69 research outputs found
κ·Όμ νλ©΄ μ격 μΌμ± μμ€ν λ€μ μ΄μ©ν μ§μμ μλ¬Ό κ³μ λ° νμ μ λ μ½λ‘μ νκ΄λ¬Όμ§ κ΄μΈ‘
νμλ
Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : νκ²½λνμ νλκ³Όμ μ‘°κ²½ν, 2022.2. λ₯μλ ¬.Monitoring phenology, physiological and structural changes in vegetation is essential to understand feedbacks of vegetation between terrestrial ecosystems and the atmosphere by influencing the albedo, carbon flux, water flux and energy. To this end, normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) from satellite remote sensing have been widely used. However, there are still limitations in satellite remote sensing as 1) satellite imagery could not capture fine-scale spatial resolution of SIF signals, 2) satellite products are strongly influenced by condition of the atmosphere (e.g. clouds), thus it is challenging to know physiological and structural changes in vegetation on cloudy days and 3) satellite imagery captured a mixed signal from over- and understory, thus it is difficult to study the difference between overstory and understory phenology separately. Therefore, in order to more accurately understand the signals observed from the satellite, further studies using near-surface remote sensing system to collect ground-based observed data are needed.
The main purpose of this dissertation is continuous observation of vegetation phenology and SIF using near-surface remote sensing system. To achieve the main goal, I set three chapters as 1) developing low-cost filter-based near-surface remote sensing system to monitor SIF continuously, 2) monitoring SIF in a temperate evergreen needleleaf forest continuously, and 3) understanding the relationships between phenology from in-situ multi-layer canopies and satellite products.
In Chapter 2, I developed the filter-based smart surface sensing system (4S-SIF) to overcome the technical challenges of monitoring SIF in the field as well as to decrease sensor cost for more comprehensive spatial sampling. I verified the satisfactory spectral performance of the bandpass filters and confirmed that digital numbers (DN) from 4S-SIF exhibited linear relationships with the DN from the hyperspectral spectroradiometer in each band (R2 > 0.99). In addition, we confirmed that 4S-SIF shows relatively low variation of dark current value at various temperatures. Furthermore, the SIF signal from 4S-SIF represents a strong linear relationship with QEpro-SIF either changing the physiological mechanisms of the plant using DCMU (3-(3, 4-dichlorophenyl)-1, 1-dimethyurea) treatment. I believe that 4S-SIF will be a useful tool for collecting in-situ data across multiple spatial and temporal scales.
Satellite-based SIF provides us with new opportunities to understand the physiological and structural dynamics of vegetation from canopy to global scales. However, the relationships between SIF and gross primary productivity (GPP) are not fully understood, which is mainly due to the challenges of decoupling structural and physiological factors that control the relationships. In Chapter 3, I reported the results of continuous observations of canopy-level SIF, GPP, absorbed photosynthetically active radiation (APAR), and chlorophyll: carotenoid index (CCI) in a temperate evergreen needleleaf forest. To understand the mechanisms underlying the relationship between GPP and SIF, I investigated the relationships of light use efficiency (LUE_p), chlorophyll fluorescence yield (Ξ¦_F), and the fraction of emitted SIF photons escaping from the canopy (f_esc) separately. I found a strongly non-linear relationship between GPP and SIF at diurnal and seasonal time scales (R2 = 0.91 with a hyperbolic regression function, daily). GPP saturated with APAR, while SIF did not. In addition, there were differential responses of LUE_p and Ξ¦_F to air temperature. While LUE_p reached saturation at high air temperatures, Ξ¦_F did not saturate. I also found that the canopy-level chlorophyll: carotenoid index was strongly correlated to canopy-level Ξ¦_F (R2 = 0.84) implying that Ξ¦_F could be more closely related to pigment pool changes rather than LUE_p. In addition, I found that the f_esc contributed to a stronger SIF-GPP relationship by partially capturing the response of LUE_p to diffuse light. These findings can help refine physiological and structural links between canopy-level SIF and GPP in evergreen needleleaf forests.
