Assessing spring phenology of a temperate woodland : a multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations

PhD ThesisVegetation phenology is the study of plant natural life cycle stages. Plant phenological events are related to carbon, energy and water cycles within terrestrial ecosystems, operating from local to global scales. As plant phenology events are highly sensitive to climate fluctuations, the timing of these events has been used as an independent indicator of climate change. The monitoring of forest phenology in a cost-effective manner, at a fine spatial scale and over relatively large areas remains a significant challenge. To address this issue, unmanned aerial vehicles (UAVs) appear to be a potential new platform for forest phenology monitoring. The aim of this research is to assess the potential of UAV data to track the temporal dynamics of spring phenology, from the individual tree to woodland scale, and to cross-compare UAV results against ground and satellite observations, in order to better understand characteristics of UAV data and assess potential for use in validation of satellite-derived phenology. A time series of UAV data were acquired in tandem with an intensive ground campaign during the spring season of 2015, over Hanging Leaves Wood, Northumberland, UK. The radiometric quality of the UAV imagery acquired by two consumer-grade cameras was assessed, in terms of the ability to retrieve reflectance and Normalised Difference Vegetation Index (NDVI), and successfully validated against ground (0.84≤R2≥0.96) and Landsat (0.73≤R2≥0.89) measurements, but only NDVI resulted in stable time series. The start (SOS), middle (MOS) and end (EOS) of spring season dates were estimated at an individual tree-level using UAV time series of NDVI and Green Chromatic Coordinate (GCC), with GCC resulting in a clearer and stronger seasonal signal at a tree crown scale. UAV-derived SOS could be predicted more accurately than MOS and EOS, with an accuracy of less than 1 week for deciduous woodland and within 2 weeks for evergreen. The UAV data were used to map phenological events for individual trees across the whole woodland, demonstrating that contrasting canopy phenological events can occur within the extent of a single Landsat pixel. This accounted for the poor relationships found between UAV- and Landsat-derived phenometrics (R2<0.45) in this study. An opportunity is now available to track very fine scale land surface changes over contiguous vegetation communities, information which could improve characterization of vegetation phenology at multiple scales.The Science without Borders program, managed by CAPES-Brazil (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)

Afromontane forest ecosystem studies with multi-source satellite data

The Afromontane Forest of north Eastern Nigeria is an important ecological ecosystem endowed with flora and fauna species. The main goals of this thesis were to explore the potential of multi-source satellite remote sensing for the assessment of the biodiversity-rich Afromontane Forest ecosystem using different methods and algorithms to retrieve two major remote sensing -essential biodiversity variables (RS-EBV) which are related and are also the major determinants of biological and ecosystem stability

Using eddy covariance, remote sensing, and in situ observations to improve models of springtime phenology in temperate deciduous forests

Phenological events in temperate forests, such as bud burst and senescence, exert strong control over seasonal fluxes of water, energy and carbon. The timing of these transitions is influenced primarily by air temperature and photoperiod, although the exact nature and magnitude of these controls is poorly understood. In this dissertation, I use in situ and remotely sensed observations of phenology in combination with surface meteorological data and measurements of biosphere-atmosphere carbon exchanges to improve understanding and develop models of canopy phenology in temperate forest ecosystems. In the first element of this research I use surface air temperatures and eddy covariance measurements of carbon dioxide fluxes to evaluate and refine widely used approaches for predicting the onset of photosynthesis in spring that account for geographic variation in thermal and photoperiod constraints on phenology. Results from this analysis show that the refined models predict the onset of spring photosynthetic activity with significantly higher accuracy than existing models. A key challenge in developing and testing these models, however, is lack of adequate data sets that characterize phenology over large areas at multi-decadal time scales. To address this need, I develop a new method for estimating long-term average and interannual dynamics in the phenology of temperate forests using time series of Landsat TM/ETM+ images. Results show that estimated spring and autumn transition dates agree closely with in-situ measurements and that Landsat-derived estimates for the start and end of the growing season in Southern New England varied by as much as 4 weeks over the 30-year record of Landsat images. In the final element of this dissertation, I use meteorological data, species composition maps, satellite remote sensing, and ground observations to develop models of springtime leaf onset in temperate deciduous forests that account for geographic differences in how forest communities respond to springtime climate forcing. Results demonstrate important differences in cumulative heating requirements and photoperiod cues among forest types and that regional differences in species composition explain substantial geographic variation in springtime phenology of temperate forests. Together, the results from this dissertation provide an improved basis for observing and modeling springtime phenology in temperate forests

