Afrakhteh, H., Azizpour, F., Ramazan Jornbyan, M. (2012). The Impact of spatial flows on rural sprawl process (Case study: Solaiman Abad Region). Physical Sacial Planning, 1(2), 9-24. [In Persion]
Afrakhteh, H. (2020). Urban peripheral spaces: Patterns, factors and consequences. Preipheral Urban Spaces Development, 2(1), 1-18.
Beaton, A., Whaley, R., Corston, K., & Kenny, F. (2019). Identifying historic river ice breakup timing using MODIS and Google Earth Engine in support of operational flood monitoring in Northern Ontario. Remote Sensing of Environment, 224(February), 352–364. https://doi.org/10.1016/j.rse.2019.02.011
Bruno, D., Sorando, R., Castellano, C., Vanessa, C., Gallardo, B., Jim, J. J., Victoria, M. L., Navarro, E., Comín, F. A., Álvarez-Farizo, B., Castellano, C., Céspedes, V., Gallardo, B., Jiménez, J. J., López, M. V., López-Flores, R., Moret-Fernández, D., Navarro, E., Picazo, F., … Comín, F. A. (2021). Depopulation impacts on ecosystem services in Mediterranean rural areas. Ecosystem Services, 52, 101369. https://doi.org/https://doi.org/10.1016/j.ecoser.2021.101369
Cengiz, S., Görmüş, S., & Oğuz, D. (2022a). Analysis of the urban growth pattern through spatial metrics; Ankara City. Land Use Policy, 112 (January 2021).
https://doi.org/10.1016/j.landusepol.2021.105812
Cengiz, S., Görmüş, S., & Oğuz, D. (2022b). Analysis of the urban growth pattern through spatial metrics; Ankara City. Land Use Policy, 112 (October 2021).
https://doi.org/10.1016/j.landusepol.2021.105812
Collins, L., Griffioen, P., Newell, G., & Mellor, A. (2018). The utility of Random Forests for wildfire severity mapping. Remote Sensing of Environment, 216(June), 374–384. https://doi.org/10.1016/j.rse.2018.07.005
Coluzzi, R., Bianchini, L., Egidi, G., Cudlin, P., Imbrenda, V., Salvati, L., & Lanfredi, M. (2022). Density matters? Settlement expansion and land degradation in Peri-urban and rural districts of Italy. Environmental Impact Assessment Review, 92, 106703.
https://doi.org/10.1016/J.EIAR.2021.106703
Crowley, M. A., Cardille, J. A., White, J. C., & Wulder, M. A. (2019). Multi-sensor, multi-scale, Bayesian data synthesis for mapping within-year wildfire progression. Remote Sensing Letters, 10(3), 302–311. https://doi.org/10.1080/2150704X.2018.1536300
Czekajlo, A., Coops, N. C., Wulder, M. A., Hermosilla, T., White, J. C., & van den Bosch, M. (2021). Mapping dynamic peri-urban land use transitions across Canada using Landsat time series: Spatial and temporal trends and associations with socio-demographic factors. Computers, Environment and Urban Systems, 88(December 2020), 101653. https://doi.org/10.1016/j.compenvurbsys.2021.101653
Dadashpoor, H., & Ahani, S. (2021). Explaining objective forces, driving forces, and causal mechanisms affecting the formation and expansion of the peri-urban areas: A critical realism approach. Land Use Policy, 102(December 2020), 105232.
https://doi.org/10.1016/j.landusepol.2020.105232
Dadashpoor, H., & Salarian, F. (2020). Urban sprawl on natural lands: analyzing and predicting the trend of land use changes and sprawl in Mazandaran city region, Iran. Environment, Development and Sustainability, 22(2), 593–614. https://doi.org/10.1007/s10668-018-0211-2
Doygun, H. (2009). Effects of urban sprawl on agricultural land: A case study of Kahramanmaraş, Turkey. Environmental Monitoring and Assessment, 158(1–4), 471–478. https://doi.org/10.1007/s10661-008-0597-7
Follmann, A., Willkomm, M., & Dannenberg, P. (2021). As the city grows, what do farmers do? A systematic review of urban and peri-urban agriculture under rapid urban growth across the Global South. Landscape and Urban Planning, 215, 104186. https://doi.org/10.1016/j.landurbplan.2021.104186
Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A., & Hasanlou, M. (2020). Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing, 167(July), 276–288.
