Applications of the Land Degradation Index (LDI) in remote sensing-based land degradation studies: An analytical review
DOI:
https://doi.org/10.17977/um063.v6.i1.2026.4Keywords:
Land Degradation Index, Remote sensing, LDI, Albedo, Machine learningAbstract
Land degradation is one of the world’s most pressing environmental issues and has become a major threat to food security and sustainable development. The Land Degradation Index (LDI) has emerged as a key tool for monitoring and assessing the extent of land degradation using remote sensing technology. This article presents methodological updates and practical applications of the LDI over the past five years (2020–2025), highlighting comprehensive methodologies that integrate multiple spectral indices, machine learning techniques and virtual infrastructure. The results show that the models developed for the LDI achieved an accuracy of up to 97% in estimating the level of land degradation, providing a robust scientific basis for decision-makers to improve sustainable management strategies.
References
Adugna, T., Xu, W., & Fan, J. (2022). Comparison of random forest and support vector machine classifiers for regional land cover mapping using coarse resolution FY-3C images. Remote Sensing, 14(3), 574. https://doi.org/10.3390/rs14030574
Aksoy, S., Yildirim, A., Gorji, T., Hamzehpour, N., Tanik, A., & Sertel, E. (2024). Potential of land degradation index for soil salinity mapping in irrigated agricultural land in a semi-arid region using Landsat-OLI and Sentinel-MSI data. Environmental Monitoring and Assessment, 196, Article 843. https://doi.org/10.1007/s10661-024-13030-1
Ali, E. A., Elnagar, A. S., Rebouh, N. Y., & Fadl, M. E. (2025). Assessing land degradation through remote sensing and geospatial techniques for sustainable development under the Mediterranean conditions. Sustainability, 17(13), 6087. https://doi.org/10.3390/su17136087
Al-Tameemi, N., Zhang, X., Shahzad, F., Mehmood, K., Xiao, L., & Zhou, J. (2025). From trends to drivers: Vegetation degradation and land-use change in Babil and Al-Qadisiyah, Iraq (2000–2023). Remote Sensing, 17(19), 3343. https://doi.org/10.3390/rs17193343
Amin, M., & Romshoo, S. A. (2025). Assessment and monitoring of land degradation indicators and processes using a geospatial approach. Modeling Earth Systems and Environment, 11, Article 20. https://doi.org/10.1007/s40808-024-02262-2
Amudha, S., Kumar, U., Murari, P. P., & Yadav, A. C. G. (2025). Advanced LSTM based deep learning system for precision fertilizer management. SGS Engineering & Sciences, 1(1). https://spast.org/index.php/techrep/index
Azeez, M. H., Al Sharaa, H. M. J., & Ziboon, A. R. T. (2025). Time series analysis of vegetation index and land degradation assessment in Dhi Qar Governorate (Iraq). Journal of Engineering and Sustainable Development, 29(5), 634. https://doi.org/10.31272/jeasd.2864
Bakr, A. J., & Al-Shrafany, D. M. (2025). High-resolution NDVI mapping for urban vegetation analysis in Erbil City: A comparative study of UAV and satellite data. Iraqi Geological Journal, 58(2C), 53–67. https://doi.org/10.46717/igj.2025.58.2C.3
Berra, E. F., Fontana, D. C., Yin, F., & Breunig, F. M. (2024). Harmonized Landsat and Sentinel-2 data with Google Earth Engine. Remote Sensing, 16(15), 2695. https://doi.org/10.3390/rs16152695
Chen, S., Woodcock, C. E., Bullock, E. L., Arévalo, P., Torchinava, P., Peng, S., & Olofsson, P. (2021). Monitoring temperate forest degradation on Google Earth Engine using Landsat time series analysis. Remote Sensing of Environment, 265, 112648. https://doi.org/10.1016/j.rse.2021.112648
Dalhel, F. T., Albayati, M. A., & Ziboon, A. R. T. (2021). Investigation soil degradation in Iraq by using geomatics techniques. Journal of Physics: Conference Series, 1973(1), 012194. https://doi.org/10.1088/1742-6596/1973/1/012194
Dhamin, T. A., Khanjer, E. F., & Mashee, F. K. (2023). The effect of temporal resolution of climatic factors on agriculture degradation in Southern Baghdad by applying remote sensing data. Iraqi Journal of Science, 64(2), 994–1006. https://doi.org/10.24996/ijs.2023.64.2.41
