بررسی تغییرات کاربری اراضی حوضه آبریز کرخه در سال‌های 1990 و 2020 با استفاده از پلتفرم گوگل ارث انجین و تصاویر ماهواره‌ای لندست

Basukala, A. K., Oldenburg, C., Schellberg, J., Sultanov, M., & Dubovyk, O. (2017). Towards improved land use mapping of irrigated croplands: Performance assessment of different image classification algorithms and approaches. European Journal of Remote Sensing, 50(1), 187-201.

Becker, W. R., Ló, T. B., Johann, J. A., & Mercante, E. (2021). Statistical features for land use and land cover classification in Google Earth Engine. Remote Sensing Applications: Society and Environment, 21, 100459.

El-Tantawi, A. M., Bao, A., Chang, C., & Liu, Y. (2019). Monitoring and predicting land use/cover changes in the Aksu-Tarim River Basin, Xinjiang-China (1990–2030). Environmental monitoring and assessment, 191(8), 1-18.

Faridatul, M. I., & Wu, B. (2018). Automatic classification of major urban land covers based on novel spectral indices. ISPRS International Journal of Geo-Information, 7(12), 453.

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.

Gumma, M. K., Thenkabail, P. S., Teluguntla, P. G., Oliphant, A., Xiong, J., Giri, C., ... & Whitbread, A. M. (2020). Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud. GIScience & Remote Sensing, 57(3), 302-322.

He, C., Shi, P., Xie, D., & Zhao, Y. (2010). Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sensing Letters, 1(4), 213-221.

Jahanbakhshi, f., Ekhtesasi, M, R., (2019). Performance evaluation of three image classification methods (random forest, support vector machine and the maximum likelihood) in land use mapping. Journal of Science and Technology of Agriculture and Natural Resources 22(4):235-247. (In Persian)

Kaabzadeh, S., Ghodousi, J., Arjmandi, R., & Jaafarzadeh Haghighifard, N. (2021). The effects of constructing and exploiting Karkheh Storage Dam on the land uses and quality of regional water. Journal of Environmental Science and Technology (In Persian).

Kazemi, H., Hashemi, H., Maghsood, F. F., Hosseini, S. H., Sarukkalige, R., Jamali, S., & Berndtsson, R. (2021). Climate vs. Human Impact: Quantitative and Qualitative Assessment of Streamflow Variation. Water, 13(17), 2404.

Lambin, E. F., Geist, H. J., & Lepers, E. (2003). Dynamics of land-use and land-cover change in tropical regions. Annual review of environment and resources, 28(1), 205-241.

Li, E., Du, P., Samat, A., Xia, J., & Che, M. (2015). An automatic approach for urban land-cover classification from Landsat-8 OLI data. International Journal of Remote Sensing, 36(24), 5983-6007.

Mallupattu, P. K., & Sreenivasula Reddy, J. R. (2013). Analysis of land use/land cover changes using remote sensing data and GIS at an Urban Area, Tirupati, India. The Scientific World Journal, 2013.

McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International journal of remote sensing, 17(7), 1425-1432.

Oliphant, A. J., Thenkabail, P. S., Teluguntla, P., Xiong, J., Congalton, R. G., Yadav, K., ... & Smith, C. (2017). NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Southeast Asia 30 m V001.

Oliphant, A. J., Thenkabail, P. S., Teluguntla, P., Xiong, J., Gumma, M. K., Congalton, R. G., & Yadav, K. (2019). Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using a random forest classifier on the Google Earth Engine Cloud. International Journal of Applied Earth Observation and Geoinformation, 81, 110-124.

Salajegheh, A., Razavizadeh, S., Khorasani, N., Hamidifar, M.,  Salajegheh, S. (2011). Land use changes and its effects on water quality (Case study: Karkheh watershed). Journal of environmental studies 37(58):81-86. (In Persian)

Shanani, H S M., Zarei, H. (2016). Investigation of land use changes during the past two last decades (Case Study: Abolabas Basin). Journal of Watershed Management Research, 14(7):237-244. (In Persian)

Steinhausen, M. J., Wagner, P. D., Narasimhan, B., & Waske, B. (2018). Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions. International journal of applied earth observation and geoinformation, 73, 595-604.

Sundarakumar, K., Harika, M., Begum, S. A., Yamini, S., & Balakrishna, K. (2012). Land use and land cover change detection and urban sprawl analysis of Vijayawada city using multitemporal landsat data. International Journal of Engineering Science and Technology, 4(01), 170-178.

Talebi, A., Goodarzi, S., Pourghsemi, H. (2018). Investigation of the possibility of landslide hazard mapping using the Random Forest algorithm (Case study: Sardarabad Watershed, Lorestan Province). Journal of Natural Environment Hazards 7(16 ):45-64. (In Persian)

Wagner, P. D., Bhallamudi, S. M., Narasimhan, B., Kumar, S., Fohrer, N., & Fiener, P. (2019). Comparing the effects of dynamic versus static representations of land use change in hydrologic impact assessments. Environmental Modelling & Software, 122, 103987.

Watershed management plan of Karkheh dam basin (1999) A model for managing water and soil resources in the vast and fertile area of the Zagros. Karkheh Dam Watershed Management Plan Office, Jihad Watershed Management Documentation Center Library (In Persian).

Yan, H., Liu, J., Huang, H. Q., Tao, B., & Cao, M. (2009). Assessing the consequence of land use change on agricultural productivity in China. Global and planetary change, 67(1-2), 13-19.

Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International journal of remote sensing, 24(3), 583-594.

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.