ارزیابی اثرات تغییر کاربری اراضی بر انتشار دی‌اکسیدکربن با استفاده از تحلیل تصاویر ماهواره ای و روش یادگیری عمیق (مطالعه موردی: شهرستان اهواز، ۲۰۱۴-۲۰۲۰)

Abiyat, M., Abiyat, M., & Abiyat, M. (2021). Modeling the Process of Spatio-Temporal Changes in Land-Use and Urban Development of Ahvaz Based on Spatial Planning Approach. Town and Country Planning, 13(1), 215-245. (in Persian).

Acker, J., Soebiyanto, R., Kiang, R., & Kempler, S. (2014). Use of the NASA Giovanni data system for geospatial public health research: example of weather-influenza connection. ISPRS International Journal of Geo-Information, 3(4), 1372-1386. https://doi.org/10.3390/ijgi3041372.

Ahmad, M. N., Shao, Z., & Javed, A. (2023). Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data. Environmental Science and Pollution Research, 30(12), 32985-33001.

Ahmad, S., Kaur, N., Badar, M.S., Shakeel, A., & Ahmed, F. (2024). Modeling Land Use and Land Cover Changes and Its Atmospheric Pollutant Concentration in the Coal Mining Area of Ramgarh District of Jharkhand, India, Using Multi‐Layer Perceptron Neural Networks (MLPNN). Environmental Quality Management, 34(2), 22351.

Alfiky, A., Kaule, G., & Salheen, M. (2012). Agricultural fragmentation of the Nile Delta; a modeling approach to measuring agricultural land deterioration in Egyptian Nile Delta. Procedia Environmental Sciences, 14, 79-97. https://doi.org/10.1016/j.proenv.2012.03.009.

Anonymous. (2000). Energy Balance of Iran. Iran Ministry of Energy, Deputy of Electricity and Energy Affairs. (in Persian).

Anonymous. (2020). Energy Balance of Iran. Iran Ministry of Energy, Deputy of Electricity and Energy Affairs. (in Persian).

Basheer, S., Wang, X., Farooque, A.A., Nawaz, R.A., Pang, T., & Neokye, E.O. (2024). A review of greenhouse gas emissions from agricultural soil. Sustainability, 16(11), 4789.

Bhosle, K., & Musande, V. (2019). Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images. Journal of the Indian Society of Remote Sensing, 47(11), 1949-1958.

Carranza-García, M., García-Gutiérrez, J., & Riquelme, J.C. (2019). A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sensing, 11(3), 274.

Chen, D., Lu, X., Hu, W., Zhang, Ch., & Lin, Y. (2021). How urban sprawl influences eco environmental quality: Empirical research in China by using the Spatial Durbin model. Ecological Indicators, 131, 108113.

Chen, Y., Jiang, H., Li, C., Jia, X., & Ghamisi, P. (2016). Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE transactions on geoscience and remote sensing, 54(10), 6232-6251.

Da silva Cruz, J., Cavalcante Blanco, C., de Oliveira Júnior, J. (2022). Modeling of land use and land cover change dynamics for future projection of the Amazon number curve. Science of The Total Environment, 811, 152348.

Dyer, J.A., Kulshreshtha, S.N., McConkey, B.G., & Desjardins, R.L. (2010). An assessment of fossil fuel energy use and CO2 emissions from farm field operations using a regional level crop and land use database for Canada. Energy, 35(5), 2261-2269.

El-Kawy, O. R., Rød, J. K., Ismail, H. A., & Suliman, A. S. (2011). Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Applied geography, 31(2), 483-494.

Falahatkar, S., & Hosseini, M, S. (2017). Predicting CO₂ emission hotspots due to land use change. Journal of Natural Environment, 70 (1), 139-148. (in Persian).

Fatemi, S.B, & Rezaei, Y. (2017). Principles of Remote Sensing. Azadeh Publications, Tehran (in Persian)

Fischedick, M., Roy, J., Acquaye, A., Allwood, J., Ceron, J. P., Geng, Y., ... & Tanaka, K. (2014). Industry In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Technical Report.

Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., ... & Snyder, P. K. (2005). Global consequences of land use. science, 309(5734), 570-574.

Grecchi, R. C., Gwyn, Q. H. J., Bénié, G. B., Formaggio, A. R., & Fahl, F. C. (2014). Land use and land cover changes in the Brazilian Cerrado: A multidisciplinary approach to assess the impacts of agricultural expansion. Applied Geography, 55, 300-312.

Hoque, M. Z., Islam, I., Ahmed, M., Hasan, S. S., & Prodhan, F. A. (2022). Spatio-temporal changes of land use land cover and ecosystem service values in coastal Bangladesh. The Egyptian Journal of Remote Sensing and Space Science, 25(1), 173-180. https://doi.org/10.1016/j.ejrs.2022.01.008.

