Acknowledgment to the Reviewers of Journal of Environmental & Earth Sciences in 2025
2026-02-06
Satellite-Based Change Detection Techniques for Land Use and Land Cover Dynamics
Authors
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Weilin Ou
Hunan Engineering Research Center for Real-scene 3D Construction and Application Technology, Hunan First Surveying and Mapping Institute, Changsha 410000, China
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Jun Qiu
Hunan Engineering Research Center for Real-scene 3D Construction and Application Technology, Hunan First Surveying and Mapping Institute, Changsha 410000, China
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Yuanzhi Li
Hunan Engineering Research Center for Real-scene 3D Construction and Application Technology, Hunan First Surveying and Mapping Institute, Changsha 410000, China
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Xin Dai
Hunan Engineering Research Center for Real-scene 3D Construction and Application Technology, Hunan First Surveying and Mapping Institute, Changsha 410000, China
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Qingchun Wang
Hunan Engineering Research Center for Real-scene 3D Construction and Application Technology, Hunan First Surveying and Mapping Institute, Changsha 410000, China
DOI:
https://doi.org/10.30564/jees.v8i8.13302Abstract
The detection of change using satellites has become a fundamental feature to track the land use and land cover (LULC) dynamics at local to global levels. Through this review, the synthesis of progress in the Earth observation data sources, preprocessing, methodological frameworks, and validation practices has been taken into consideration, upon which credible mapping of LULC changes relies. We then give a brief overview of applications of optical, synthetic aperture radar (SAR), thermal, and emerging hyperspectral/structural products, with the outlook of how resolution-revisit trade-offs, co-registration, atmosphere/terrain normalization, vapor and speckle suppressing capabilities, and time-series separation of performance. We then classify change detection methodologies as formulations of the problem, including binary no change, multi-class transition mapping, and the estimation of change timing. Classical differencing and change vector analysis will be useful in interpretable settings with low data, whereas statistical breakpoint and trajectory models allow time and persistence reasoning that are sensitive to seasonality. Deep learning designs have been used to learn spatial-temporal representations using engineered multi-temporal features, and machine learning algorithms, such as Siamese and encoder-decoder networks and temporal convolutional and transformer models, can learn directly on imagery. New self-supervised and foundation model paradigms seek to enhance transferability in domain shift as well as label scarcity. We also check multi-sensor fusion and object-based approaches to the enhancement of semantic coherence at high resolution. Lastly, we speak about benchmark datasets, evaluation measures, and validation procedures, pointing out pitfalls due to spatial leakage and class imbalance, and emphasizing uncertainty-aware statistically defendable area estimation. We finish with operational monitoring that is driven by application and is open to challenges towards scalable, interpretable, and trustworthy operational monitoring.
Keywords:
Land Use/Land Cover; Change Detection; Time-Series Remote Sensing; SAR-Optical Fusion; Uncertainty QuantificationReferences
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Ou, W., Qiu, J., Li, Y., Dai, X., & Wang, Q. (2026). Satellite-Based Change Detection Techniques for Land Use and Land Cover Dynamics. Journal of Environmental & Earth Sciences, 8(8), 155–180. https://doi.org/10.30564/jees.v8i8.13302
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Copyright © 2026 Weilin Ou, Jun Qiu, Yuanzhi Li, Xin Dai, Qingchun Wang
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
