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The use of land cover indices for rapid surface urban heat island detection from multi-temporal Landsat imageries / Nagihan Aslan in ISPRS International journal of geo-information, vol 10 n° 6 (June 2021)
[article]
Titre : The use of land cover indices for rapid surface urban heat island detection from multi-temporal Landsat imageries Type de document : Article/Communication Auteurs : Nagihan Aslan, Auteur ; Dilek Koc-San, Auteur Année de publication : 2021 Article en page(s) : n° 416 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] ilot thermique urbain
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] image proche infrarouge
[Termes IGN] Normalized Difference Built-up Index
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Normalized Difference Water Index
[Termes IGN] occupation du sol
[Termes IGN] Soil Adjusted Vegetation Index
[Termes IGN] température au sol
[Termes IGN] Turquie
[Termes IGN] utilisation du solRésumé : (auteur) The aims of this study were to determine surface urban heat island (SUHI) effects and to analyze the land use/land cover (LULC) and land surface temperature (LST) changes for 11 time periods from the years 2002 to 2020 using Landsat time series images. Bursa, which is the fourth largest metropolitan city in Turkey, was selected as the study area, and Landsat multi-temporal images of the summer season were used. Firstly, the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), modified normalized difference water index (MNDWI) and index-based built-up index (IBI) were created using the bands of Landsat images, and LULC classes were determined by applying automatic thresholding. The LST values were calculated using thermal images and SUHI effects were determined. The results show that NDVI, SAVI, MNDWI and IBI indices can be used effectively for the determination of the urban, vegetation and water LULC classes for SUHI studies, with overall classification accuracies between 89.60% and 95.90% for the used images. According to the obtained results, generally the LST values increased for almost all land cover areas between the years 2002 and 2020. The SUHI magnitudes were computed by using two methods, and it was found that there was an important increase in the 18-year time period. Numéro de notice : A2021-516 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10060416 Date de publication en ligne : 16/06/2021 En ligne : https://doi.org/10.3390/ijgi10060416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97936
in ISPRS International journal of geo-information > vol 10 n° 6 (June 2021) . - n° 416[article]A deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration / Daeyong Jin in Remote sensing, vol 13 n°10 (May-2 2021)
[article]
Titre : A deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration Type de document : Article/Communication Auteurs : Daeyong Jin, Auteur ; Eojin Lee, Auteur ; Kyonghwan Kwon, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2003 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] chlorophylle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Corée du sud
[Termes IGN] distribution spatiale
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] hydrodynamique
[Termes IGN] image COMS-GOCIRésumé : (auteur) In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a. Numéro de notice : A2021-417 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13102003 Date de publication en ligne : 20/05/2021 En ligne : https://doi.org/10.3390/rs13102003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97759
in Remote sensing > vol 13 n°10 (May-2 2021) . - n° 2003[article]Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data : The superiority of deep learning over a semi-empirical model / S.M. Ghosh in Computers & geosciences, vol 150 (May 2021)
[article]
Titre : Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data : The superiority of deep learning over a semi-empirical model Type de document : Article/Communication Auteurs : S.M. Ghosh, Auteur ; M.D. Behera, Auteur Année de publication : 2021 Article en page(s) : n° 104737 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande C
[Termes IGN] biomasse aérienne
[Termes IGN] classification par réseau neuronal
[Termes IGN] forêt tropicale
[Termes IGN] image Sentinel-SAR
[Termes IGN] Inde
[Termes IGN] mangrove
[Termes IGN] R (langage)Résumé : (auteur) The availability of advanced Machine Learning algorithms has made the estimation process of biophysical parameters more efficient. However, the efficiency of those methods seldom compared with the efficiency of already established semi-empirical procedures. Aboveground biomass (AGB) of mangrove forests is a crucial biophysical parameter as it is positively correlated to the carbon stocks and fluxes. The free availability of Sentinel-1 C-band SAR data and machine learning algorithms hold promises in estimating AGB of tropical mangrove forests. We reported high AGB (70 t/ha to 666 t/ha) using 185 field quadrats of 0.04ha each from Bhitarkanika Wildlife Sanctuary, located on the eastern Indian coast that could be attributed to species composition. The AGB maps generated using Interferometric Water Cloud Model (IWCM) and Deep Learning models were different from each other as they rely on different variables. IWCM was more dependent, especially on ground and vegetation components of coherence, while canopy height acted as the most crucial variable in the Deep Learning model. However, the negligible variations in Deep Learning-based AGB maps can be attributed to interpreting the importance of coherence and VH backscatter. Due to low canopy penetration power of C-band SAR, high temporal decorrelation resulting from longer time gap between interferometric image pairs, and high spatial heterogeneity of mangrove forests, IWCM found as an unsuitable method for AGB estimation. Interestingly, a Deep Learning algorithm could translate the exact relationship between predictor variables and mangrove AGB in Bhitarkanika Wildlife Sanctuary. The AGB estimation studies in mangrove forests using Sentinel data should focus more on using machine learning algorithms like Deep Learning rather than semi-empirical models. Numéro de notice : A2021-941 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104737 En ligne : https://doi.org/10.1016/j.cageo.2021.104737 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99751
in Computers & geosciences > vol 150 (May 2021) . - n° 104737[article]An area merging method in map generalization considering typical characteristics of structured geographic objects / Chengming Li in Cartography and Geographic Information Science, vol 48 n° 3 (May 2021)
[article]
Titre : An area merging method in map generalization considering typical characteristics of structured geographic objects Type de document : Article/Communication Auteurs : Chengming Li, Auteur ; Yong Yin, Auteur ; Pengda Wu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 210 - 224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Chine
