150 research outputs found
μκ³΅κ° ν΄μλ ν₯μμ ν΅ν μμ λ³ν λͺ¨λν°λ§
νμλ
Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : νκ²½λνμ νλκ³Όμ μ‘°κ²½ν, 2023. 2. λ₯μλ ¬.μ‘μ μνκ³μμ λκΈ°κΆκ³Ό μλ¬ΌκΆμ μνΈ μμ©μ μ΄ν΄νκΈ° μν΄μλ μμ λ³νμ λͺ¨λν°λ§μ΄ νμνλ€. μ΄ λ, μμ±μμμ μ§νλ©΄μ κ΄μΈ‘νμ¬ μμμ§λλ₯Ό μ 곡ν μ μμ§λ§, μ§νλ³νμ μμΈν μ 보λ ꡬλ¦μ΄λ μμ± μ΄λ―Έμ§μ κ³΅κ° ν΄μλμ μν΄ μ νλμλ€. λν μμ±μμμ μκ³΅κ° ν΄μλκ° μμμ§λλ₯Ό ν΅ν κ΄ν©μ± λͺ¨λν°λ§μ λ―ΈμΉλ μν₯μ μμ ν λ°νμ§μ§ μμλ€.
λ³Έ λ
Όλ¬Έμμλ κ³ ν΄μλ μμ μ§λλ₯Ό μΌλ¨μλ‘ μμ±νκΈ° μμ± μμμ μκ³΅κ° ν΄μλλ₯Ό ν₯μμν€λ κ²μ λͺ©νλ‘ νμλ€. κ³ ν΄μλ μμ±μμμ νμ©ν μμ λ³ν λͺ¨λν°λ§μ μ곡κ°μ μΌλ‘ νμ₯νκΈ° μν΄ 1) μ μ§κΆ€λ μμ±μ νμ©ν μμμ΅ν©μ ν΅ν΄ μκ°ν΄μλ ν₯μ, 2) μ λμ μμ±λ€νΈμν¬λ₯Ό νμ©ν 곡κ°ν΄μλ ν₯μ, 3) μ곡κ°ν΄μλκ° λμ μμ±μμμ ν μ§νΌλ³΅μ΄ κ· μ§νμ§ μμ 곡κ°μμ μλ¬Ό κ΄ν©μ± λͺ¨λν°λ§μ μννμλ€. μ΄μ²λΌ, μμ±κΈ°λ° μ격νμ§μμ μλ‘μ΄ κΈ°μ μ΄ λ±μ₯ν¨μ λ°λΌ νμ¬ λ° κ³Όκ±°μ μμ±μμμ μκ³΅κ° ν΄μλ μΈ‘λ©΄μμ ν₯μλμ΄ μμ λ³νμ λͺ¨λν°λ§ ν μ μλ€.
