• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.362-365
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• 2011

단위면적당 증발산량 중 일부가 식생의 물질 생산에 이용될 수 있는지를 나타내는 지표인 수분 이용효율 (Water Use Efficiency, WUE)은 총 일차생산성(Gross Primary Productivity, GPP)과 단위면적당 증발산량(Evapotranspiration, ET)의 비로 산출된다. 이전 연구들에서의 수분 이용효율의 적용은 수분 스트레스에 대한 작물의 생산성 차이 분석과 같은 작물학과 농림학 분야의 연구들이 대부분이었지만. 기후 변화가 생태계 생산성 또는 에너지 수지에 영향을 미치는 등의 전 지구적 규모의 수문학적 연구에도 적용할 수 있다. 본 연구에서는, Moderate Resolution Imaging Spectroradiometer (MODIS) 영상자료에서 1km 해상도로 8일 단위의 총 일차생산성과 증발산량을 산정함으로써 수분 이용효율을 구하였다. 향후에는 산정된 이 지표를 남한지역에 적용하여 수분 이용효율에 대한 지도를 작성하고, 실측된 총 일차생산량과 증발산 값을 이용하여 검증한 후 알고리즘을 개선해 나갈 계획이다.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.779-793
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• 2021

Water stress and environmental drivers are important factors to explain the variance of gross primary production (GPP). Environmental drivers are used to generate GPP in Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm and process-based model. However, MODIS algorithm only consider the vapor pressure deficit (VPD) data while the process-based biogeochemical model also uses limited data to express water stress. We compared the relationship between environmental drivers and GPP from eddy covariance method, MODIS algorithm, and Community Land Model 4 (CLM 4) simulation in normal years and drought years. To consider water stress specifically, we used VPD and evaporative fraction (EF). We evaluated the effects from environmental drivers and EF towards GPP products using the structural equation modeling (SEM) in South Korea. We found that GPP products from MODIS algorithm and model simulation results were not restricted from VPD data if VPD was underestimated. We also found that in the cropland area, irrigation effects can relieve VPD effects to GPP. However, GPP products derived from MODIS and CLM 4 had limitation to explain the irrigation effects to GPP. Overall, these results will enhance the understanding of GPP products derived from MODIS and CLM 4.

• Journal of Wetlands Research
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• v.14 no.4
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• pp.503-518
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• 2012

Models estimating carbon budget at land surface are mainly interested in vertical flux of carbon. On the other hand, studies on horizontal flux are obviously lacked to confirm that relationship between the hydrological flux of organic carbon discharged from catchment and terrestrial carbon production, a relation between Total Organic Carbon(TOC) and Gross Primary Production(GPP) tried analysis through cross correlation. The best correlation structure is correlation between GPP and TOC of flow-weighted mean concentration from watershed without delay. Furthermore, cross correlation analysis was performed by consider periodicity. The correlation between TOC and GPP in summer was similar to correlation without periodicity. Therefore, correlation between GPP and TOC was most regulated by the correlation between GPP and TOC at summer. As a result, the vegetation carbon and organic carbon from watershed is recognized a close relationship on the seasonal. Therefore, future research is correlation analyzing between vegetation variables according season, GPP and TOC, we are expected to use quantitative understanding that horizontal flux flow of carbon from the surface.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.203-203
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• 2023

총일차생산량(GPP, Gross Primary Productivity)은 생태계의 유기물 생산량을 나타내는 지표로써 생태계 생산성과 안정성을 파악할 수 있는 중요한 지표로 알려져 있다. GPP를 산출하는 대표적인 방법에는 다중 센서를 탑재한 원격 탐사 자료를 활용하는 방법과 플럭스타워를 통해 관측한 에디공분산을 분석하는 방법이 있다. 본 연구에서는 Landsat과 MODIS와 같이 시공간 해상도가 다른 원격 탐사 자료들을 기반으로 초고해상도 GPP 자료를 산출하기 위한 공간자료 융합 연구를 수행하였다. 이를 위해 GAN(Generative Adversarial Networks)과 같은 머신러닝 알고리즘을 활용하였으며 최종적으로 산정된 GPP 정보는 설마천과 청미천 등에 설치된 플럭스타워로부터 획득한 자료와의 비교·검증을 통해 평가되었다. 본 연구의 성과는 향후 증발산 자료, 생태계 호흡량 자료 등과의 조합을 통해 얻을 수 있는 물이용효율(WUE, Water Use Efficiency), 탄소이용효율(CUE, Carbon Uptake Efficiency)과 같은 지표 산정 시 적극 활용될 수 있을 것으로 기대된다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• The Korean Journal of Ecology
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• v.28 no.4
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• pp.215-222
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• 2005

