From Land Cover Change Modeling to Precision Conservation: A Review of Methods, Applications, and Future Directions

Ming Gao

doi:10.71222/g804nk45

Authors

Ming Gao Department of Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne, UK Author

DOI:

https://doi.org/10.71222/g804nk45

Keywords:

LUCC modeling, precision conservation, green infrastructure, Ecological Security Patterns, scenario planning, urban resilience

Abstract

As global urbanization and climate change intensify, the demand for data-driven spatial strategies to enhance landscape resilience has become paramount. This paper provides a comprehensive review of the evolution of Land Use and Land Cover Change (LUCC) modeling and its critical role in advancing Precision Conservation and Green Infrastructure (GI) planning. We analyze the methodological trajectory from traditional statistical models, such as Logistic Regression and Markov Chains, to modern intelligent frameworks involving Cellular Automata (CA), Machine Learning (ML), and Deep Learning (DL). The review highlights how these predictive tools enable a shift from reactive environmental protection to proactive spatial design. Specifically, LUCC modeling facilitates the identification of vulnerable biodiversity hotspots and the delineation of Ecological Security Patterns (ESP) by simulating "Sources," "Corridors," and "Strategic Points." Furthermore, we explore the integration of modeling into GI planning through scenario-based analysis (e.g., Business-as-Usual vs. Ecological Priority) and multi-objective optimization algorithms to ensure multifunctional urban resilience. Despite these advancements, significant challenges persist, including spatial-temporal data constraints, the "black box" nature of complex algorithms, and the "pixel-to-parcel gap" in policy implementation. We conclude that the future of resilient landscape management lies in the development of Digital Twins and a strengthened transdisciplinary collaboration between data scientists, ecologists, and urban planners to close the implementation gap between theoretical simulation and actionable spatial policy.

References

1. X. Zhang, K. Li, Y. Dai, and S. Yi, “Modeling the land cover change in Chesapeake Bay area for precision conservation and green infrastructure planning,” Remote Sensing, vol. 16, no. 3, p. 545, 2024. doi: 10.3390/rs16030545

2. Z. Zhang, X. Li, X. Liu, and K. Zhao, “Dynamic simulation and projection of land use change using system dynamics model in the Chinese Tianshan mountainous region, Central Asia,” Ecological Modelling, vol. 487, Art. no. 110564, 2024.

3. Y. Zhang, J. Yang, D. Wang, J. Wang, L. Yu, F. Yan, et al., “An integrated CNN model for reconstructing and predicting land use/cover change: A case study of the Baicheng area, Northeast China,” Remote Sensing, vol. 13, no. 23, Art. no. 4846, 2021.

4. G. Wang, “Performance evaluation and optimization of photovoltaic systems in urban environments,” Int. J. New Dev. Eng. Soc., vol. 9, pp. 42–49, 2025, doi: 10.25236/IJNDES.2025.090106.

5. M. Druga and J. Minár, “Cost distance and potential accessibility as alternative spatial approximators of human influence in LUCC modelling,” Land Use Policy, vol. 132, Art. no. 106840, 2023.

6. H. Matsumoto, T. Gu, S. Yo, M. Sasahira, S. Monden, T. Ninomiya, M. Osawa, O. Handa, E. Umegaki, and A. Shiotani, “Fecal microbiota transplantation using donor stool obtained from exercised mice suppresses colonic tumor development induced by azoxymethane in high-fat diet-induced obese mice,” Microorganisms, vol. 13, no. 5, p. 1009, 2025.

7. X. Xie, M. Peng, L. Zhang, M. Chen, J. Li, and Y. Tuo, “Assessing the impacts of climate and land use change on water conservation in the three-river headstreams region of China based on the integration of the InVEST model and machine learning,” Land, vol. 13, no. 3, Art. no. 352, 2024.

8. W. Sun, “Integration of Market-Oriented Development Models and Marketing Strategies in Real Estate,” European Journal of Business, Economics & Management, vol. 1, no. 3, pp. 45–52, 2025

9. Q. Huang, D. Ma, T. Wang, Q. Wang, and H. Xu, “Effects of LUCC changes on ecosystem service supply and trade-off–synergy relationships in the Bohai Rim region,” Journal of Environmental Management, vol. 396, Art. no. 128035, 2025.

10. S. Yuan, “Data Flow Mechanisms and Model Applications in Intelligent Business Operation Platforms”, Financial Economics Insights, vol. 2, no. 1, pp. 144–151, 2025, doi: 10.70088/m66tbm53.

11. Z. Yang, X. Dai, H. Lu, C. Liu, R. Nie, M. Zhang, et al., “Evaluation and prediction of water conservation of the Yellow River Basin in Sichuan Province, China, based on Google Earth Engine and CA-Markov,” Heliyon, vol. 9, no. 7, 2023.