نقشه‌برداری رقومی شوری خاک سطحی در بخش مرکزی استان خوزستان با استفاده از الگوریتم‌های طبقه‌بندی نظارت شده

Abbas, A., and Khan, S. (2007). Using remote sensing techniques for appraisal of irrigated soil salinity. MODSIM 2007 International Congress on Modelling and Simulation, 2632–2638.

Abbas, A., Khan, S., Hussain, N., Hanjra, M. A., and Akbar, S. (2013). Characterizing soil salinity in irrigated agriculture using a remote sensing approach. *Physics and Chemistry of the Earth, Parts A/B/C, 55–57, 43–52.

Allbed, A., Kumar, L., and Aldakheel, Y. Y. (2014). Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region. Geoderma, 230–231, 1–11.

Banaei, M. H. (1998). Map of moisture and temperature regimes of Iranian soils. Soil and Water Research Institute. (In Persian)

Baret, F., and Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 35(2–3), 161–173.

Bertani, T. C., Novack, T., Hayakawa, E. H., and Zani, H. (2010). Detection of saline and non-saline lakes on the Pantanal of Nhecolândia (Brazil) using object-based image analysis. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38.

Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and regression trees. Wadsworth.

Chaudhuri, B. B., and Sarkar, N. (1995). Texture segmentation using fractal dimension. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(1), 72–77.

Chatrenor, M., Landi, A., Farrokhian Firouzi, A., Noroozi, A. and Bahrami, H. (2019). Using VIS-NIR spectroscopy to model soil salinity in dust-prone areas of Khuzestan Province. Iranian Journal of Soil and Water Research, 50(8), 1951–1962. (In Persian)

Cortes, C., and Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.

Cover, T. M., and Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27.

Crippen, R. E. (1990). Calculating the vegetation index faster. Remote Sensing of Environment, 34(1), 71–73.

Dehni, A., and Lounis, M. (2012). Remote sensing techniques for salt affected soil mapping: Application to the Oran region of Algeria. Procedia Engineering, 33, 188–198.

Douaoui, A. E. K., Nicolas, H., and Walter, C. (2006). Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma, 134, 217–230.

Elhag, M. (2016). Evaluation of different soil salinity mapping using remote sensing techniques in arid ecosystems, Saudi Arabia. Journal of Sensors, 2016, 7596175.

Elnaggar, A. A., and Noller, J. S. (2009). Application of remote-sensing data and decision-tree analysis to mapping salt-affected soils over large areas. Remote Sensing, 2(1), 151–165.

Food and Agriculture Organization (FAO). (2015). Global Soil Partnership: Status of the World's Soil Resources. Food and Agriculture Organization of the United Nations.

Forouzani, M., and Mardani, M. (2021). The effects of climate change adaptation behavior on agricultural water consumption in Bavi County. The Second National Conference on Agricultural and Environmental Research in Iran, Khuzestan University of Agricultural Sciences and Natural Resources.

Gorji, T., Sertel, E., and Tanik, A. (2017). Monitoring soil salinity via remote sensing technology under data scarce conditions: A case study from Turkey. Ecological Indicators, 74, 384–391.

Haykin, S. (1999). Neural networks: A comprehensive foundation (2nd ed.). Prentice Hall.

Hopmans, J. W., Qureshi, A. S., Kisekka, I., Munns, R., Grattan, S. R., Rengasamy, P., Ben-Gal, A., Assouline, S., Javaux, M., and Minhas, P. S. (2021). Critical knowledge gaps and research priorities in global soil salinity. Advances in Agronomy, 169, 1–191.

Hosseini, S. K., Forouzani, M. and Abdeshahi, A. (2023). Investigating the effect of livelihood assets on diversity of villagers' livelihoods (the case of Bavi County). Iranian Journal of Agricultural Economics and Development Research, 54(2), 487-503. (In Persian)

Ivushkin, K., Bartholomeus, H., Bregt, A. K., Pulatov, A., Kempen, B., and de Sousa, L. (2019). Global mapping of soil salinity change. Remote Sensing of Environment, 231, 111260.

Jordan, C. F. (1969). Derivation of leaf-area index from quality of light on the forest floor. Ecology, 50(4), 663–666.

Katawatin, R., and Kotrapat, W. (2005). Use of LANDSAT-7 ETM+ with ancillary data for soil salinity mapping in Northeast Thailand. Proceedings of SPIE, 5852, 708–717.