A novel approach to modelling mangrove phenology from satellite images: a case study from Northern Australia

Around the world, the effects of changing plant phenology are evident in many ways: from earlier and longer growing seasons to altering the relationships between plants and their natural pollinators. Plant phenology is often monitored using satellite images and parametric methods. Parametric methods assume that ecosystems have unimodal phenologies and that the phenology model is invariant through space and time. In evergreen ecosystems such as mangrove forests, these assumptions may not hold true. Here we present a novel, data-driven approach to extract plant phenology from Landsat imagery using Generalized Additive Models (GAMs). Using GAMs, we created models for six different mangrove forests across Australia. In contrast to parametric methods, GAMs let the data define the shape of the phenological curve, hence showing the unique characteristics of each study site. We found that the Enhanced Vegetation Index (EVI) model is related to leaf production rate (from in situ data), leaf gain and net leaf production (from the published literature). We also found that EVI does not respond immediately to leaf gain in most cases, but has a two- to three-month lag. We also identified the start of season and peak growing season dates at our field site. The former occurs between September and October and the latter May and July. The GAMs allowed us to identify dual phenology events in our study sites, indicated by two instances of high EVI and two instances of low EVI values throughout the year. We contribute to a better understanding of mangrove phenology by presenting a data-driven method that allows us to link physical changes of mangrove forests with satellite imagery. In the future, we will use GAMs to (1) relate phenology to environmental variables (e.g., temperature and rainfall) and (2) predict phenological changes

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

SATELLITE-BASED CHARACTERIZATION OF CROP TYPE AND PRODUCTIVITY OF AGROECOSYSTEMS: CASE STUDIES IN NORTHEAST CHINA, SOUTHERN AFRICA, AND CONTERMINOUS USA

Agroecosystem, or agricultural ecosystems, is the important anthropogenic ecosystem to meet the human demand for food, fiber, and feed, and it covers approximately 40-50% of the earth’s land surface. Accurate estimates of agricultural land use and land cover and Gross Primary Production (GPP) are indispensable for global food security and understanding variations in the terrestrial carbon budgets. This dissertation aimed to strengthen the capacities of remote sensing to produce the high-quality products of crop type maps and primary productivity on large regional scales. In chapter 2, we designed simple algorithms to identify paddy rice of two different phenological phases (flooding/transplanting and ripening) at regional scales using 30-m multi-temporal Landsat images. Sixteen Landsat images from 2010 - 2012 were used to generate the paddy rice map in the Sanjiang Plain, northeast China - one of the intensive paddy rice cultivation regions in Northern Asia. The user and producer accuracies of paddy rice on the resultant Landsat-based paddy rice map were 90% and 94%, respectively, and was an improvement over the paddy rice dataset derived through visual interpretation and digitalization on the fine-resolution satellite images and traditional agricultural census data. Chapter 3 evaluated the capacities of the temporal MODIS vegetation indices and the satellite-based Vegetation Photosynthesis Model (VPM) to describe phenology and model the seasonal dynamics of GPP for savanna woodlands in Southern Africa on the site level. The results showed that the VPM-based GPP estimates tracked the seasonal dynamics and interannual variation of GPP estimated from eddy covariance measurements at flux tower sites. This study suggests that the VPM is a valuable tool for estimating GPP of semi-arid and semi-humid savanna woodland ecosystems in Southern Africa. Chapter 4 assessed the accuracies of air temperature and downward shortwave radiation of the North America Regional Reanalysis (NARR) by the National Centers for Environmental Prediction (NCEP), and evaluated impacts of the accuracies of regional climate inputs on the VPM-based GPP estimates for U.S. Midwest cropland. The results implied that the bias of NARR downward shortwave radiation introduced significant uncertainties into the VPM-based GPP estimates, suggesting that more accurate surface radiation datasets are needed to estimate primary production of terrestrial ecosystems at regional and global scales. Chapter 5 presented independent and complementary analyses of the impact of 2012 flash drought on productivity in the U.S. Midwest using multiple sources of evidences, i.e., in-situ AmeriFlux CO2 data, satellite observations of vegetation indices and solar-induced chlorophyll fluorescence (SIF), and scaled ecosystem modeling. The results showed that phenological activities of all biomes advanced 1-2 weeks earlier in 2012 compared to other years of 2010-2014, and the drought threatened the U.S. Midwest agroecosystems. The growth of grassland/prairie and cropland was suppressed from June and it didn’t recover until the end of the growing season. As the frequency and severity of droughts have been predicted to increase in future, this study provides better insights into the impacts of flash droughts on vegetation productivity and carbon cycling of major biomes in the U.S. Midwest