We do not fully understand what satellite NDVI derived leaf-out and full leaf dates actually observe because deciduous broadleaf forest consists of multi-layer canopies typically and mixed-signal from multi-layer canopies could affect satellite observation. Ultimately, we have the following question: What phenology do we actually see from space compared to ground observations on multi-layer canopy phenology? In Chapter 4, I reported the results of 8 years of continuous observations of multi-layer phenology and climate variables in a deciduous broadleaf forest, South Korea. Multi-channel spectrometers installed above and below overstory canopy allowed us to monitor over- and understory canopy phenology separately, continuously. I evaluated the widely used phenology detection methods, curvature change rate and threshold with NDVI observed above top of the canopy and compared leaf-out and full leaf dates from both methods to in-situ observed multi-layer phenology. First, I found that NDVI from the above canopy had a strong linear relationship with satellites NDVI (R2=0.95 for MODIS products and R2= 0.85 for Landsat8). Second, leaf-out dates extracted by the curvature change rate method and 10% threshold were well matched with understory leaf-out dates. Third, the full-leaf dates extracted by the curvature change rate method and 90% threshold were similar to overstory full-leaf dates. Furthermore, I found that overstory leaf-out dates were closely correlated to accumulated growing degree days (AGDD) while understory leaf-out dates were related to AGDD and also sensitive to the number of chill days (NCD). These results suggest that satellite-based leaf-out and full leaf dates represent understory and overstory signals in the deciduous forest site, which requires caution when using satellite-based phenology data into future prediction as overstory and understory canopy show different sensitivities to AGDD and NCD.μλ¬Ό κ³μ λ° μμμ μ리νμ , ꡬ쑰μ μΈ λ³νλ₯Ό μ§μμ μΌλ‘ λͺ¨λν°λ§ νλ κ²μ μ‘μμνκ³μ λκΈ°κΆ μ¬μ΄μ μλμ§, νμ μν λ±μ νΌλλ°±μ μ΄ν΄νλλ° νμμ μ΄λ€. μ΄λ₯Ό κ΄μΈ‘νκΈ° μνμ¬ μμ±μμ κ΄μΈ‘λ μ κ·ν μμ μ§μ (NDVI) νμ μ λ μ½λ‘μ νκ΄λ¬Όμ§ (SIF)λ λμ€μ μΌλ‘ μ¬μ©λκ³ μλ€. κ·Έλ¬λ, μ°μ£Ό μμ± κΈ°λ°μ μλ£λ λ€μκ³Ό κ°μ νκ³μ λ€μ΄ μ‘΄μ¬νλ€. 1) μμ§κΉμ§ κ³ ν΄μλμ μμ± κΈ°λ° SIF μλ£λ μκ³ , 2) μμ± μλ£λ€μ λκΈ°μ μ§ (μ, ꡬλ¦)μ μν₯μ λ°μ, νλ¦° λ μ μμμ μ리νμ , ꡬ쑰μ λ³νλ₯Ό νμ§νκΈ° νλ€λ€. λν, 3) μμ± μ΄λ―Έμ§λ μλΆ μμκ³Ό νλΆ μμμ΄ νΌν©λμ΄ μμΈ μ νΈλ₯Ό νμ§νκΈ° λλ¬Έμ, κ° μΈ΅μ μλ¬Ό κ³μ μ κ°κ° μ°κ΅¬νκΈ°μ μ΄λ €μμ΄ μλ€. κ·Έλ¬λ―λ‘, μμ±μμ νμ§ν μ νΈλ₯Ό νκ°νκ³ , μμμ μ리νμ , ꡬ쑰μ λ³νλ₯Ό λ³΄λ€ μ νν μ΄ν΄νκΈ° μν΄μλ κ·Όμ νλ©΄ μ격 μΌμ± μμ€ν
μ μ΄μ©ν μ€μΈ‘ μλ£ κΈ°λ°μ μ°κ΅¬λ€μ΄ μꡬλλ€. λ³Έ νμλ
Όλ¬Έμ μ£Ό λͺ©μ μ κ·Όμ νλ©΄ μ격 μΌμ± μμ€ν
μ μ΄μ©νμ¬ μλ¬Ό κ³μ λ° SIFλ₯Ό νμ₯μμ μ§μμ μΌλ‘ μ€μΈ‘νκ³ , μμ± μμ κΈ°λ°μ μ°κ΅¬κ° κ°κ³ μλ νκ³μ λ° κΆκΈμ¦λ€μ ν΄κ²° λ° λ³΄μνλ κ²μ΄λ€. μ΄ λͺ©μ μ λ¬μ±νκΈ° μνμ¬, μλμ κ°μ μΈκ°μ§ Chapter: 1) SIFλ₯Ό κ΄μΈ‘νκΈ° μν νν° κΈ°λ°μ μ λ ΄ν κ·Όμ νλ©΄ μΌμ± μμ€ν
κ°λ°, 2)μ¨λ μΉ¨μ½μλ¦Όμμμ μ°μμ μΈ SIF κ΄μΈ‘, 3)μμ± κΈ°λ°μ μλ¬Ό κ³μ κ³Ό μ€μΈ‘ν λ€μΈ΅ μμμ μλ¬Ό κ³μ λΉκ΅λ‘ ꡬμ±νκ³ , μ΄λ₯Ό μ§ννμλ€.
SIFλ μμμ ꡬ쑰μ , μ리νμ λ³νλ₯Ό μ΄ν΄νκ³ , μΆμ νλ μΈμλ‘ μ¬μ©λ μ μμ΄, SIFλ₯Ό νμ₯μμ κ΄μΈ‘νκΈ° μν λ€μν κ·Όμ νλ©΄ μ격 μΌμ± μμ€ν
λ€μ΄ μ΅κ·Ό μ μλμ΄ μ€κ³ μλ€. κ·Έλ¬λ, μμ§κΉμ§ SIFλ₯Ό κ΄μΈ‘νκΈ° μν μμ
μ μΌλ‘ μ ν΅λλ κ΄μΈ‘ μμ€ν
μ νμ ν λΆμ‘±νλ©°, λΆκ΄κ³μ ꡬ쑰μ νΉμ±μ νμ₯μμ κ΄μΈ‘ μμ€ν
μ 보μ λ° κ΄λ¦¬νκΈ°κ° μ΄λ €μ λμ μ§μ SIFλ₯Ό μ·¨λνλ κ²μ λ§€μ° λμ μ μΈ λΆμΌμ΄λ€. λ³Έ νμ λ
Όλ¬Έμ Chapter 2μμλ SIFλ₯Ό νμ₯μμ λ³΄λ€ μμ½κ² κ΄μΈ‘νκΈ° μν νν° κΈ°λ°μ κ·Όμ νλ©΄ μΌμ± μμ€ν
(Smart Surface Sensing System, 4S-SIF)μ κ°λ°νμλ€. μΌμλ λμ νν°λ€κ³Ό ν¬ν λ€μ΄μ€λκ° κ²°ν©λμ΄ μμΌλ©°, μ보 λͺ¨ν°λ₯Ό μ¬μ©νμ¬ λμ νν° λ° κ΄μΈ‘ λ°©ν₯μ μλμ μΌλ‘ λ³κ²½ν¨μΌλ‘μ¨, ν κ°μ ν¬ν λ€μ΄μ€λκ° 3κ°μ νμ₯ λ²μ(757, 760, 770 nm)μ λΉ λ° νμμΌλ‘λΆν° μ
μ¬λλ κ΄λκ³Ό μμμΌλ‘ λ°μ¬/λ°©μΆλ κ΄λμ κ΄μΈ‘ν μ μλλ‘ κ³ μλμλ€. ν¬ν λ€μ΄μ€λλ‘λΆν° μΈμλ λμ§νΈ μμΉ κ°μ μμ
μ μΌλ‘ ν맀λλ μ΄κ³ ν΄μλ λΆκ΄κ³(QE Pro, Ocean Insight)μ λλ ·ν μ ν κ΄κ³λ₯Ό 보μ΄λ κ²μ νμΈνμλ€ (R2 > 0.99). μΆκ°μ μΌλ‘, 4S-SIFμμ κ΄μΈ‘λ SIFμ μ΄κ³ ν΄μλ λΆκ΄κ³λ₯Ό μ΄μ©νμ¬ μΆμΆν SIFκ° μ νμ μΈ κ΄κ³λ₯Ό μ΄λ£¨λ κ²μ νμΈνμλ€. μμμ μ리νμ λ³νλ₯Ό μΌμΌν€λ νν λ¬Όμ§μΈ DCMU(3-(3, 4-dichlorophenyl)-1, 1-dimethyurea)μ μ²λ¦¬νμμλ λΆκ΅¬νκ³ , μ°μΆλ SIFλ€μ μ ν κ΄κ³λ₯Ό 보μλ€. λ³Έ μΌμλ κΈ°μ‘΄ μμ€ν
λ€μ λΉν΄ μ¬μ©νκΈ° μ½κ³ κ°λ¨νλ©°, μ λ ΄νκΈ° λλ¬Έμ λ€μν μ곡κ°μ μ€μΌμΌμ SIFλ₯Ό κ΄μΈ‘ν μ μλ€λ μ₯μ μ΄ μλ€.