Khan, N. M., Rastoskuev, V. V., Shalina, E. V., and Sato, Y. (2001). Mapping salt-affected soils using remote sensing indicators. In Proceedings of the 22nd Asian Conference on Remote Sensing (Vol. 1, pp. 5–9).

Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J., and Goetz, A. F. H. (1993). The spectral image processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment, 44(2–3), 145–163.

Lohrabi, Y., Abasi, M., Soltani, A., and Riyahi bakhtyari, H. R. (2018). Determination of the most suitable method for forest type mapping in central Zagros using landsat-8 satellite Images. Forest Research and Development, 4(2), 191-205. (In Persian)

Ma, G., Ding, J., Han, L., Zhang, Z., and Ran, S. (2021). Digital mapping of soil salinization based on Sentinel-1 and Sentinel-2 data combined with machine learning algorithms. Regional Sustainability, 2(2), 177–188.

Marashi, A., Mahmoodi, A., and Eslami, M. R. (2012). Reduction of groundwater level by selecting potential cropping patterns in Bavi County. National Conference on Climate Change and Sustainable Agricultural and Natural Resources Development Engineering, Hamedan, Iran. (In Persian)

Mather, P. M., and Tso, B. (2009). Classification methods for remotely sensed data (2nd ed). CRC Press.

McDaniel, K. C., and Haas, R. H. (1982). Assessing mesquite-grass vegetation condition from Landsat. Photogrammetric Engineering and Remote Sensing, 48(3), 441–450.

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.

Metternicht, G. I., and Zinck, J. A. (2003). Remote sensing of soil salinity: Potentials and constraints. Remote Sensing of Environment, 85(1), 1–20.

Minasny, B., and McBratney, A. B. (2006). A conditioned Latin hypercube method for sampling in the presence of ancillary information. Computers and Geosciences, 32(9), 1378–1388.

Mohammadzadeh, K. and Feizizadeh, B. (2020). Identifying and Monitoring Soil Salinization in the Eastern Part of Urmia lake Together With Comparing Capability of Object Based Image Analysis Techniques. Journal of Water and Soil Conservation, 27(3), 65-84. (In Persian)

Mmombeni, M., Arekhi, S. and Arami, S. A. (2022). Changes in the salinity using remote sensing and GIS (Case study: South Khuzestan). Desert Ecosystem Engineering, 4(6), 27-34. (In Persian)

Moradi, A. R., Jafari, M., Arzani, H., and Ebrahimi, M. (2016). Assessment of land use changes into dry land using satellite images and Geographical information system (GIS). RS and GIS Techniques for Natural Resources, 7(1), 89-100. (In Persian)

Mudler, V. L., Bruin, S., and Schaepman, M. E. (2013). Representing major soil variability at regional scale by constrained Latin Hypercube Sampling of remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 21, 301-310.

Nguyen, K. A., Liou, Y. A., Tran, H. P., Hoang, P. P., and Nguyen, T. H. (2020). Soil salinity assessment by using near-infrared channel and vegetation soil salinity index derived from Landsat 8 OLI data: A case study in the Tra Vinh Province, Mekong Delta, Vietnam. Progress in Earth and Planetary Science, 7(1), 1–16.

Pahlavan Rad, M. R., Khormali, F., Toomanian, N., Kiani, F., and Komaki, Ch. B. (2015). Digital soil mapping using Random Forest model in Golestan province. Journal of Water and Soil Conservation, 21(6), 73-93.

Peng, J., et al. (2019). Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma, 337, 1309–1319.

Rhoades, J. D. (1996). Salinity: Electrical conductivity and total dissolved solids. In Methods of soil analysis: Part 3 Chemical methods (pp. 417–435). SSSA.

Richards, J. A., and Jia, X. (2006). Remote sensing digital image analysis: An introduction (4th ed.). Springer.

Richards, L. A. (1954). Diagnosis and improvement of saline and alkali soils. LWW.

Rostami, R., Mohammad Khorshiddoust, A. and Bakuoyi, M. (2023). Investigating the trend of land use changes in eastern Lake of Urmia with an emphasis on soil salinity by object-oriented method. Journal of Geography and Planning, 26(82), 83-93.

Saadat, S., esmaeelnejad, L., rezaei, H. and Mirkhani, R. (2024). Monitoring soil Salinity of the Sistan plain using field data and remote sensing. Iranian Journal of Soil and Water Research, 55(8), 1297-1309. (In Persian)

Savari, M., Sheheytavi, A., Shokati Amghani, M. (2023). Factors underpinning Iranian farmers’ intention to conserve biodiversity at the farm level. Journal for Nature Conservation, 73, 1-10.

