The role of forests is increasing because of rapid land use changes worldwide that have implications on ecosystems and the carbon cycle. Therefore, it is necessary to obtain accurate information about forests and build forest inventories. However, it is difficult to assess the internal structure of the forest through 2D remote sensing techniques and fieldwork. In this aspect, we proposed a method for estimating the vertical structure of forests based on full-waveform light detection and ranging (FW LiDAR) data in this study. Voxel-based tree point density maps were generated by estimating the number of canopy height points in each voxel grid from the raster digital terrain model (DTM) and canopy height points after pre-processing the LiDAR point clouds. We applied an unsupervised classification algorithm to the voxel-based tree point density maps and identified seven classes by profile pattern analysis for the forest vertical types. The classification accuracy was found to be 72.73% from the validation from 11 field investigation sites, which was additionally confirmed through comparative analysis with aerial images. Based on this pre-classification reference map, which is assumed to be ground truths, the deep neural network (DNN) model was finally applied to perform the final classification. As a result of accuracy assessment, it showed accuracy of 92.72% with a good performance. These results demonstrate the potential of vertical structure estimation for extensive forests using FW LiDAR data and that the distinction between one-storied and two-storied forests can be clearly represented. This technique is expected to contribute to efficient and effective management of forests based on accurate information derived from the proposed method.

This study presents a comprehensive analysis of contaminant transport in estuarine environments, focusing on the impact of tidal creeks and flats. The research employs advanced hydrodynamic models with irregular grid systems and conducts a detailed residual current analysis to explore how these physical features influence the movement and dispersion of contaminants. The methodology involves simulating residual currents and Lagrangian particle trajectories in both ‘Creek’ and ‘No Creek’ cases, under varying tidal conditions. The results indicate that tidal creeks significantly affect particle retention and transport, with notable differences observed in the dispersion patterns between the two scenarios. The ‘Creek’ case demonstrates enhanced material retention along the creek pathways, while the ‘No Creek’ case shows broader dispersion, potentially leading to increased sedimentation in open sea areas. The discussion highlights the implications of these findings for sediment dynamics, contaminant transport, and estuarine ecology, emphasizing the role of tidal creeks in modulating flow and material transport. The research underlines the necessity of incorporating detailed environmental features in estuarine models for accurate contaminant transport prediction and effective estuarine management. This study contributes to a deeper understanding of estuarine hydrodynamics and offers valuable insights for environmental policy and management in coastal regions.

The Changjiang Diluted Water (CDW) plays a crucial role in shaping the hydrography and ecosystem dynamics of the East China Sea (ECS), particularly during summer when freshwater discharge enhances stratification and modulates biogeochemical processes. Despite its importance, the detailed progression of CDW and its short‐term impacts remain poorly understood due to the limited availability of high‐resolution observations. Using high‐resolution in situ observations from the Ieodo Ocean Research Station in summer 2020, we examined the sequential evolution of CDW and its effects on the upper‐ocean structure and phytoplankton blooms. CDW evolution was categorized into six distinct phases, characterized by abrupt shifts in salinity and stratification, driven by monsoonal winds, tides, and typhoon‐induced mixing. CDW intrusion formed a persistent barrier layer that suppressed vertical mixing and trapped surface heat, leading to increased sea surface temperatures. Nutrient influx from CDW, enriched in nitrate and phosphate, promoted the proliferation of diatoms relative to picoplankton and dinoflagellates, although all groups increased during the CDW‐influenced period. Internal wave‐induced mixing facilitated nutrient replenishment from deeper layers, partially alleviating phosphate deficiency inherent in CDW and enhancing phytoplankton productivity. Typhoon Bavi disrupted stratification through strong vertical mixing, resulting in a high‐salinity state that differed from preintrusion hydrography. This study provides a time‐resolved view of CDW‐driven variability and demonstrates how episodic freshwater inputs, coupled with physical forcing, regulate stratification, nutrient supply, and ecosystem response in the ECS.