μμ± κΈ°λ°μ SIFλ₯Ό μ΄μ©νμ¬ μ΄μΌμ°¨μμ°μ±(gross primary productivity, GPP)μ μΆμ νλ μ°κ΅¬λ μ΅κ·Ό νμ μν μ°κ΅¬ λΆμΌμμ κ°κ΄λ°κ³ μλ μ°κ΅¬ μ£Όμ μ΄λ€. κ·Έλ¬λ, SIFμ GPPμ κ΄κ³λ μ¬μ ν λ§μ λΆνμ€μ±μ μ§λκ³ μλλ°, μ΄λ SIF-GPP κ΄κ³λ₯Ό μ‘°μ νλ μμμ ꡬ쑰μ λ° μ리νμ μμΈμ λ°λ‘ λΆλ¦¬νμ¬ κ³ μ°°ν μ°κ΅¬λ€μ΄ λΆμ‘±νκΈ° λλ¬Έμ΄λ€. λ³Έ νμ λ
Όλ¬Έμ Chapter 3μμλ μ§μμ μΌλ‘ SIF, GPP, ν‘μλ κ΄ν©μ±μ ν¨λ³΅μ¬λ (absorbed photosynthetically active radiation, APAR), κ·Έλ¦¬κ³ ν΄λ‘λ‘νκ³Ό μΉ΄λ‘ν°λ
Έμ΄λμ λΉμ¨ μΈμ (chlorophyll: carotenoid index, CCI)λ₯Ό μ¨λμΉ¨μ½μλ¦Όμμ μ°μμ μΌλ‘ κ΄μΈ‘νμλ€. SIF-GPP κ΄κ³μ ꡬ체μ μΈ λ©μ»€λμ¦ κ΄κ³λ₯Ό λ°νκΈ° μνμ¬, κ΄ μ΄μ©ν¨μ¨ (light use efficiency, LUE_p), μ½λ‘μ νκ΄ μλλ₯ (chlorophyll fluorescence yield, Ξ¦_F) κ·Έλ¦¬κ³ SIF κ΄μκ° κ΅°λ½μΌλ‘λΆν° λ°©μΆλλ λΉμ¨ (escape fraction, f_esc)μ κ°κ° λμΆνκ³ νꡬνμλ€. SIFμ GPPμ κ΄κ³λ λλ ·ν λΉ μ νμ μΈ κ΄κ³λ₯Ό 보μ΄λ κ²μ νμΈνμΌλ©°(R2 = 0.91 with a hyperbolic regression function, daily), μΌμ£ΌκΈ° λ¨μμμ SIFλ APARμ λν΄ μ νμ μ΄μμ§λ§ GPPλ APARμ λν΄ λλ ·ν ν¬ν κ΄κ³λ₯Ό 보μ΄λ κ²μ νμΈνμλ€. μΆκ°μ μΌλ‘ LUE_p μ Ξ¦_F κ° λκΈ° μ¨λμ λ°λΌ λ°μνλ μ λκ° λ€λ₯Έ κ²μ νμΈνμλ€. LUE_pλ λμ μ¨λμμ ν¬ν λμμ§λ§, Ξ¦_Fλ ν¬ν ν¨ν΄μ νμΈν μ μμλ€. λν, κ΅°λ½ μμ€μμμ CCIμ Ξ¦_Fκ° λλ ·ν μκ΄ κ΄κ³λ₯Ό 보μλ€(R2 = 0.84). μ΄λ Ξ¦_Fκ° μ½λ‘μ μμμ μν₯μ LUE_pμ λΉν΄ λ κ°ν κ΄κ³κ° μμ μ μμμ μμ¬νλ€. λ§μ§λ§μΌλ‘, f_escκ° νμκ΄μ μ°λλ μ λμ λ°λΌ λ°μμ νμ¬, Ξ¦_Fμ LUE_pμ κ°ν μκ΄ κ΄κ³λ₯Ό νμ±νλλ° κΈ°μ¬νλ κ²μ νμΈνμλ€. μ΄λ¬ν λ°κ²¬μ μ¨λ μΉ¨μ½μλ¦Όμμ κ΅°λ½ μμ€μ SIF-GPPκ΄κ³λ₯Ό μ리νμ λ° κ΅¬μ‘°μ μΈ‘λ©΄μμ μ΄ν΄νκ³ κ·λͺ
νλλ° ν° λμμ΄ λ κ²μ΄λ€.
