• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.481-487
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• 2005

In this study, a GIS model to simulate the behavior of surface forest fires was developed on the basis of forest fire growth prediction algorithm. This model consists of three modules for data-handling, simulation and report writing. The data-handling module was designed to interpret such forest fire environment factors as terrain, fuel and weather and provide sets of data required in analyzing fire behavior. The simulation module simulates the fire and determines spread velocity, fire intensity and burnt area over time associated with terrain slope, wind, effective humidity and such fuel condition factors as fuel depth, fuel loading and moisture content for fire extinction. The module is equipped with the functions to infer the fuel condition factors from the information extracted from digital vegetation map sand the fuel moisture from the weather conditions including effective humidity, maximum temperature, precipitation and hourly irradiation. The report writer has the function to provide results of a series of analyses for fire prediction. A performance test of the model with the 2002 Chungyang forest fire showed the predictive accuracy of 61% in spread rate.

• Korean Journal of Environment and Ecology
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• v.22 no.2
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• pp.119-127
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• 2008

This research managed to establish the space information on incidence factors of landslide targeting Bukhansan National Park and aimed at suggesting a basic data for disaster prevention of a landslide for the period to come in Bukhansan National Park through drawing up the map indicating vulnerability to a landslide and ecological risks by the use of overlay analysis and adding-up estimation matrix analysis methods. This research selected slope angle, slope aspect, slope length, drainage, vegetation index(NDVI) and land use as an assessment factor of a landslide and constructed the spatial database at a level of '$30m\times30m$' resolution. The analysis result was that there existed high vulnerability to a landslide almost all over Uidong and Dobong valleys. As for ecological risks, Dobong valley, Yongueocheon valley, Jeongneung valley and Pyeongchang valley were analyzed to be higher, so it is judged that the impact on a landslide risk should be also considered in time of establishing a management plan for these districts for the time to come.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.5
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• pp.495-502
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• 2016

Italian Ryegrass (IRG), which is known as high yielding and the highest quality winter annual forage crop, is grown in mid-south area in Korea. The objective of this study was to evaluate the use of unmanned aerial vehicle (UAV) for the monitoring IRG growth. Unmanned aerial vehicle imagery obtained from middle March to late May in Nonsan, Chungcheongnam-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between $NDVI_{UAV}$ of IRG and biophysical measurements such as plant height, fresh weight, and dry weight over an entire IRG growth period. The similar trend between $NDVI_{UAV}$ and growth parameters was shown. Correlation analysis between $NDVI_{UAV}$ and IRG growth parameters revealed that $NDVI_{UAV}$ was highly correlated with fresh weight (r=0.988), plant height (r=0.925), and dry weight (r=0.853). According to the relationship among growth parameters and $NDVI_{UAV}$, the temporal variation of $NDVI_{UAV}$ was significant to interpret IRG growth. Four different regression models, such as (1) Linear regression function, (2) Linear regression through the origin, (3) Power function, and (4) Logistic function were developed to evaluate the relationship between temporal $NDVI_{UAV}$ and measured IRG growth parameters. The power function provided higher accurate results to predict growth parameters than linear or logistic functions using coefficient of determination. The spatial distribution map of IRG growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when $NDVI_{UAV}$ was applied to power function. From these results, $NDVI_{UAV}$ can be used as a new tool for monitoring IRG growth.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.19 no.3
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• pp.103-119
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• 2016

This study aims to assess habitat feature on the large-scale spawning ground of the Boreal Digging Frog Kaloula borealis in Daemyung retarding basin of Daegu, and to analyze the relationships between species abundance and meteorological factors for each habitat. Fifty-seven(57) pitfalls were installed to collect species abundance of 4 survey regions, and high-resolution satellite image, soil sampling equipment, digital topographic map, and GPS were used to develop habitat features such as terrain, soil, vegetation, human disturbance. The analysis shows that the frog is most abundant in sloped region with densely herbaceous cover in southern part of the retarding basin. In the breeding season, lowland regions, where Phragmites communis and P. japonica dominant wetlands and temporary ponds distributed, are heavily concentrated by the species for spawning and foraging. Located in between legally protected Dalsung wetands and lowland regions of the retarding basin, riverine natural levee is ecologically important area as core habitat for Kaloula borealis, and high number of individuals were detected both breeding and non-breeding seasons. Temperate- and pressure-related meteorological elements are selected as statistically significant variables in species abundance of non-breeding season in lowland and highland regions. However, in sloped regions, only a few variables are statistically significant during non-breeding season. Moreover, breeding activities in sloped regions are statistically significant with minimum temperature, grass minimum temperature, dew point temperature, and vapor pressure. Significant meteorological factors with habitat features are effectively applied to establish species conservation strategy of the retarding basin and to construct for avoiding massive road-kills on neighboring roads of the study sites, particularly post-breeding movements from spawning to burrowing areas.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.426-428
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• 2003