MODIS (Moderate Resolution Image Spectrometer) is a core satellite sensor boarded on Terra and Aqua satellite of NASA Earth Observing System since 1999 and 2001, respectively. MODIS LAI, FPAR, and GPP provide useful means to monitor plant phonology and material cycles in terrestrial ecosystems. In this study, LAI, FPAR, and GPP in Korea were evaluated and errors associated with cloud contamination on MODIS pixels were eliminated for years $2001\sim2003$. Three-year means of cloud-corrected annual GPP were 1836, 1369, and 1460g C $m^{-2}y^{-1}$ for evergreen needleleaf forest, deciduous broadleaf forest, and mixed forest, respectively. The cloud-originated errors were 8.5%, 13.1%, and 8.4% for FPAR, LAI, and GPP, respectively. Summertime errors from June to September explained by 78% of the annual accumulative errors in GPP. This study indicates that cloud-originated errors should be mitigated for practical use of MODIS vegetation products to monitor seasonal and annual changes in plant phonology and vegetation production in Korea.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.481-494
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• 2011

Investigation of the $CO_2$ exchange between biosphere and atmosphere at regional, continental, and global scales can be directed to combining remote sensing with carbon cycle process to estimate vegetation productivity. NASA Earth Observing System (EOS) currently produces a regular global estimate of gross primary productivity (GPP) and annual net primary productivity (NPP) of the entire terrestrial earth surface at 1 km spatial resolution. While the MODIS GPP algorithm uses meteorological data provided by the NASA Data Assimilation Office (DAO), the sub-pixel heterogeneity or complex terrain are generally reflected due to coarse spatial resolutions of the DAO data (a resolution of $1{\circ}\;{\times}\;1.25{\circ}$). In this study, we estimated inputs retrieved from MODIS products of the AQUA and TERRA satellites with 5 km spatial resolution for the purpose of finer GPP and/or NPP determinations. The derivatives included temperature, VPD, and solar radiation. Seven AmeriFlux data located in the Corn Belt region were obtained to use for evaluation of the input data from MODIS. MODIS-derived air temperature values showed a good agreement with ground-based observations. The mean error (ME) and coefficient of correlation (R) ranged from $-0.9^{\circ}C$ to $+5.2^{\circ}C$ and from 0.83 to 0.98, respectively. VPD somewhat coarsely agreed with tower observations (ME = -183.8 Pa ~ +382.1 Pa; R = 0.51 ~ 0.92). While MODIS-derived shortwave radiation showed a good correlation with observations, it was slightly overestimated (ME = -0.4 MJ $day^{-1}$ ~ +7.9 MJ $day^{-1}$; R = 0.67 ~ 0.97). Our results indicate that the use of inputs derived MODIS atmosphere and land products can provide a useful tool for estimating crop GPP.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.242-252
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• 2016

A process-based ecosystem model, BIOME-BGC, was applied to simulate seasonal and inter-annual dynamics of carbon and water processes for potential evergreen needleleaf forest (ENF) biome in Korea. Two simulation sites, Milyang and Unljin, were selected to reflect warm-and-dry and cool-and-wet climate regimes, where massive diebacks of pines including Pinus densiflora, P. koraiensis and P thunbergii, were observed in 2009 and 2014, respectively. Standard Precipitation Index (SPI) showed periodic drought occurrence at every 5 years or so for both sites. Since mid-2000s, droughts occurred with hotter climate condition. Among many model variables, Cpool (i.e., a temporary carbon pool reserving photosynthetic compounds before allocations for new tissue production) was identified as a useful proxy variable of tree carbon starvation caused by reduction of gross primary production (GPP) and/or increase of maintenance respiration (Rm). Temporal Cpool variation agreed well with timings of pine tree diebacks for both sites. Though water stress was important, winter- and spring-time warmer temperature also played critical roles in reduction of Cpool, especially for the cool-and-wet Uljin. Shallow soil depth intensified the drought effect, which was, however, marginal for soil depth shallower than 0.5 m. Our modeling analysis implicates seasonal drought and warmer climate can intensify vulnerability of ENF dieback in Korea, especially for shallower soils, in which multi-year continued stress is of concern more than short-term episodic stress.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.235-250
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• 2021

