Manifold embeddings achieve comparable performance with multispectral imagery for time-series based land disturbance detection
Dense long-term time series multispectral imagery is crucial for monitoring Earth's surface and detecting disturbances in near-real-time. However, the massive storage requirements of such data pose significant challenges. Dimensionality reduction techniques have been widely applied in remote se...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2523481 |
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| Summary: | Dense long-term time series multispectral imagery is crucial for monitoring Earth's surface and detecting disturbances in near-real-time. However, the massive storage requirements of such data pose significant challenges. Dimensionality reduction techniques have been widely applied in remote sensing to address the curse of dimensionality, yet achieving lossless recovery of multispectral data across global locations and varying surface conditions remains difficult. Additionally, it is unclear whether reduced features retain temporal continuity and can integrate effectively with time series algorithms for disturbance detection. This study leverages Uniform Manifold Approximation and Projection (UMAP) for multispectral dimensionality reduction, trained on Harmonized Landsat Sentinel-2 (HLS) imagery. The resulting manifold embeddings are applied to the Continuous Change Detection and Classification (CCDC) algorithm for land disturbance detection. Two key findings emerge: (1) We developed a general UMAP-based dimensionality reduction model that works across global seasons, with manifold embeddings preserving time series coherence and exhibiting stable value ranges. (2) The embeddings achieved comparable performance to full-spectrum multispectral data in image prediction and disturbance detection with CCDC. This research highlights the potential of manifold learning to efficiently store and process dense satellite imagery while maintaining the ability to detect diverse land disturbances. |
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| ISSN: | 1753-8947 1753-8955 |