μ 2μ₯μμλ μ μ§κΆ€λμμ±μμμ νμ©νλ μκ³΅κ° μμμ΅ν©μΌλ‘ μλ¬Όμ κ΄ν©μ±μ λͺ¨λν°λ§ νμ λ, μκ°ν΄μλκ° ν₯μλ¨μ 보μλ€. μκ³΅κ° μμμ΅ν© μ, ꡬλ¦νμ§, μλ°©ν₯ λ°μ¬ ν¨μ μ‘°μ , κ³΅κ° λ±λ‘, μκ³΅κ° μ΅ν©, μκ³΅κ° κ²°μΈ‘μΉ λ³΄μ λ±μ κ³Όμ μ κ±°μΉλ€. μ΄ μμμ΅ν© μ°μΆλ¬Όμ κ²½μκ΄λ¦¬ λ±μΌλ‘ μμ μ§μμ μ°κ° λ³λμ΄ ν° λ μ₯μ(λκ²½μ§μ λμ½μλ¦Ό)μμ νκ°νμλ€. κ·Έ κ²°κ³Ό, μκ³΅κ° μμμ΅ν© μ°μΆλ¬Όμ κ²°μΈ‘μΉ μμ΄ νμ₯κ΄μΈ‘μ μμΈ‘νμλ€ (R2 = 0.71, μλ νΈν₯ = 5.64% λκ²½μ§; R2 = 0.79, μλ νΈν₯ = -13.8%, νμ½μλ¦Ό). μκ³΅κ° μμμ΅ν©μ μμ μ§λμ μκ³΅κ° ν΄μλλ₯Ό μ μ§μ μΌλ‘ κ°μ νμ¬, μλ¬Ό μμ₯κΈ°λμ μμ±μμμ΄ νμ₯ κ΄μΈ‘μ κ³Όμ νκ°λ₯Ό μ€μλ€. μμμ΅ν©μ λμ μκ³΅κ° ν΄μλλ‘ κ΄ν©μ± μ§λλ₯Ό μΌκ°κ²©μΌλ‘ μμ±νκΈ°μ μ΄λ₯Ό νμ©νμ¬ μμ± μμμ μ νλ μκ³΅κ° ν΄μλλ‘ λ°νμ§μ§ μμ μλ¬Όλ³νμ κ³Όμ μ λ°κ²¬νκΈΈ κΈ°λνλ€.
μμμ 곡κ°λΆν¬μ μ λ°λμ
κ³Ό ν μ§ νΌλ³΅ λ³ν λͺ¨λν°λ§μ μν΄ νμμ μ΄λ€. κ³ ν΄μλ μμ±μμμΌλ‘ μ§κ΅¬ νλ©΄μ κ΄μΈ‘νλ κ²μ μ©μ΄νκ² ν΄μ‘λ€. νΉν Planet Fusionμ μ΄μνμμ±κ΅° λ°μ΄ν°λ₯Ό μ΅λν νμ©ν΄ λ°μ΄ν° κ²°μΈ‘μ΄ μλ 3m κ³΅κ° ν΄μλμ μ§ν νλ©΄ λ°μ¬λμ΄λ€. κ·Έλ¬λ κ³Όκ±° μμ± μΌμ(Landsatμ κ²½μ° 30~60m)μ κ³΅κ° ν΄μλλ μμμ 곡κ°μ λ³νλ₯Ό μμΈ λΆμνλ κ²μ μ ννλ€. μ 3μ₯μμλ Landsat λ°μ΄ν°μ κ³΅κ° ν΄μλλ₯Ό ν₯μνκΈ° μν΄ Planet Fusion λ° Landsat 8 λ°μ΄ν°λ₯Ό μ¬μ©νμ¬ μ΄μ€ μ λμ μμ± λ€νΈμν¬(the dual RSS-GAN)λ₯Ό νμ΅μμΌ, κ³ ν΄μλ μ κ·ν μμ μ§μ(NDVI)μ μλ¬Ό κ·Όμ μΈμ λ°μ¬(NIRv)λλ₯Ό μμ±νλ νλ€. νμκΈ°λ° νμ₯ μμμ§μ(μ΅λ 8λ
)μ λλ‘ κΈ°λ° μ΄λΆκ΄μ§λλ‘ the dual RSS-GANμ μ±λ₯μ λνλ―Όκ΅ λ΄ λ λμμ§(λκ²½μ§μ νμ½μλ¦Ό)μμ νκ°νλ€. The dual RSS-GANμ Landsat 8 μμμ 곡κ°ν΄μλλ₯Ό ν₯μμμΌ κ³΅κ° ννμ 보μνκ³ μμ μ§μμ κ³μ μ λ³νλ₯Ό ν¬μ°©νλ€(R2> 0.96). κ·Έλ¦¬κ³ the dual RSS-GANμ Landsat 8 μμ μ§μκ° νμ₯μ λΉν΄ κ³Όμ νκ°λλ κ²μ μννλ€. νμ₯ κ΄μΈ‘μ λΉν΄ μ΄μ€ RSS-GANκ³Ό Landsat 8μ μλ νΈν₯ κ° κ°κ° -0.8% μμ -1.5%, -10.3% μμ -4.6% μλ€. μ΄λ¬ν κ°μ μ Planet Fusionμ 곡κ°μ 보λ₯Ό μ΄μ€ RSS-GANλ‘ νμ΅νμκΈ°μ κ°λ₯νλ€. ν€λΉ μ°κ΅¬ κ²°κ³Όλ Landsat μμμ κ³΅κ° ν΄μλλ₯Ό ν₯μμμΌ μ¨κ²¨μ§ κ³΅κ° μ 보λ₯Ό μ 곡νλ μλ‘μ΄ μ κ·Ό λ°©μμ΄λ€.