Ebrahimi, A., Zolfaghari, F., Ghodsi, M., & Narmashiri, F. (2024). Assessing the accuracy of spectral indices obtained from Sentinel images using field research to estimate land degradation. PLOS ONE, 19(7), e0305758. https://doi.org/10.1371/journal.pone.0305758
Gaznayee, H. A. A., & Al-Quraishi, A. M. F. (2021). Drought trend analysis in a semi-arid area of Iraq based on Normalized Difference Vegetation Index, Normalized Difference Water Index and Standardized Precipitation Index. Journal of Arid Land, 13(5), 421–434. https://doi.org/10.1007/s40333-021-0062-9
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
Hashemi, Z., Sodaiezadeh, H., Mokhtari, M. H., & Hakimzadeh Ardakani, M. A. (2024). Monitoring and forecasting desertification and land degradation using remote sensing and machine learning techniques in Sistan Plain, Iran. Journal of African Earth Sciences, 218, 105375. https://doi.org/10.1016/j.jafrearsci.2024.105375
Hassan, H. M., & Dakheel, H. S. (2023). Using the Normalized Difference Vegetation Index (NDVI) to study the change of vegetation cover in Thi-Qar Governorate, southern Iraq for the period from 1990–2022. Texas Journal of Agriculture and Biological Sciences, 13. https://zienjournals.com
Hosen, M.-A., & Tang, L. (2025). A physically-informed long short-term memory-based tool for long-term, large-scale and spatially informed drought prediction using an enhanced combined drought index (ECDI). Journal of Hydrology, 740, 132178. https://doi.org/10.1016/j.jhydrol.2025.132178
Jasim, A. A., Hason, M. M., Sahar, A. A., & Kadhim, T. H. (2025). Desertification phenomenon assessment in Al-Hay District, Wasit/Iraq using remote sensing techniques. Iraqi Bulletin of Geology and Mining, 21(1), 491–507. https://doi.org/10.59150/ibgm2101a26
Machine learning and SHAP-based analysis of deforestation and forest degradation dynamics along the Iraq–Turkey border. (2025). Earth, 6(2), 49. https://doi.org/10.3390/earth6020049
Mutale, B., Withanage, N. C., Mishra, P. K., Shen, J., Abdelrahman, K., & Fnais, M. S. (2024). A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: A case from Lusaka and Colombo. Frontiers in Environmental Science, 12. https://doi.org/10.3389/fenvs.2024.1431645
Republic of Iraq, Ministry of Agriculture. (2017). Land Degradation Neutrality target setting: National report. UNCCD.
Republic of Iraq, Ministry of Agriculture. (2018). Iraq LDN target setting national report. United Nations Convention to Combat Desertification. https://www.unccd.int/sites/default/files/ldn_targets/2019-08/Iraq%20LDN%20TSP%20Country%20Report.pdf
Science Publishing Group. (2025). The threat of climate change to vegetation health and land degradation in Iraq’s diverse climatic environment. American Journal of Agriculture and Forestry.
Shao, H., Liu, M., Shao, Q., Sun, X., Wu, J., Xiang, Z., & Yang, W. (2024). A novel dual-threshold assessment method for formulating land degradation neutrality priority governance strategies in Central Asia under SDG 15.3.1. Environmental Impact Assessment Review, 109, 107630.
Singh, S., Kumar, R., Bhardwaj, A., Sam, L., Shekhar, M., Singh, A., Kumar, R., & Gupta, A. (2024). Monitoring vegetation degradation using remote sensing and machine learning over India: A multi-sensor, multi-temporal and multi-scale approach. Frontiers in Forests and Global Change, 7, 1382557. https://doi.org/10.3389/ffgc.2024.1382557
Wang, Y., Shi, L., Hu, Y., Hu, X., Song, W., & Wang, L. (2024). A comprehensive study of deep learning for soil moisture prediction. Hydrology and Earth System Sciences, 28(4), 917. https://doi.org/10.5194/hess-28-917-2024
Yousefi, S., Pourghasemi, H. R., Avand, M., Janizadeh, S., Tavangar, S., & Santosh, M. (2021). Assessment of land degradation using machine-learning techniques: A case of declining rangelands. Land Degradation & Development, 32(3), 1452–1466. https://doi.org/10.1002/ldr.3794
Zhang, W., Wang, Y., Chen, Y., Zhang, X., & Feng, M. (2024). Exploration of the utilization of a new land degradation index in Lake Ebinur Basin in China. Scientific Reports, 14, 17670. https://doi.org/10.1038/s41598-024-68639-6
Zwain, H. M., Al-Hamdani, A. H. K., & Hadi, A. A. (2021). A study of desertification using remote sensing: Basra Governorate as a case study. Iraqi Journal of Science, 62(3), 912–926.
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