• 대한공간정보학회지
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• 제24권4호
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• pp.105-113
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• 2016

산불방사열에너지(fire radiative power)는 산불로부터 방출되는 에너지로서 산불의 연소과정에서 발생하는 온실가스를 추정하기 위한 기초자료로 이용된다. 유럽, 아프리카, 아메리카 지역의 정지궤도 위성센서들은 준실시간의 산불방사열에너지를 산출 및 제공하고 있지만 아시아권에는 아직까지 정지궤도 위성기반의 공식적인 산불방사열에너지 산출물이 제공되지 않고 있다. 본 연구에서는 중적외 복사휘도법(mid-infrared radiance method)을 이용하여 히마와리(Himawari-8) 위성 기반의 산불방사열에너지를 최초로 산출하였으며, 산출정확도를 검증하기 위해 인도네시아 수마트라 지역에 대해 Aqua/Terra 위성의 MODIS(moderate resolution imaging spectroradiometer) 산불방사열에너지 산출물과의 비교검증을 실시하였다. 이 과정에서 NDVI(normalized difference vegetation index)와 FVC(fraction of vegetation coverage)를 이용하여 중적외 복사휘도법의 중요인자인 지표면 방출률을 지면피복 종류에 따라 계산하였으며, 최적화 실험을 통하여 히마와리 AHI(advanced Himawari imager)의 센서계수 a = 3.11을 도출하였다. 본 연구를 통해 산출된 히마와리 산불방사열에너지는 MODIS를 기준으로 약 20%의 평균절대백분비오차를 나타내었으며 이는 미국과 유럽연합의 정지궤도위성의 산불방사열에너지 검증결과와 유사한 수준의 정확도로 평가된다. 히마와리 산불방사열에너지의 산출정확도는 산불의 크기와 위성관측각에 따라 일부 차이를 보였으나 태양천정각과 토지피복에 따른 영향은 거의 없는 것을 알 수 있었다. 이 연구는 아시아권의 정지궤도위성 산불방사열에너지 산출을 위한 참고자료로서 활용가치가 있으며 산불방출 온실가스 추정에 기초자료로 활용될 수 있을 것으로 기대한다.

• 대한원격탐사학회지
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• 제33권4호
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• pp.401-410
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• 2017

Estimation of snow depth using optical image is conducted by using correlation with Snow Cover Fraction (SCF). Various algorithms have been proposed for the estimation of snow cover fraction based on Normalized Difference Snow Index (NDSI). In this study we tested linear, quadratic, and exponential equations for the generation of snow cover fraction maps using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite in order to evaluate their applicability to the complex terrain of South Korea and to search for improvements to the estimation of snow depth on this landscape. The results were validated by comparison with in-situ snowfall data from weather stations, with Root Mean Square Error (RMSE) calculated as 3.43, 2.37, and 3.99 cm for the linear, quadratic, and exponential approaches, respectively. Although quadratic results showed the best RMSE, this was due to the limitations of the data used in the study; there are few number of in-situ data recorded on the station at the time of image acquisition and even the data is mostly recorded on low snowfall. So, we conclude that linear-based algorithms are better suited for use in South Korea. However, in the case of using the linear equation, the SCF with a negative value can be calculated, so it should be corrected. Since the coefficients of the equation are not optimized for this area, further regression analysis is needed. In addition, if more variables such as Normalized Difference Vegetation Index (NDVI), land cover, etc. are considered, it could be possible that estimation of national-scale snow depth with higher accuracy.

• 대한원격탐사학회지
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• 제34권4호
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• pp.625-638
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• 2018

In this paper, we derive i) a function to estimate snow cover fraction (SCF) from a MODIS satellite image that has a wide observational area and short re-visit period and ii) a function to determine snow depth from the estimated SCF map. The SCF equation is important for estimating the snow depth from optical images. The proposed SCF equation is defined using the Gaussian function. We found that the Gaussian function was a better model than the linear equation for explaining the relationship between the normalized difference snow index (NDSI) and the normalized difference vegetation index (NDVI), and SCF. An accuracy test was performed using 38 MODIS images, and the achieved root mean square error (RMSE) was improved by approximately 7.7 % compared to that of the linear equation. After the SCF maps were created using the SCF equation from the MODIS images, a relation function between in-situ snow depth and MODIS-derived SCF was defined. The RMSE of the MODIS-derived snow depth was approximately 3.55 cm when compared to the in-situ data. This is a somewhat large error range in the Republic of Korea, which generally has less than 10 cm of snowfall. Therefore, in this study, we corrected the calculated snow depth using the relationship between the measured and calculated values for each single image unit. The corrected snow depth was finally recorded and had an RMSE of approximately 2.98 cm, which was an improvement. In future, the accuracy of the algorithm can be improved by considering more varied variables at the same time.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.446-446
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• 2022

