• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.4
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• pp.384-398
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• 2015

In this paper, the high-resolution Weather Research and Forecasting/Noah-MultiParameterization (WRF/Noah-MP) modeling system is configured for the Cheongmicheon Farmland site in Korea (CFK), and its performance in land and atmospheric simulation is evaluated using the observed data at CFK during the 2014 special observation period (21 August-10 September). In order to explore the usefulness of turning on Noah-MP dynamic vegetation in midterm simulations of surface and atmospheric variables, two numerical experiments are conducted without dynamic vegetation and with dynamic vegetation (referred to as CTL and DVG experiments, respectively). The main results are as following. 1) CTL showed a tendency of overestimating daytime net shortwave radiation, thereby surface heat fluxes and Bowen ratio. The CTL experiment showed reasonable magnitudes and timing of air temperature at 2 m and 10 m; especially the small error in simulating minimum air temperature showed high potential for predicting frost and leaf wetness duration. The CTL experiment overestimated 10-m wind and precipitation, but the beginning and ending time of precipitation were well captured. 2) When the dynamic vegetation was turned on, the WRF/Noah-MP system showed more realistic values of leaf area index (LAI), net shortwave radiation, surface heat fluxes, Bowen ratio, air temperature, wind and precipitation. The DVG experiment, where LAI is a prognostic variable, produced larger LAI than CTL, and the larger LAI showed better agreement with the observed. The simulated Bowen ratio got closer to the observed ratio, indicating reasonable surface energy partition. The DVG experiment showed patterns similar to CTL, with differences for maximum air temperature. Both experiments showed faster rising of 10-m air temperature during the morning growth hours, presumably due to the rapid growth of daytime mixed layers in the Yonsei University (YSU) boundary layer scheme. The DVG experiment decreased errors in simulating 10-m wind and precipitation. 3) As horizontal resolution increases, the models did not show practical improvement in simulation performance for surface fluxes, air temperature, wind and precipitation, and required three-dimensional observation for more agricultural land spots as well as consistency in model topography and land cover data.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.426-441
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• 2023

This study investigated the predictive accuracy of a model of landslide displacement in Jecheon-si, where a great number of landslides were triggered by heavy rain on both natural (non-clear-cut) and clear-cut slopes during August 2020. This was accomplished by applying three flow direction methods (single flow direction, SFD; multiple flow direction, MFD; infinite flow direction, IFD) and the degree of root cohesion to an infinite slope stability equation. The application assumed that the soil saturation and any changes in root cohesion occurred following the timber harvest (clear-cutting). In the study area, 830 landslide locations were identified via landslide inventory mapping from satellite images and 25 cm resolution aerial photographs. The results of the landslide modeling comparison showed the accuracy of the models that considered changes in the root cohesion following clear-cutting to be improved by 1.3% to 2.6% when compared with those not considered in the area under the receiver operating characteristics (AUROC) analysis. Furthermore, the accuracy of the models that used the MFD algorithm improved by up to 1.3% when compared with the models that used the other algorithms in the AUROC analysis. These results suggest that the discriminatory application of the root cohesion, which considers changes in the vegetation condition, and the selection of the flow direction method may influence the accuracy of landslide predictive modeling. In the future, the results of this study should be verified by examining the root cohesion and its dynamic changes according to the tree species using the field hydrological monitoring technique.

• Journal of Environmental Impact Assessment
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• v.24 no.1
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• pp.35-50
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• 2015

The purposes of this study are to apply species distribution modeling in urban management planning for habitat conservation in non-urban area and to provide a detailed classification method for management zone. To achieve these objectives, Species Distribution Model was used to generate species richness and then to compare with the results from land suitability assessment. 59 species distribution models were developed by Maxent. This study used 15 model variables (5 topographical variables, 4 vegetation variables, and 6 distance variables) for Maxent models. Then species richness was created by sum of predicted species distributions. Land suitability assessment was conducted with criteria from type I of "Guidelines for land suitability assessment". After acquiring evaluation values from species richness and land suitability assessment, the results from these two models were compared according to the five grades of classification. The areas with the identical grade in Species richness and land suitability assessment are categorized and then compared each other. The comparison results are Grade1 10.92%, Grade2 37.10%, Grade3 34.56%, Grade4 20.89% and Grade5 1.73%. Grade1 and Grade5 showed the lowest agreement rate. Namely, development or conservation grade showed high disagreement between two assessment system. Therefore, the areas located between urban, agriculture, forest, and reserve have a tendency to change easily by development plans. Even though management areas are not the core area of reserve, it is important to provide a venue for species habitat and eco-corridor to protect and improve biodiversity in terms of landscape ecology. Consequently, adoption of species richness in three levels of management area classification such as conservation, production, planning should be considered in urban management plan.

