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The cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 1 (January 2023)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 1 (January 2023) . - pp 47 - 55 Titre : The cellular automata approach in dynamic modelling of land use change detection and future simulations based on remote sensing data in Lahore Pakistan Type de document : Article/Communication Auteurs : Muhammad Nasar Ahmad, Auteur ; Zhenfeng Shao, Auteur ; Akib Javed, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 47 - 55 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] automate cellulaire [Termes IGN] carte thématique [Termes IGN] classification semi-dirigée [Termes IGN] détection de changement [Termes IGN] données vectorielles [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] MNS SRTM [Termes IGN] modèle dynamique [Termes IGN] occupation du sol [Termes IGN] Pakistan [Termes IGN] surveillance de l'urbanisation [Termes IGN] utilisation du sol Résumé : (auteur) Rapid urbanization has become an immense problem in Lahore city, causing various socio-economic and environmental problems. Therefore, it is noteworthy to monitor land use/land cover (LULC) change detection and future LULC patterns in Lahore. The present study focuses on evaluating the current extent and modeling the future LULC developments in Lahore, Pakistan. Therefore, the semi-automatic classification model has been applied for the classification of Landsat satellite imagery from 2000 to 2020. And the Modules of Land Use Change Evaluation (MOLUSCE) cellular automata (CA-ANN) model was implemented to simulate future land use trends for the years 2030 and 2040. This study project made use of Landsat, Shuttle Radar Topography Mission Digital Elevation Model, and vector data. The research methodology includes three main steps: (i) semi-automatic land use classification using Landsat data from 2000 to 2020; (ii) future land use prediction using the CA-ANN (MOLUSCE) model; and (iii) monitoring change detection and interpretation of results. The research findings indicated that there was a rise in urban areas and a decline in vegetation, barren land, and water bodies for both the past and future projections. The results also revealed that about 27.41% of the urban area has been increased from 2000 to 2020 with a decrease of 42.13% in vegetation, 2.3% in barren land, and 6.51% in water bodies, respectively. The urban area is also expected to grow by 23.15% between 2020 and 2040, whereas vegetation, barren land, and water bodies will all decline by 28.05%, 1.8%, and 12.31%, respectively. Results can also aid in the long-term, sustainable planning of the city. It was also observed that the majority of the city's urban area expansion was found to have occurred in the city's eastern and southern regions. This research also suggests that decision-makers and municipal Government should reconsider city expansion strategies. Moreover, the future city master plans of 2050 must emphasize the relevance of rooftop urban planting and natural resource conservation. Numéro de notice : A2023-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : https://doi.org/10.14358/PERS.22-00102R2 Date de publication en ligne : 01/01/2023 En ligne : https://doi.org/10.14358/PERS.22-00102R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102357 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2023011	SL	Revue	Centre de documentation	Revues en salle	Disponible

Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models / Saadia Sultan Wahlaa in Geocarto international, vol 37 n° 27 ([20/12/2022])  

Public [article]  inGeocarto international > vol 37 n° 27 [20/12/2022] . - pp Titre : Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models Type de document : Article/Communication Auteurs : Saadia Sultan Wahlaa, Auteur ; Jamil Hasan Kazmi, Auteur ; Alireza Sharifi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] changement climatique [Termes IGN] classification par arbre de décision [Termes IGN] évapotranspiration [Termes IGN] Indice de précipitations antérieures [Termes IGN] modèle de simulation [Termes IGN] Pakistan [Termes IGN] prévision météorologique [Termes IGN] sécheresse Résumé : (auteur) Droughts may inflict significant damage to agricultural and water supplies, resulting in substantial financial losses as well as the death of people and livestock. This study intends to anticipate droughts by studying the changes of an acceptable index using appropriate climatic factors. This study was divided into three phases, first being the determination of the Standardized Precipitation Evapotranspiration (SPEI) index for the Cholistan, Punjab, Pakistan area based on a dataset spanning 1980 to 2020. The indices are calculated at different monthly intervals which could to predict short-term periods for the Cholistan in Pakistan, we selected two distinctive time periods of one month (SPEI–1) and three months (SPEI–3). The second phase involved dividing the data into three sample sizes, which were used for training data from 1980 to 2010, testing data from 2011 to 2015 and validation data from 2016 to 2020. The utilization of the random forest (RF) algorithm to train and evaluate the data using a variety of climate variables e.g. potential evapotranspiration, rainfall, vapor pressure cloud cover, and mean, minimum and maximum, temperature. The final phase was to analyze the performance of the model based on statistical metrics and drought classes. Based on these considerations, statistical measures, such as the Coefficient of Determination (R2) and the Root Mean Square Error (RMSE) approach, were used to evaluate the performance of the test group throughout the testing period. The model's performance revealed the satisfactory results with R2 values of 0.80 and 0.78, for SPEI–1 and SPEI–3 situations, respectively. Following the data analysis, it was discovered that the validation period had a receiving operating curve and area under the Curve (ROC-AUC) of 0.87 for the SPEI–1 case and 0.85 for the SPEI–3 case. In this context, the results indicate that the SPEI may be useful as a prediction tool for drought prediction and the performances the RF model was suitable for both timescales. However, a more rigorous analysis with a larger dataset or a combination of datasets from different areas might be more beneficial for generalization over more extended time periods provide additional insights. Numéro de notice : A2022-934 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2093411 Date de publication en ligne : 30/06/2022 En ligne : https://doi.org/10.1080/10106049.2022.2093411 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102672 [article]

