGN] modèle de simulation [Termes IGN] Picea (genre) [Termes IGN] Pinus ponderosa [Termes IGN] Pseudotsuga menziesii [Termes IGN] puits de carbone [Termes IGN] rendement [Termes IGN] Utah (Etas-Unis) [Termes IGN] variation saisonnière [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Forest management has the potential to contribute to the removal of greenhouse gasses from the atmosphere via carbon sequestration and storage. To identify management actions that will maximize carbon removal and storage over the long term, models are needed that accurately and realistically represent forest responses to changing climate. The most widely used growth and yield model in the United States (U.S.), the Forest Vegetation Simulator (FVS), which also forms the basis for several forest carbon calculators, does not currently include the direct effect of climate variation on tree growth. We incorporated the effects of climate on tree diameter growth by combining tree-ring data with forest inventory data to parameterize a suite of alternative models characterizing the growth of three dominant tree species in the arid and moisture-limited state of Utah. These species, Pinus ponderosa Dougl. ex Laws, Pseudotsuga menziesii var. glauca Mayr (Franco), and Picea engelmannii Parry ex Engelm., encompass the full elevational range of montane forest types. The alternative models we considered differed progressively from the current FVS large-tree diameter growth model, first by changing to an annual time step, then by adding interannual climate effects, followed by model simplification (removal of predictors), and finally, complexification, including effects of spatial variation in climate and two-way interactions between predictors. We validated diameter growth predictions from these models with independent observations, and evaluated model performance in terms of accuracy, precision, and bias. We then compared predictions of future growth made by the existing large-tree diameter growth model used in FVS, i.e., without climate effects, to those of our updated models, including those with climate effects. We found that simpler models of tree growth outperform the current FVS model, and that the incorporation of climate effects improves model performance for two out of three species, in which growth is currently overpredicted by FVS. Diameter growth projected with improved, climate-sensitive models is less than the future tree growth projected by the current climate-insensitive FVS model. Tree rings can be used to identify and incorporate drivers of growth variation into a stand-level growth and yield model, giving more accurate predictions of the carbon uptake potential of forests under climate change. Numéro de notice : A2022-390 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.foreco.2022.120256 Date de publication en ligne : 12/05/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120256 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100681 [article]

Simulation of the potential impact of urban expansion on regional ecological corridors: A case study of Taiyuan, China / Wei Hou in Sustainable Cities and Society, vol 83 (August 2022)  

Public [article]  inSustainable Cities and Society > vol 83 (August 2022) . - n° 103933 Titre : Simulation of the potential impact of urban expansion on regional ecological corridors: A case study of Taiyuan, China Type de document : Article/Communication Auteurs : Wei Hou, Auteur ; Wen Zhou, Auteur ; Jingyang Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103933 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] carte d'occupation du sol [Termes IGN] Chansi (Chine) [Termes IGN] corridor biologique [Termes IGN] croissance urbaine [Termes IGN] étalement urbain [Termes IGN] modèle de simulation [Termes IGN] modélisation spatiale [Termes IGN] trame verte et bleue Résumé : (auteur) Urbanization caused by intensive land-use change is the main driving force of environment degradation. The development and protection of ecological corridors to connect green natural features has been recognized as an effective means for improving the resilience of cities. It is especially important for the cities which are in the rapid urbanizing process. In this paper, we used high-resolution spatial data to extract the distribution of ecological corridors by using the Least Cost Path model for the city of Taiyuan, China. Then, a prediction of the urban area for the year 2035 was achieved by using the SLEUTH model. Finally, we analyzed the negative impact of urban expansion on the corridors under current urbanization trends. Our results show that the regional corridors are mostly distributed in the western mountainous area, connecting large natural areas. There are also three ecological corridors crossing the city from the east to west. According to our prediction, Taiyuan will mainly grow southward along the urban edge and road network. As a result, two ecological corridors in the central and southern parts of Taiyuan will be largely occupied, especially the corridors in Xiaodian district which account for 72.51% of the total occupied corridor area. To avoid further conflict, the urbanization intensity along the sides of the corridors should be restricted to maintain corridors of a certain width. Our research findings can provide urban planners insightful suggestions for conservation and restoration of ecological networks and assist in spatial planning. Numéro de notice : A2022-487 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.103933 Date de publication en ligne : 07/05/2022 En ligne : https://doi.org/10.1016/j.scs.2022.103933 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100952 [article]