• Journal of Korean Society of Forest Science
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• v.61 no.1
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• pp.69-79
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• 1983

To develope the fundamental models suitable for slope stabilization and scenic effect improvement of the roadside slopes, this study has continuously been conducted for last about 10 years through the field survey and observations on the roadside slopes of 100 plots located in the Capital region. The results obtained could be summarized as follows: 1) In general, due to unsuitable treatments and constructions to the man-made bare slope characteristics of the roadsides, the treatment aims for stabilizing and improving the scenic beauty of the slopes have not been successfully reached in the surveyed regions. 2) Particularly, because of insufficiency of the follow-up maintenance techniques to the roadside slopes treated, denudations of slope scenery established as well as the withering of the vegetation planted have been accelerated for the most part of the slopes treated. 3) 6 fundamental models for the roadside slope treatments have been developed and could be edaptable to the nation-wide purposes. The fundamental models are the model of forest scenery match plantation, roadside scenery establishment, denuded land rehabilitation, rock slope greenification, absolute stabilization, and environmental plantation belt establishment, respectively.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.3
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• pp.74-99
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• 2022

Surface ozone is produced by photochemical reactions of nitrogen oxides(NOx) and volatile organic compounds(VOCs) emitted from vehicles and industrial sites, adversely affecting vegetation and the human body. In South Korea, ozone is monitored in real-time at stations(i.e., point measurements), but it is difficult to monitor and analyze its continuous spatial distribution. In this study, surface ozone concentrations were interpolated to have a spatial resolution of 1.5km every hour using the stacking ensemble technique, followed by a 5-fold cross-validation. Base models for the stacking ensemble were cokriging, multi-linear regression(MLR), random forest(RF), and support vector regression(SVR), while MLR was used as the meta model, having all base model results as additional input variables. The results showed that the stacking ensemble model yielded the better performance than the individual base models, resulting in an averaged R of 0.76 and RMSE of 0.0065ppm during the study period of 2020. The surface ozone concentration distribution generated by the stacking ensemble model had a wider range with a spatial pattern similar with terrain and urbanization variables, compared to those by the base models. Not only should the proposed model be capable of producing the hourly spatial distribution of ozone, but it should also be highly applicable for calculating the daily maximum 8-hour ozone concentrations.

• Korean Journal of Environment and Ecology
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• v.14 no.2
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• pp.111-118
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• 2000

본연구는 울릉도의 성인봉과 태하령 지역의 산림 식생을 대상으로 199년 7-8월에 식생조사와 토양조사에 의한 너도밤나무 군락의 임분구조 및 DCCA ordination을 이용하여 분석한 결과는 다음과 같다. 1. 울릉도 산림의 중요치를 각 군락별로 살펴보면 너도밤나무-섬조릿대 군락에서 중요치가 높은 종은 너도밤나무, 우산고로쇠, 마가목, 섬단풍, 섬벚나무 등의 순으로 너도 밤나무-일색고사리 군락은 우산고로쇠 너도밤나무, 마가목 층층나무, 등수국 등의 순으로 나타났다 그리고 너도밤나무-큰두루미꽃 군락에서 중요치가 높은 종은 너도밤나무 우산고로쇠 등수국 마가목 음나무등의 순으로 솔송나무-섬잣나무 군락은 섬잣나무, 너도밤나무, 솔송나무, 회솔나무, 섬피나무 등의 순으로 나타났다. 2. DCCA ordination에 의하면 산림군락과 환경요인과의 상관관계는 다음과 같다 너도밤나무-섬조릿대 군락은 해발고가 높고 네 군락 중 토양수분이나 전절소 유기물 등이 많은 지역에 분포하고 있었다. 너도밤나무-일색고사리 군락은 해발고가 다른 군집보다 높고 토양수분이나 전질소, 유기물 등이 많아 너도밤나무-섬조릿대 군락과 매우 유사한 입지환경을 가진 지역이나 토성 중 clay 가 많이 함유된 지역에 분포하고 있었다. 너도밤나무-큰두루미꽃 군락은 해발고가 네 군락 중 중간지역에 분포하고 있으며 토양수분이나 유기물, 전질소 등도 중간인 지역에 분포하고 있었다. 솔송나무-섬잣나무 군락은 해발고가 낮고 토양수분이나 전질소, 유기물이 적고 sand가 많이 함유된 토양에 분포하고 있었다. 3. 울릉도 산림군락으 Shannon의 종다양도 지수는 0.5455~0.8801으로 비교적 낮은 수치를 나타내고 있다. 또한 너도밤나무 군락에서 분포하고 있는 주요 종의 조서열 중요치 곡선을 보면 전체의 기울기가 완만하여 너도밤나무 군락은 안정적이라 할 수 있다.단 생산성 향상을 위한 세포의 고농도 배양에는 조사한 여러 배양 시스템 중에 가장 효율적인 시스템임올 알 수 있었다 하지만 이 시스템 에서 포도당을 낮은 level로 유지할 수 있었으나, 초산의 과도한 축적으로 항체 생산성의 향상은 예상에 비해 크지 않았다. 81%), C18 0(12.38%), C18: 1(25.93%), C22:6(9.95%)이며 결합지방질(結合脂肪質)은 C14 : 0(11.60%), C16 : 0(18.94%), C16: 1(10.42%). C18 : 1(10.89%), C22 : 6(23.44%)이었다. 총필수지방산(總必須脂肪酸) 함량(含量)은 극성지방질(極成脂肪質)$(20.14{\sim}31.12%)$이 비극성지방질(非極成脂肪質)$(6.97{\sim}11.13%)$보다 훨씬 높았고, 결합지방질(結合脂肪質)이 유리지방질(遊離脂肪質)보다 높았으며 부위별(部位別)로는 피부(皮部)$(15.18{\sim}15.41%)$가 육질부(肉質部)$(6.97{\sim}11.13%)$보다 높았다. 또${\omega}3$고도부포화지방산(高度不飽和脂肪酸) 함량(含量)은 육질부(肉質部)$(15.15{\sim}28.32%)$가 피부(皮部)$(6.77{\sim}18.18%)$나 내장부(內臟部)$(8.35{\sim}9.74%)$보다 높았으며, 육질부(肉質部)에서는 극성지방질(極成脂肪質)$(26.28{\sim}34.18%)$이 비극성지방질(非極成脂肪質)$(15.15{\sim}28.32%)$보다 높았다.veral world-wide prediction models. Based on the analysis, we can easilty know

