• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.170-173
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• 2004

When the data from the artificial satellite is analyzed, recent years it is perceived to vegetation index using BRF(Bi-directional Reflectance Factor) of the observation target. To make the BRF models, it is important to measure the 3D structure of the observation target actually. In this study, it is proposed to the observation technique by using multi-angle stereo pair image, and shown the observation result in grassland area. Also, our team has been operating the radio controlled helicopter which can fly over the tall forest canopy and it can be equipped the measurement system.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• 제3권3호
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• pp.151-158
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• 1999

Biogenic source emissions refer to naturally occuring emissions from vegetation, microbial activities in soil, lightening, and so on. Vegetation is especially known to emit a considerable amout of volatile organic compounds into the atmosphere. Therefore, biogenic source emissions are an important input to photochemical air quality models. since most biogenic source emissions are calculated at the county-level, they should be geographically allocated to the computational grid cells of a photochemical air quality model prior to running the model. The traditional method for the spatial allocation for biogenic source emissions has been to use a "spatial surrogate indicator" such as a county area. In order to examine the applicability of such approximations, this study developed more detailed surrogate indicators to improve the spatial allocation method for biogenic source emissions. Due to the spatially variable nature of biogenic source emissions, Geographic Information Systems(GIS) were introduced as new tools to develop more detailed spatial surrogate indicators. Use of these newly developed spatial surrogate indicators for biogenic source emission allocation provides a better resolution than the standard spatial surrogate indicator.indicator.

• The Korean Journal of Ecology
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• 제10권2호
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• pp.91-107
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• 1987

This study is concerned with some methods and applications, used as a basis on information and entropy analysis of vegetation data. These methods are adopted for the evaluating the effect of sampling intensity on information, which repersnets the departure of observed variable from standard component. Classes on the data matrix are caluculated by using marginal dispersion array for rank and weighting information program. Finally the information and entropy are computed by applying seven options. On the application of vegetation studies, two models for cluster analysis and analysis of concentration are explained in detail. Cluster analysis is based on use of equivocation information and Rajski's metrics. The analysis of concentration utilizes coherence coefficience being transformed values, which has been adjusted from blocks and entropy values. The relationship btween three begetation clusters and four stands of Naejangsan data is highly significant in 79% of total variance. Cluster A relatively tends to prefer north side, and cluster C south side.

• Journal of Mechanical Science and Technology
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• 제18권10호
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• pp.1799-1808
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• 2004

Knowledge of airflow characteristics in nasal cavity is essential to understand the physiological and pathological aspects of nasal breathing. Several studies have utilized physical models of the healthy nasal cavity to investigate the relationship between nasal anatomy and airflow. Since the final goal of these works is their contribution to the diagnosisand treatment of nasal diseases, the next step on this topic is naturally studies for disordered nasal cavities. In this paper, as the first application, airflows in the normal and abnormal nasal cavities with adenoid vegetation are investigated experimentally by PIV, and comparisons of both cases are appreciated. Dense CT data and careful treatment of model surface under the ENT doctor's advice provide more sophisticatedcavity model. The CBC PIV algorithm with window offset is used for PIV flow analysis. Average and RMS distributions are obtained for inspirational and expirational nasal airflows. Airflow characteristics that are related with the abnormalities in nasal cavity are presented.

• Ecology and Resilient Infrastructure
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• 제1권2호
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• pp.68-81
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• 2014

본 연구에서는 2차원 흐름/유사이동 모형에 식생생장모형을 추가하여 하도의 식생 활착 및 성장에 의한 지형변화 과정과 특성을 수치모의 하였다. 교호사주가 발달하는 조건에서 식생 이입 및 활착은 사주의 이동을 감소시켰다. 식생면적 및 하폭의 변화는 저유량 지속시간보다 상류유량 변화에 더 크게 영향을 받았다. 상류유량이 감소하면 식생면적은 증가하고 하도폭은 감소하였다. 망상하도가 발달하는 조건에서 하도내 식생 이입 및 활착은 지형변화 특성에 크게 영향을 미쳤다. 망도하도에서 식생은 망상의 수를 감소시키고 결국 상류유량이 크게 감소하면 하도지형을 망상하도에서 단일수로로 변화시켰다. 식생면적은 상류유량이 증가함에 따라 감소하였다. 하도폭은 식생 도입 후 급격히 줄어들었고 상류유량 감소와 함께 감소하였다. 수치모의를 이용하여 홍수량 감소가 하도 내의 식생 이입 및 활착을 가속시키고 이로 인해 하도변화 특성에 미치는 영향을 정성적으로 확인할 수 있음을 보였다.