The simulation and prediction of land surface temperature based on SCP and CA-ANN models using remote sensing data: A case study of Lahore / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 12 (December 2022) . - pp 783 - 790 Titre : The simulation and prediction of land surface temperature based on SCP and CA-ANN models using remote sensing data: A case study of Lahore Type de document : Article/Communication Auteurs : Muhammad Nasar Ahmad, Auteur ; Shao Zhengfeng, Auteur ; Andaleeb Yaseen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 783 - 790 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] changement climatique [Termes IGN] changement d'utilisation du sol [Termes IGN] classification par réseau neuronal [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] MNS SRTM [Termes IGN] modèle de simulation [Termes IGN] Pakistan [Termes IGN] planification urbaine [Termes IGN] température au sol Résumé : (auteur) Over the last two decades, urban growth has become a major issue in Lahore, accelerating land surface temperature (LST) rise. The present study focused on estimating the current situation and simulating the future LST patterns in Lahore using remote sensing data and machine learning models. The semi-automated classification model was applied for the estimation of LST from 2000 to 2020. Then, the cellular automata-artificial neural networks (CA-ANN) module was implemented to predict future LST patterns for 2030 and 2040, respectively. Our research findings revealed that an average of 2.8 °C of land surface temperature has increased, with a mean LST value from 37.25 °C to 40.10 °C in Lahore during the last two decades from 2000 to 2020. Moreover, keeping CA-ANN simulations for land surface temperature, an increase of 2.2 °C is projected through 2040, and mean LST values will be increased from 40.1 °C to 42.31 °C by 2040. The CA-ANN model was validated for future LST simulation with an overall Kappa value of 0.82 and 86.2% of correctness for the years 2030 and 2040 using modules for land-use change evaluation. The study also indicates that land surface temperature is an important factor in environmental changes. Therefore, it is suggested that future urban planning should focus on urban rooftop plantations and vegetation conservation to minimize land surface temperature increases in Lahore. Numéro de notice : A2022-886 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00071R2 Date de publication en ligne : 01/12/2022 En ligne : https://doi.org/10.14358/PERS.22-00071R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102208 [article]

PS-InSAR based validated landslide susceptibility modelling: a case study of Ghizer valley, Northern Pakistan / Sajid Hussain in Geocarto international, vol 37 n° 13 ([15/07/2022])  

Public [article]  inGeocarto international > vol 37 n° 13 [15/07/2022] . - pp 3941 - 3962 Titre : PS-InSAR based validated landslide susceptibility modelling: a case study of Ghizer valley, Northern Pakistan Type de document : Article/Communication Auteurs : Sajid Hussain, Auteur ; Sun Hongxing, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3941 - 3962 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] aléa [Termes IGN] effondrement de terrain [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] Pakistan Résumé : (auteur) Northern Pakistan is a rugged mountainous area that is seismically active, high gradients, disintegrated lithology, and glaciers in the high peaks. District Ghizer lies among the most vulnerable areas and experience landslides every year due to different causative factors. This study has carried out to prepare a detailed landslide inventory and to develop a susceptibility model for the area. The most followed and probabilistic approach, Frequency Ratio (FR) model and a semi-qualitative Analytical Hierarchy Process (AHP) approach were applied to find the correlation between causative factors and mapped landslides. Persistent Scatterer Interferometry (PSI) Interferometric Synthetic Aperture Radar (InSAR) technique was applied to check deformation movement in the susceptible zones of extracted models, which showed the high Line of Sight (LOS) deformation velocity in high susceptible zones of both models. The extracted Landslide Susceptibility Index (LSI) models showed 82.82% and 73.43% of prediction accuracy for FR and AHP method calculated by Area Under Curve (AUC) of Receiver operating characteristic (ROC) method. The models revealed Slope, barrenness, and Geology are the main causative factors of landslide activities in the study area. Finally, both Landslide susceptibility index maps were classified into five susceptibility classes. As the study area is very prone to landslide disasters so these susceptibility models will be helpful to delineate hazardous zones for the medication of future landslides disasters in the area as well as it can be used as a tool in the planning strategies by decision-makers in development projects in the area. Numéro de notice : A2022-589 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1870165 Date de publication en ligne : 11/02/2021 En ligne : https://doi.org/10.1080/10106049.2020.1870165 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101363 [article]

