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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)
[article]
Titre : The fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Tianyu Guo, Auteur ; Chengye Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 665 - 671 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] Chine
[Termes IGN] couvert végétal
[Termes IGN] Google Earth Engine
[Termes IGN] hétérogénéité spatiale
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] industrie minière
[Termes IGN] mine
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] régression géographiquement pondérée
[Termes IGN] régression multiple
[Termes IGN] réseau neuronal artificielRésumé : (auteur) To determine the fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas, six types of data were used as driving factors and three methods —multi-linear regression (MLR), geographically weighted regression (GWR), and geographically weighted artificial neural network (GWANN)— were adopted in the modeling. The experiments, conducted in Shengli mining areas located in Xilinhot city, China, show that the MLR model without consideration of spatial heterogeneity and spatial non-stationarity performs the worst and that the GWR model presents obvious location differences, since it predefines a linear relationship which is unable to describe FVC for some locations. The GWANN model, improving on these defects, is the most suitable model for the FVC driving process in mining areas; it outperforms the other two models, with root-mean-square error (RMSE) and mean absolute percentage error (MAPE) reaching 0.16 and 0.20. It has improvements of approximately 24% in RMSE and 33% in MAPE compared to the MLR model, and those values grow to 59% and 71% when compared with the GWR model. Numéro de notice : A2022-813 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00070R3 Date de publication en ligne : 01/10/2022 En ligne : https://doi.org/10.14358/PERS.21-00070R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101973
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 10 (October 2022) . - pp 665 - 671[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2022101 SL Revue Centre de documentation Revues en salle Disponible Simulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices / Najmeh Mozaffaree Pour in Environmental Monitoring and Assessment, vol 194 n° 9 (September 2022)
[article]
Titre : Simulation of land use/land cover changes and urban expansion in Estonia by a hybrid ANN-CA-MCA model and utilizing spectral-textural indices Type de document : Article/Communication Auteurs : Najmeh Mozaffaree Pour, Auteur ; Oleksandr Karasov, Auteur ; Iuliia Burdun, Auteur ; Tõnu Oja, Auteur Année de publication : 2022 Article en page(s) : n° 584 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] chaîne de Markov
[Termes IGN] croissance urbaine
[Termes IGN] Estonie
[Termes IGN] Google Earth Engine
[Termes IGN] image Landsat-8
[Termes IGN] modèle de simulation
[Termes IGN] occupation du sol
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Over the recent two decades, land use/land cover (LULC) drastically changed in Estonia. Even though the population decreased by 11%, noticeable agricultural and forest land areas were turned into urban land. In this work, we analyzed those LULC changes by mapping the spatial characteristics of LULC and urban expansion in the years 2000–2019 in Estonia. Moreover, using the revealed spatiotemporal transitions of LULC, we simulated LULC and urban expansion for 2030. Landsat 5 and 8 data were used to estimate 147 spectral-textural indices in the Google Earth Engine cloud computing platform. After that, 19 selected indices were used to model LULC changes by applying the hybrid artificial neural network, cellular automata, and Markov chain analysis (ANN-CA-MCA). While determining spectral-textural indices is quite common for LULC classifications, utilization of these continues indices in LULC change detection and examining these indices at the landscape scale is still in infancy. This country-wide modeling approach provided the first comprehensive projection of future LULC utilizing spectral-textural indices. In this work, we utilized the hybrid ANN-CA-MCA model for predicting LULC in Estonia for 2030; we revealed that the predicted changes in LULC from 2019 to 2030 were similar to the observed changes from 2011 to 2019. The predicted change in the area of artificial surfaces was an increased rate of 1.33% to reach 787.04 km2 in total by 2030. Between 2019 and 2030, the other significant changes were the decrease of 34.57 km2 of forest lands and the increase of agricultural lands by 14.90 km2 and wetlands by 9.31 km2. These findings can develop a proper course of action for long-term spatial planning in Estonia. Therefore, a key policy priority should be to plan for the stable care of forest lands to maintain biodiversity. Numéro de notice : A2022-458 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE/URBANISME Nature : Article DOI : 10.1007/s10661-022-10266-7 Date de publication en ligne : 13/07/2022 En ligne : http://dx.doi.org/10.1007/s10661-022-10266-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101258