κ³ ν΄μλμμ μλ¬Ό κ΄ν©μ± μ§λλ ν μ§νΌλ³΅μ΄ 볡μ‘ν 곡κ°μμ νμ μν λͺ¨λν°λ§μ νμμ μ΄λ€. κ·Έλ¬λ Sentinel-2, Landsat λ° MODISμ κ°μ΄ νμ λμ‘° κΆ€λμ μλ μμ±μ κ³΅κ° ν΄μλκ° λκ±°λ μκ° ν΄μλ λμ μμ±μμλ§ μ 곡ν μ μλ€. μ΅κ·Ό λ°μ¬λ μ΄μνμμ±κ΅°μ μ΄λ¬ν ν΄μλ νκ³μ 극볡ν μ μλ€. νΉν Planet Fusionμ μ΄μνμμ± μλ£μ μκ³΅κ° ν΄μλλ‘ μ§νλ©΄μ κ΄μΈ‘ν μ μλ€. 4μ₯μμ, Planet Fusion μ§νλ°μ¬λλ₯Ό μ΄μ©νμ¬ μμμμ λ°μ¬λ κ·Όμ μΈμ 볡μ¬(NIRvP)λ₯Ό 3m ν΄μλ μ§λλ₯Ό μΌκ°κ²©μΌλ‘ μμ±νλ€. κ·Έλ° λ€μ λ―Έκ΅ μΊλ¦¬ν¬λμμ£Ό μν¬λΌλ©ν -μ νΈμν¨ λΈνμ νλμ€ νμ λ€νΈμν¬ λ°μ΄ν°μ λΉκ΅νμ¬ μλ¬Ό κ΄ν©μ±μ μΆμ νκΈ° μν NIRvP μ§λμ μ±λ₯μ νκ°νμλ€. μ 체μ μΌλ‘ NIRvP μ§λλ μ΅μ§μ μ¦μ μμ λ³νμλ λΆκ΅¬νκ³ κ°λ³ λμμ§μ μλ¬Ό κ΄ν©μ±μ μκ°μ λ³νλ₯Ό ν¬μ°©νμλ€. κ·Έλ¬λ λμμ§ μ 체μ λν NIRvP μ§λμ μλ¬Ό κ΄ν©μ± μ¬μ΄μ κ΄κ³λ NIRvP μ§λλ₯Ό νλμ€ νμ κ΄μΈ‘λ²μμ μΌμΉμν¬ λλ§ λμ μκ΄κ΄κ³λ₯Ό 보μλ€. κ΄μΈ‘λ²μλ₯Ό μΌμΉμν¬ κ²½μ°, NIRvP μ§λλ μλ¬Ό κ΄ν©μ±μ μΆμ νλ λ° μμ΄ νμ₯ NIRvPλ³΄λ€ μ°μν μ±λ₯μ 보μλ€. μ΄λ¬ν μ±λ₯ μ°¨μ΄λ νλμ€ νμ κ΄μΈ‘λ²μλ₯Ό μΌμΉμν¬ λ, μ°κ΅¬ λμμ§ κ°μ NIRvP-μλ¬Ό κ΄ν©μ± κ΄κ³μ κΈ°μΈκΈ°κ° μΌκ΄μ±μ 보μκΈ° λλ¬Έμ΄λ€. λ³Έ μ°κ΅¬ κ²°κ³Όλ μμ± κ΄μΈ‘μ νλμ€ νμ κ΄μΈ‘λ²μμ μΌμΉμν€λ κ²μ μ€μμ±μ 보μ¬μ£Όκ³ λμ μκ³΅κ° ν΄μλλ‘ μλ¬Ό κ΄ν©μ±μ μ격μΌλ‘ λͺ¨λν°λ§νλ μ΄μνμμ±κ΅° μλ£μ μ μ¬λ ₯μ 보μ¬μ€λ€.Monitoring changes in terrestrial vegetation is essential to understanding interactions between atmosphere and biosphere, especially terrestrial ecosystem. To this end, satellite remote sensing offer maps for examining land surface in different scales. However, the detailed information was hindered under the clouds or limited by the spatial resolution of satellite imagery. Moreover, the impacts of spatial and temporal resolution in photosynthesis monitoring were not fully revealed.