Optical and microwave remote sensing data have been widely used in land cover and land use classification. Thanks to the spectral absorption characteristics of ground object in visible and near infrared region, optical data enables to extract different land cover types according to their material composition like water body, vegetation cover or bare land. On the other hand, microwave sensor receives backscatter radiance which contains information on surface roughness, object density and their 3-D structure that are very important complementary information to interpret land use and land cover. Separate use of these data have brought many successful results in practice. However, the accuracy of the land use / land cover established by this methodology still has some problems. One of the way to improve accuracy of the land use / land cover classification is just combination of both optical and microwave data in analysis. In this paper for the research, the author used LANDSAT TM scene 127/45 acquired on October 21, 1992, JERS-1 SAR scene 119/265 acquired on October 27, 1992 and aerial photographs taken on October 21, 1992. The study area has been selected in Hanoi City and surrounding area, Vietnam. This is a flat agricultural area with various land use types as water rice, secondary crops like maize, cassava, vegetables cultivation as cucumber, tomato etc. mixed with human settlement and some manufacture facilities as brick and ceramic factories. The use of only optical or microwave data could result in misclassification among some land use features as settlement and vegetables cultivation using frame stages. By combination of multitemporal JERS-1 SAR and TM data these errors have been eliminated so that accuracy of the final land use / land cover map has been improved. The paper describes a methodology for data combination and presents results achieved by the proposed approach.

• Journal of Korean Society of Water and Wastewater
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• v.31 no.3
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• pp.197-204
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• 2017

This study was carried out to apply the UAV(Unmanned Aerial Vehicle) coupled with Multispectral sensor for the algae bloom monitoring in river. The study acquired remote sensing data using UAV on the midstream area of Gum River, one of four major rivers in South Korea. Normalized difference vegetation index (NDVI) is used for monitoring algae change. This study conducted water sampling and analysis in the field for correlating with NDVI values. Among the samples analyzed, the chlorophyll concentration exhibited strong and significant linear relationships with NDVI, and thus NDVI was chosen for algae bloom index to identify emergence aspect of phytoplankton in river. Aerial remote sensing technology can provide more accurate, flexible, cheaper, and faster monitoring methods of detecting and predicting eutrophication and therefore cyanobacteria bloom in water reservoirs compared to currently used technology. As a result, there was high level of correlation in chlorophyll-a and NDVI. It is expected that when this remote water quality and pollution monitoring technology is applied in the field, it would be able to improve capabilities to deal with the river water quality and pollution at the early stage.

• Journal of The Geomorphological Association of Korea
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• v.24 no.3
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• pp.105-118
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• 2017

The debris slope landforms which are existent in Korean mountains is generally on the steep slopes and mostly covered by vegetation, it is difficult to investigate the landform. Therefore a scientific method is required to come up with an effective field investigation plan. For this purpose, the use of Remote Sensing and GIS technologies for a spatial analysis is essential. This study has extracted the potential area of debrisslope landform formation using Fuzzy set and Bayesian Predictive Discriminate Model as mathematical data integration methods. The first step was to obtain information about debris locations and their related factors. This information was verified through field investigation and then used to build a database. In the second step, the map that zoning the study area based on the degree of debris formation possibility was generated using two modeling methods, and then cross validation technique was applied. In order to quantitatively analyze the accuracy of two modeling methods, the calculated potential rate of debrisformation within the study area was evaluated by plotting SRC(Success Rate Curve) and calculating AUC(Area Under the Curve). As a result, the prediction accuracy of Fuzzy set model wes 83.1% and Bayesian Predictive Discriminate Model wes 84.9%. It showed that two models are accurate and reliable and can contribute to efficient field investigation and debris landform management.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.2 no.4
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• pp.219-228
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• 2021