Satellite-based observations of sea wind are useful for forecasting marine weather and performing marine disaster management. Meteorological Operational Satellite-B (MetOp-B) is one of the satellites that provide wind products through a scatterometer named the Advanced Scatterometer (ASCAT). Since the linear regression method has been conventionally employed for calibrating remotely-sensed wind data, deviations and biases remain un-resolved to some degree. For coastal applications, these remotely-sensed wind data need to be calibrated again using local in-situ measurements in order to provide more accurate and realistic information. Thus, this study proposed a new method to calibrate ASCAT-based wind speed estimates using artificial neural networks. Herein, a deep neural network (DNN) model was applied. Wind databases collected during a period from 2012 to 2019 by the MetOp-B ASCAT and ten buoy stations in Korean seas were considered for deep learning-based calibration. ASCAT-based wind data and in-situ measurements were collocated in space and time. They were then separated into training and validation sets. A DNN model was designed and trained using multiple input variables such as observation location, sensing date and time, wind speed, and wind direction of the training set. The DNN-based best fit calibration model was evaluated using the validation set. The mean of biases between ASCAT-based and in-situ wind speeds was found to be decreased from 0.41 to 0.05 m/s on average for all buoy locations. The root mean squared error (RMSE) of wind speed was reduced from 1.38 m/s to 0.93 m/s. Moreover, the DNN-based calibration considerably improved the quality of wind speeds of less than 4 m/s, and of high wind speeds of 11–25 m/s. These results suggest that ASCAT-based observations can accurately represent real wind fields if a DNN-based calibration approach is applied.

This study addresses the critical challenge of predicting sediment behavior in a semi-enclosed estuary, where the interplay between artificial freshwater discharge and seawater significantly impacts turbidity. Such environments are characterized by complex hydrodynamic interactions that lead to cycles of sediment settling and resuspension, influenced by tidal forces. To tackle this problem, we employed machine learning, leveraging its capability to analyze and predict complex non-linear phenomena. Our approach involved extensive transect observations conducted over two years, encompassing 11 ebb tide and 9 flood tide cycles. These observations were crucial for training the machine learning model, ensuring it captured the nuanced dynamics of sediment behavior under varying hydrodynamic conditions. The necessity of this research lies in its potential to enhance our understanding of sediment dynamics in estuaries, a vital aspect for environmental management and engineering projects. The findings demonstrate a promising alignment between the machine learning model’s predictions and the theoretically assumed sediment behavior, highlighting the model’s effectiveness in deciphering and predicting turbidity patterns in these challenging environments.

This paper present efficient methods for merging KOMPSAT-3A (Korea Multi-Purpose Satellite) medium wave Infrared (MIR) and panchromatic (PAN) images. Spatial sharpening techniques have been developed to create an image with both high spatial and high spectral resolution by combining the desired qualities of a PAN image with high spatial and low spectral resolution and an MS/MIR image with low spatial and high spectral resolution. The proposed methods can extract an optimal scaling factor, and uses the tactics of appropriately controlling the balance between the spatial and spectral resolutions. KOMPSAT-3A PAN and MIR images were used to test and evaluate the performance of the proposed methods. A qualitative assessment were performed using the image quality index (Q4), the cross correlation index (CC) and the relative global dimensional synthesis error (Spectral/Spatial ERGAS). These tests indicate that the proposed methods preserve the spectral and spatial characteristics of the original MIR and PAN images. Visual analysis reveals that the spectral and spatial information derived from the proposed methods were well retained in the test images. A comparison of the results of the proposed methods with those obtained from applying existing ones such as the Multi Sensor Fusion (MSF) technique or the Guide Filter Based Fusion (GF) show the efficiency of the new fusion process to be superior to the one of the others. The results showed a significant improvement in fusion capability for KOMPSAT-3A MIR imagery.