Land change modeler and CA-Markov chain analysis for land use land cover change using satellite data of Peshawar, Pakistan
Tài liệu tham khảo
Abdullahi, 2018, Land use change modeling and the effect of compact city paradigms: integration of GIS-based cellular automata and weights-of-evidence techniques, Environ. Earth Sci., 77, 1, 10.1007/s12665-018-7429-z
Ahmed, 2012, Modeling urban land cover growth dynamics using multioral satellite images: a case study of Dhaka, Bangladesh, ISPRS Int. J. Geo-Inf., 1, 3, 10.3390/ijgi1010003
Al-Najjar, 2019, Land cover classification from fused DSM and UAV images using convolutional neural networks, Rem. Sens., 11, 1, 10.3390/rs11121461
Arsanjani, 2013, Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion, Int. J. Appl. Earth Obs. Geoinf., 21, 265
Arsanjani, 2012, Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion, Int. J. Appl. Earth Obs. Geoinf., 21, 265
Bagan, 2015, Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data, GIScience Remote Sens., 52, 765, 10.1080/15481603.2015.1072400
Baqa, 2021, Monitoring and modeling the patterns and trends of urban growth using urban sprawl matrix and CA-markov model: a case study of Karachi, Pakistan, Land, 10, 700, 10.3390/land10070700
Baqa, 2022, Characterizing spatiotemporal variations in the urban thermal environment related to land cover changes in Karachi, Pakistan, from 2000 to 2020, Rem. Sens., 14, 2164, 10.3390/rs14092164
Behera, 2012, Modelling and analyzing the watershed dynamics using Cellular Automata (CA)-Markov model - a geo-information based approach, J. Earth Syst. Sci., 121, 1011, 10.1007/s12040-012-0207-5
Bera, 2022, Integrated influencing mechanism of potential drivers on seasonal variability of LST in Kolkata municipal corporation, India, Land, 11, 1461, 10.3390/land11091461
Bhunia, 2018, Assessment of spatial variability of soil properties using geostatistical approach of lateritic soil (West Bengal, India), Ann. Agrar. Sci., 16, 436, 10.1016/j.aasci.2018.06.003
Chou, 2019, Research on the variation characteristics of climatic elements from April to September in China's main grain-producing areas, Theor. Appl. Climatol., 137, 3197, 10.1007/s00704-019-02795-y
Dadras, 2015, Spatio-temporal analysis of urban growth from remote sensing data in Bandar Abbas city, Iran. Egypt, J. Remote Sens. Sp. Sci., 18, 35
Dang, 2011, Evaluation of food risk parameters in the day river flood diversion area, red river delta, vietnam, Nat. Hazards, 56, 169, 10.1007/s11069-010-9558-x
Das, 2021, Assessment of variation of land use/land cover and its impact on land surface temperature of Asansol subdivision. Egypt, J. Remote Sens. Sp. Sci., 24, 131
Deilami, 2018, Urban heat island effect: a systematic review of spatio-temporal factors, data, methods, and mitigation measures, Int. J. Appl. Earth Obs. Geoinf., 67, 30
Deng, 2017, Spatio-temporal change of lake water extent in Wuhan urban agglomeration based on Landsat images from 1987 to 2015, Rem. Sens., 9, 10.3390/rs9030270
Dong, 2009, Spatio-temporal changes in annual accumulated temperature in China and the effects on cropping systems, 1980s to 2000, Clim. Res., 40, 37, 10.3354/cr00823
Elliott, 2015, Urbanization as socioenvironmental succession: the case of hazardous industrial site accumulation, Am. J. Sociol., 120, 1736, 10.1086/681715
Fan, 2008, Temporal and spatial change detecting (1998-2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images, Environ. Monit. Assess., 137, 127, 10.1007/s10661-007-9734-y
Fu, 2018, Deriving suitability factors for CA-Markov land use simulation model based on local historical data, J. Environ. Manag., 206, 10, 10.1016/j.jenvman.2017.10.012
Galicia, 2007, Land use and land cover change in highland temperate forest in the Izta-Popo National Park, Central Mexico, Mt. Res. Dev., 27, 48, 10.1659/0276-4741(2007)27[48:LUALCC]2.0.CO;2
Ghaderizadeh, 2022, Multiscale dual-branch residual spectral-spatial network with attention for hyperspectral image classification, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 15, 5455, 10.1109/JSTARS.2022.3188732
Haq, 2021, Exploring and understanding the floristic richness, life-form, leaf-size spectra and phenology of plants in protected forests: a case study of Dachigam National Park in Himalaya, Asia, Acta Ecol. Sin., 41, 479, 10.1016/j.chnaes.2021.07.010
