Centre de documentation
scientifique

Impacts of forest management on stand and landscape-level microclimate heterogeneity of European beech forests / Joscha H. Menge in Landscape ecology, vol 38 n° 4 (April 2023)  

Public [article]  inLandscape ecology > vol 38 n° 4 (April 2023) . - pp 903 - 917 Titre : Impacts of forest management on stand and landscape-level microclimate heterogeneity of European beech forests Type de document : Article/Communication Auteurs : Joscha H. Menge, Auteur ; Paul Magdon, Auteur ; Stephan Wöllauer, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 903 - 917 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse comparative [Termes IGN] données lidar [Termes IGN] éclaircie (sylviculture) [Termes IGN] écosystème forestier [Termes IGN] Fagus (genre) [Termes IGN] forêt équienne [Termes IGN] forêt inéquienne [Termes IGN] gestion forestière [Termes IGN] hêtraie [Termes IGN] microclimat [Termes IGN] régression multiple [Termes IGN] semis de points [Termes IGN] température de l'air [Termes IGN] Thuringe Résumé : (auteur) Context: Forest microclimate influences biodiversity and plays a crucial role in regulating forest ecosystem functions. It is modified by forest management as a result of changes in forest structure due to tree harvesting and thinning. Objectives: Here, we investigate the impacts of even-aged and uneven-aged forest management on stand- and landscape-level heterogeneity of forest microclimates, in comparison with unmanaged, old-growth European beech forest. Methods: We combined stand structural and topographical indices derived from airborne laser scanning with climate observations from 23 meteorological stations at permanent forest plots within the Hainich region, Germany. Based on a multiple linear regression model, we spatially interpolated the diurnal temperature range (DTR) as an indicator of forest microclimate across a 4338 ha section of the forest with 50 m spatial resolution. Microclimate heterogeneity was measured as α-, β-, and γ-diversity of thermal niches (i.e. DTR classes). Results: Even-aged forests showed a higher γ-diversity of microclimates than uneven-aged and unmanaged forests. This was mainly due to a higher β-diversity resulting from the spatial coexistence of different forest developmental stages within the landscape. The greater structural complexity at the stand-level in uneven-aged stands did not increase α-diversity of microclimates. Predicted DTR was significantly lower and spatially more homogenous in unmanaged forest compared to both types of managed forest. Conclusion: If forest management aims at creating a wide range of habitats with different microclimates within a landscape, spatially co-existing types of differently managed and unmanaged forests should be considered, instead of focusing on a specific type of management, or setting aside forest reserves only. Numéro de notice : A2023-224 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1007/s10980-023-01596-z Date de publication en ligne : 30/01/2023 En ligne : https://doi.org/10.1007/s10980-023-01596-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103175 [article]

A comparative assessment of the statistical methods based on urban population density estimation / Merve Yılmaz in Geocarto international, vol 38 n° 1 ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° 1 [01/01/2023] . - n° 2152494 Titre : A comparative assessment of the statistical methods based on urban population density estimation Type de document : Article/Communication Auteurs : Merve Yılmaz, Auteur Année de publication : 2023 Article en page(s) : n° 2152494 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] densité de population [Termes IGN] planification urbaine [Termes IGN] population urbaine [Termes IGN] régression géographiquement pondérée [Termes IGN] régression multiple [Termes IGN] Turquie Résumé : (auteur) Population density is important spatial information for addressing the use and access to land resources in cities under the Sustainable Development Goals. This is because the spatial data support appropriate spatial policies at the spatial scale and predicts how much land will be consumed in the future. The study aims to compare and evaluate the regression tools in the context of estimating the population density difference. The three analysis tools used are Random Forest-Based Classification, Multiple Linear Regression, and Geographically Weighted Regression. The sampling area covers cities around Türkiye. Comparative results showed that the two most important descriptive variables in the Random Forest-Based Classification model are the density difference of the new developed area and the connectivity. The three main explanatory variables of the Multiple Linear Regression model are centrality, vehicle ownership, and accessibility. The results of the Multiple Linear Regression model (a non-spatial model) and the Geographically Weighted Regression model (a spatial model), were found to be quite similar. The importance of accessibility and connectivity is more evident in the Multiple Linear Regression model when the Random Forest-Based Classification model highlights the density values in the new development areas. Numéro de notice : A2023-055 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10106049.2022.2152494 Date de publication en ligne : 28/12/2022 En ligne : https://doi.org/10.1080/10106049.2022.2152494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102388 [article]