• Korean Journal of Environment and Ecology
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• v.26 no.3
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• pp.394-405
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• 2012

The aim of this study was to propose methods to transplant for the ecosystem damage borrow pits. The research site is Junggun-dong Gwangyang-si Jeollanam-do. The total area of the site is approximately $199,026m^2$, but the area damaged by exploitation of soil and rocks is about $84,200m^2$. This signals the transplanting method to solve the problems of ecological destruction. The research will focus on the areas either which are evaluated as damaged or in which the development is inevitable. Therefore, this study will investigate the vegetation structure and their evolution, topological and soil character, and annual ring structure; in the end, the study will propose compensating and restoring options. This study proposed the selection of trees and their planting methods by using the models of the community transplantation(Quercus mongolica trees) and the tree transplantation(Pinus thunbergii trees). The study set out plans that will attempt to restore the Quercus mongolica forests and 89 Quercus mongolica trees of the canopy layer trees, 153 middle layer trees, and 661 shrubs are suitable. The tree transplantation utilized the existing Pinus thunbergii trees. The number of transplantation is 2,648. The total area of the transplantation topsoil is calculated to be $15,353m^3$. These study results must be contributed to reduce a damaged ecosystems and compensated damaged ecosystems for solving the problem of damaged borrow pits.

• Korean Journal of Remote Sensing
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• v.29 no.5
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• pp.461-476
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• 2013

Carnegie-Ames-Stanford Approach (CASA) model is one of the most quick, convenient and accurate models to estimate the NPP (Net Primary Productivity) of vegetation. The purposes of this study are (1) to examine the spatial and temporal patterns of vegetation NPP of the paddy field area in Korea from 2002 to 2012, and (2) to investigate how the rice productivity responded to inter-annual NPP variability, and (3) to estimate rice yield in Korea using CASA model applied to MOderate Resolution Imaging Spectroradiometer (MODIS) products and solar radiation. MODIS products; MYD09 for NIR and SWIR bands, MYD11 for LST, MYD15 for FPAR, respectively from a NASA web site were used. Finally, (4) its applicability is to be reviewed. For those purposes, correlation coefficients (linear regression for monthly NPP and accumulated NPP with rice yield) were examined to evaluate the spatial and temporal patterns of the relations. As a result, the total accumulated NPP and Sep. NPP tend to have high correlation with rice yield. The rice yield in 2012 was estimated to be 526.93kg/10a by accumulated NPP and 520.32 kg/10a by Sep. NPP. RMSE were 9.46kg/10a and 12.93kg/10a, respectively, compared with the yield forecast of the National Statistical Office. This leads to the conclusion that NPP changes in the paddy field were well reflected rice yield in this study.