Houghton, R. A. (2003). Revised estimates of the annual net flux of carbon to the atmosphere from changes in land use and land management 1850–2000. Tellus B: Chemical and Physical Meteorology, 55(2), 378-390. https://doi.org/10.3402/tellusb.v55i2.16764.

Jiang, H., Hu, H., Zhong, R., Xu, J., Xu, J., Huang, J., ... & Lin, T. (2020). A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level. Global change biology, 26(3), 1754-1766. https://doi.org/10.1111/gcb.14885.

Khachoo, Y.H., Cutugno, M., Robustelli, U., Pugliano, G. (2024). Impact of land use and land cover (LULC) changes on carbon stocks and economic implications in Calabria using Google Earth Engine (GEE). Sensors, 24(17), 5836.

Li, Y., Zhang, H., & Shen, Q. (2017). Spectral–spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sensing, 9(1), 67.

Lillesand, T., Kiefer, R. W., & Chipman, J. (2015). Remote sensing and image interpretation. John Wiley & Sons.

Magazzino, C., Cerulli, G., Shahzad, U., & Khan, S. (2023). The nexus between agricultural land use, urbanization, and greenhouse gas emissions: Novel implications from different stages of income levels. Atmospheric Pollution Research, 14(9), 101846.

Mousavi, S. M., Ebadi, H., & Kiani, A. (2019). Provide an Optimal Deep-network Method for Spectral-spatial Classifying of High Resolution Images. Journal of Geomatics Science and Technology, 9(2), 151-170. (in Persian).

Murugan, M., Selvaraj, R., & Nagarajan, S. (2022). Assessment of land use land cover change detection in multitemporal satellite images using machine learning algorithms. In Cognitive Systems and Signal Processing in Image Processing (pp. 27-45). Academic Press.

Ramankutty, N., & Foley, J. A. (1999). Estimating historical changes in global land cover: Croplands from 1700 to 1992. Global biogeochemical cycles, 13(4), 997-1027.

Ranjbar, A., Heydarnejad, S., Mousavi, S. H., & Mirzaei, R. (2019). Mapping desertification potential using life cycle assessment method: a case study in Lorestan Province, Iran. Journal of Arid Land, 11, 652-663. https://doi.org/10.1007/s40333-019-0064-z.

Rehman, A., Ma, H., Ozturk, I., Murshed, M., & Dagar, V. (2021). The dynamic impacts of CO2 emissions from different sources on Pakistan’s economic progress: a roadmap to sustainable development. Environment, Development and Sustainability, 23(12), 17857-17880.

Salem, M., Tsurusaki, N., & Divigalpitiya, P. (2020). Remote sensing-based detection of agricultural land losses around Greater Cairo since the Egyptian revolution of 2011. Land use policy, 97, 104744. https://doi.org/10.1016/j.landusepol.2020.104744.

Seto, K. C., Güneralp, B., & Hutyra, L. R. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences, 109(40), 16083-16088. https://doi.org/10.1073/pnas.1211658109.

Shojaeeian, A., Mokhtari Chelche, S., Keshtkar, L., & Soleymani Rad, E. (2015). Comparing the Performance of Parametric and NonparametricMethods in Land Cover Classification using Landsat-8 Satellite Images (Case study: A part of Dezful city). Scientific-Research Quarterly of Geographical Data (SEPEHR), 24(93), 53-64. (in Persian).

Taheri Dehkordi, A., & Valadan Zoej, M. J. (2021). Classification of croplands using sentinel-2 satellite images and a novel deep 3D convolutional neural network (case study: Shahrekord). Iranian Journal of Soil and Water Research, 52(7), 1941-1953. (in Persian).

Tahmasebi Moghadam, H., Ghaed Rahmati, S., & Shahrokhi far, Z. (2020). Comparative evaluation of urban sprawl with emphasis on land use changes during the period of 1987-2016 (case study: Amel and Babol cities). Journal of geography and urban-regional planning, 27, 149-166. (in Persian).

Tejaswini, M., Pranuthi.P., Ravichand, S., & Anuradha. T. (2019). Land cover change detection using convolution neural network. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 791-794). IEEE.

United Nations. (2018). World Urbanization Prospects 2018: Highlights. United Nations Department of Economic and Social Affairs, Population Division.

Wang, J., Bretz, M., Dewan, M. A. A., & Delavar, M. A. (2022). Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects. Science of the Total Environment, 822, 153559. https://doi.org/10.1016/j.scitotenv.2022.153559.

Wang, Z.B., Chen, J., Mao, S.C., Han, Y.C., Chen, F., Zhang, L.F., Li, Y.B., & Li, C.D. (2017). Comparison of greenhouse gas emissions of chemical fertilizer types in China's crop production. Journal of Cleaner Production, 141,1267-1274.

Yang, C., Rottensteiner, F., & Heipke, C. (2018). Classification of land cover and land use based on convolutional neural networks. ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 4, 251-258.