본 연구에서는 MODIS(MODerate resolution Imaging Spectroradiometer) 다중 위성영상을 기반으로 전국 시공간 토양수분 및 토양수분 기반의 가뭄지수 SWDI(Soil Water Deficit Index)를 산정하였다. 시공간 토양수분의 산정을 위해 입력자료로 MODIS 위성의 지표면온도(Land Surface Temperature, LST), 증발산 및 식생(Enhanced Vegetation Index, EVI; Fraction of Photosynthetically Active Radiation, FPAR; Leaf Area Index, LAI; Normalized Difference Vegetation Index, NDVI) 관련 산출물 자료와 지상 관측자료인 일 단위 강수량 자료를 구축하였다. MODIS 위성영상은 산출물별로 제공되는 QC(Quality Control) 영상을 활용해 보정을 수행하였고, 공간 강수량 자료는 기상청에서 제공하는 전국 92개 지점의 종관기상관측자료를 구축하여 공간보간기법인 역거리가중법을 적용해 생성하였다. 실측 토양수분은 농촌진흥청에서 제공하는 76개 지점의 토양 깊이 10 cm에 설치된 TDR(Time Domain Reflectomerty) 센서에서 측정된 토양수분 자료를 활용하였으며, 토양수분 모의 시 토양 속성을 고려하기 위해 국립농업과학원에서 제공하는 토양도를 구축하여 활용하였다. 토양수분 산정 모형은 다중선형회귀모형(Multiple Linear Regression Model, MLRM)을 활용하였으며, 계절 및 토성에 따른 회귀식을 산정하였다. 회귀식 기반의 토양수분과 토성별 포장용수량 및 영구위조점 값을 이용하여 SWDI를 산정하고, 실제 가뭄 발생 시기 및 지역과의 비교하고자 한다.

• 대한원격탐사학회지
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• 제21권3호
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• 대한원격탐사학회지
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• 제21권6호
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• pp.457-468
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• 2005

도시화에 따른 불투수 지표면의 증가는 도시환경에 부정적인 영향을 미치게 된다. 따라서 도시 내 불투수 지역의 시공간적 변화 사항을 탐색하고 정량화하는 작업은 도시환경을 연구함에 있어 무엇보다 중요한 일이라 할 수 있다 지난 시기 도시지역의 불투수 지표면을 탐색하는 방법으로는 전통적인 영상분류 기법이 많이 사용되었다. 그러나 기존의 전통적인 영상분류 기법은 영상을 구성하는 각 셀이 지표면에 존재하는 다양한 객체들의 분광특성이 혼합된 결과임에도 불구하고 단 하나의 클래스로만 구분하는 단점을 가진다. 또한 불투수 지표면의 비율을 산정하기 위해서는 영상분류 후 각 분류항목에 불투수율을 할당해야하는 2중의 노력이 필요하며, 각 클래스에 단일한 불투수율을 지정해야만 하는 단점을 갖는다. 본 논문에서는 기존의 영상 분류방법이 갖는 이러한 단점을 보완하고자 불투수 지표면의 비율을 산정하기 위해 분광혼합분석 (spectral mixture analysis) 기법을 이용하였다. 분광혼합분석 기법을 적용하기 위해 식생, 토양, low albedo, high albedo 등 4가지 요소를 엔드멤버로 선택하였으며, 불투수율은 low albedo와 high albedo의 합으로 산정하였다. 대상 연구지역은 지난 십여년 동안 급격한 도시화가 진행된 탄천유역을 선정하였으며, 1988, 1994, 2001년의 Landsat 영상을 이용하여 신도시 건설에 따른 불투수 지표면의 변화율을 검토하였다. 분석결과 탄천유역의 불투수율은 88년 $15.6\%$, 94년 $20.1\%$, 2001년 $24\%$로 증가된 것으로 나타났다. 결론적으로 도시 불투수율을 분석 시 분광혼합분석 기법을 적용할 경우 추가적인 노력 없이 비교적 정확한 불투수율을 얻을 수 있음을 확인할 수 있었다.