Scudiero, E., Skaggs, T. H., and Corwin, D. L. (2015). Regional-scale soil salinity assessment using Landsat ETM+ canopy reflectance. Remote Sensing of Environment, 169, 335–343.

Shahid, S. A., Zaman, M., and Heng, L. (2018). Soil salinity: Historical perspectives and a world overview of the problem. In Guideline for salinity assessment, mitigation and adaptation using nuclear and related techniques (pp. 43–53). Springer.

Shahryari, A. (2013). Investigation the spatial changes of some soil properties in Atabiyeh lands (Khuzestan Province) using geostatistical, interpolation and GIS methods. Master's thesis in soil science, Faculty of Agriculture, Shahid Chamran University of Ahvaz.

Shammi, M., Rahman, M., Bondad, S. E., and Bodrud-Doza, M. (2019). Impacts of salinity intrusion in community health: A review of experiences on drinking water sodium from coastal areas of Bangladesh. Healthcare, 7(1), 50.

Shao, H., Chu, L., Lu, H., Qi, W., Chen, X., Liu, J., Kuang, S., Tang, B., and Wong, V. (2019). Towards sustainable agriculture for the salt-affected soil. Land Degradation and Development, 30(5), 574–579.

Singh, A. (2022). Soil salinity: A global threat to sustainable development. Soil Use and Management, 38(1), 39–67.

Statistical Yearbook of Khuzestan Province. (2022). Statistical Center of Iran. (In Persian)

Stals, J. P. (2007). Mapping potential soil salinization using rule based object-oriented image analysis [Doctoral dissertation, Stellenbosch University].

Stavi, I., Thevs, N., and Priori, S. (2021). Soil salinity and sodicity in drylands: A review of causes, effects, monitoring, and restoration measures. Frontiers in Environmental Science, 9, 712831.

Taghadosi, M. M., Hasanlou, M. and Eftekhari, K. (2019). Evaluating and comparing supervised classification algorithms with the aim of mapping soil salinity levels using Sentinel-2 satellite imageries. Scientific- Research Quarterly of Geographical Data (SEPEHR), 28(110), 37-52. (In Persian)

Tarolli, P., Luo, J., Park, E., Barcaccia, G., and Masin, R. (2024). Soil salinization in agriculture: Mitigation and adaptation strategies combining nature-based solutions and bioengineering. iScience, 27(2).

Thaker, P. N., Brahmbhatt, N., and Shah, K. (2021). A review: Impact of soil salinity on ecological, agricultural and socio-economic concerns. International Journal of Advanced Research, 9, 979–986.

Tomic, O., Luciano, G., Nilsen, A., Hyldig, G., Lorensen, K., and Næs, T. (2009). Analysing sensory panel performance in a proficiency test using the PanelCheck software. European Food Research and Technology, 228(3), 497–511.

United States Geological Survey (USGS). (2022). Landsat 8 data users handbook (Version 5.0).

Volschenk, T., Fey, M. V., and Zietsman, H. L. (2005). Situation analysis of problems for water quality management in the Lower Orange River region (WRC Report No. 1236/1/05). Water Research Commission.

Wicaksono, P., and Hafizt, M. (2017). Dark target effectiveness for dark-object subtraction atmospheric correction method on mangrove above-ground carbon stock mapping. IET Image Processing, 12(4), 582–587.

Wu, W., et al. (2014). Mapping soil salinity changes using remote sensing in Central Iraq. Geoderma Regional, 2–3, 21–31.

Yang, L., Li, X., Shi, J., Shen, F., Qi, F., Gao, B., Chen, Z., Zhu, A., and Zhou, C. (2020). Evaluation of conditioned Latin hypercube sampling for soil mapping based on a machine learning method. Geoderma, 369, 114337.

Zhang, J., and Jia, L. (2014). A comparison of pixel-based and object-based land cover classification methods in an arid/semi-arid environment of Northwestern China. 2014 3rd International Workshop on Earth Observation and Remote Sensing Applications, 246–250.

Zhang, T., Qi, J., Gao., Y., Ouyang, Z., Zeng, S., and Zhao, B. (2011). Detecting soil salinity with remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 289–297.

Zhang, Z., Niu, B., Li, X., Kang, X., and Hu, Z. (2022). Estimation and dynamic analysis of soil salinity based on UAV and Sentinel-2A multispectral imagery in the coastal area, China. Land, 11(12), 2307.