Can machine learning improve small area population forecasts? A forecast combination approach / Irina Grossman in Computers, Environment and Urban Systems, vol 95 (July 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 95 (July 2022) . - n° 101806 Titre : Can machine learning improve small area population forecasts? A forecast combination approach Type de document : Article/Communication Auteurs : Irina Grossman, Auteur ; Kasun Bandara, Auteur ; Tom Wilson, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101806 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse en composantes principales [Termes IGN] apprentissage automatique [Termes IGN] Australie [Termes IGN] démographie [Termes IGN] Extreme Gradient Machine [Termes IGN] infrastructure [Termes IGN] lissage de données [Termes IGN] modèle de simulation [Termes IGN] modèle empirique [Termes IGN] Nouvelle-Zélande [Termes IGN] planification stratégique [Termes IGN] pondération [Termes IGN] série temporelle Résumé : (auteur) Generating accurate small area population forecasts is vital for governments and businesses as it provides better grounds for decision making and strategic planning of future demand for services and infrastructure. Small area population forecasting faces numerous challenges, including complex underlying demographic processes, data sparsity, and short time series due to changing geographic boundaries. In this paper, we propose a novel framework for small area forecasting which combines proven demographic forecasting methods, an exponential smoothing based algorithm, and a machine learning based forecasting technique. The proposed forecasting combination contains four base models commonly used in demographic forecasting, a univariate forecasting model specifically suitable for forecasting yearly data, and a globally trained Light Gradient Boosting Model (LGBM) that exploits the similarities between a collection of population time series. In this study, three forecast combination techniques are investigated to weight the forecasts generated by these base models. We empirically evaluate our method, by preparing small area population forecasts for Australia and New Zealand. The proposed framework is able to achieve competitive results in terms of forecasting accuracy. Moreover, we show that the inclusion of the LGBM model always improves the accuracy of combination models on both datasets, relative to combination models which only include the demographic models. In particular, the results indicate that the proposed combination framework decreases the prevalence of relatively poor forecasts, while improving the reliability of small area population forecasts. Numéro de notice : A2022-374 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101806 Date de publication en ligne : 19/04/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101806 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100621 [article]

Mixed geographically and temporally weighted regression for spatio-temporal deformation modelling / Zhijia Yang in Survey review, vol 54 n° 385 (July 2022)  

Public [article]  inSurvey review > vol 54 n° 385 (July 2022) . - pp 290 - 300 Titre : Mixed geographically and temporally weighted regression for spatio-temporal deformation modelling Type de document : Article/Communication Auteurs : Zhijia Yang, Auteur ; Wujiao Dai, Auteur ; Wenkun Yu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 290 - 300 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] barrage [Termes IGN] déformation d'édifice [Termes IGN] méthode fondée sur le noyau [Termes IGN] modèle de simulation [Termes IGN] modélisation spatio-temporelle [Termes IGN] régression [Termes IGN] régression géographiquement pondérée [Termes IGN] surveillance d'ouvrage Résumé : (auteur) When the regression coefficient of independent variable has both global stationarity and spatio-temporal non-stationarity properties, the deformation model based on the geographically and temporally weighted regression (GTWR) will no longer be applicable. In order to resolve this problem, we propose an improved method to establish the spatio-temporal deformation model using mixed geographically and temporally weighted regression (MGTWR). In this method, both the global regression coefficient and the variable regression coefficient are selected for regression coefficient hypothesis test, and the local linear two-step estimation method is used to fit the MGTWR model. A dam deformation modelling example shows that the MGTWR model improves the average prediction accuracy by 57.6% compared to the GTWR model when the regression coefficients have both global stationarity and spatio-temporal non-stationarity properties. Numéro de notice : A2022-534 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1935578 Date de publication en ligne : 10/06/2021 En ligne : https://doi.org/10.1080/00396265.2021.1935578 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101090 [article]