https://doi.org/10.1016/j.isprsjprs.2020.07.013
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031
Huang, H., Chen, Y., Clinton, N., Wang, J., Wang, X., Liu, C., Gong, P., Yang, J., Bai, Y., Zheng, Y., & Zhu, Z. (2017). Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine. Remote Sensing of Environment, 202, 166–176. https://doi.org/10.1016/j.rse.2017.02.021
Huang, X., Wang, H., & Xiao, F. (2022). Land Use Policy Simulating urban growth affected by national and regional land use policies : Case study from Wuhan , China. Land Use Policy, 112(May 2021), 105850. https://doi.org/10.1016/j.landusepol.2021.105850
Johnson, M. P. (2001). Environmental impacts of urban sprawl: A survey of the literature and proposed research agenda. Environment and Planning A, 33(4), 717–735. https://doi.org/10.1068/a3327
Jozak, A., Lotfi, S., Nikpour, A. (2022). Analyzing the impacts and consequences of village annexation to cities (A Case study of Amol city). Preipheral Urban Spaces Development, 4(1), 25-46.
Kumar, L., & Mutanga, O. (2018). Google Earth Engine applications since inception: Usage, trends, and potential. Remote Sensing, 10(10), 1–15.
https://doi.org/10.3390/rs10101509
Lei, Y., Flacke, J., & Schwarz, N. (2021). Does Urban planning affect urban growth pattern? A case study of Shenzhen, China. Land Use Policy, 101(November 2019), 105100. https://doi.org/10.1016/j.landusepol.2020.105100
Li, X., Zhou, Y., Zhu, Z., Liang, L., Yu, B., & Cao, W. (2018). Mapping annual urban dynamics ( 1985 – 2015 ) using time series of Landsat data. Remote Sensing of Environment, 216(July), 674–683.
https://doi.org/10.1016/j.rse.2018.07.030
Liu, X., Hu, G., Chen, Y., Li, X., Xu, X., Li, S., Pei, F., & Wang, S. (2018). High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform. Remote Sensing of Environment, 209(February), 227–239. https://doi.org/10.1016/j.rse.2018.02.055
Mondal, D., & Banerjee, A. (2021). Exploring peri-urban dynamism in India: Evidence from Kolkata Metropolis. Journal of Urban Management, 10(4), 382–392. https://doi.org/10.1016/j.jum.2021.06.004
Mondal, D., & Sen, S. (2020). Methodological Dimensions of Delineating Peri-urban Areas: The Case of Kolkata Metropolis. Environment and Urbanization ASIA, 11(2), 183–194. https://doi.org/10.1177/0975425320938519
Patela, N. N., Angiuli, E., Gamba, P., Gaughan, A., Lisini, G., Stevens, F. R., Tatem, A. J., & Trianni, G. (2015). Multitemporal settlement and population mapping from landsatusing google earth engine. International Journal of Applied Earth Observation and Geoinformation, 35(PB), 199–208. https://doi.org/10.1016/j.jag.2014.09.005
Perbet, P., Fortin, M., Ville, A., & Béland, M. (2019). Near real-time deforestation detection in Malaysia and Indonesia using change vector analysis with three sensors. International Journal of Remote Sensing, 40(19), 7439–7458. https://doi.org/10.1080/01431161.2019.1579390
Qu, Y., Zhan, L., Jiang, G., Ma, W., & Dong, X. (2021). How to Address “Population Decline and Land Expansion (PDLE)" of rural residential areas in the process of Urbanization:A comparative regional analysis of human-land interaction in Shandong Province. Habitat International, 117, 102441. https://doi.org/10.1016/J.HABITATINT.2021.102441
R, J. V. C. I., Huang, F., Yu, Y., & Feng, T. (2019). Automatic extraction of urban impervious surfaces based on deep learning and multi-source remote sensing data q. Journal of Visual Communication and Image Representation, 60, 16–27.
https://doi.org/10.1016/j.jvcir.2018.12.051
Roy, B., & Kasemi, N. (2021). Monitoring urban growth dynamics using remote sensing and GIS techniques of Raiganj Urban Agglomeration, India. Egyptian Journal of Remote Sensing and Space Science, 24(2), 221–230. https://doi.org/10.1016/j.ejrs.2021.02.001
Shao, Y., Li, G. L., Guenther, E., & Campbell, J. B. (2015). Evaluation of Topographic Correction on Subpixel Impervious Cover Mapping With CBERS-2B Data. 12(8), 1675–1679.
