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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)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 5 (May 2021) . - n° 308 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 public Résumé : (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 [article]

Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions / Xin-Ming Zhu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 2680 - 2697 Titre : Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions Type de document : Article/Communication Auteurs : Xin-Ming Zhu, Auteur ; Xiao-Ning Song, Auteur ; Pei Leng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2680 - 2697 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Chine [Termes IGN] correction atmosphérique [Termes IGN] hétérogénéité spatiale [Termes IGN] image Landsat-8 [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] régression [Termes IGN] température au sol [Termes IGN] variable biophysique (végétation) Résumé : (Auteur) Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi’an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases. Numéro de notice : A2021-219 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3002821 date de publication en ligne : 26/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3002821 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97211 [article]

Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling / Stefanos Georganos in Geocarto international, vol 36 n° 2 ([01/02/2021])  

Public [article]  inGeocarto international > vol 36 n° 2 [01/02/2021] . - pp 121 -1 36 Titre : Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling Type de document : Article/Communication Auteurs : Stefanos Georganos, Auteur ; Tais Grippa, Auteur ; Assane Niang Gadiaga, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 121 -1 36 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] autocorrélation spatiale [Termes IGN] classification par forêts aléatoires [Termes IGN] Dakar [Termes IGN] densité de population [Termes IGN] distribution spatiale [Termes IGN] hétérogénéité spatiale [Termes IGN] modèle dynamique [Termes IGN] population [Termes IGN] utilisation du sol Résumé : (auteur) Machine learning algorithms such as Random Forest (RF) are being increasingly applied on traditionally geographical topics such as population estimation. Even though RF is a well performing and generalizable algorithm, the vast majority of its implementations is still ‘aspatial’ and may not address spatial heterogenous processes. At the same time, remote sensing (RS) data which are commonly used to model population can be highly spatially heterogeneous. From this scope, we present a novel geographical implementation of RF, named Geographical Random Forest (GRF) as both a predictive and exploratory tool to model population as a function of RS covariates. GRF is a disaggregation of RF into geographical space in the form of local sub-models. From the first empirical results, we conclude that GRF can be more predictive when an appropriate spatial scale is selected to model the data, with reduced residual autocorrelation and lower Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values. Finally, and of equal importance, GRF can be used as an effective exploratory tool to visualize the relationship between dependent and independent variables, highlighting interesting local variations and allowing for a better understanding of the processes that may be causing the observed spatial heterogeneity. Numéro de notice : A2021-080 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1595177 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1595177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96822 [article]

Deriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 / Helena Bergstedt in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6008 - 6019 Titre : Deriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 Type de document : Article/Communication Auteurs : Helena Bergstedt, Auteur ; Annett Bartsch, Auteur ; Anton Neureiter, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6008 - 6019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Autriche [Termes IGN] bande C [Termes IGN] courbe de Pearson [Termes IGN] dégel [Termes IGN] Finlande [Termes IGN] fonte des glaces [Termes IGN] hétérogénéité spatiale [Termes IGN] image MetOp-ASCAT [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] pergélisol [Termes IGN] rétrodiffusion [Termes IGN] série temporelle [Termes IGN] télédétection en hyperfréquence [Termes IGN] température au sol Résumé : (auteur) Surface state data derived from spaceborne microwave sensors with suitable temporal sampling are to date only available in low spatial resolution (25–50 km). Current approaches do not adequately resolve spatial heterogeneity in landscape-scale freeze–thaw processes. We propose to derive a frozen fraction instead of binary freeze–thaw information. This introduces the possibility to monitor the gradual freezing and thawing of complex landscapes. Frozen fractions were retrieved from Advanced Scatterometer (ASCAT, C-band) backscatter on a 12.5-km grid for three sites in noncontinuous permafrost areas in northern Finland and the Austrian Alps. To calibrate the retrieval approach, frozen fractions based on Sentinel-1 synthetic aperture radar (SAR, C-band) were derived for all sites and compared to ASCAT backscatter. We found strong relationships for ASCAT backscatter with Sentinel-1 derived frozen fractions (Pearson correlations of −0.85 to −0.96) for the sites in northern Finland and less strong relationships for the Alpine site (Pearson correlations −0.579 and −0.611, including and excluding forested areas). Applying the derived linear relationships, predicted frozen fractions using ASCAT backscatter values showed root mean square error (RMSE) values between 7.26% and 16.87% when compared with Sentinel-1 frozen fractions. The validation of the Sentinel-1 derived freeze–thaw classifications showed high accuracy when compared to in situ near-surface soil temperature (84.7%–94%). Results are discussed with regard to landscape type, differences between spring and autumn, and gridding. This article serves as a proof of concept, showcasing the possibility to derive frozen fraction from coarse spatial resolution scatterometer time series to improve the representation of spatial heterogeneity in landscape-scale surface state. Numéro de notice : A2020-525 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2967364 date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2967364 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95702 [article]