μλ¬Ό κ³μ μ μμμ΄ μ² μ λ°λΌ μ£ΌκΈ°μ μΌλ‘ λνλ΄λ λ³νλ₯Ό κ΄μΈ‘νλ λ°μμ΄λ€. μλ¬Ό κ³μ μ μ‘μμνκ³μ λκΈ°κΆ μ¬μ΄μ λ¬Όμ§ μνμ μ΄ν΄νλλ° λ§€μ° μ€μνλ€. μμ± κΈ°λ°μ NDVIλ μλ¬Ό κ³μ μ νμ§νκ³ μ°κ΅¬νλλ° κ°μ₯ λμ€μ μΌλ‘ μ¬μ©λλ€. κ·Έλ¬λ, νμ½μλ¦Όμμμ μμ± NDVI κΈ°λ°μ κ°μ½ μκΈ° λ° μ±μ μκΈ°κ° μ€μ μ΄λ μμ μ νμ§νλμ§λ λΆλΆλͺ
νλ€. μ€μ νμ½μλ¦Όμ λ€μΈ΅ μμ ꡬ쑰μ μΌμ°¨μμΌλ‘ μ΄λ£¨μ΄μ Έ μλ λ°λ©΄, μμ± μμμ λ€μΈ΅ μμμ μ νΈκ° μμ¬ μλ μ΄μ°¨μμ κ²°κ³Όλ¬Όμ΄κΈ° λλ¬Έμ΄λ€. λ°λΌμ, μμ± NDVI κΈ°λ°μ μλ¬Ό κ³μ μ΄ λ€μΈ΅ μμ ꡬ쑰λ₯Ό μ΄λ£¨κ³ μλ νμ½μλ¦Όμμ μ€μ νμ₯ κ΄μΈ‘κ³Ό λΉκ΅νμμ λ μ΄λ μμ μ νμ§νλμ§μ λν κΆκΈμ¦μ΄ λ¨λλ€. λ³Έ νμ λ
Όλ¬Έμ Chapter 4μμλ μ§μμ μΌλ‘ 8λ
λμ νμ½μλ¦Όλ΄μ λ€μΈ΅ μμμ μλ¬Ό κ³μ μ κ·Όμ νλ©΄ μ격 μΌμ± μμ€ν
μ μ΄μ©νμ¬ κ΄μΈ‘νκ³ , μμ± NDVI κΈ°λ°μ μλ¬Ό κ³μ κ³Ό λΉκ΅νμλ€. λ€μ±λ λΆκ΄κ³λ₯Ό μλΆ μμμ μμ μλμ μ€μΉν¨μΌλ‘μ¨, μλΆ μμκ³Ό νλΆ μμμ μλ¬Ό κ³μ μ κ°κ° μ°μμ μΌλ‘ κ΄μΈ‘νμλ€. μλ¬Ό κ³μ μ νμ§νκΈ° μνμ¬ κ°μ₯ λ§μ΄ μ¬μ©λλ λ°©λ²μΈ 1) μμΉλ₯Ό μ΄μ©νλ λ°©λ²κ³Ό 2) μ΄κ³λν¨μλ₯Ό μ΄μ©νλ λ°©λ²μ μ¬μ©νμ¬ κ°μ½ μκΈ° λ° μ±μ μκΈ°λ₯Ό κ³μ°νκ³ μ΄λ₯Ό λ€μΈ΅ μμμ μλ¬Ό κ³μ κ³Ό λΉκ΅νμλ€. λ³Έ μ°κ΅¬ κ²°κ³Ό, 첫λ²μ§Έλ‘, κ΅°λ½μ μμΈ΅λΆμμ μ€μΈ‘ν NDVIμ μμ± κΈ°λ°μ NDVIκ° κ°ν μ ν κ΄κ³λ₯Ό 보μ΄λ κ²μ νμΈνλ€ (R2=0.95 λ MODIS μμλ€ λ° R2= 0.85 λ Landsat8). λλ²μ§Έλ‘, μ΄κ³λν¨μ λ°©λ²κ³Ό 10%μ μμΉ κ°μ μ΄μ©ν λ°©λ²μ΄ λΉμ·ν κ°μ½ μκΈ°λ₯Ό μΆμ νλ κ²μ νμΈνμμΌλ©°, νλΆ μμμ κ°μ½ μκΈ°μ λΉμ·ν μκΈ°μμ νμΈνμλ€. μΈλ²μ§Έλ‘, μ΄κ³λν¨μ λ°©λ²κ³Ό 90%μ μμΉ κ°μ μ΄μ©ν λ°©λ²μ΄ λΉμ·ν μ±μ μκΈ°λ₯Ό μ°μΆνμμΌλ©°, μ΄λ μλΆ μμμ μ±μ μκΈ°μ λΉμ·νμλ€. μΆκ°μ μΌλ‘ μλΆ μμμ κ°μ½ μκΈ°μ νλΆ μμμ κ°μ½ μκΈ°κ° μ¨λμ λ°μνλ μ λκ° λλ ·νκ² μ°¨μ΄κ° λλ κ²μ νμΈν μ μμλ€. μλΆ μμμ κ°μ½ μκΈ°λ μ μ° μμ₯ μ¨λ μΌμ (AGDD)μ κ°ν μκ΄μ±μ 보μκ³ , νλΆ μμμ κ°μ½ μκΈ°λ AGDDμ μ°κ΄μ±μ κ°κ³ μμ λΏλ§ μλλΌ μΆμ μΌμ(NCD)μλ λ―Όκ°νκ² λ°μνλ κ²μ νμΈνμλ€. μ΄λ¬ν κ²°κ³Όλ μμ± NDVI κΈ°λ°μ κ°μ½ μκΈ°λ νλΆ μμμ κ°μ½ μκΈ°μ μ°κ΄μ±μ΄ λκ³ , μ±μ μκΈ°λ μλΆ μμμ μ±μ μκΈ°μ λΉμ·νλ€λ κ²μ μλ―Ένλ€. λν, μλΆ μμκ³Ό νλΆ μμμ΄ μ¨λμ λ€λ₯Έ λ―Όκ°μ±μ κ°κ³ μμ΄, μμ±μμ μ°μΆλ μλ¬Ό κ³μ μ μ΄μ©νμ¬ κΈ°νλ³νλ₯Ό μ΄ν΄νκ³ μ ν λ, μ΄λ€ μΈ΅μ μμμ΄ μμ± μμμ μ£Όλ μν₯μ λ―ΈμΉλμ§ κ³ λ €ν΄μΌ νλ€λ κ²μ μμ¬νλ€.
