Fine-Scale Mapping and Uncertainty Quantification of Intertidal Sediment Grain Size Using Geostatistical Simulation Integrated with Machine Learning and High-Resolution Remote Sensing Imagery

초록

Highlights What are the main findings? Incorporating satellite-derived features into the random forest model improved the accuracy of grain size prediction compared to using field samples alone. The proposed geostatistical simulation framework enabled explicit quantification of prediction uncertainty. What is the implication of the main finding? Satellite-derived features, when integrated with machine learning and multivariate geostatistics, can be used to complement limited field data in tidal flats. The proposed approach provides fine-scale grain-size maps with associated uncertainty information to support coastal management.Highlights What are the main findings? Incorporating satellite-derived features into the random forest model improved the accuracy of grain size prediction compared to using field samples alone. The proposed geostatistical simulation framework enabled explicit quantification of prediction uncertainty. What is the implication of the main finding? Satellite-derived features, when integrated with machine learning and multivariate geostatistics, can be used to complement limited field data in tidal flats. The proposed approach provides fine-scale grain-size maps with associated uncertainty information to support coastal management.Abstract This study presents a geostatistical simulation approach for fine-scale grain size mapping in tidal flats, which complements sparse field survey data with high-resolution optical satellite imagery and quantifies prediction uncertainty at unsampled locations. Within a multi-Gaussian regression kriging (MGRK) framework, a random forest (RF) regression model is used to estimate the trend component of grain size variability in Gaussian space. Residual components are estimated using kriging, and the trend and residual components are combined to construct conditional cumulative distribution functions for uncertainty modeling. Sequential Gaussian simulation based on the CCDFs generates alternative realizations of grain size, allowing for quantification of prediction uncertainty. The potential of this integrated approach was tested on the Baramarae tidal flat in Korea using KOMPSAT-2 imagery. Three spectral features, the green band, red band, and normalized difference water index (NDWI), explained 42.74% of the grain size variability, with NDWI identified as the most influential feature, contributing 40.8% compared with 31.7% for the red band and 27.5% for the green band. MGRK effectively captured local grain size variations, reducing the mean absolute error from 0.554 to 0.280 compared with univariate kriging based solely on field survey data, corresponding to an improvement of approximately 49.5%. The benefit of the proposed approach was validated by a reduction in prediction uncertainty, with the mean standard deviation decreasing from 0.743 in simulations based solely on field data to 0.280 in MGRK-based simulations. These findings indicate that the proposed geostatistical approach, integrating satellite-derived features, is a reliable method for fine-scale mapping of intertidal sediment grain size by providing both predictions and associated uncertainty estimates.

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