Hassan, 2016, Dynamics of land use and land cover change (LULCC) using geospatial techniques: a case study of Islamabad Pakistan, SpringerPlus, 5, 812, 10.1186/s40064-016-2414-z
Hou, 2019, Scenario-based modelling for urban sustainability focusing on changes in cropland under rapid urbanization: a case study of Hangzhou from 1990 to 2035, Sci. Total Environ., 661, 422, 10.1016/j.scitotenv.2019.01.208
Hu, 2013, Exploring the use of google earth imagery and object-based methods in land use/cover mapping, Rem. Sens., 5, 6026, 10.3390/rs5116026
Hu, 2015, Deep convolutional neural networks for hyperspectral image classification, J. Sens., 10.1155/2015/258619
Huang, 2002, An assessment of support vector machines for land cover classification, Int. J. Rem. Sens., 23, 725, 10.1080/01431160110040323
Hussain, 2022, Spatiotemporal variation in land use land cover in the response to local climate change using multispectral remote sensing data, Land, 11, 595, 10.3390/land11050595
Iqbal, 2000, A Baseline survey for the development of livestock sector in Cholistan, Jt. Publ. AERU, AARI, Faisalabad, SSI, NARC, Islam. GTZ, Lahore, 4
Jalayer, 2022, Modeling and predicting land use land cover spatiotemporal changes: a case study in chalus watershed, Iran, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 15, 5496, 10.1109/JSTARS.2022.3189528
Jalayer, 2022, Modeling and predicting land use land cover spatiotemporal changes: a case study in chalus watershed, Iran, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 15, 5496, 10.1109/JSTARS.2022.3189528
Kavzoglu, 2009, A kernel functions analysis for support vector machines for land cover classification, Int. J. Appl. Earth Obs. Geoinf., 11, 352
Keshtkar, 2016, A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models, Model. Earth Syst. Environ., 2, 1, 10.1007/s40808-015-0068-4
Khaiter, 2020
Khalil, 2022, Comparative analysis of machine learning and multi-criteria decision making techniques for landslide susceptibility mapping of Muzaffarabad district, Front. Environ. Sci., 10, 1, 10.3389/fenvs.2022.1028373
Khan, 2021, Use of gis and remote sensing data to understand the impacts of land use/land cover changes (Lulcc) on snow leopard (panthera uncia) habitat in Pakistan, Sustain. Times, 13
Laongmanee, 2013, Assessment of Spatial resolution in estimating leaf area index from satellite images : a case study with Avicennia marina plantations in Thailand, Int. J. Geoinformatics, 9, 69
2020
Li, 2017, The surface urban heat island response to urban expansion: a panel analysis for the conterminous United States, Sci. Total Environ., 426, 10.1016/j.scitotenv.2017.06.229
Magarotto, 2019, Analysis of urban growth in coastal areas supported by 2D/2.5D GIS data. A comparative study of Boa Viagem Beach (Brazil) and Rocha Beach (Portugal), J. Coast Conserv., 23, 1081, 10.1007/s11852-019-00715-w
Majeed, 2022, Spatiotemporal distribution patterns of climbers along an abiotic gradient in jhelum district, Punjab, Pakistan, Forests, 13, 1244, 10.3390/f13081244
Majeed, 2022, A detailed ecological exploration of the distribution patterns of wild poaceae from the jhelum district (Punjab), Pakistan, Sustainability, 14, 3786, 10.3390/su14073786
Majumdar, 2020, Assessment and detection of land cover changes in the southern fringe of Kolkata using remotely sensed data, Geogr. Environ. Sustain., 13, 121, 10.24057/2071-9388-2020-65
Mannan, 2019, Application of land-use/land cover changes in monitoring and projecting forest biomass carbon loss in Pakistan, Glob. Ecol. Conserv., 17
Mansour, 2022, Forecasting of built-up land expansion in a desert urban environment, Rem. Sens., 14, 10.3390/rs14092037
Montaner-Fernández, 2020, Spatio-temporal variation of the urban heat island in Santiago, Chile during summers 2005–2017, Rem. Sens., 12, 1, 10.3390/rs12203345
Mosammam, 2017, Monitoring land use change and measuring urban sprawl based on its spatial forms: the case of Qom city. Egypt, J. Remote Sens. Sp. Sci., 20, 103
Mumtaz, 2020, Modeling spatio-temporal land transformation and its associated impacts on land surface temperature (LST), Rem. Sens., 12, 10.3390/rs12182987