• 한국습지학회지
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• 제19권1호
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• pp.144-154
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• 2017

수변식생은 코리도형 생태계로서 수생태계와 육상생태계 사이의 전이지대이며, 구조적으로나 기능적으로 연계를 통해 생태계 건강성을 향상시킬 수 있게 된다. 이러한 수변생태계는 서식처, 종의 공급원과 수요처, 이동통로, 필터와 장벽 등의 기능을 갖는다. 본 연구는 표준생태계의 식생구조를 바탕으로 저수지 수위변동구간의 수문환경 및 토양환경을 고려하여 도입 가능한 식생모델을 구축하기 위해 수행되었다. 경사도 10%이내의 완경사지를 중심으로 식생모델 적용 대상지 2개소를 선정하였다. 수변식생 모델은 GIS분석과 위성영상, 현지조사 결과에 따라 수위 및 침수빈도, 토양 및 지형인자, 토지피복(이용) 형태를 기준으로 구축하였으며, 구체적으로는 다음과 같다. 첫째, 수변식생 도입 가능구역으로는 계획홍수위 지점에서 만수위의 -5m 지점까지로 하였다. 둘째, 식재를 위한 수변지역 경사도는 $10^{\circ}$ 이하이다. 셋째, 연평균 100일 침수까지 생육 가능한 수종은 버드나무, 왕버들, 갯버들 등이다. 넷째, 토지피복(이용) 형태에 따라 야생초지(개여뀌, 이삭사초, 강아지풀, 가막사리, 물억새, 갈대, 망초, 달맞이꽃 등), 묵논(기존 경작지), 밭(기존 경작지)으로 분류하였다. 다섯째, 수변식생 기능을 생태경관림으로 설정하여 모델을 구축하였다. 이를 바탕으로 대청호 2개소를 선정하여 모델식재를 제안하였다. 본 연구에서 제안된 수변식생 모델은 저수지 및 하천 수변식생대의 생태적 기능과 경관성을 향상하기 위한 기초자료로 유용할 것이다.

• Ecology and Resilient Infrastructure
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• 제10권4호
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• pp.237-249
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• 2023

본 연구는 우리나라 고유의 기후와 식생, 그리고 숲의 경관생태적 특성을 기반으로 새로운 식재경관 중심의 정원화 모형을 정립하기 위해 수행되었다. 또한 산림군집의 경관 특성을 기존의 정원 식재유형과의 통합적 접근을 통해 보다 혁신적인 숲정원 모델을 개발하고자 하였다. 일차적으로 우리나라 주요 산림 식생군집을 구성하는 핵심 경관요소를 도출하고, 군집구조의 수직적인 층위와 수평적인 분포양상에 대해 우점종과 표징종 및 식별종 등 진단종과 이들의 성상 및 우점도-군집도를 분석·평가하였다. 이를 기준으로 시각적 우세요소에 따라 주 경관, 부 경관, 세부 경관으로 계층화하여 표준화된 식생경관의 구조와 형식을 개발하였다. 또한 이를 우리나라 온대중북부지역의 대표 식생군락인 낙엽활엽수림을 대상으로 미술사적인 차용의 개념과 범위를 적용하여 숲정원 모델로의 적용성을 검토하였다. 아울러 숲정원의 개념정의 및 유형화, 그리고 숲 식생의 전략적인 접근을 토대로한 통합적인 숲정원 모델을 제안하였다.