in Environmental Monitoring and Assessment > vol 194 n° 9 (September 2022) . - n° 584[article]Spatial–spectral attention network guided with change magnitude image for land cover change detection using remote sensing images / Zhiyong Lv in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)
[article]
Titre : Spatial–spectral attention network guided with change magnitude image for land cover change detection using remote sensing images Type de document : Article/Communication Auteurs : Zhiyong Lv, Auteur ; Fengjun Wang, Auteur ; Guoqing Cui, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4412712 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] détection de changement
[Termes IGN] image Landsat-TM
[Termes IGN] jeu de données
[Termes IGN] occupation du sol
[Termes IGN] prévention des risques
[Termes IGN] réseau neuronal siamoisRésumé : (auteur) Land cover change detection (LCCD) using remote sensing images (RSIs) plays an important role in natural disaster evaluation, forest deformation monitoring, and wildfire destruction detection. However, bitemporal images are usually acquired at different atmospheric conditions, such as sun height and soil moisture, which usually cause pseudo and noise change in the change detection map. Changed areas on the ground also generally have various shapes and sizes, consequently making the utilization of spatial contextual information a challenging task. In this article, we design a novel neural network with a spatial–spectral attention mechanism and multiscale dilation convolution modules. This work is based on the previously demonstrated promising performance of convolutional neural network for LCCD with RSIs and attempts to capture more positive changes and further enhance the detection accuracies. The learning of the proposed neural network is guided with a change magnitude image. The performance and feasibility of the proposed network are validated with four pairs of RSIs that depict real land cover change events on the Earth’s surface. Comparison of the performance of the proposed approach with that of five state-of-art methods indicates the superiority of the proposed network in terms of ten quantitative evaluation metrics and visual performance. Such as, the proposed network achieved an improvement of about 0.08%–14.87% in terms of overall accuracy (OA) for Dataset-A. Numéro de notice : A2022-660 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3197901 Date de publication en ligne : 17/08/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3197901 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101516
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 8 (August 2022) . - n° 4412712[article]Heat wave-induced augmentation of surface urban heat islands strongly regulated by rural background / Shiqi Miao in Sustainable Cities and Society, vol 82 (July 2022)
[article]
Titre : Heat wave-induced augmentation of surface urban heat islands strongly regulated by rural background Type de document : Article/Communication Auteurs : Shiqi Miao, Auteur ; Wenfeng Zhan, Auteur ; Jiameng Lai, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103874 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] changement climatique
[Termes IGN] Chine
[Termes IGN] climat tropical
[Termes IGN] couvert végétal
[Termes IGN] densité de la végétation
[Termes IGN] données environnementales
[Termes IGN] forêt
[Termes IGN] humidité de l'air
[Termes IGN] ilot thermique urbain
[Termes IGN] image Terra-MODIS
[Termes IGN] nuit
[Termes IGN] température au sol
[Termes IGN] zone humide
[Termes IGN] zone ruraleRésumé : (auteur) The impact of heat waves (HWs) on surface urban heat islands (SUHIs) has been widely studied, but the spatial pattern of SUHI responsiveness to HWs across various climates remains unclear, and the influence of HW intensity on SUHI responsiveness has not been systematically quantified. Using MODIS land surface temperature data, here we investigated the responsiveness of SUHI to HWs (quantified as ∆I) as well as its variations with HW intensity in 354 cities in seven climate zones across China. We find that during HW periods, the SUHI and surface urban cool island are augmented in the humid and arid regions of China, respectively. The inter-city heterogeneity in rural vegetation coverage accounts for such a spatial pattern. In eastern China, the ∆I peaks in the north subtropical climate (0.72 ± 0.54 K for daytime and 0.29 ± 0.23 K for the nighttime) probably for its specific rural farming method. With the intensification of HWs, the augmentation effect can be further enhanced for the north subtropical, warm temperate, and arid temperate climates during the day and for almost all the climates at night. These findings can help advance the understanding of the responsiveness of SUHI to extreme climatic events. Numéro de notice : A2022-375 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.scs.2022.103874 Date de publication en ligne : 13/04/2022 En ligne : https://doi.org/10.1016/j.scs.2022.103874 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100624