• 농업과학연구
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• 제43권4호
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• pp.522-536
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• 2016

Fluorine is unique chemical element which occurs naturally, but is not an essential nutrient for plants. Fluoride toxicity can arise due to excessive fluoride intake from a variety of natural or manmade sources. Fluoride is phytotoxic to most plants. Plants which are sensitive for fluorine exposure even low concentrations of fluorine can cause leave damage and a decline in growth. All vegetation contains some fluoride absorbed from soil and water. The highest levels of F in field-grown vegetables are found up to $40mg\;kg^{-1}$ fresh weight although fluoride is relatively immobile and is not easily leached in soil because most of the fluoride was not readily soluble or exchangeable. Also, high concentrations of fluoride primarily associated with the soil colloid or clay fraction can increase fluoride levels in soil solution, increasing uptake via the plant root. In soils more than 90 percent of the natural fluoride ranging from 20 to $1,000{\mu}g\;g^{-1}$ is insoluble, or tightly bound to soil particles. The excess accumulation of fluorides in vegetation leads to visible leaf injury, damage to fruits, changes in the yield. The amount of fluoride taken up by plants depending on the type of plant, the nature of the soil, and the amount and form of fluoride in the soil should be controlled. Conclusively, fluoride is possible and long-term pollution effects on plant growth through accumulation of the fluoride retained in the soil.

• 대기
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• 제26권3호
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• pp.483-492
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• 2016

We have examined the random error of eddy covariance (EC) measurements on the basis of two-tower approach during daytime. Two EC towers were placed on the grassland with different vegetation density near Gumi-weir. We calculated the random error using three different methods. The first method (M1) is two-tower method suggested by Hollinger and Richardson (2005) where random error is based on differences between simultaneous flux measurements from two towers in very similar environmental conditions. The second one (M2) is suggested by Kessomkiat et al. (2013), which is extended procedure to estimate random error of EC data for two towers in more heterogeneous environmental conditions. They removed systematic flux difference due to the energy balance deficit and evaporative fraction difference between two sites before determining the random error of fluxes using M1 method. Here, we introduce the third method (M3) where we additionally removed systematic flux difference due to available energy difference between two sites. Compared to M1 and M2 methods, application of M3 method results in more symmetric random error distribution. The magnitude of estimated random error is smallest when using M3 method because application of M3 method results in the least systematic flux difference between two sites among three methods. An empirical formula of random error is developed as a function of flux magnitude, wind speed and measurement height for use in single tower sites near Nakdong River. This study suggests that correcting available energy difference between two sites is also required for calculating the random error of EC data from two towers at heterogeneous site where vegetation density is low.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 춘계학술대회 논문집
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• pp.228-233
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• 2007

지표면 온도(Land Surface Temperature, LST)는 지표와 대기간의 수증기 교환을 조절하는 중요한 기상변수중의 하나이다. 그 외에도 지표면 온도는 토양의 상태나 식생의 성장에도 밀접한 관계가 있어 임업과 농업에도 널리 활용되고 있다. 본 연구의 목적은 위성 지표면 자료를 이용하여 지상관측점의 열적 공간 대표성을 알아내는 것이다. 전국에총 76개의 관측소가 있으며 그중에서 선정된 6곳 의 관측소(서울，부산，대전，대구，광주，춘천)를 MODIS LST product와 비교를 하였다. 비교 방법은 위성 자료의 pixel size를 $3{\time}\;3$, $5{\time}\;5$, $7{\time}\;7$, $9{\time}\;9$, $11{\time}\;11$, $15{\time}\;15$, $19{\time}\;19$, $25{\time}\;25$로 변환하여 각 pixel size별 평균값을 계산하여 MODIS product와 비교하여 선형분석을 하였다. 분석의 요소로 Fraction Vegetation Cover(FVC)와 Digital Elevation Model(DEM)을 사용하였으며 분석 결과 FVC의 상관관계과 DEM보다 높은 상관성을 보여주었다. 선형분석으로 도출한 식으로 지표면 온도를 재산출한 뒤 지상관측값과의 RMSE를 산출하였다. 대표성 규명을 위한 RMSE는 일 최고 기온 산출 모델에 관한 연구를 참고하여 $^{\circ}C$로 결정하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.351-353
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• 2003

We present a retrieval scheme for the remote sensing of evapotranspiration (ET) over rice paddy. To perform the retrieval, high-resolution airborne imagery of multi-spectral visible and thermal infrared data, and ground-based meteorological measurements are utilized. Our ET retrieval scheme is based on the basic principal of surface energy budget, which is a result of balance in longwave and shortwave radiation, latent heat, sensible heat, and energy flux into the ground. To partition the latent and sensible heat fluxes of interest from the energy balance equation, three basic parameters are of most concern, including albedo, surface temperature, and normalized difference vegetation index (NDVI). The NDVI and albedo can be easily derived from the visible and near infrared spectral data, while the surface tem-perature can be determined through the analysis of the infrared data with the Stefan Boltzmann law. From the airborne imagery taken on 28 April 2003, we observe very good dry and wet pixels that can be easily corre-sponded to the radiation and evaporation controlled crite-ria, respectively, and, hence, for the further use in defin-ing the evaporative fraction needed to partition sensible and latent heat fluxes from the net energy flux. The de-rived ET is compared with the in situ measurements.