Integrating Remote Sensing and Ecosystem Models for Terrestrial Vegetation Analysis: Phenology, Biomass, and Stand Age

Terrestrial vegetation plays an important role in global carbon cycling and climate change by assimilating carbon into biomass during the growing season and releasing it due to natural or anthropogenic disturbances. Remote sensing and ecosystem models can help us extend our studies of vegetation phenology, aboveground biomass, and disturbances from field sites to regional or global scales. Nonetheless, remote sensing-derived variables may differ in fundamental and important ways from ground measurements. With the growth of remote sensing as a key tool in geoscience research, comparisons to ground data and intercomparisons among satellite products are needed. Here I conduct three separate but related analyses and show promising comparisons of key ecosystem states and processes derived from remote sensing and theoretical modeling to those observed on the ground. First, I show that the Moderate Resolution Imaging Spectroradiometer (MODIS) greenup product is significantly correlated with the earliest ground phenology event for North America. Spring greenup indices from different satellites demonstrate similar variability along latitudes, but the number of ground phenology observations in summer, fall, and winter is too limited to interpret the remote sensing-derived phenology products. Second, I estimate aboveground biomass (AGB) for California and show that it agrees with inventory-based regional biomass assessments. In this approach, I present a new remote sensing-based approach for mapping live forest AGB based on a simple parametric model that combines high-resolution estimates of Leaf Area Index derived from Landsat and canopy maximum height from the space-borne Geoscience Laser Altimeter System (GLAS) sensor. Third, I built a theoretical model to estimate stand age in primary forests by coupling a carbon accumulation function to the probability density of disturbance occurrences, and then ran the model with satellite-derived AGB and net primary production. The validated remote sensing data, integrated with ecosystem models, are particularly useful for large-region vegetation research in areas with sparse field measurements, and will help us to explore the long-term vegetation dynamics