During our observations of changes in halophyte distribution in Hampyeong Bay over a period of five years, we found that the distribution area showed a maintenance for Phragmites communis community, a tendency of gradual increase for Zoysia sinica community, gradual decrease for Suaeda maritima community, and disappearance for Limonium tetragonum community during the studied period. The Phragmites communis community stably settled in areas adjacent to land and appeared not to be significantly affected by physical factors (such as tides and waves) or disturbances caused by biological factors (such as interspecific competition). Among studied species, germination time was shown to be the fastest for Suaeda maritima. In addition, this species showed certain characteristics that allowed it to settle primarily in new habitats formed by sand deposition as its growth was not halted under conditions with high amounts of sand and high organic matter content. However, in areas where Zoysia sinica and Suaeda maritima resided together, the area inhabited by Suaeda maritima gradually decreased due to interspecific competition between the two species. This was believed to be the result of a sharp decrease in the germination of Suaeda maritima since May, while the germination of Zoysia sinica was continuously maintained, indicating that the latter had an advantage in terms of seedling competition. In the case of the Limonium tetragonum community, its habitat was found to have been completely destroyed because it was covered by sand. The study area was confirmed to have undergone a large change in topography as tides and waves resulted in sand deposition onto these lands. Hampyeong Bay is considered to have experienced changes in halophyte distribution related to certain complex factors, such as changes in physical habitats and changes in biological factors such as interspecific competition.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.83-101
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• 2022

Accurate atmospheric correction is essential for the analysis of land surface and environmental monitoring. Aerosol optical depth (AOD) information is particularly important in atmospheric correction because the radiation attenuation by Mie scattering makes the differences between the radiation calculated at the satellite sensor and the radiation measured at the land surface. Thus, it is necessary to use high-quality AOD data for an appropriate atmospheric correction of high-resolution satellite images. In this study, we examined the Second Simulation of a Satellite Signal in the Solar Spectrum (6S)-based atmospheric correction results for the Sentinel-2 images in South Korea using raster AOD (MODIS) and single-point AOD (AERONET). The 6S result was overall agreed with the Sentinel-2 level 2 data. Moreover, using raster AOD showed better performance than using single-point AOD. The atmospheric correction using the single-point AOD yielded some inappropriate values for forest and water pixels, where as the atmospheric correction using raster AOD produced stable and natural patterns in accordance with the land cover map. Also, the Sentinel-2 normalized difference vegetation index (NDVI) after the 6S correction had similar patterns to the up scaled drone NDVI, although Sentinel-2 NDVI had relatively low values. Also, the spatial distribution of both images seemed very similar for growing and harvest seasons. Future work will be necessary to make efforts for the gap-filling of AOD data and an accurate bi-directional reflectance distribution function (BRDF) model for high-resolution atmospheric correction. These methods can help improve the land surface monitoring using the future Compact Advanced Satellite 500 in South Korea.

• Atmosphere
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• v.33 no.1
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• pp.61-72
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• 2023

Accurate estimation of forest carbon stocks is important in establishing greenhouse gas reduction plans. In this study, we estimate the spatial distribution of forest carbon stocks using machine learning techniques based on high-resolution remote sensing data and detailed field survey data. The high-resolution remote sensing data used in this study are Landsat indices (EVI, NDVI, NDII) for monitoring vegetation vitality and Shuttle Radar Topography Mission (SRTM) data for describing topography. We also used the forest growing stock data from the National Forest Inventory (NFI) for estimating forest biomass. Based on these data, we built a model based on machine learning methods and optimized for Korean forest types to calculate the forest carbon stocks per grid unit. With the newly developed estimation model, we created forest carbon stocks maps and estimated the forest carbon stocks in South Korea. As a result, forest carbon stock in South Korea was estimated to be 432,214,520 tC in 2020. Furthermore, we estimated the loss of forest carbon stocks due to the Donghae-Uljin forest fire in 2022 using the forest carbon stock map in this study. The surrounding forest destroyed around the fire area was estimated to be about 24,835 ha and the loss of forest carbon stocks was estimated to be 1,396,457 tC. Our model serves as a tool to estimate spatially distributed local forest carbon stocks and facilitates accounting of real-time changes in the carbon balance as well as managing the LULUCF part of greenhouse gas inventories.