[Termes IGN] conflit d'intégration
[Termes IGN] détection de contours
[Termes IGN] fusion de données
[Termes IGN] généralisation automatique de données
[Termes IGN] objet géographique zonal
[Termes IGN] occupation du sol
[Termes IGN] programmation adaptée à l'objet
[Termes IGN] structure spatiale
[Termes IGN] tessellation
[Termes IGN] ville
[Termes IGN] zone tampon
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Merging is an important operation in the map generalization of land-cover and other coverages. We define structured geographic objects as collections of adjacent areas with homogeneous semantics that are regularly arranged as spatial structures. Existing studies have concentrated on unstructured objects, which will lead to the structured ones losing part or even most of the typical characteristics during merging. Therefore, as a supplement to the existing mature merging method, a targeted method was proposed in this paper to address the merging problem of structured geographic objects. First, structured geographic objects were classified into four typical patterns, and they were identified automatically according to seven spatial structure parameters. Second, a Miter-type buffer transformation was introduced to extract the overall boundary of structured geographic objects, and areas inside the overall boundary were processed with the most appropriate merging operations for their pattern. Finally, the corresponding merged results of structured geographic objects were inserted back into the merged result of the original land-cover data by using the NOT operation, and the spatial conflicts near the boundary were adjusted. We test our method for a dataset of geographical census data for a city in China. The experimental results revealed that compared with state-of-the-art method, the proposed method produces more reasonable generalization result by effectively identifying and maintaining the typical spatial structures; moreover, the proposed method also preserves the planar tessellation characteristic of land-cover data and the balance of area variation in each land-cover class. Numéro de notice : A2021-489 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1863862 Date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1080/15230406.2020.1863862 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97530
in Cartography and Geographic Information Science > vol 48 n° 3 (May 2021) . - pp 210 - 224[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 032-2021031 RAB Revue Centre de documentation En réserve L003 Disponible Cellular automata based land-use change simulation considering spatio-temporal influence heterogeneity of light rail transit construction: A case in Nanjing, China / Jiaming Na in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)
[article]
Titre : Cellular automata based land-use change simulation considering spatio-temporal influence heterogeneity of light rail transit construction: A case in Nanjing, China Type de document : Article/Communication Auteurs : Jiaming Na, Auteur ; Jie Zhu, Auteur ; Jiazhu Zheng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 308 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] automate cellulaire
[Termes IGN] changement d'utilisation du sol
[Termes IGN] données spatiotemporelles
[Termes IGN] hétérogénéité spatiale
[Termes IGN] modèle de simulation
[Termes IGN] Nankin (Kiangsou)
[Termes IGN] réseau ferroviaire
[Termes IGN] train
[Termes IGN] transport ferroviaire
[Termes IGN] transport publicRésumé : (auteur) Light rail transit (LRT), an essential urban public transport system in China, significantly reshaped the urban land-use (LU) pattern. Although the LRT impact and land-use change (LUC) analysis plays an essential role in urban planning policy, the spatiotemporal heterogeneity of LRT impacts have not been considered in LUC simulation studies. This study simulates the urban LU change, considering the spatiotemporal heterogeneity of LRT construction impacts on urban LUC. LUC from 1995 to 2005 in Nanjing, China, is chosen as a case study. At first, the distance decay function is employed to verify the quantitative impact of LRT construction on LU change. Accordingly, the variation trends of each LU type during different stages are described in time and space. A cellular automata model incorporated by the generated LRT impact is established and then implemented for simulation. According to model performance assessment results, the proposed model can produce a realistic urban pattern with Freedom of Movement (FoM) exceeding 24% and a significantly lower relative error than the CA simulation without considering LRT influence. Numéro de notice : A2021-390 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 0.3390/ijgi10050308 Date de publication en ligne : 06/05/2020 En ligne : https://doi.org/10.3390/ijgi10050308 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97675
in ISPRS International journal of geo-information > vol 10 n° 5 (May 2021) . - n° 308[article]Detection of rainstorm pattern in arid regions using MODIS NDVI time series analysis / Mohamed E. Hereher in Geocarto international, vol 36 n° 8 ([01/05/2021])PermalinkElectrical resistivity, remote sensing and geographic information system approach for mapping groundwater potential zones in coastal aquifers of Gurpur watershed / H.S. Virupaksha in Geocarto international, vol 36 n° 8 ([01/05/2021])PermalinkEstimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])PermalinkForest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method / Hongliang Lu in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)PermalinkIdentifying urban neighborhoods with higher potential for social investment using GIS-FIS approach / Hossein Aghajani in Applied geomatics, vol 13 n° 1 (May 2021)PermalinkIntegrating a forward feature selection algorithm, random forest, and cellular automata to extrapolate urban growth in the Tehran-Karaj region of Iran / Hossein Shafizadeh-Moghadam in Computers, Environment and Urban Systems, vol 87 (May 2021)PermalinkIntegration of laser scanner and photogrammetry for heritage BIM enhancement / Yahya Alshawabkeh in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)PermalinkSelf-thinning tree mortality models that account for vertical stand structure, species mixing and climate / David I. Forrester in Forest ecology and management, Vol 487 ([01/05/2021])PermalinkA Voronoi-based method for land-use optimization using semidefinite programming and gradient descent algorithm / Vorapong Suppakitpaisarn in International journal of geographical information science IJGIS, vol 35 n° 5 (May 2021)PermalinkScalable deep learning to identify brick kilns and aid regulatory capacity / Jihyeon Lee in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 118 n° 17 (27 April 2021)Permalink