Mustafa, 2020, Study for predicting land surface temperature (lst) using Landsat data: a comparison of four algorithms, Adv. Civ. Eng., 10.1155/2020/7363546
2017
Pontius, 2011, Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment, Int. J. Rem. Sens., 32, 4407, 10.1080/01431161.2011.552923
Qian, 2016, Impact of land use/land cover change on changes in surface solar radiation in eastern China since the reform and opening up, Theor. Appl. Climatol., 123, 131, 10.1007/s00704-014-1334-5
Roy, 2014, Landsat-8: science and product vision for terrestrial global change research, Remote Sens. Environ., 145, 154, 10.1016/j.rse.2014.02.001
Sahoo, 2016, Index-based groundwater vulnerability mapping using quantitative parameters, Environ. Earth Sci., 75, 10.1007/s12665-016-5395-x
Sayemuzzaman, 2014, Modeling of future land cover land use change in North Carolina using Markov chain and cellular automata model, Am. J. Eng. Appl. Sci., 7, 295, 10.3844/ajeassp.2014.295.306
Shao, 2019, Deep learning-based fusion of Landsat-8 and Sentinel-2 images for a harmonized surface reflectance product, Remote Sens. Environ., 235, 10.1016/j.rse.2019.111425
Singh, 2018, Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India, Geocarto Int., 33, 1202, 10.1080/10106049.2017.1343390
Tariq, 2020, CA-Markov chain analysis of seasonal land surface temperature and land use landcover change using optical multi-temporal satellite data of, Rem. Sens., 12, 1, 10.3390/rs12203402
Tariq, 2020, CA-Markov chain analysis of seasonal land surface temperature and land use landcover change using optical multi-temporal satellite data of Faisalabad, Pakistan, Rem. Sens., 12, 1, 10.3390/rs12203402
Tariq, 2021, Monitoring land use and land cover changes using geospatial techniques, a case study of Fateh Jang, Attock, Pakistan, Geogr. Environ. Sustain., 14, 41, 10.24057/2071-9388-2020-117
Tariq, 2021, Forest fire monitoring using spatial-statistical and Geo-spatial analysis of factors determining forest fire in Margalla Hills, Islamabad, Pakistan, Geomatics, Nat. Hazards Risk, 12, 1212, 10.1080/19475705.2021.1920477
Tariq, 2021, Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning methods, J. For. Res., 13, 12
Tariq, 2022, Impact of spatio-temporal land surface temperature on cropping pattern and land use and land cover changes using satellite imagery, Hafizabad District, Punjab, Province of Pakistan, Arabian J. Geosci., 15, 1045, 10.1007/s12517-022-10238-8
Tran, 2017, Characterizing the relationship between land use land cover change and land surface temperature, ISPRS J. Photogrammetry Remote Sens., 124, 119, 10.1016/j.isprsjprs.2017.01.001
Ullah, 2019, Remote sensing-based quantification of the relationships between land use land cover changes and surface temperature over the lower Himalayan region, Sustain. Times, 11
Ur Rehman, 2018, Land use/land cover changes through satellite remote sensing approach: a case study of Indus delta, Pakistan, Pakistan J. Sci. Ind. Res. Ser. A Phys. Sci., 61, 156, 10.52763/PJSIR.PHYS.SCI.61.3.2018.156.162
Wahla, 2022, Assessing spatio-temporal mapping and monitoring of climatic variability using SPEI and RF machine learning models, Geocarto Int., 1
Wang, 2013, Markov Random Field modeling, inference & learning in computer vision & image understanding: a survey, Comput. Vis. Image Understand., 117, 1610, 10.1016/j.cviu.2013.07.004
2019
Xiong, 2017, Automated cropland mapping of continental Africa using Google Earth Engine cloud computing, ISPRS J. Photogrammetry Remote Sens., 126, 225, 10.1016/j.isprsjprs.2017.01.019
Yin, 2011, Monitoring urban expansion and land use/land cover changes of Shanghai metropolitan area during the transitional economy (1979-2009) in China, Environ. Monit. Assess., 177, 609, 10.1007/s10661-010-1660-8
Yulianto, 2019, Analysis of the dynamics of land use change and its prediction based on the integration of remotely sensed data and CA-Markov model, in the upstream Citarum Watershed, West Java, Indonesia, Int. J. Digit. Earth, 12, 1151, 10.1080/17538947.2018.1497098
Zullo, 2019, Effects of urban growth spatial pattern (UGSP) on the land surface temperature (LST): a study in the Po Valley (Italy), Sci. Total Environ., 650, 1740, 10.1016/j.scitotenv.2018.09.331