μμ±μ λμ μ§μμ λ³νλ₯Ό μμ½κ² λͺ¨λν°λ§ν μ μμ΄ λ§μ κ°λ₯μ±μ κ°κ³ μλ λꡬμ΄μ§λ§, λ³΄λ€ μ νν μμ± κ΄μΈ‘ κ°μ μ΄ν΄νκΈ° μν΄μλ νμ₯μμ κ΄μΈ‘λ μλ£λ₯Ό κΈ°λ°μΌλ‘ ν κ²μ¦μ΄ μꡬλλ€. λ³Έ νμ λ
Όλ¬Έμμλ 1) κ·Όμ νλ©΄ μΌμ± μμ€ν
μ κ°λ°, 2) κ·Όμ νλ©΄ μΌμ± μμ€ν
μ νμ©ν μμμ μ리νμ ꡬ쑰μ λ³νμ μ§μμ μΈ κ΄μΈ‘, 3) λ€μΈ΅ μμ ꡬ쑰μμ κ΄μΈ‘λλ μλ¬Ό κ³μ λ° μμ±μμ μΆμ λ μλ¬Ό κ³μ μ μ°κ΄μ± νκ°λ₯Ό μννμλ€. κ°λ°ν κ·Όμ νλ©΄ μΌμλ μμ
μΌμλ€κ³Ό λΉκ΅νμ λ, κ°κ²©μ μΌλ‘ μ λ ΄νκ³ μ μ½κ² μ¬μ©ν μ μμμΌλ©°, μ±λ₯μ μΌλ‘λ λΆμ‘±ν¨μ΄ μμλ€. κ·Όμ νλ©΄ μΌμ± μμ€ν
μ μ΄μ©νμ¬ SIFλ₯Ό μ¨λ μΉ¨μ½μλ¦Όμμ μ§μμ μΌλ‘ κ΄μΈ‘ν κ²°κ³Ό, μ΄μΌμ°¨μμ°μ±κ³Ό SIFλ λΉμ ν κ΄κ³λ₯Ό κ°λ κ²μ νμΈνμλ€. μ΄λ λ§μ μ ν μ°κ΅¬λ€μμ λ°νν μμ± κΈ°λ°μ SIFμ GPPκ° μ νμ μΈ κ΄κ³λ₯Ό 보μΈλ€λ κ²κ³Όλ λ€μ μλ°λ κ²°κ³Όμ΄λ€. λ€μΈ‘ μμμ λ΄μ² μλ¬Ό κ³μ μ μ°μμ μΌλ‘ κ΄μΈ‘νκ³ , μμ± κΈ°λ°μ μλ¬Ό κ³μ κ³Ό λΉκ΅νκ°ν μ°κ΅¬μμλ μμ± κΈ°λ°μ κ°μ½ μκΈ°λ νλΆ μμμ μν₯μ μ£Όλ‘ λ°κ³ , μ±μ μκΈ°λ μλΆ μμμ μκΈ°μ λΉμ·ν κ²μ νμΈνμλ€. μ¦, κ·Όμ νλ©΄ μΌμ± μμ€ν
μ μ΄μ©νμ¬ νμ₯μμ μ€μΈ‘ν κ²°κ³Όλ μμ± μμμ νμ©ν μ°κ΅¬λ€κ³Όλ λ€λ₯Έ κ²°κ³Όλ₯Ό λ³΄μΌ μλ μμΌλ©°, μμ± μμμ νκ° λ° μ΄ν΄νλλ° μ¬μ©λ μ μλ€. λ°λΌμ, λ³΄λ€ μ νν μμμ ꡬ쑰μ , μ리νμ λ©μ»€λμ¦μ μ΄ν΄νκΈ° μν΄μλ κ·Όμ νλ©΄ μΌμ±μ νμ©ν νμ₯μμ ꡬμΆν μλ£ κΈ°λ°μ λ λ§μ μ°κ΅¬λ€μ΄ νμνλ€λ κ²μ μμ¬νλ€.Abstract i
Chapter 1. Introduction 2
1. Background 2
2. Purpose 5
Chapter 2. Monitoring SIF using a filter-based near surface remote sensing system 9
1. Introduction 9
2. Instrument desing and technical spefications of the filter-based smart surface sensing system (4S-SIF) 12
2.1. Ultra-narrow band pass filter 14
2.2. Calibration of 4S-SIF 15
2.3. Temperature and humidity response 16
2.4. Evaluate SIF quality from 4S-SIF in the field 17
3. Results 20
4. Discussion 23
Chapter 3. SIF is non-linearly related to canopy photosynthesis in a temperate evergreen needleleaf forest during fall transition 27
1. Introduction 27
2. Methods and Materials 31
2.1. Study site 31
2.2. Leaf-level fluorescence measurement 32
2.3. Canopy-level SIF and spectral reflectance measurement 34
2.4. SIF retrieval 37
2.5. Canopy-level photosynthesis estimates 38
2.6. Meteorological variables and APAR 39
2.7. Statistical analysis 40
3. Results 41
4. Discussion 48
4.1. Non-linear relationships between SIF and GPP 49
4.2. Role of f_esc in SIF-GPP relationship 53
4.3. Implications of non-linear SIF-GPP relationship in temperate ENF 54
5. Conclusion 57
6. Appendix 59
Chapter 4. Monitoring spring phenology of multi-layer canopy in a deciduous broadleaf forest: What signal do satellites actually see in space 65
1. Introduction 65
2. Materials and Methods 69
2.1. Study site 69
2.2. Multi-layer spectral reflectance and transmittance measurement 70
2.3. Phenometrics detection 72
2.4. In-situ multi-layer phenology 74
2.5. Satellite remote sensing data 75
2.6. Meteorological variables 75
3. Results 76
3.1. Seasonal to interannual variations of NDVI, 1-transmittance, and air temperature 76
3.2. Inter-annual variation of leaf-out and full-leaf dates 78
3.3. The relationships between dates calculated according tothreshold and in-situ multi-layer phenology 80
3.4. The relationship between multi-layer phenology, AGDD and NCD 81
4. Discussion 82
4.1. How do satellite-based leaf-out and full-leaf dates differ from in-situ multi-layer phenology 83
4.2. Are the 10 % and 90 % thresholds from satellite-basedNDVI always well matched with the leaf-out and full-leaf dates calculated by the curvature change rate 86
4.3. What are the implications of the difference between satellite-based and multi-layer phenology 87
4.4. Limitations and implications for future studies 89
5. Conclusion 91
6. Appendix 92
Chapter 5. Conclusion 114
Abstract in Korean 115λ°
μ λ ΄ν κ·Όμ νλ©΄ μΌμλ₯Ό μ΄μ©ν μμμ§μ, μ½λ©΄μ μ§μ, κ΄ν©μ±μ ν¨λ³΅μ¬λμ ν‘μλ₯ κ΄μ°°
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μλͺ
κ³Όνλν μνμ‘°κ²½Β·μ§μμμ€ν
곡νλΆ, 2017. 8. λ₯μλ ¬.Monitoring vegetation indices, fraction of absorbed photosynthetically active radiation (fPAR) and leaf area index (LAI) has advanced our understanding of biosphere-atmosphere interactions. Although there are continuous observations for each variable, monitoring vegetation indices, fPAR and LAI simultaneously is still lacking. Recent advances of technology provide unprecedented opportunities to integrate various low-cost sensors as an intelligent near surface observation system for monitoring ecosystem structure and functions. In this study, we developed a Smart Surface Sensing System (4S), which can automatically collect, transfer, process and analyze data, and then publish time series results on public-available website. The system is composed of micro-computers, micro-controllers, multi-spectral spectrometers made from Light Emitting Diode (LED), micro cameras, and Internet module. We did intensive tests and calibrations in the lab. Then, we conducted in-situ observations of normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), fraction of absorbed photosynthetically active radiation (fPAR), and leaf area index (LAI) continuously at a rice paddy field during the growing season. NDVI and EVI obtained by 4S showed linear relationships with those from a reference hyperspectrometer (R2 = 0.98NDVI, R2 = 0.96EVI). 4S derived fPAR and LAI were comparable to LAI-2200 and destructive measurements in both magnitude and seasonal trajectory. We retrieved vegetation indices, fPAR and LAI independently and continuously and show that after the reproductive stage, fPAR remained constant, whereas LAI and NDVI decreased continuously after their peak because of non-photosynthetic materials such as grain and yellow leaf. In addition, using vegetation index to estimate fPAR has limitation because the spectral reflectance could not capture the diurnal pattern. On the other hand, fPAR changes abruptly depending on the sky conditions and the amount of light transmitted. We believe that 4S will be useful in the expansion of ecological sensing networks across multiple spatial and temporal scales.1 Introduction 1