근접 표면 원격 센싱 시스템들을 이용한 지속적 식물 계절 및 태양 유도 엽록소 형광물질 관측

학위논문(박사) -- 서울대학교대학원 : 환경대학원 협동과정 조경학, 2022.2. 류영렬.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 (Φ_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 Φ_F to air temperature. While LUE_p reached saturation at high air temperatures, Φ_F did not saturate. I also found that the canopy-level chlorophyll: carotenoid index was strongly correlated to canopy-level Φ_F (R2 = 0.84) implying that Φ_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.식물 계절 및 식생의 생리학적, 구조적인 변화를 지속적으로 모니터링 하는 것은 육상생태계와 대기권 사이의 에너지, 탄소 순환 등의 피드백을 이해하는데 필수적이다. 이를 관측하기 위하여 위성에서 관측된 정규화 식생 지수 (NDVI) 태양 유도 엽록소 형광물질 (SIF)는 대중적으로 사용되고 있다. 그러나, 우주 위성 기반의 자료는 다음과 같은 한계점들이 존재한다. 1) 아직까지 고해상도의 위성 기반 SIF 자료는 없고, 2) 위성 자료들은 대기의 질 (예, 구름)에 영향을 받아, 흐린 날의 식생의 생리학적, 구조적 변화를 탐지하기 힘들다. 또한, 3) 위성 이미지는 상부 식생과 하부 식생이 혼합되어 섞인 신호를 탐지하기 때문에, 각 층의 식물 계절을 각각 연구하기에 어려움이 있다. 그러므로, 위성에서 탐지한 신호를 평가하고, 식생의 생리학적, 구조적 변화를 보다 정확히 이해하기 위해서는 근접 표면 원격 센싱 시스템을 이용한 실측 자료 기반의 연구들이 요구된다. 본 학위논문의 주 목적은 근접 표면 원격 센싱 시스템을 이용하여 식물 계절 및 SIF를 현장에서 지속적으로 실측하고, 위성 영상 기반의 연구가 갖고 있는 한계점 및 궁금증들을 해결 및 보완하는 것이다. 이 목적을 달성하기 위하여, 아래와 같은 세가지 Chapter: 1) SIF를 관측하기 위한 필터 기반의 저렴한 근접 표면 센싱 시스템 개발, 2)온대 침엽수림에서의 연속적인 SIF 관측, 3)위성 기반의 식물 계절과 실측한 다층 식생의 식물 계절 비교로 구성하고, 이를 진행하였다. SIF는 식생의 구조적, 생리학적 변화를 이해하고, 추정하는 인자로 사용될 수 있어, SIF를 현장에서 관측하기 위한 다양한 근접 표면 원격 센싱 시스템들이 최근 제시되어 오고 있다. 그러나, 아직까지 SIF를 관측하기 위한 상업적으로 유통되는 관측 시스템은 현저히 부족하며, 분광계의 구조적 특성상 현장에서 관측 시스템을 보정 및 관리하기가 어려워 높은 질의 SIF를 취득하는 것은 매우 도전 적인 분야이다. 본 학위 논문의 Chapter 2에서는 SIF를 현장에서 보다 손쉽게 관측하기 위한 필터 기반의 근접 표면 센싱 시스템(Smart Surface Sensing System, 4S-SIF)을 개발하였다. 센서는 대역 필터들과 포토다이오드가 결합되어 있으며, 서보 모터를 사용하여 대역 필터 및 관측 방향을 자동적으로 변경함으로써, 한 개의 포토다이오드가 3개의 파장 범위(757, 760, 770 nm)의 빛 및 태양으로부터 입사되는 광량과 식생으로 반사/방출된 광량을 관측할 수 있도록 고안되었다. 포토다이오드로부터 인식된 디지털 수치 값은 상업적으로 판매되는 초고해상도 분광계(QE Pro, Ocean Insight)와 뚜렷한 선형 관계를 보이는 것을 확인하였다 (R2 > 0.99). 