AI for Restoring Degraded Lands: Mapping Degradation, Predicting Climate Risk and Valuing Ecosystem Services
DOI:
https://doi.org/10.36808/if/2026/v152i6A/171245Keywords:
Artificial Intelligence, Land restoration, Climate risk prediction, Ecosystem services, Land degradation mapping, Remote sensing, Machine learning.Abstract
Land degradation is a major global challenge, with up to 40% of the world's land estimated to be degraded, affecting more than 3 billion people and weakening food security, biodiversity, climate resilience and ecosystem services. In this context, the objective of this review is to examine how artificial intelligence (AI), remote sensing and geospatial analytics can support land restoration by integrating three connected domains: land degradation mapping, climate-risk prediction and ecosystem service valuation. The review focuses on AI-based approaches for identifying degradation hotspots, assessing vegetation and soil stress, predicting drought, heat, evapotranspiration and fire-related risks, and estimating ecosystem services such as carbon sequestration, soil retention, water regulation, forage productivity and biodiversity support. Methodologically, the paper adopts a thematic synthesis of peer-reviewed literature, with emphasis on machine learning, deep learning, hybrid geospatial models and multi-source data integration. The synthesis indicates that AI can improve restoration planning by strengthening spatial diagnosis, capturing nonlinear land-climate interactions, anticipating future risk and estimating likely ecosystem service gains from restoration interventions. However, the review also finds that operational adoption remains constrained by data gaps, limited field validation, scale mismatch, uncertainty and weak interpretability of complex models. The paper therefore argues for a shift from isolated AI applications towards integrated, explainable and decision-oriented frameworks that combine Earth Observation, climate data, field evidence and ecosystem service indicators. Future research should prioritise groundvalidated datasets, multi-scale modelling, uncertainty reporting, local ecological knowledge and operational decision-support systems. Such integration can help identify where restoration is most urgently needed, where it is most likely to succeed, and what ecological and livelihood benefits it can generate. This review provides state-of-the-art insights on using AI-enabled land degradation mapping, climate-risk prediction and ecosystem service valuation as decision-support tools for sustainable land management, ecosystem resilience and evidence-based restoration of degraded landscapes.
References
Adhikari K. and Hartemink A.E. (2016). Linking soils to ecosystem services: A global review. Geoderma, 262: 101–111. https://doi.org/10.1016/j.geoderma.2015.08.009
Amani S. and Shafizadeh-Moghadam H. (2023). A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data. Agricultural Water Management, 284: 108324. https://doi.org/10.1016/j.agwat.2023.108324
Andrianarivony H.S. and Akhloufi M.A. (2024). Machine learning and deep learning for wildfire spread prediction: A review. Fire, 7(12): 482. https://doi.org/10.3390/fire7120482
D'Acunto F., Marinello F. and Pezzuolo A. (2024). Rural land degradation assessment through remote sensing: Current technologies, models, and applications. Remote Sensing, 16(16): 3059. https://doi.org/10.3390/rs16163059
Dikshit A., Pradhan B. and Huete A. (2021). An improved SPEI drought forecasting approach using the long short-term memory neural network. Journal of Environmental Management, 283: 111979. https://doi.org/10.1016/j.jenvman.2021.111979
Dikshit A., Pradhan B. and Santosh M. (2022). Artificial neural networks in drought prediction in the 21st century: A scientometric analysis. Applied Soft Computing, 114: 108080. https://doi.org/10.1016/j.asoc.2021.108080
Gu, Z., and Zeng, M. (2024). The use of artificial intelligence and satellite remote sensing in land cover change detection: Review and perspectives. Sustainability, 16(1): 274. https://doi.org/ 10.3390/su16010274
Intergovernmental Panel on Climate Change. (2019). Climate change and land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. IPCC. https://www.ipcc.ch/srccl/Intergovernmental
Science-Policy Platform on Biodiversity and Ecosystem Services. (2018). The IPBES assessment report on land degradation and restoration. IPBES Secretariat. https://files.ipbes.net/ipbes-web-prod-public-files/2018_ldr_ full_report_book_v4_pages.pdf
Manley K., Nyelele, C. and Egoh B. N. (2022). A review of machine learning and big data applications in addressing ecosystem service research gaps. Ecosystem Services, 57: 101478. https://doi.org/10.1016/j.ecoser.2022.101478
Materia S., García L.P., Van straaten C., Oh S., Mamalakis A., Cavicchia L., Coumou D., de Luca, P., Kretschmer M. and Donat M.G. (2024). Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives. WIREs Climate Change, 15(6): e914. https://doi.org/10.1002/wcc.914
Morid S., Smakhtin V. and Moghaddasi M. (2007). Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology, 27(15): 2103–2111. https://doi.org/10.1002/joc.1498
Nascimento C.M., Silva, S.H.G., de Menezes M.D., Curi N., Gonçalves M.G.M., Marques J.J. and Guilherme L.R.G. (2021). Soil degradation index developed by multitemporal remote sensing images, climate variables, terrain and soil attributes. Journal of Environmental Management, 277: 111364. https://doi.org/10.1016/j.jenvman.2020.111316
Pacheco F.A.L., Fernandes L.F.S., Valle Junior R.F., Valera C.A. and Pissarra T.C.T. (2018). Land degradation: Multiple environmental consequences and routes to neutrality. Current Opinion in Environmental Science and Health, 5: 79–86. https://doi.org/10.1016/j.coesh.2018.07.002
Page M.J., McKenzie J.E., Bossuyt P.M., Boutron I., Hoffmann T.C., Mulrow C.D., Shamseer L., Tetzlaff J.M., Akl E.A., Brennan S.E., Chou R., Glanville J., Grimshaw J.M., McDonald S., McGuinness L.A., Stewart L.A., Thomas J., Tricco A.C., Welch V.A., Whiting P., and Moher D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372: n71. https://doi.org/10.1136/bmj.n71
Scowen M., Athanasiadis I.N., Bullock J.M., Eigenbrod F. and Willcock S. (2021). The current and future uses of machine learning in ecosystem service research. Science of the Total Environment, 800: 149263. https://doi.org/10.1016/ j.scitotenv.2021.149263
Torabi Haghighi A., Darabi H., Karimidastenaei Z., Davudirad A.A., Sadegh M., and Kløve B. (2021). Land degradation risk mapping using topographic, human-induced, and geoenvironmental variables and machine learning algorithms, for the Pole-Doab watershed, Iran. Environmental Earth Sciences, 80: 1. https://doi.org/10.1007/s12665-020-09327-2
United Nations Convention to Combat Desertification. (2025). Desertification and Drought Day 2025 factsheet. UNCCD. https://www.unccd.int/resources/factsheets/desertificationand-drought-day-2025-factsheet
Wang J., Zhen J., Hu W., Chen S., Lizaga I., Zeraatpisheh M., and Yang X. (2023). Remote sensing of soil degradation: Progress and perspective. International Soil and Water Conservation Research, 11(3): 429–454. https://doi.org/10.1016/j.iswcr.2023.03.002
Willcock S., Martínez-López J., Hooftman D.A.P., Bagstad K. J., Balbi S., Marzo A., Prato C., Sciandrello S., Signorello G., Voigt B., Villa F. and Bullock J. M. (2018). Machine learning for ecosystem services. Ecosystem Services, 33: 165–174. https://doi.org/10.1016/j.ecoser.2018.04.004
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