2 Method and materials 4
2.1 Development and calibration of 4S 4
2.2 Testing the 4S LED spectrometer 6
2.2.1 Site description 10
2.2.2 4S in-situ 12
2.2.3 Reference data collection 15
2.2.4 Satellite remote sensing data 16
3 Results 17
3.1 Seasonal variation in 4S LED sensor 17
3.2 Seasonal variation in 4S camera sensor 20
3.3 Comparison of NDVI obtained from 4S and satellite with different resolutions 22
4 Discussion 23
4.1 What are the advantages of 4S development 23
4.2 What are the advantages of observing vegetation indices, fPAR and LAI independently 25
4.3 What are the advantages of continuous observation compared to different sensors 29
5 Conclusion 32
6 References 33
7 Abstract (Korean) 38Maste
Highly efficient gene knockout in mice and zebrafish with RNA-guided endonucleases
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : ννλΆ(μννμ 곡), 2014. 2. κΉμ§μ.Zinc finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs) , engineered nucleases, are composed of designable DNA-binding domains and a non-specific nuclease domain and enable a broad range of genomic modification by inducing double-strand breaks (DSBs) that stimulate intrinsic cellular repair mechanisms such as non-homologous recombination (NHEJ) and homologous recombination (HR) at specific genomic locations. This technology has been described earlier as promising tools for targeted genome engineering in cells and many organisms. Recently, RNA-guided endonucleases (RGENs) derived from bacterial type-II CRISPR/Cas system, have been described as site-specific endonucleases whose specificities are programmed by small RNA components. RGEN also has been applied in cells and organisms as genome engineering tool. Here in this study, injection of RGENs as Cas9 protein: guide RNA complexes or Cas9 mRNA plus guide RNA into one-cell stage embryos of mice and zebrafish efficiently disrupts a target gene in both species. RGENs efficiently generated germ-line transmittable mutations in up to 93% of newborn mice with minimal toxicity. RGEN-induced mutations in the mouse Prkdc gene that encodes an enzyme critical for DNA double strand break repair resulted in immunodeficiency both in F0 and F1 mice. I propose that RGEN-mediated mutagenesis in animals will greatly expedite the creation of genetically-engineered model organisms accelerating functional genomic research.Table of contents
ABSTRACT .............................................................................................. 1
TABLE OF CONTENTS ......................................................................... 3
LIST OF FIGURES .................................................................................. 5
LIST OF TABLES .................................................................................... 7
I. INTRODUCTION ................................................................................ 8
II. MATERIALS AND METHODS ....................................................... 11
1. RGEN COMPONENTS. ..................................................................... 11
2. In vitro cleavage reactions. ................................................................. 11
3. Microinjection of RGENs into mouse embryos. .............................. 12
4. Fluorescent PCR. ................................................................................ 13
5. Genotyping, sequence analyses, and phenotyping of mutant mice. 14
6. Characterization of immune cells by flow cytometry. ..................... 15
7. Procedures of RGEN-mediated mutagenesis in zebrafish embryos. .................................................................................................................. 16
III. RESULTS ......................................................................................... 17
4
1. RGEN Design and Production. ......................................................... 17
2. Generation of Founder Mice with RGEN-induced Mutations. ...... 18
3. Gene Disruption by Cas9 protein-sgRNA Complex in Mice and Zebrafish. ................................................................................................ 20
4. Analysis of Off-target Effects of RGENs. ......................................... 22
5. Phenotype Analysis and Germ-line Transmission of Prkdc-mutant Mice. ........................................................................................................ 23
IV. DISCUSSION ................................................................................... 26
V. REFERENCE ..................................................................................... 55
Abstract in Koreanβ¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦β¦........61Maste
μ μ μμ μ¬μ©μ μ λ ₯μΌλ‘λΆν° μΈκ° λμμ ν©μ± λ° νΈμ§