• 한국작물학회지
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• 제50권4호
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• pp.268-275
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• 2005

Early predictions of crop yields call provide information to producers to take advantages of opportunities into market places, to assess national food security, and to provide early food shortage warning. The objectives of this study were to identify the most useful parameters for estimating yields and to compare two model selection methods for finding the 'best' model developed by multiple linear regression. This research was conducted in two 65ha corn/soybean rotation fields located in east central South Dakota. Data used to develop models were small temporal variability information (STVI: elevation, apparent electrical conductivity $(EC_a)$, slope), large temporal variability information (LTVI : inorganic N, Olsen P, soil moisture), and remote sensing information (green, red, and NIR bands and normalized difference vegetation index (NDVI), green normalized difference vegetation index (GDVI)). Second order Akaike's Information Criterion (AICc) and Stepwise multiple regression were used to develop the best-fitting equations in each system (information groups). The models with $\Delta_i\leq2$ were selected and 22 and 37 models were selected at Moody and Brookings, respectively. Based on the results, the most useful variables to estimate corn yield were different in each field. Elevation and $EC_a$ were consistently the most useful variables in both fields and most of the systems. Model selection was different in each field. Different number of variables were selected in different fields. These results might be contributed to different landscapes and management histories of the study fields. The most common variables selected by AICc and Stepwise were different. In validation, Stepwise was slightly better than AICc at Moody and at Brookings AICc was slightly better than Stepwise. Results suggest that the Alec approach can be used to identify the most useful information and select the 'best' yield models for production fields.

• 한국산림과학회지
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• 제96권5호
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• pp.591-601
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• 2007

Spatial information on forest biomass is an important factor to evaluate the capability of forest as a carbon sequestrator and is a core independent variable required to drive models which describe ecological processes such as carbon budget, hydrological budget, and energy flow. The objective of this study is to understand the relationship between satellite image and field data, and to quantitatively estimate and map the spatial distribution of forest biomass. Landsat Enhanced Thematic Mapper (ETM+) derived vegetation indices and field survey data were applied to estimate the biomass distribution of mountainous forest located in Gwangneung Experimental Forest (230 ha). Field survey data collected from the ground plots were used as the dependent variable, forest biomass, while satellite image reflectance data (Band 1~5 and Band 7), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and RVI (Ratio Vegetation Index) were used as the independent variables. The mean and total biomass of Gwangneung catchment area were estimated to be about 229.5 ton/ha and $52.8{\times}10^3$ tons respectively. Regression analysis revealed significant relationships between the measured biomass and Landsat derived variables in both of deciduous forest ($R^2=0.76$, P < 0.05) and coniferous forest ($R^2=0.75$, P < 0.05). However, there still exist many uncertainties in the estimation of forest ecosystem parameters based on vegetation remote sensing. Developing remote sensing techniques with adequate filed survey data over a long period are expected to increase the estimation accuracy of spatial information of the forest ecosystem.

• 한국대기환경학회지
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• 제24권4호
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• pp.423-438
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• 2008

BEIS2 (Biogenic Emissions Inventory System version 2) and BEIS3.12 (BEIS version 3.12) were used to estimate hourly biogenic emissions over South Korea using a set of vegetation and meteorological data simulated with the MM5 (Mesoscale Model version 5). Two biogenic emission models utilized different emission factors and showed different responses to solar radiations, resulting in about $10{\sim}20%$ difference in the nationwide isoprene emission estimates. Among the 11-vegetation classes, it was found that mixed forest and deciduous forest are the most important vegetation classes producing isoprene emissions over South Korea comprising ${\sim}90%$ of the total. The simulated isoprene concentrations over Seoul metropolitan area show that diurnal and daily variations match relatively well with the PAMS (Photochemical Air Monitoring Station) measurements during the period of June 3${\sim}$June 10, 2004. Compared to BEIS2, BEIS3.12 yielded ${\sim}35%$ higher isoprene concentrations during daytime and presented better matches to the high peaks observed over the Seoul area. This study showed that the importance of vegetation data and emission factors to estimate biogenic emissions. Thus, it is expected to improve domestic vegetation categories and emission factors in order to better represent biogenic emissions over South Korea.