Integration of geospatial technologies with multiple regression model for urban land use land cover change analysis and its impact on land surface temperature in Jimma City, southwestern Ethiopia / Mitiku Badasa Moisa in Applied geomatics, vol 14 n° 4 (December 2022)  

Public [article]  inApplied geomatics > vol 14 n° 4 (December 2022) . - pp 653 - 667 Titre : Integration of geospatial technologies with multiple regression model for urban land use land cover change analysis and its impact on land surface temperature in Jimma City, southwestern Ethiopia Type de document : Article/Communication Auteurs : Mitiku Badasa Moisa, Auteur ; Indale Niguse Dejene, Auteur ; Dessalegn Obsi Gemeda, Auteur Année de publication : 2022 Article en page(s) : pp 653 - 667 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] changement climatique [Termes IGN] changement d'occupation du sol [Termes IGN] climat urbain [Termes IGN] espace vert [Termes IGN] étalement urbain [Termes IGN] Ethiopie [Termes IGN] flore urbaine [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] modèle de régression [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] régression multiple [Termes IGN] surface imperméable [Termes IGN] urbanisation [Termes IGN] utilisation du sol Résumé : (auteur) Rapid urbanization and population growth are the main problems faced by developing countries that lead to natural resource depletion in the periphery of the city. This research attempts to analyze the impacts of urban land use land cover (LULC) change on land surface temperature (LST) from 1991 to 2021 in Jimma city, southwestern Ethiopia. Landsat Thematic Mapper (TM) 1991, Landsat Enhanced Thematic Mapper Plus (ETM +) 2005, and Landsat-8 Operational land imagery (OLI)/Thermal Infrared Sensor (TIRS) 2021 were used in this study. Multispectral bands and thermal infrared bands of Landsat images were used to calculate LULC change, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and LST. The LULC of the study area was classified using a supervised classification method with the maximum likelihood algorithm. The results of this study clearly showed that there is a negative correlation between vegetation cover and LST. The decrease in vegetation coverage and expansion of impervious surfaces lead to elevated LST in urban areas. The loss of vegetation cover contributed to the increasing trend of LST. Moreover, the conversion of vegetation cover to impervious surfaces aggravates the problem of LST. The results revealed that the built-up area was increased at a rate of 0.4 km2/year from 1991 to 2021. The vegetation cover in the city declined due to urban expansion to the periphery of the city. Consequently, the dense vegetation and sparse vegetation were converted into built-up areas by approximately 5.2 km2 during the study period. The mean LST of the study area increased by 10.3 °C from 1991 to 2021 during the winter season in daytime. To improve the problems of climate change around urban areas, all stakeholders should work together to increase the urban green space coverage, which will contribute a significant role in mitigating LST and the urban heat island effect. More specifically, all residents could be accessible to public green spaces around big cities. Numéro de notice : A2022-893 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s12518-022-00463-x Date de publication en ligne : 22/08/2022 En ligne : https://doi.org/10.1007/s12518-022-00463-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102241 [article]

Urban wetland fragmentation and ecosystem service assessment using integrated machine learning algorithm and spatial landscape analysis / Das Subhasis in Geocarto international, vol 37 n° 25 ([01/12/2022])  