추가적으로, 4S-SIF에서 관측된 SIF와 초고해상도 분광계를 이용하여 추출한 SIF가 선형적인 관계를 이루는 것을 확인하였다. 식생의 생리학적 변화를 일으키는 화학 물질인 DCMU(3-(3, 4-dichlorophenyl)-1, 1-dimethyurea)을 처리했음에도 불구하고, 산출된 SIF들은 선형 관계를 보였다. 본 센서는 기존 시스템들에 비해 사용하기 쉽고 간단하며, 저렴하기 때문에 다양한 시공간적 스케일의 SIF를 관측할 수 있다는 장점이 있다. 위성 기반의 SIF를 이용하여 총일차생산성(gross primary productivity, GPP)을 추정하는 연구는 최근 탄소 순환 연구 분야에서 각광받고 있는 연구 주제이다. 그러나, SIF와 GPP의 관계는 여전히 많은 불확실성을 지니고 있는데, 이는 SIF-GPP 관계를 조절하는 식생의 구조적 및 생리학적 요인을 따로 분리하여 고찰한 연구들이 부족하기 때문이다. 본 학위 논문의 Chapter 3에서는 지속적으로 SIF, GPP, 흡수된 광합성유효복사량 (absorbed photosynthetically active radiation, APAR), 그리고 클로로필과 카로티노이드의 비율 인자 (chlorophyll: carotenoid index, CCI)를 온대침엽수림에서 연속적으로 관측하였다. SIF-GPP 관계의 구체적인 메커니즘 관계를 밝히기 위하여, 광 이용효율 (light use efficiency, LUE_p), 엽록소 형광 수득률 (chlorophyll fluorescence yield, Φ_F) 그리고 SIF 광자가 군락으로부터 방출되는 비율 (escape fraction, f_esc)을 각각 도출하고 탐구하였다. SIF와 GPP의 관계는 뚜렷한 비 선형적인 관계를 보이는 것을 확인했으며(R2 = 0.91 with a hyperbolic regression function, daily), 일주기 단위에서 SIF는 APAR에 대해 선형적이었지만 GPP는 APAR에 대해 뚜렷한 포화 관계를 보이는 것을 확인하였다. 추가적으로 LUE_p 와 Φ_F 가 대기 온도에 따라 반응하는 정도가 다른 것을 확인하였다. LUE_p는 높은 온도에서 포화 되었지만, Φ_F는 포화 패턴을 확인할 수 없었다. 또한, 군락 수준에서의 CCI와 Φ_F가 뚜렷한 상관 관계를 보였다(R2 = 0.84). 이는 Φ_F가 엽록소 색소에 영향을 LUE_p에 비해 더 강한 관계가 있을 수 있음을 시사한다. 마지막으로, f_esc가 태양광의 산란된 정도에 따라 반응을 하여, Φ_F와 LUE_p의 강한 상관 관계를 형성하는데 기여하는 것을 확인하였다. 이러한 발견은 온대 침엽수림에서 군락 수준의 SIF-GPP관계를 생리학적 및 구조적 측면에서 이해하고 규명하는데 큰 도움이 될 것이다. 식물 계절은 식생이 철을 따라 주기적으로 나타내는 변화를 관측하는 반응이다. 식물 계절은 육상생태계와 대기권 사이의 물질 순환을 이해하는데 매우 중요하다. 위성 기반의 NDVI는 식물 계절을 탐지하고 연구하는데 가장 대중적으로 사용된다. 그러나, 활엽수림에서의 위성 NDVI 기반의 개엽 시기 및 성숙 시기가 실제 어느 시점을 탐지하는지는 불분명하다. 실제 활엽수림은 다층 식생 구조의 삼차원으로 이루어져 있는 반면, 위성 영상은 다층 식생의 신호가 섞여 있는 이차원의 결과물이기 때문이다. 따라서, 위성 NDVI 기반의 식물 계절이 다층 식생 구조를 이루고 있는 활엽수림에서 실제 현장 관측과 비교하였을 때 어느 시점을 탐지하는지에 대한 궁금증이 남는다. 본 학위 논문의 Chapter 4에서는 지속적으로 8년 동안 활엽수림내의 다층 식생의 식물 계절을 근접 표면 원격 센싱 시스템을 이용하여 관측하고, 위성 NDVI 기반의 식물 계절과 비교하였다. 