• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.715-727
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• 2023

To provide detailed and appropriate meteorological information in mountainous areas, the Korea Forest Service has established an Automatic Mountain Meteorology Observation Station (AMOS) network in major mountainous regions since 2012, and 464 stations are currently operated. In this study, we proposed an optimal kriging technique with lapse rate correction to produce gridded temperature data suitable for Korean forests using AMOS point observations. First, the outliers of the AMOS temperature data were removed through statistical processing. Then, an optimized theoretical variogram, which best approximates the empirical variogram, was derived to perform the optimal kriging with lapse rate correction. A 500-meter resolution Kriging map for temperature was created to reflect the elevation variations in Korean mountainous terrain. A blind evaluation of the method using a spatially unbiased validation sample showed a correlation coefficient of 0.899 to 0.953 and an error of 0.933 to 1.230℃, indicating a slight accuracy improvement compared to regular kriging without lapse rate correction. However, the critical advantage of the proposed method is that it can appropriately represent the complex terrain of Korean forests, such as local variations in mountainous areas and coastal forests in Gangwon province and topographical differences in Jirisan and Naejangsan and their surrounding forests.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.637-654
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• 2023

In recent years, the number of users has been increasing with the rapid development of earth observation satellites. In response, the Committee on Earth Observation Satellites (CEOS) has been striving to provide user-friendly satellite images by introducing the concept of Analysis Ready Data (ARD) and defining its requirements as CEOS ARD for Land (CARD4L). In ARD, a mask called an Unusable Data Mask (UDM), identifying unnecessary pixels for land analysis, should be provided with a satellite image. UDMs include clouds, cloud shadows, terrain shadows, etc. Terrain shadows are generated in mountainous terrain with large terrain relief, and these areas cause errors in analysis due to their low radiation intensity. previous research on terrain shadow detection focused on detecting terrain shadow pixels to correct terrain shadows. However, this should be replaced by the terrain correction method. Therefore, there is a need to expand the purpose of terrain shadow detection. In this study, to utilize CAS500-4 for forest and agriculture analysis, we extended the scope of the terrain shadow detection to shaded areas. This paper aims to analyze the potential for terrain shadow detection to make a terrain shadow mask for South and North Korea. To detect terrain shadows, we used a Hill-shade algorithm that utilizes the position of the sun and a surface's derivatives, such as slope and aspect. Using RapidEye images with a spatial resolution of 5 meters and Sentinel-2 images with a spatial resolution of 10 meters over the Korean Peninsula, the optimal threshold for shadow determination was confirmed by comparing them with the ground truth. The optimal threshold was used to perform terrain shadow detection, and the results were analyzed. As a qualitative result, it was confirmed that the shape was similar to the ground truth as a whole. In addition, it was confirmed that most of the F1 scores were between 0.8 and 0.94 for all images tested. Based on the results of this study, it was confirmed that automatic terrain shadow detection was well performed throughout the Korean Peninsula.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.3
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• pp.620-628
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• 2017

In this paper, we suggest a route plan sharing service for the navigation assistance service which is the second item of 16 items in maritime service portfolios (MSPs) for safety navigation based on interview process. Also, we developed scenarios for effectiveness evaluation of the proposed service, and conducted simulations using full mission ship handling simulator (FMSS) for effectiveness evaluation of proposed services based on the developed scenarios. Through the simulations, we analyzed proximity measures, controllability statistics and subjective evaluations to assess the usefulness of suggested service. If accomplishing the test for new services to apply real ships and vessel traffic (VTS) center, there has possibilities to occur various risks in terms of time/cost problems. Therefore, there is needs for the preliminary effectiveness evaluation processes necessarily when adopts and implements new services. Because we expected the service that is helpful for safety navigation, but the test results are not when conducted a simulation.

• Economic and Environmental Geology
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• v.53 no.3
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• pp.321-336
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• 2020

The Headquarters for Earthquake Research Promotion (HERP) represents the upper-most level of Japan's earthquake disaster prevention governance. Its policy committee establishes the national earthquake investigation research promotion plan. The earthquake investigation committee of HERP collects survey geo-data and evaluates the research results of each earthquake disaster prevention agency. The establishment of an earthquake-related geo-resilience research strategy is both necessary and desirable for Korea. The concept of geo-resilience entails the ability to improve disaster resilience through the application of research results and the convergence of geoscience with science and technology (S&T) including the humanities and social sciences. The achievement of geo-resilience requires a national long-term roadmap and strategy for earthquake prediction research, the development of earthquake disaster prediction and prevention technology, Geo-ICT convergence technology development, implementation of a geocyber physics system (Geo-CPS), the use of geo-mimetics, and geoscientific R&D as it relates to local communities. Through such efforts, the national research institutes of Korea will be able to develop earthquake prediction capacities in relevant fields, reinforce proactive response capabilities, enhance community-level confidence in geodata and its research results, foster next-generation geoscientific manpower, and expand geoscientific infrastructure.