Public [article]  inGeocarto international > vol 37 n° 25 [01/12/2022] . - pp 7800 - 7818 Titre : Urban wetland fragmentation and ecosystem service assessment using integrated machine learning algorithm and spatial landscape analysis Type de document : Article/Communication Auteurs : Das Subhasis, Auteur ; Partha Pratim Adhikary, Auteur ; Pravat Kumar Shit, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 7800 - 7818 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse du paysage [Termes IGN] analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] Calcutta [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] Inde [Termes IGN] occupation du sol [Termes IGN] QGIS [Termes IGN] régression multiple [Termes IGN] service écosystémique [Termes IGN] zone humide [Termes IGN] zone urbaine Résumé : (auteur) Dynamics of ecosystem service value (ESV) of various wetlands has been assessed by researchers globally. But the impact of spatio-temporal variation of landscape metrics on ESV in the lower Gangetic plains has not been examined, fully. The present work has established linkages between landscape metrics and ESV in Kolkata urban agglomeration using support vector machine and multivariate regression analysis. Result indicates that wetland area has been reduced by 5.26%, 13.67% and 9.03% during the periods 1990–2000, 2000–2010 and 2010–2020, respectively and the ESV contributed by wetlands has been decreased by $131428, $323674 and $184649, respectively during the same period at an annual rate of 0.85%. Number of patches, mean patch area and edge density are the main determinants of wetland fragmentation and decreased by 44.12%, 10.23% and 8.65%, respectively during the last three decades. A wetland restoration strategy based on dynamic restoration, reactive restoration and wetland creation for the study area has been formulated, which can guide for sustainable management of wetland resources in Kolkata urban agglomeration. Numéro de notice : A2022-930 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1080/10106049.2021.1985174 Date de publication en ligne : 03/11/2021 En ligne : https://doi.org/10.1080/10106049.2021.1985174 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102665 [article]

Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid / Zhen Dai in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 11 (November 2022) . - pp 2248 - 2269 Titre : Geographically convolutional neural network weighted regression: a method for modeling spatially non-stationary relationships based on a global spatial proximity grid Type de document : Article/Communication Auteurs : Zhen Dai, Auteur ; Sensen Wu, Auteur ; Yuanyuan Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2248 - 2269 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] distribution spatiale [Termes IGN] modèle de régression [Termes IGN] régression géographiquement pondérée [Termes IGN] régression linéaire [Termes IGN] réseau neuronal convolutif Résumé : (auteur) Geographically weighted regression (GWR) is a classical method of modeling spatially non-stationary relationships. The geographically neural network weighted regression (GNNWR) model solves the problem of the inaccurate construction of spatial weight kernels using a spatially weighted neural network. However, when the spatial distribution of observations is uneven, the spatial proximity expression in the input of GWR and GNNWR models does not fully represent the impact of the whole research space on the estimating point. Therefore, we established a global spatial proximity grid (GSPG) to express the spatial proximity of each estimating point and proposed a spatially weighted convolutional neural network (SWCNN) to extract the relationship between the GSPG and spatial weights. Finally, we proposed a geographically convolutional neural network weighted regression (GCNNWR) model combining SWCNN and ordinary linear regression (OLR) model to estimate spatial non-stationarity. We used two case studies of simulated data and real environment data to demonstrate the advancements of the GCNNWR model. The GCNNWR model achieved higher estimation accuracy and greater predictive power than the OLR, GWR, multi-scale GWR (MGWR), and GNNWR models. Moreover, the GCNNWR model maintained its better stability and accuracy in estimating spatially non-stationary relationships when the distribution of observations was uneven. Numéro de notice : A2022-773 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2100892 Date de publication en ligne : 27/09/2022 En ligne : https://doi.org/10.1080/13658816.2022.2100892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101954 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2022111	SL	Revue	Centre de documentation	Revues en salle	Disponible

Human mobility and COVID-19 transmission: a systematic review and future directions / Mengxi Zhang in Annals of GIS, vol 28 n° 4 (November 2022)  

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Improving accuracy of local geoid model using machine learning approaches and residuals of GPS/levelling geoid height / Mosbeh R. Kaloop in Survey review, vol 54 n° 387 (November 2022)  

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Comparison of change and static state as the dependent variable for modeling urban growth / Yongjiu Feng in Geocarto international, vol 37 n° 23 ([15/10/2022])  

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Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks / Abdelkrim Bouasria in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

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Machine learning and natural language processing of social media data for event detection in smart cities / Andrei Hodorog in Sustainable Cities and Society, vol 85 (October 2022)  

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The fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas / Jun Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 10 (October 2022)  

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The FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation / Shuaijun Liu in Remote sensing of environment, vol 279 (September-15 2022)  

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Analysis of structure from motion and airborne laser scanning features for the evaluation of forest structure / Alejandro Rodríguez-Vivancos in European Journal of Forest Research, vol 141 n° 3 (June 2022)  

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GIS and machine learning for analysing influencing factors of bushfires using 40-year spatio-temporal bushfire data / Wanqin He in ISPRS International journal of geo-information, vol 11 n° 6 (June 2022)  

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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)  

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