• Title/Summary/Keyword: Eddy Covariance

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Air Sampling and Isotope Analyses of Water Vapor and CO2 using Multi-Level Profile System (다중연직농도시스템(Multi-Level Profile System)을 이용한 수증기와 이산화탄소 시료채취 및 안정동위원소 조성 분석)

  • Lee, Dong-Ho;Kim, Su-Jin;Cheon, Jung-Hwa;Kim, Joon
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.12 no.4
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    • pp.277-288
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    • 2010
  • The multi-level $H_2O/CO_2$ profile system has been widely used to quantify the storage and advection effects on energy and mass fluxes measured by eddy covariance systems. In this study, we expanded the utility of the profile system by accommodating air sampling devices for isotope analyses of water vapor and $CO_2$. A pre-evacuated 2L glass flask was connected to the discharge of an Infrared Gas Analyzer (IRGA) of the profile system so that airs with known concentration of $H_2O$ and $CO_2$ can be sampled. To test the performance of this sampling system, we sampled airs from 8 levels (from 0.1 to 40 m) at the KoFlux tower of Gwangneung deciduous forest, Korea. Air samples in the 2L flask were separated into its component gases and pure $H_2O$ and $CO_2$ were extracted by using a vacuum extraction line. This novel technique successfully produced vertical profiles of ${\delta}D$ of $H_2O$ and ${\delta}^{13}C$ of $CO_2$ in a mature forest, and estimated ${\delta}D$ of evapotranspiration (${\delta}D_{ET}$) and ${\delta}^{13}C$ of $CO_2$ from ecosystem respiration (${\delta}^{13}C_{resp}$) by using Keeling plots. While technical improvement is still required in various aspects, our sampling system has two major advantages over other proposed techniques. First, it is cost effective since our system uses the existing structure of the profile system. Second, both $CO_2$ and $H_2O$ can be sampled simultaneously so that net ecosystem exchange of $H_2O$ and $CO_2$ can be partitioned at the same temporal resolution, which will improve our understanding of the coupling between water and carbon cycles in terrestrial ecosystems.

A Sensitivity Analysis of JULES Land Surface Model for Two Major Ecosystems in Korea: Influence of Biophysical Parameters on the Simulation of Gross Primary Productivity and Ecosystem Respiration (한국의 두 주요 생태계에 대한 JULES 지면 모형의 민감도 분석: 일차생산량과 생태계 호흡의 모사에 미치는 생물리모수의 영향)

  • Jang, Ji-Hyeon;Hong, Jin-Kyu;Byun, Young-Hwa;Kwon, Hyo-Jung;Chae, Nam-Yi;Lim, Jong-Hwan;Kim, Joon
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.12 no.2
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    • pp.107-121
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    • 2010
  • We conducted a sensitivity test of Joint UK Land Environment Simulator (JULES), in which the influence of biophysical parameters on the simulation of gross primary productivity (GPP) and ecosystem respiration (RE) was investigated for two typical ecosystems in Korea. For this test, we employed the whole-year observation of eddy-covariance fluxes measured in 2006 at two KoFlux sites: (1) a deciduous forest in complex terrain in Gwangneung and (2) a farmland with heterogeneous mosaic patches in Haenam. Our analysis showed that the simulated GPP was most sensitive to the maximum rate of RuBP carboxylation and leaf nitrogen concentration for both ecosystems. RE was sensitive to wood biomass parameter for the deciduous forest in Gwangneung. For the mixed farmland in Haenam, however, RE was most sensitive to the maximum rate of RuBP carboxylation and leaf nitrogen concentration like the simulated GPP. For both sites, the JULES model overestimated both GPP and RE when the default values of input parameters were adopted. Considering the fact that the leaf nitrogen concentration observed at the deciduous forest site was only about 60% of its default value, the significant portion of the model's overestimation can be attributed to such a discrepancy in the input parameters. Our finding demonstrates that the abovementioned key biophysical parameters of the two ecosystems should be evaluated carefully prior to any simulation and interpretation of ecosystem carbon exchange in Korea.