Modeling human–human interaction with attention-based high-order GCN for trajectory prediction / Yanyan Fang in The Visual Computer, vol 38 n° 7 (July 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 7 (July 2022) . - pp 2257 - 2269 Titre : Modeling human–human interaction with attention-based high-order GCN for trajectory prediction Type de document : Article/Communication Auteurs : Yanyan Fang, Auteur ; Zhiyu Jin, Auteur ; Zhenhua Cui, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2257 - 2269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] attention (apprentissage automatique) [Termes IGN] détection de cible [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] interaction spatiale [Termes IGN] modèle de simulation [Termes IGN] objet mobile [Termes IGN] piéton [Termes IGN] réseau neuronal de graphes [Termes IGN] trajet (mobilité) Résumé : (auteur) This paper presents a novel high-order graph convolutional network (GCN) for pedestrian trajectory prediction. Specifically, the walking state of a target pedestrian depends on both its historical trajectory, which encodes its speed, walking direction and acceleration information, as well as the movement of its neighbors. Thus we propose to leverage GCNs to aggregate the trajectory features of the target pedestrian and its neighbors to predict the movement of the target pedestrian. Considering that the movement of the neighbors’ neighbors affects the movement of the target pedestrian’s neighbors, thus indirectly affecting the movement of the target pedestrian, we propose to use a high-order GCN for human–human interaction modelling. Such a high-order GCN considers the target pedestrian’s neighbors as well as its neighbors’ neighbors. Further, a pedestrian avoids collision with others by estimating its locations and its neighbors’ upcoming locations, and it slows down or changes direction if it believes a collision may occur, especially in very crowded scenes. In light of this, we propose to model such anticipation-based decision making behavior as attention and combine it with our high-order GCN. Thus we first roughly estimate the future trajectories of all pedestrians with a simple method. By using the coarse predicted future trajectory and GCN outputs, we calculate the attention in our attention-based high-order GCN and predict future trajectory. Extensive experiments validate the effectiveness of our approach. In addition, our model shows a higher data efficiency. On the ETH&UCY dataset, using only 5% of the training data for each training epoch, our model outperforms the state of the art. Numéro de notice : A2022-507 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-021-02109-2 Date de publication en ligne : 01/07/2021 En ligne : https://doi.org/10.1007/s00371-021-02109-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101040 [article]

Multi-frequency phase-only PPP-RTK model applied to BeiDou data / Pengyu Hou in GPS solutions, vol 26 n° 3 (July 2022)  

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Mapping and prediction of soil organic carbon by an advanced geostatistical technique using remote sensing and terrain data / Santanu Malik in Geocarto international, vol 37 n° 8 ([22/06/2022])  

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How large-scale bark beetle infestations influence the protective effects of forest stands against avalanches: A case study in the Swiss Alps / Marion E. Caduff in Forest ecology and management, vol 514 (15 June 2022)  

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Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction / Tianhong Zhao in Computers, Environment and Urban Systems, vol 94 (June 2022)  

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Management or climate and which one has the greatest impact on forest soil’s protective value? A case study in Romanian mountains / Cosmin Cosofret in Forests, vol 13 n° 6 (June 2022)  

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Virtual laser scanning of dynamic scenes created from real 4D topographic point cloud data / Lukas Winiwarter in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

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Research on automatic identification method of terraces on the Loess plateau based on deep transfer learning / Mingge Yu in Remote sensing, vol 14 n° 10 (May-2 2022)  

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Impacts of spatiotemporal resolution and tiling on SLEUTH model calibration and forecasting for urban areas with unregulated growth patterns / Damilola Eyelade in International journal of geographical information science IJGIS, vol 36 n° 5 (May 2022)  

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Landslide susceptibility assessment considering spatial agglomeration and dispersion characteristics: A case study of Bijie City in Guizhou Province, China / Kezhen Yao in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)  

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Coupling fossil records and traditional discrimination metrics to test how genetic information improves species distribution models of the European beech Fagus sylvatica / Pedro Poli in European Journal of Forest Research, vol 141 n° 2 (April 2022)  

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