Extraction of Spatiotemporal Information of Rainfall-Induced Landslides from Remote Sensing
Abstract
:1. Introduction
2. Study Area and Datasets
2.1. Study Area
2.2. Multispectral Remote Sensing Imagery Data
2.3. Soil Moisture Products
3. Methods
3.1. Landslide Detection Based on Multisource Remote Sensing Data
3.2. Determining Landslide Timing Based on Hydrometeorological Thresholds
3.3. Model Validation
4. Results
4.1. Identification of Landslide Events and Spatial Distribution
4.2. Temporal Identification of Rainfall-Induced Landslides
4.3. Accuracy Evaluation
5. Discussion
6. Conclusions
- We proposed an automated algorithm to extract spatial information of landslide events based on multisource remote sensing data. First, this approach optimizes the spatial information extraction method for remote sensing landslide events. Utilizing a bi-temporal NDVI change detection method, it considers both the quantity and area accuracy of landslide event extraction. Then, a regional-scale automated algorithm for extracting spatial information of landslide events was designed on the GEE platform, enabling rapid extraction of landslide event spatial information on a regional scale.
- We identified temporal information of rainfall-induced landslide events based on hydrometeorological thresholds. First, a hydrometeorological threshold model was developed considering the antecedent soil saturation and recent rainfall, making it effective for landslide prediction. Second, using this method to classify and temporally identify landslide events extracted from remote sensing, 87.3% of the temporal information recognition errors are noted within 7 days. The temporal identification accuracy of regional rainfall-induced landslide events was enhanced. This demonstrates that the method can establish spatiotemporal information of landslides for areas without recorded landslide data.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Product | Temporal Coverage | Temporal Resolution | Spatial Resolution | Soil Depth |
---|---|---|---|---|
SMAP-PE | 2015 to present | Daily | 9 km × 9 km | 0–5 cm |
SMAP-Sur | Every 3 h | 9 km × 9 km | 0–5 cm | |
SMAP-RZ | 0–100 cm |
73.5%—20 mm | 75.5%—19 mm | 75.5%—20 mm | 78.5%—20 mm | |
---|---|---|---|---|
TPR | 0.82 | 0.70 | 0.83 | 0.83 |
FPR | 0.39 | 0.21 | 0.32 | 0.22 |
d | 0.43 | 0.37 | 0.36 | 0.28 |
73.5%—20 mm | 75.5%—19 mm | 75.5%—20 mm | 78.5%—20 mm | |
---|---|---|---|---|
TPR | 0.72 | 0.79 | 0.84 | 0.83 |
FPR | 0.21 | 0.24 | 0.29 | 0.21 |
d | 0.35 | 0.32 | 0.33 | 0.27 |
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Zeng, T.; Zhang, J.; Chen, Y.; Zhu, S. Extraction of Spatiotemporal Information of Rainfall-Induced Landslides from Remote Sensing. Remote Sens. 2024, 16, 3089. https://doi.org/10.3390/rs16163089
Zeng T, Zhang J, Chen Y, Zhu S. Extraction of Spatiotemporal Information of Rainfall-Induced Landslides from Remote Sensing. Remote Sensing. 2024; 16(16):3089. https://doi.org/10.3390/rs16163089
Chicago/Turabian StyleZeng, Tongxiao, Jun Zhang, Yulin Chen, and Shaonan Zhu. 2024. "Extraction of Spatiotemporal Information of Rainfall-Induced Landslides from Remote Sensing" Remote Sensing 16, no. 16: 3089. https://doi.org/10.3390/rs16163089