Dynamic linkage between urbanization, electrical power consumption, and suitability analysis using remote sensing and GIS techniques / Muhammad Nasar Ahmad in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 3 (March 2022) . - pp 171 - 179 Titre : Dynamic linkage between urbanization, electrical power consumption, and suitability analysis using remote sensing and GIS techniques Type de document : Article/Communication Auteurs : Muhammad Nasar Ahmad, Auteur ; Qimin Cheng, Auteur ; Fang Luo, Auteur Année de publication : 2022 Article en page(s) : pp 171 - 179 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] consommation [Termes IGN] densité de population [Termes IGN] éclairage public [Termes IGN] électricité [Termes IGN] étalement urbain [Termes IGN] image DMSP-OLS [Termes IGN] image Landsat-OLI [Termes IGN] image Landsat-TM [Termes IGN] Pakistan [Termes IGN] prise de vue nocturne [Termes IGN] urbanisation Résumé : (auteur) This article proposes an estimation method for assessing urban sprawl using multispectral remote sensing data: SNPP-VIIRS, DMSP/OLS, Landsat 5-TM, and Landsat 8-OLI. This study focuses on the impacts of human activities, in terms of increased electrical-power consumption (EPC) due to urbanization. For this purpose, night-time light data are used to measure the EPC growth from 2000 to 2020. We also perform a suitability analysis using geographic information-systems techniques to propose a new urban town in Lahore to mitigate urbanization and EPC increase. We found an overall increase of 33% in urban area and an EPC increase of 21.6% in the last two decades. We also find that the best proposed site for the new urban town is in the northwest of Lahore. Numéro de notice : A2022-201 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00026R3 Date de publication en ligne : 01/03/2022 En ligne : https://doi.org/10.14358/PERS.21-00026R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100004 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2022031	SL	Revue	Centre de documentation	Revues en salle	Disponible

Analysis of factors affecting adoption of volunteered geographic information in the context of national spatial data infrastructure / Munir Ahmad in ISPRS International journal of geo-information, vol 11 n° 2 (February 2022)  

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Simulation of the meltwater under different climate change scenarios in a poorly gauged snow and glacier-fed Chitral River catchment (Hindukush region) / Huma Hayat in Geocarto international, vol 37 n° 1 ([01/01/2022])  

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Vegetation index and dynamics / Eusebio Cano Carmona (2022)  

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Estimation of surface deformation due to Pasni earthquake using RADAR interferometry / Muhammad Ali in Geocarto international, vol 36 n° 14 ([01/08/2021])  

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GIS-based spatial landslide distribution analysis of district Neelum, AJ&K, Pakistan / Shah Naseer in Natural Hazards, vol 106 n° 1 (March 2021)  

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Geo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan / Muhammad Imran in Geocarto international, vol 36 n° 2 ([01/02/2021])  

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Mitigating urban visual pollution through a multistakeholder spatial decision support system to optimize locational potential of billboards / Khydija Wakil in ISPRS International journal of geo-information, vol 10 n° 2 (February 2021)  

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Remote sensing of precipitation, 2. Volume 2 / Silas Michaelides (2019)  

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Geological structures control on earthquake ruptures: The Mw7.7, 2013, Balochistan earthquake, Pakistan / A. Vallage in Geophysical research letters, vol 43 n° 19 (15 October 2016)  

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Inelastic surface deformation during the 2013 Mw 7.7 Balochistan, Pakistan, earthquake / A. Vallage in Geology, vol 43 n° 12 (December 2015)  

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