Centre de documentation
scientifique

Improving methods to predict aboveground biomass of Pinus sylvestris in urban forest using UFB model, LiDAR and digital hemispherical photography / Ihor Kozak in Urban Forestry & Urban Greening, vol 79 (January 2023)  

Public [article]  inUrban Forestry & Urban Greening > vol 79 (January 2023) . - n° 127793 Titre : Improving methods to predict aboveground biomass of Pinus sylvestris in urban forest using UFB model, LiDAR and digital hemispherical photography Type de document : Article/Communication Auteurs : Ihor Kozak, Auteur ; Mikhail Popov, Auteur ; Igor Semko, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 127793 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] biomasse aérienne [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] forêt urbaine [Termes IGN] houppier [Termes IGN] image hémisphérique [Termes IGN] Leaf Area Index [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle de régression [Termes IGN] modèle numérique de terrain [Termes IGN] photographie numérique [Termes IGN] Pinus sylvestris [Termes IGN] Pologne [Termes IGN] semis de points [Termes IGN] surface terrière Résumé : (auteur) The article proposes methods for combining Airborne Laser Scanning (ALS) with Digital Hemispherical Photography (DHP) data required by the Urban Forest Biomass (UFB) model to predict the aboveground biomass (AGB) of Scotch pine (Pinus sylvestris L.) in urban forests of Lublin (Poland). The article also demonstrates the potential of ALS and DHP data in urban AGB estimation. ALS and Leaf Area Index (LAI) data were calculated using a voxels-vector approach based on the measurements taken at eight permanent sample plots (PSPs). The research was conducted in 2014 and the prediction was made until 2030. It was found that the determination coefficients (R2) for the Basal Area (BA) of the trees are 0.97, and the BA modeling parameters have a high correlation with those observed in the field (model efficiency (ME) 0.94). 83 % growth trajectory based on the measured BA was appropriately modeled using the UFB model (P > 0.9). The results for AGB show that the degree of fitting and accuracy are greatest for the Monte Carlo (MC) simulation technique based on ALS and DHP data (UBF with ALS and DHP) where R2 = 0.98, RMSE = 2.97 t/ha, MAE = 2.35 t/ha, rRMSE = 1.28 %, which performed better than MC simulation technique without ALS and DHP (UBF without ALS and DHP) where R2 = 0.94, RMSE = 4.58 t/ha, MAE = 3.64 t/ha, rRMSE = 3.29 %. The results indicate that the proposed method based on combining the UFB model, LiDAR and DHP allows us to improve the accuracy of the AGB prediction. Numéro de notice : A2023-023 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.ufug.2022.127793 Date de publication en ligne : 23/11/2022 En ligne : https://doi.org/10.1016/j.ufug.2022.127793 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102246 [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]

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

Measuring visual walkability perception using panoramic street view images, virtual reality, and deep learning / Yunqin Li in Sustainable Cities and Society, vol 86 (November 2022)  

Public [article]  inSustainable Cities and Society > vol 86 (November 2022) . - n° 104140 Titre : Measuring visual walkability perception using panoramic street view images, virtual reality, and deep learning Type de document : Article/Communication Auteurs : Yunqin Li, Auteur ; Nobuyoshi Yabuki, Auteur ; Tomohiro Fukuda, Auteur Année de publication : 2022 Article en page(s) : n° 104140 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] corrélation [Termes IGN] image panoramique [Termes IGN] image Streetview [Termes IGN] modèle de régression [Termes IGN] piéton [Termes IGN] réalité virtuelle [Termes IGN] scène urbaine [Termes IGN] segmentation sémantique [Termes IGN] vision Résumé : (auteur) Measuring perceptions of visual walkability in urban streets and exploring the associations between the visual features of the street built environment that make walking attractive to humans are both theoretically and practically important. Previous studies have used either environmental audits and subjective evaluations that have limitations in terms of cost, time, and measurement scale, or computer-aided audits based on natural street view images (SVIs) but with gaps in real perception. In this study, a virtual reality panoramic image-based deep learning framework is proposed for measuring visual walkability perception (VWP) and then quantifying and visualizing the contributing visual features. A VWP classification deep multitask learning (VWPCL) model was first developed and trained on human ratings of panoramic SVIs in virtual reality to predict VWP in six categories. Second, a regression model was used to determine the degree of correlation of various objects with one of the six VWP categories based on semantic segmentation. Furthermore, an interpretable deep learning model was used to assist in identifying and visualizing elements that contribute to VWP. The experiment validated the accuracy of the VWPCL model for predicting VWP. The results represent a further step in understanding the interplay of VWP and street-level semantics and features. Numéro de notice : A2022-816 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scs.2022.104140 Date de publication en ligne : 21/08/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104140 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101982 [article]

Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression / Haoyu Wang in Remote sensing of environment, vol 278 (September 2022)  

Public [article]  inRemote sensing of environment > vol 278 (September 2022) . - n° 113088 Titre : Mapping annual urban evolution process (2001–2018) at 250 m: A normalized multi-objective deep learning regression Type de document : Article/Communication Auteurs : Haoyu Wang, Auteur ; Xiuyuan Zhang, Auteur ; Shihong Du, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113088 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] apprentissage profond [Termes IGN] carte d'occupation du sol [Termes IGN] cartographie thématique [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] croissance urbaine [Termes IGN] image Terra-MODIS [Termes IGN] modèle de régression [Termes IGN] série temporelle [Termes IGN] surface cultivée Résumé : (auteur) Global urbanization changes land cover patterns and affects the living environment of humans. However, urbanization and its evolution process, i.e., conversions among diverse land covers, are hard to measure, as existing land cover maps usually have low temporal resolutions; conversely, long-term and temporally dense land cover maps, such as vegetation-impervious-soil decomposition maps base on MODIS, ignore the important land cover of cropland in urban evolution process (UEP). To resolve the issue, this study suggests a novel model named time-extended non-crop vegetation-impervious-cropland (Time V-I-C) to represent and quantify different stages of UEP; then, a normalized multi-objective T-ConvLSTM (NMT) method is proposed to unmix cropland, non-crop vegetation, and impervious based on the intra-annual remotely-sensed time series, and obtain their fractions in each pixel for generating UEP maps. Consequently, UEP maps from 2001 to 2018 are generated for two Chinese urban agglomerations, i.e., Beijing-Tianjin-Hebei and Yangtze River Delta urban agglomerations. The mapping results have high accuracies with a small standard error of regression (SER) of 13.1%, small root mean square error (RMSE) of 12.6%, and small mean absolute error (MAE) of 8.4%, and the maps reveal the different UEP in the two urban agglomerations. Therefore, this study provides a new idea for expressing UEP and contributes to a wide range of urbanization studies and sustainable city development. Numéro de notice : A2022-511 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.rse.2022.113088 Date de publication en ligne : 25/05/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113088 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101049 [article]

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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Developing a data-fusing method for mapping fine-scale urban three-dimensional building structure / Xinxin Wu in Sustainable Cities and Society, vol 80 (May 2022)  

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Identifying locations for new bike-sharing stations in Glasgow: an analysis of spatial equity and demand factors / Jeneva Beairsto in Annals of GIS, vol 28 n° 2 (April 2022)  

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Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 5 ([01/03/2022])  

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Influence of determinant factors towards soil erosion using ordinary least squared regression in GIS domain / Imran Ahmad in Applied geomatics, vol 14 n° 1 (March 2022)  

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A novel regression method for harmonic analysis of time series / Qiang Zhou in ISPRS Journal of photogrammetry and remote sensing, vol 185 (March 2022)  

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Aboveground biomass estimation of an agro-pastoral ecology in semi-arid Bundelkhand region of India from Landsat data: a comparison of support vector machine and traditional regression models / Dibyendu Deb in Geocarto international, vol 37 n° 4 ([15/02/2022])  

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Simulating fire-safe cities using a machine learning-based algorithm for the complex urban forms of developing nations: a case of Mumbai India / Vaibhav Kumar in Geocarto international, vol 37 n° 4 ([15/02/2022])  

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Dynamic modelling of rice leaf area index with quad-source optical imagery and machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 3 ([01/02/2022])  

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Etude de la propagation de l’onde de crue du bassin versant de la Save / Emma Bonzom (2022) 

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