다채널 분광계를 상부 식생의 위와 아래에 설치함으로써, 상부 식생과 하부 식생의 식물 계절을 각각 연속적으로 관측하였다. 식물 계절을 탐지하기 위하여 가장 많이 사용되는 방법인 1) 역치를 이용하는 방법과 2) 이계도함수를 이용하는 방법을 사용하여 개엽 시기 및 성숙 시기를 계산하고 이를 다층 식생의 식물 계절과 비교하였다. 본 연구 결과, 첫번째로, 군락의 상층부에서 실측한 NDVI와 위성 기반의 NDVI가 강한 선형 관계를 보이는 것을 확인했다 (R2=0.95 는 MODIS 영상들 및 R2= 0.85 는 Landsat8). 두번째로, 이계도함수 방법과 10%의 역치 값을 이용한 방법이 비슷한 개엽 시기를 추정하는 것을 확인하였으며, 하부 식생의 개엽 시기와 비슷한 시기임을 확인하였다. 세번째로, 이계도함수 방법과 90%의 역치 값을 이용한 방법이 비슷한 성숙 시기를 산출하였으며, 이는 상부 식생의 성숙 시기와 비슷하였다. 추가적으로 상부 식생의 개엽 시기와 하부 식생의 개엽 시기가 온도와 반응하는 정도가 뚜렷하게 차이가 나는 것을 확인할 수 있었다. 상부 식생의 개엽 시기는 적산 생장 온도 일수 (AGDD)와 강한 상관성을 보였고, 하부 식생의 개엽 시기는 AGDD와 연관성을 갖고 있을 뿐만 아니라 추위 일수(NCD)에도 민감하게 반응하는 것을 확인하였다. 이러한 결과는 위성 NDVI 기반의 개엽 시기는 하부 식생의 개엽 시기와 연관성이 높고, 성숙 시기는 상부 식생의 성숙 시기와 비슷하다는 것을 의미한다. 또한, 상부 식생과 하부 식생이 온도에 다른 민감성을 갖고 있어, 위성에서 산출된 식물 계절을 이용하여 기후변화를 이해하고자 할 때, 어떤 층의 식생이 위성 영상에 주된 영향을 미치는지 고려해야 한다는 것을 시사한다. 위성은 넓은 지역의 변화를 손쉽게 모니터링할 수 있어 많은 가능성을 갖고 있는 도구이지만, 보다 정확한 위성 관측 값을 이해하기 위해서는 현장에서 관측된 자료를 기반으로 한 검증이 요구된다. 본 학위 논문에서는 1) 근접 표면 센싱 시스템을 개발, 2) 근접 표면 센싱 시스템을 활용한 식생의 생리학적 구조적 변화의 지속적인 관측, 3) 다층 식생 구조에서 관측되는 식물 계절 및 위성에서 추정된 식물 계절의 연관성 평가를 수행하였다. 개발한 근접 표면 센서는 상업 센서들과 비교했을 때, 가격적으로 저렴하고 손 쉽게 사용할 수 있었으며, 성능적으로도 부족함이 없었다. 근접 표면 센싱 시스템을 이용하여 SIF를 온대 침엽수림에서 지속적으로 관측한 결과, 총일차생산성과 SIF는 비선형 관계를 갖는 것을 확인하였다. 이는 많은 선행 연구들에서 발표한 위성 기반의 SIF와 GPP가 선형적인 관계를 보인다는 것과는 다소 상반된 결과이다. 다측 식생의 봄철 식물 계절을 연속적으로 관측하고, 위성 기반의 식물 계절과 비교평가한 연구에서는 위성 기반의 개엽 시기는 하부 식생에 영향을 주로 받고, 성숙 시기는 상부 식생의 시기와 비슷한 것을 확인하였다. 즉, 근접 표면 센싱 시스템을 이용하여 현장에서 실측한 결과는 위성 영상을 활용한 연구들과는 다른 결과를 보일 수도 있으며, 위성 영상을 평가 및 이해하는데 사용될 수 있다. 따라서, 보다 정확한 식생의 구조적, 생리학적 메커니즘을 이해하기 위해서는 근접 표면 센싱을 활용한 현장에서 구축한 자료 기반의 더 많은 연구들이 필요하다는 것을 시사한다.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 115박

The global tree carrying capacity (keynote)

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