• Journal of Korean Society of Forest Science
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• v.110 no.2
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• pp.233-243
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• 2021

Considering worldwide efforts to mitigate repercussions of climate change, the South Korean government has declared to reach net zero by 2050 to achieve a carbon-neutral sustainable society. For full implementation of NDCs, the government has actively reflected its forestry sector into these strategies. Since coarse woody debris (CWD) in forests represents an enduring carbon storage, it is of particular significance to determine characteristics of changes in carbon stocks of CWD by utilizing data on dead trees monitored in permanent sample plots within national forest inventories (NFIs). In this study, therefore, both occurrence and carbon stocks of CWD were estimated in such plots using data on CWD from the 5^th, 6^th, and 7^th NFIs. Subsequently, characteristics of changes in carbon stocks over time were analyzed. Based on the analysis of 2,021 plots available for monitoring in each NFI of Gangwon Province, the volume of CWD (m³ ha^-1) was found to be 4.71 in the 5^th NFI and 4.09 in the 6^th NFI. However, the volume of CWD declined to 3.09 in the 7^th NFI. Moreover, the annual carbon stocks of CWD (ton C ha^-1) were estimated to be 0.67 in 2009, 0.64 in 2014, and 0.41 in 2019, showing a downward trend over time. This study provides a basis for future research to investigate long-term changes and estimate carbon stocks of CWD in South Korea forests.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1407-1422
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• 2022

This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F₁ regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.318-329
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• 2022

Plant phenology including flowering, leaf unfolding, and leaf coloring in a forest is important to understand the forest ecosystem. Temperature rise due to recent climate change, however, can lead to plant phenology change as well as snowfall in winter season. Therefore, accurate monitoring of forest environment changes such as plant phenology and snow cover is essential to understand the climate change effect on forest management. These changes can monitor using a digital camera system. This paper introduces the detection methods for plant phenology and snow cover at the mountain region using an unmanned camera system that is a way to monitor the change of forest environment. In this study, the Automatic Mountain Meteorology Stations (AMOS) operated by Korea Forest Service (KFS) were selected as the testbed sites in order to systematize the plant phenology and snow cover detection in complex mountain areas. Multi-directional Internet Protocol (IP) camera system that is a kind of unmanned camera was installed at AMOS located in Seoul, Pyeongchang, Geochang, and Uljin. To detect the forest plant phenology and snow cover, the Red-Green-Blue (RGB) analysis based on the IP camera imagery was developed. The results produced by using image analysis captured from IP camera showed good performance in comparison with in-situ data. This result indicates that the utilization technique of IP camera system can capture the forest environment effectively and can be applied to various forest fields such as secure safety, forest ecosystem and disaster management, forestry, etc.

• Journal of Korean Society of Forest Science
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• v.112 no.2
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• pp.209-216
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• 2023

This study aimed to use national forest inventory data to develop a forest productivity index and yield prediction model of a Tilia amurensis stand. The site index displaying the forest productivity of the Tilia amurensis stand was developed as a Schumacher model, and the site index classification curve was generated from the model results; its distribution growth in Korea ranged from 8-16. The growth model using age as an independent variable for breast height and height diameter estimation was derived from the Chapman-Richards and Weibull model. The Fitness Indices of the estimation models were 0.32 and 0.11, respectively, which were generally low values, but the estimation-equation residuals were evenly distributed around 0, so we judged that there would be no issue in applying the equation. The stand basal area and site index of the Tilia amurensis stand had the greatest effect on the stand-volume change. These two factors were used to derive the Tilia amurensis stand yield model, and the model's determination coefficient was approximately 94%. After verifying the residual normality of the equation and autocorrelation of the growth factors in the yield model, no particular problems were observed. Finally, the growth and yield models of the Tilia amurensis stand were used to produce the makeshift stand yield table. According to this table, when the Tilia amurensis stand is 70 years old, the estimated stand-volume per hectare would be approximately 208 m³ . It is expected that these study results will be helpful for decision-making of Tilia amurensis stands management, which have high value as a forest resource for honey and timber.

• Journal of the Society of Disaster Information
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• v.13 no.1
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• pp.118-129
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• 2017

This paper introduces analyzing the feature of drowning accidents in Korea and reviewing the method for reducing it. A lot of drowning accidents happen at inland areas such as lakes and rivers in Korea. Also, when the accident happens, systematic notify and rescue activity are carried out, but in terms of an accident prevention, there are many problems such as lack of management manpower and the difficulty of continuous monitoring about dangerous areas, etc. In order to resolve the problems about prevention of drowning accidents and the lack of the management, the role of the local governments is important, and using the ICT technology, it is effective to make use of monitoring remotely dangerous areas and developing control technology.