8 research outputs found
Ecosystem aboveground structures of an open-canopy black spruce forest in interior Alaska for ecosystem modeling
Recent warming trends in the Arctic and boreal regions far exceed the global average, and this trend is likely to continue in the future. To establish accurate benchmarks for models and represent the current status of carbon stocks and flows, it is essential to provide detailed descriptions of forest ecosystem structures, including understory plant communities alongside carbon flux data. In this data paper, we present a synthesis data set of aboveground forest structural characteristics. Field campaigns were performed from 2010 to 2018 at the Poker Flat Research Range of the Geophysical Institute of the University of Alaska, Fairbanks, located on the outskirts of Fairbanks, AK, USA. The forest is a sparse evergreen needle-leaf forest. The field campaigns include tree census surveys in the years 2010 and 2014, direct measurement of tree aboveground biomass and their vertical profiles in 2012, tree leaf area index (LAI) measurement using a non-destructive method in 2018, and biomass and LAI survey of forest floor vegetation through direct sampling in 2018. The ecosystem structure data sets described in this paper are helpful for the calibration of the forest stand representations in the ecosystem and land surface models and for the validation of the simulated forest growth and stand structures. The data are available for download from the Japanese National Institute of Polar Research Arctic Data archive System, listed under Kobayashi et al. 2023
In Situ Observations Reveal How Spectral Reflectance Responds to Growing Season Phenology of an Open Evergreen Forest in Alaska
Plant phenology timings, such as spring green-up and autumn senescence, are essential state information characterizing biological responses and terrestrial carbon cycles. Current efforts for the in situ reflectance measurements are not enough to obtain the exact interpretation of how seasonal spectral signature responds to phenological stages in boreal evergreen needleleaf forests. This study shows the first in situ continuous measurements of canopy scale (overstory + understory) and understory spectral reflectance and vegetation index in an open boreal forest in interior Alaska. Two visible and near infrared spectroradiometer systems were installed at the top of the observation tower and the forest understory, and spectral reflectance measurements were performed in 10 min intervals from early spring to late autumn. We found that canopy scale normalized difference vegetation index (NDVI) varied with the solar zenith angle. On the other hand, NDVI of understory plants was less sensitive to the solar zenith angle. Due to the influence of the solar geometry, the annual maximum canopy NDVI observed in the morning satellite overpass time (10β11 am) shifted to the spring direction compared with the standardized NDVI by the fixed solar zenith angle range (60β70Β°). We also found that the in situ NDVI time-series had a month-long high NDVI plateau in autumn, which was completely out of photosynthetically active periods when compared with eddy covariance net ecosystem exchange measurements. The result suggests that the onset of an autumn high NDVI plateau is likely to be the end of the growing season. In this way, our spectral measurements can serve as baseline information for the development and validation of satellite-based phenology algorithms in the northern high latitudes
In Situ Observations Reveal How Spectral Reflectance Responds to Growing Season Phenology of an Open Evergreen Forest in Alaska
Plant phenology timings, such as spring green-up and autumn senescence, are essential state information characterizing biological responses and terrestrial carbon cycles. Current efforts for the in situ reflectance measurements are not enough to obtain the exact interpretation of how seasonal spectral signature responds to phenological stages in boreal evergreen needleleaf forests. This study shows the first in situ continuous measurements of canopy scale (overstory + understory) and understory spectral reflectance and vegetation index in an open boreal forest in interior Alaska. Two visible and near infrared spectroradiometer systems were installed at the top of the observation tower and the forest understory, and spectral reflectance measurements were performed in 10 min intervals from early spring to late autumn. We found that canopy scale normalized difference vegetation index (NDVI) varied with the solar zenith angle. On the other hand, NDVI of understory plants was less sensitive to the solar zenith angle. Due to the influence of the solar geometry, the annual maximum canopy NDVI observed in the morning satellite overpass time (10β11 am) shifted to the spring direction compared with the standardized NDVI by the fixed solar zenith angle range (60β70Β°). We also found that the in situ NDVI time-series had a month-long high NDVI plateau in autumn, which was completely out of photosynthetically active periods when compared with eddy covariance net ecosystem exchange measurements. The result suggests that the onset of an autumn high NDVI plateau is likely to be the end of the growing season. In this way, our spectral measurements can serve as baseline information for the development and validation of satellite-based phenology algorithms in the northern high latitudes
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°