in Sustainable Cities and Society > vol 82 (July 2022) . - n° 103874[article]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)
[article]
Titre : Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction Type de document : Article/Communication Auteurs : Tianhong Zhao, Auteur ; Zhengdong Huang, Auteur ; Wei Tu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage profond
[Termes IGN] bati
[Termes IGN] données spatiotemporelles
[Termes IGN] gestion de trafic
[Termes IGN] graphe
[Termes IGN] logement
[Termes IGN] migration pendulaire
[Termes IGN] modèle de simulation
[Termes IGN] régression géographiquement pondérée
[Termes IGN] service public
[Termes IGN] Shenzhen
[Termes IGN] système de transport intelligent
[Termes IGN] transport public
[Termes IGN] transport urbainRésumé : (auteur) Accurate and robust short-term bus travel prediction facilitates operating the bus fleet to provide comfortable and flexible bus services. The built environment, including land use, buildings, and public facilities, has an important influence on bus travel demand prediction. However, previous studies regarded the built environment as a static feature thus even ignored its influence on bus travel in deep learning framework. To fill this gap, we propose a graph deep learning-based approach coupling with spatiotemporal influence of built environment (GDLBE) to enhance short-term bus travel demand prediction. A time-dependent geographically weighted regression method is used to resolve the dynamic influence of the built environment on bus travel demand at different times of the day. A graph deep learning module is used to capture the comprehensive spatial and temporal dependency behind massive bus travel demand. The short-term bus travel demand is predicted by fusing the dynamic built environment influences and spatiotemporal dependency. An experiment in Shenzhen is conducted to evaluate the performance of the proposed approach. Baseline methods are compared, and the results demonstrate that the proposed approach outperforms the baselines. These results will help bus fleet dispatch for smart transportation. Numéro de notice : A2022-245 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101776 Date de publication en ligne : 12/03/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100185
in Computers, Environment and Urban Systems > vol 94 (June 2022) . - n° 101776[article]A GIS-based approach for identification of optimum runoff harvesting sites and storage estimation: a study from Subarnarekha-Kangsabati Interfluve, India / Manas Karmakar in Applied geomatics, vol 14 n° 2 (June 2022)PermalinkGraph-based block-level urban change detection using Sentinel-2 time series / Nan Wang in Remote sensing of environment, vol 274 (June 2022)PermalinkLarge-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images / Lingdong Mao in Landscape and Urban Planning, vol 222 (June 2022)PermalinkMulti-objective optimization of urban environmental system design using machine learning / Peiyuan Li in Computers, Environment and Urban Systems, vol 94 (June 2022)PermalinkThe interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria / Alfred S. Alademomi in Applied geomatics, vol 14 n° 2 (June 2022)PermalinkMulti-resolution representation using graph database / Yizhi Huang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)PermalinkClassification of vegetation classes by using time series of Sentinel-2 images for large scale mapping in Cameroon / Hermann Tagne in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)PermalinkFusion of optical, radar and waveform LiDAR observations for land cover classification / Huiran Jin in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)PermalinkSmartphone digital photography for fractional vegetation cover estimation / Gaofei Yin in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)PermalinkDetecting land use and land cover change on Barbuda before and after the Hurricane Irma with respect to potential land grabbing: A combined volunteered geographic information and multi sensor approach / Andreas Rienow in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)Permalink