νμλ
Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ, 2014. 8. μ΄μ ν¬.An ideal 3D character animation system can easily synthesize and edit human motion and also will provide an efficient user interface for an animator. However, despite advancements of animation systems, building effective systems for synthesizing and editing realistic human motion still remains a difficult problem. In the case of a single character, the human body is a significantly complex structure because it consists of as many as hundreds of degrees of freedom. An animator should manually adjust many joints of the human body from user inputs. In a crowd scene, many individuals in a human crowd have to respond to user inputs when an animator wants a given crowd to fit a new environment. The main goal of this thesis is to improve interactions between a user and an animation system.
As 3D character animation systems are usually driven by low-dimensional inputs, there is no method for a user to directly generate a high-dimensional character animation. To address this problem, we propose a data-driven mapping model that is built by motion data obtained from a full-body motion capture system, crowd simulation, and data-driven motion synthesis algorithm. With the data-driven mapping model in hand, we can transform low-dimensional user inputs into character animation because motion data help to infer missing parts of system inputs. As motion capture data have many details and provide realism of the movement of a human, it is easier to generate a realistic character animation than without motion capture data.
To demonstrate the generality and strengths of our approach, we developed two animation systems that allow the user to synthesize a single character animation in realtime and edit crowd animation via low-dimensional user inputs interactively. The first system entails controlling a virtual avatar using a small set of three-dimensional (3D) motion sensors. The second system manipulates large-scale crowd animation that consists of hundreds of characters with a small number of user constraints. Examples show that our system is much less laborious and time-consuming than previous animation systems, and thus is much more suitable for a user to generate desired character animation.Contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . II
Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IV
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Background 10
2.1 Performance Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.1.1 Performance-based Interfaces for Character Animation . . . . . . . 11
2.1.2 Statistical Models for Motion Synthesis . . . . . . . . . . . . . . . 12
2.1.3 Retrieval of Motion Capture Data . . . . . . . . . . . . . . . . . . 13
2.2 Crowd Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.1 Crowd Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.2 Motion Editing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.3 Geometry Deformation . . . . . . . . . . . . . . . . . . . . . . . . 15
3 Realtime Performance Animation Using Sparse 3D Motion Sensors 17
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.2 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.3 Sensor Data and Calibration . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.4 Motion Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.4.1 Online Local Model . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.4.2 Kernel CCA-based Regression . . . . . . . . . . . . . . . . . . . . 25
3.4.3 Motion Post-processing . . . . . . . . . . . . . . . . . . . . . . . 27
3.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4 Interactive Manipulation of Large-Scale Crowd Animation 40
4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.2 Crowd Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.3 Cage-based Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.3.1 Cage Construction . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.3.2 Cage Representation . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4 Editing Crowd Animation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.4.1 Spatial Manipulation . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.4.2 Temporal Manipulation . . . . . . . . . . . . . . . . . . . . . . . . 57
4.5 Collision Avoidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5 Conclusion 69
Bibliography I
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XI
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XIIIDocto
νμμμμ κ΄ν νμ±κ³Όμ
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λ λΆλ°°κ° κ΄νμ μΌλ‘ μ΄λ£¨μ΄μ§κ³ μμμ μ μ μλ€. λ³Έ μ°κ΅¬μμλ λ΄μ¬ λͺ¨νμ
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κ³Όλ₯Ό λ³λμ§μ λν΄ μ§ν κ²μμ μ κ·Ό λ°©μμ μ΄μ©νμ¬ λΆμνκ³ μ νλ€. λ³Έ μ°κ΅¬μ
μλ λ€λ₯Έ κ²½κΈ°μλ€μ λͺ¨λ°©νλ ννλ₯Ό 보μ΄λ κ²½κΈ°μλ€μ΄ λλλ‘ μ νμ μμ΄μ μλνμ§ μμ μ€μλ₯Ό νλ€λ μ μ κ°μ ν κ²°κ³Ό, κ· λ±λΆν μ΄ μ μΌνκ² νλ₯ μ μμ μ±μ μ§λκ³ μλ ν΄μμ 보μ΄κ³ μλ€.μ΄ μ°κ΅¬λ μμΈλνκ΅ κ²½μ μ°κ΅¬μ κΈ°μ
κ²½μλ ₯ μ°κ΅¬μΌν°μ μ§μλ μμΈλνκ΅ λ°μ κΈ°κΈμ μ°
κ΅¬λΉ μ§μμ ν΅ν΄ μνλμλ€
λͺ©μ§κ³ κΈ°μ§λ‘λΆν° μμ§μ± ν¨λͺ¨λ₯Ό μ΄μ©ν μ°λ£μ© μνμ¬ μμ°μ κ΄ν μ°κ΅¬
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Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :곡μ
ννκ³Ό,1997.Maste
Development and characterization of artificial nuclear receptor and target reporter.