Modelling housing rents using spatial autoregressive geographically weighted regression: a case study in cracow, Poland / Mateusz Tomal in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 20 p. Titre : Modelling housing rents using spatial autoregressive geographically weighted regression: a case study in cracow, Poland Type de document : Article/Communication Auteurs : Mateusz Tomal, Auteur Année de publication : 2020 Article en page(s) : 20 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] autorégression [Termes IGN] bien immobilier [Termes IGN] Cracovie (Pologne) [Termes IGN] distribution spatiale [Termes IGN] économétrie [Termes IGN] évaluation foncière [Termes IGN] gestion foncière [Termes IGN] hétérogénéité spatiale [Termes IGN] méthode de Monte-Carlo [Termes IGN] méthode des moindres carrés [Termes IGN] régression géographiquement pondérée Résumé : (auteur) The proportion of tenants will undoubtedly rise in Poland, where at present, the ownership housing model is very dominant. As a result, the rental housing market in Poland is currently under-researched in comparison with owner-occupancy. In order to narrow this research gap, this study attempts to identify the determinants affecting rental prices in Cracow. The latter were obtained from the internet platform otodom.pl using the web scraping technique. To identify rent determinants, ordinary least squares (OLS) regression and spatial econometric methods were used. In particular, traditional spatial autoregressive model (SAR) and spatial autoregressive geographically weighted regression (GWR-SAR) were employed, which made it possible to take into account the spatial heterogeneity of the parameters of determinants and the spatially changing spatial autocorrelation of housing rents. In-depth analysis of rent determinants using the GWR-SAR model exposed the complexity of the rental market in Cracow. Estimates of the above model revealed that many local markets can be identified in Cracow, with different factors shaping housing rents. However, one can identify some determinants that are ubiquitous for almost the entire city. This concerns mainly the variables describing the area of the flat and the age of the building. Moreover, the Monte Carlo test indicated that the spatial autoregressive parameter also changes significantly over space. Numéro de notice : A2020-314 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060346 date de publication en ligne : 26/05/2020 En ligne : https://doi.org/10.3390/ijgi9060346 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95169 [article]

A comprehensive framework for studying diffusion patterns of imported dengue with individual-based movement data / Haiyan Tao in International journal of geographical information science IJGIS, vol 34 n° 3 (March 2020)  

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The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study / Yanan Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

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Streambank topography: an accuracy assessment of UAV-based and traditional 3D reconstructions / Benjamin U. Meinen in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)  

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Accurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits / Tawanda W. Gara in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

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Testing spatial heterogeneity in geographically weighted principal components analysis / Javier Roca-Pardiñas in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)  

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Exposure-related forest-steppe: A diverse landscape type determined by topography and climate / Martin Hais in Journal of Arid Environments, vol 135 (December 2016)  

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Combined effects of area, connectivity, history and structural heterogeneity of woodlands on the species richness of hoverflies (Diptera: Syrphidae) / Pierre-Alexis Herrault in Landscape ecology, vol 31 n° 4 (May 2016)

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Mapping the value of ecosystem services: A case study from the Austrian Alps / Alessandro Paletto in Annals of forest research, vol 58 n° 1 (January 2015)  

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Spatial chromatic model in high-dimensional spaces and the uniqueness of chromatic code: a new perspective of geographic entity–space relationship / Weining Zhu in International journal of geographical information science IJGIS, vol 29 n° 1 (January 2015)  

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La construction d'une matrice de flux à partir de traces de téléphones portables / Françoise Bahoken in Cartes & Géomatique, n° 217 (septembre 2013)    

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