The overarching question of this study is how a typical rice cultivation system in Gimje, Korea was keeping up with the triple-win challenge of climate-smart agriculture (CSA). To answer this question, we have employed (1) quantitative data from direct measurement of energy, water, carbon and information flows in and out of a rice cultivation system and (2) appropriate metrics to assess production, efficiency, GHG fluxes, and resilience. The study site was one of the Korean Network of Flux measurement (KoFlux) sites (i.e., GRK) located at Gimje, Korea, managed by National Academy of Agricultural Science, Rural Development Administration. Fluxes of energy, water, carbon dioxide (CO₂) and methane (CH₄) were directly measured using eddy-covariance technique during the growing seasons of 2011, 2012 and 2014. The production indicators include gross primary productivity (GPP), grain yield, light use efficiency (LUE), water use efficiency (WUE), and carbon uptake efficiency (CUE). The GHG mitigation was assessed with indicators such as fluxes of carbon dioxide (F_CO2), methane (F_CH4), and nitrous oxide (F_N2O). Resilience was assessed in terms of self-organization (S), using information-theoretic approach. Overall, the results demonstrated that the rice cultivation system at GRK was climate-smart in 2011 in a relative sense but failed to maintain in the following years. Resilience was high and changed little for three year. However, the apparent competing goals or trade-offs between productivity and GHG mitigation were found within individual years as well as between the years, causing difficulties in achieving the triple-win scenario. The pursuit of CSA requires for stakeholders to prioritize their goals (i.e., governance) and to practice opportune interventions (i.e., management) based on the feedback from real-time assessment of the CSA indicators (i.e., monitoring) - i.e., a purpose-driven visioneering.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2013.11a
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• pp.26-27
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• 2013

산림생태계의 에너지, 물질, 정보의 교환 과정과 그 변화를 이해하려면 먼저 생태계의 구조와 기능이 어떻게 상호작용하는지를 이해해야 한다. 생태계의 기능은 한, 두 가지의 특징에 의해서만 이루어지는 것이 아니다. 그렇기 때문에 그 기능을 파악하고 적절히 이용하거나 대응하기 위해서는 한 생태계와 주변 환경 전체를 바라볼 수 있는 시스템 사고가 필요하다. 이에 우리는 생태계의 '구조'를 파악함으로써 생태계의 '상태'를 이해하고자 한다. 본 연구에서는 Ruddell and Kumar (2009)의 접근법을 따라, 어떻게 한 생태계의 상태를 파악할 수 있는가라는 질문을 광릉활엽수림에 적용하여 답하고자 한다. 즉, 우리는 산림생태계가 열린 복잡계라고 가정하고, 생태계 내에서 다양한 프로세스들 간의 시시각각 변하는 네트워크의 구조가 각 시점의 시스템의 상태를 나타내는 지표가 될 수 있다고 가정하였다. 이 연구에서는 그 구조적 특징을 정량화하여 나타내는데 초점을 맞추었다. 각각의 프로세스를 대표하는 상태 변수들 간의 정보 흐름의 양과 방향, 시간 규모를 계산해냄으로써 네트워크 구조를 파악하고자 하였다. 온대 산악지형 활엽수림인 GDK의 2008년 순생태계교환량(NEE), 총일차생산량(GPP), 생태계호흡량(RE), 현열플럭스(H), 잠열플럭스(LE), 하향단파복사(Rg), 강수량(Precipitation), 기압(Pressure), 기온(T), 포차(VPD)의 시계열 자료를 월별로 나누어 최장 18 시간 규모의 정보 흐름을 계산하였다. 정보 흐름의 구조를 파악하기 위하여 변수들 간의 전이엔트로피(Transfer entropy)와 상호정보(Mutual Information)를 계산하는 방법을 사용하였다. 또한 시계열 자료를 이용함으로써 변수들 간에 정보가 전달되는 시간 규모의 특성을 파악할 수 있었다. 최종적으로, 계산한 정보 흐름을 시각화하여 프로세스 네트워크 구조를 나타내었다. 결과는 월별로 생태계의 정보 흐름의 종류, 방향과 시간 규모, 그에 따른 프로세스 간 상호 작용의 특징 등을 보여준다. 이를 통해 계절적 환경 변화에 따라 시스템의 네트워크 구조와 상태가 어떻게 변화하는지 이해할 수 있을 것이다. 이 연구는 추후 우리 연구실에서 생산한 8 년 자료에 적용함으로써 다양한 날씨 및 기후변화와 환경 변화에 따라 생태계의 구조와 상태가 어떻게 변화하는지 연구하는 시작점이 될 것이다. 이 접근법은 단위나 차원에 무관하게 다양한 종류의 자료에 적용할 수 있는 반면에, 일관성 있게 정의된 시스템의 상태 및 그 상태를 구성하는 주요 하부 시스템들의 네트워크 상태를 이해하는데 이용될 수 있다. 본 연구는 비평형 열역학과 복잡계의 관점에서 바라 본 시스템 사고를 적용하려 하는 여러 연구 분야에 새로운 도전을 촉발할 좋은 선행연구가 될 것이라 기대된다.