μκ³Όνμ¬μ
λ¨/μμ¬[νκΈ]
νΉμ μ μ μμ μ리μ μΈ κΈ°λ₯μ μ°κ΅¬νλ κΈ°μ΄μ°κ΅¬λ μ μ μμΉλ£ κ°μ μμ©μ°κ΅¬μ μ¬μ©νκΈ° μν inducible expression systemμ κ°λ°νκΈ° μνμ¬, κΈ°μ‘΄μ GAL4 DNA binding domain (DBD)μ μ΄μ©ν
inducible expression systemμ κ°λν artificial nuclear receptorμ target reporterλ₯Ό κ°λ°νμλ€. νμ¬ λ€μν μ’
λ₯μ inducible expression systemμ΄ κ°λ° λμλλ°, κ·Έλ€ λλΆλΆμ μ μ μμ λ°νμ νΉμ ligandλ‘ μ‘°μ νκΈ° μν΄ prokaryoteλ eukaryoteμμ μ λλ ligand binding domain (LBD)μ μ¬μ©νλ©°,
activation domain (AD)μΌλ‘ herpes simplex virusμ VP16 ADλ₯Ό μ£Όλ‘ μ¬μ©νλ€. μλ‘μ΄ κ°λ°ν inducible expression systemμμ artificial nuclear receptorλ yeastμ GAL4 DBDμ human
progesterone/estrogen receptor LBD, SREBP1aμ ADλ₯Ό μ¬μ©νμλ€. Estrogen receptor LBDμ κ²½μ°μ estrogenκ³Ό anti-estrogenμΈ 4-OHT (hydroxytamoxifen)μ΄ λͺ¨λ κ²°ν©νμ§λ§, human progesterone receptor LBDμ κ²½μ° C-terminal λ§λ¨μ 892-933 μλ―Έλ
Έμ°μ μ κ±°νμ¬ progesteroneκ³Όλ κ²°ν©νμ§ μκ³ progesterone antagonistμΈ RU486 (mifepristone)μλ§ νΉμ΄μ μΈ λ°μμ λνλ΄κ² ν LBDλ₯Ό μ¬μ©νμλ€.
Reporter geneμ GAL4 response element (RE)λ₯Ό 5λ² λ°λ³΅ν λΆμμ ACL minimal promoter -60/+67, luciferase geneμ μ°κ²°νμλ€.
μλ‘ κ°λ°ν inducible expression systemμ λ κ°μ§ κ°μ μ μ΄ μμλ€. 첫째λ‘, artificial nuclear receptorμ SREBP1a ADμ μ¬μ©ν¨μΌλ‘μ¨ μ μ μλ°νμ λμ± νμ±ν μν¬ μ μμκ³ λμ§Έλ‘, reporter geneμ promoterμΈ minimal TATA-boxλ₯Ό ACL minimal promoter -60/+67λ‘ λ체μν€κ³ , μ¬κΈ°μ SV40 enhancerλ₯Ό μ°κ²°νμ¬ μ μ μμ λ°νμ λμ± μ¦κ° μν¬ μ μμλ€. μ΄λ¬ν κ°λλ inducible expression systemμ exogenous geneμ κΈ°λ₯μ μ°κ΅¬νλ κΈ°μ΄μ°κ΅¬λ μ μ μμΉλ£ κ°μ μμ©μ°κ΅¬μμ λ§€μ° μ μ©νκ² μ΄μ©λ μ μμ κ²μ΄λ€.
[μλ¬Έ]
To develop an inducible expression system for use in basic research and applied research, we generated the artificial nuclear receptor and target reporter. A number of ligand-regulated artificial nuclear receptor have been generated by various means, using ligand binding domain (LBD) derived
from either prokaryotes or eukaryotes, and activation domain (AD) derived from herpes simplex virus.
In our inducible expression system, the artificial nuclear receptor contains the yeast GAL4 DNA binding domain, LBD of human progesterone/estrogen receptor and SREBP1a AD. The estrogen
receptor LBD bind to estrogen and anti-estrogen (4-OHT). But progesterone receptor LBD specifically bind to anti-progesterone (mifepristone, RU486) instead of endogenous hormone. The target
reporter construct contains the five copies of GAL4 responsive element, ACL minimal promoter -60/+67 and luciferase gene. In the presence of ligand, artificial nuclear receptor specifically activates the expression of luciferase gene.
Two major improvements of this system were made. First, artificial nuclear receptor GAL4-PR/ER was rendered more potent by fusing the strong activation domain, SREBP1a AD. Second, target reporter gene expression was more increase by replaced minimal TATA box promoter with ACL minimal promoter -60/+67, and fusing the SV40 enhancer. This enhanced inducible expression
system can restrict its specificity to the target gene, and enable to provide powerful tools that may be applied to the study of exogenous gene function and gene therapy.ope
λλν΅ν©μ νμ μ¬μ μ§μΆλ³νμ κ΄ν μ°κ΅¬
νμλ
Όλ¬Έ(μμ¬)--μμΈλνκ΅ νκ²½λνμ :νκ²½κ³ννκ³Ό λμλ°μ§μκ³νμ 곡,1997.Maste
μλΆν΄μ± iodophor microsphere μ λμΉ¨μ§μ μ 건μ κΈ° μ μ° μ λ°© κ°μΌ μλ°© ν¨κ³Ό
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Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :μμνκ³Ό μμλ΄κ³Όνμ 곡,2002.Maste
(A)Study on the debarment and the suspension in the U.S. government procurement contract law
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Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :λ²νκ³Ό,2005.Maste
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