Application of Machine Learning Algorithm and Remote-sensed Data to Estimate Forest Gross Primary Production at Multi-sites Level (산림 총일차생산량 예측의 공간적 확장을 위한 인공위성 자료와 기계학습 알고리즘의 활용)

  • Lee, Bora;Kim, Eunsook;Lim, Jong-Hwan;Kang, Minseok;Kim, Joon
    • Korean Journal of Remote Sensing
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    • v.35 no.6_2
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    • pp.1117-1132
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    • 2019
  • Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

Sapflux Measurement Database Using Granier's Heat Dissipation Method and Heat Pulse Method (수액류 측정 데이터베이스: 그래니어(Granier) 센서 열손실탐침법(Heat Dissipation Method)과 열파동법(Heat Pulse Method)을 이용한 수액류 측정)

  • Lee, Minsu;Park, Juhan;Cho, Sungsik;Moon, Minkyu;Ryu, Daun;Lee, Hoontaek;Lee, Hojin;Kim, Sookyung;Kim, Taekyung;Byeon, Siyeon;Jeon, Jihyun;Bhusal, Narayan;Kim, Hyun Seok
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.22 no.4
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    • pp.327-339
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    • 2020
  • Transpiration is the movement of water into the atmosphere through leaf stomata of plant, and it accounts for more than half of evapotranspiration from the land surface. The measurements of transpiration could be conducted in various ways including eddy covariance and water balance method etc. However, the transpiration measurements of individual trees are necessary to quantify and compare the water use of each species and individual component within stands. For the measurement of the transpiration by individual tree, the thermometric methods such as heat dissipation and heat pulse methods are widely used. However, it is difficult and labor consuming to maintain the transpiration measurements of individual trees in a wide range area and especially for long-term experiment. Therefore, the sharing of sapflow data through database should be useful to promote the studies on transpiration and water balance for large spatial scale. In this paper, we present sap flow database, which have Granier type sap flux data from 18 Korean pine (Pinus koraiensis) since 2011 and 16 (Quercus aliena) since 2013 in Mt.Taehwa Seoul National University forest and 18 needle fir (Abies holophylla), seven (Quercus serrata), three (Carpinus laxiflora and C. cordata each since 2013 in Gwangneung. In addition, the database includes the sapling transpiration of nine species (Prunus sargentii, Larix kaempferii, Quercus accutisima, Pinus densiflora, Fraxinus rhynchophylla, Chamecypans obtuse, P. koraiensis, Betulla platyphylla, A. holophylla, Pinus thunbergii), which were measured using heat pulse method since 2018. We believe this is the first database to share the sapflux data in Rep. of Korea, and we wish our database to be used by other researchers and contribute a variety of researches in this field.

On Using Near-surface Remote Sensing Observation for Evaluation Gross Primary Productivity and Net Ecosystem CO2 Partitioning (근거리 원격탐사 기법을 이용한 총일차생산량 추정 및 순생태계 CO2 교환량 배분의 정확도 평가에 관하여)

  • Park, Juhan;Kang, Minseok;Cho, Sungsik;Sohn, Seungwon;Kim, Jongho;Kim, Su-Jin;Lim, Jong-Hwan;Kang, Mingu;Shim, Kyo-Moon
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.23 no.4
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    • pp.251-267
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    • 2021
  • Remotely sensed vegetation indices (VIs) are empirically related with gross primary productivity (GPP) in various spatio-temporal scales. The uncertainties in GPP-VI relationship increase with temporal resolution. Uncertainty also exists in the eddy covariance (EC)-based estimation of GPP, arising from the partitioning of the measured net ecosystem CO2 exchange (NEE) into GPP and ecosystem respiration (RE). For two forests and two agricultural sites, we correlated the EC-derived GPP in various time scales with three different near-surface remotely sensed VIs: (1) normalized difference vegetation index (NDVI), (2) enhanced vegetation index (EVI), and (3) near infrared reflectance from vegetation (NIRv) along with NIRvP (i.e., NIRv multiplied by photosynthetically active radiation, PAR). Among the compared VIs, NIRvP showed highest correlation with half-hourly and monthly GPP at all sites. The NIRvP was used to test the reliability of GPP derived by two different NEE partitioning methods: (1) original KoFlux methods (GPPOri) and (2) machine-learning based method (GPPANN). GPPANN showed higher correlation with NIRvP at half-hourly time scale, but there was no difference at daily time scale. The NIRvP-GPP correlation was lower under clear sky conditions due to co-limitation of GPP by other environmental conditions such as air temperature, vapor pressure deficit and soil moisture. However, under cloudy conditions when photosynthesis is mainly limited by radiation, the use of NIRvP was more promising to test the credibility of NEE partitioning methods. Despite the necessity of further analyses, the results suggest that NIRvP can be used as the proxy of GPP at high temporal-scale. However, for the VIs-based GPP estimation with high temporal resolution to be meaningful, complex systems-based analysis methods (related to systems thinking and self-organization that goes beyond the empirical VIs-GPP relationship) should be developed.