Sojasi qeidari, H., sadrossadat, A. (2015). Analyzing factors affecting land use change at rural areas of Mashhad’s urban fringe. Journal of Rural Research, 6(4), 831-856. doi: 10.22059/jrur.2015.57360
Song, X., Feng, Q., Xia, F., Li, X., & Scheffran, J. (2021). Impacts of changing urban land-use structure on sustainable city growth in China: A population-density dynamics perspective. Habitat International, 107(November 2020). https://doi.org/10.1016/j.habitatint.2020.102296
Sudalayandi, R. S., Srinivasan, E., & Kasaragod, G. R. (2021). Urban growth analysis of Tamil Nadu state, India using VIIRS DNB night data during 2012 and 2016. Remote Sensing Applications: Society and Environment, 23(May), 100559. https://doi.org/10.1016/j.rsase.2021.100559
Sun, Z. (2011). Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine. Journal of Applied Remote Sensing, 5(1), 053501. https://doi.org/10.1117/1.3539767
Sun, Z., Wang, C., Guo, H., & Shang, R. (2017). A Modified Normalized Difference Impervious Surface Index (MNDISI) for Automatic Urban Mapping from Landsat Imagery. Remote Sensing , 9(9), 1–18. https://doi.org/10.3390/rs9090942
Sun, Z., Xu, R., Du, W., Wang, L., & Lu, D. (2019). High-resolution urban land mapping in China from sentinel 1A/2 imagery based on Google Earth Engine. Remote Sensing, 11(7), 1–22. https://doi.org/10.3390/rs11070752
Taleshi M, Azizpour F, dolati G. The Impact Analysis of Spatial Flows on Physio-Spatial Transformation of Peripheral Villages (Case Study: Peripheral Rural Settlements of Karaj Metropolitan, Tankanan Shomali District). JHRE. 2019; 38 (166) :79-94 [In Persion].
Teluguntla, P., Thenkabail, P., Oliphant, A., Xiong, J., Gumma, M. K., Congalton, R. G., Yadav, K., & Huete, A. (2018). A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform. ISPRS Journal of Photogrammetry and Remote Sensing, 144(February), 325–340. https://doi.org/10.1016/j.isprsjprs.2018.07.017
Wang, Z., Gang, C., Li, X., & Chen, Y. (2015). International Journal of Remote Application of a normalized difference impervious index ( NDII ) to extract urban impervious surface features based on Landsat TM images. February, 37–41.
https://doi.org/10.1080/01431161.2015.1007250
Wu, M., Zhao, X., Sun, Z., & Guo, H. (2019). A Hierarchical Multiscale Super-Pixel-Based Classification Method for Extracting Urban Impervious Surface Using Deep Residual Network from WorldView-2 and LiDAR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(1), 210–222.
https://doi.org/10.1109/JSTARS.2018.2886288
Yılmaz, M., & Terzi, F. (2021). Measuring the patterns of urban spatial growth of coastal cities in developing countries by geospatial metrics. Land Use Policy, 107(April), 105487. https://doi.org/10.1016/j.landusepol.2021.105487
yousefi, A., molaeih, N., rezaei, P. (2021). Evaluation of the Physical and Environmental Factors Affecting Land Use Changes in Rural Areas of Rasht. Geographical Engineering of Territory, (), -.
Zarei, A., Dadashpoor, H., & Amini, M. (2016). Determination of the optimal land use allocation pattern in Nowshahr County, Northern Iran. Environment, Development and Sustainability, v. 18(1), 37-56–2016 v.18 no.1. https://doi.org/10.1007/s10668-015-9620-7
Zhang, Q., Li, B., Thau, D., & Moore, R. (2015). Building a better Urban picture: Combining day and night remote sensing imagery. Remote Sensing, 7(9), 11887–11913. https://doi.org/10.3390/rs70911887
Zhang, Y., Zhang, H., & Lin, H. (2014). Improving the impervious surface estimation with combined use of optical and SAR remote sensing images. Remote Sensing of Environment, 141, 155–167. https://doi.org/10.1016/j.rse.2013.10.028
Zia Tavana, M., Ghadermazy, H. (2009). Land Use Changes of Peri-Urban Villages in Urban Sprawl Process: Case study: Naisar & Hassanabad Villages of Sanandaj. Human Geography Research, 42(68), -.
Zhu, X., Qian, T., & Wei, Y. (2020). Socio-Economic Planning Sciences Do high-speed railways accelerate urban land expansion in China ? A study based on the multi-stage difference-in-differences model. Socio-Economic Planning Sciences, November 2019, 100846.
https://doi.org/10.1016/j.seps.2020.100846
Zondag, B., & Borsboom, J. (2009). DRIVING FORCES OF LAND-USE CHANGE. In 49th ERSA Conference, August, 1–16
)