In this dissertation, I aimed to enhance the spatial and temporal resolution of satellite imagery towards daily gap-free vegetation maps with high spatial resolution. In order to expand vegetation change monitoring in time and space using high-resolution satellite images, I 1) improved temporal resolution of satellite dataset through image fusion using geostationary satellites, 2) improved spatial resolution of satellite dataset using generative adversarial networks, and 3) showed the use of high spatiotemporal resolution maps for monitoring plant photosynthesis especially over heterogeneous landscapes. With the advent of new techniques in satellite remote sensing, current and past datasets can be fully utilized for monitoring vegetation changes in the respect of spatial and temporal resolution.
In Chapter 2, I developed the integrated system that implemented geostationary satellite products in the spatiotemporal image fusion method for monitoring canopy photosynthesis. The integrated system contains the series of process (i.e., cloud masking, nadir bidirectional reflectance function adjustment, spatial registration, spatiotemporal image fusion, spatial gap-filling, temporal-gap-filling). I conducted the evaluation of the integrated system over heterogeneous rice paddy landscape where the drastic land cover changes were caused by cultivation management and deciduous forest where consecutive changes occurred in time. The results showed that the integrated system well predict in situ measurements without data gaps (R2 = 0.71, relative bias = 5.64% at rice paddy site; R2 = 0.79, relative bias = -13.8% at deciduous forest site). The integrated system gradually improved the spatiotemporal resolution of vegetation maps, reducing the underestimation of in situ measurements, especially during peak growing season. Since the integrated system generates daily canopy photosynthesis maps for monitoring dynamics among regions of interest worldwide with high spatial resolution. I anticipate future efforts to reveal the hindered information by the limited spatial and temporal resolution of satellite imagery.
Detailed spatial representations of terrestrial vegetation are essential for precision agricultural applications and the monitoring of land cover changes in heterogeneous landscapes. The advent of satellite-based remote sensing has facilitated daily observations of the Earths surface with high spatial resolution. In particular, a data fusion product such as Planet Fusion has realized the delivery of daily, gap-free surface reflectance data with 3-m pixel resolution through full utilization of relatively recent (i.e., 2018-) CubeSat constellation data. However, the spatial resolution of past satellite sensors (i.e., 30β60 m for Landsat) has restricted the detailed spatial analysis of past changes in vegetation. In Chapter 3, to overcome the spatial resolution constraint of Landsat data for long-term vegetation monitoring, we propose a dual remote-sensing super-resolution generative adversarial network (dual RSS-GAN) combining Planet Fusion and Landsat 8 data to simulate spatially enhanced long-term time-series of the normalized difference vegetation index (NDVI) and near-infrared reflectance from vegetation (NIRv). We evaluated the performance of the dual RSS-GAN against in situ tower-based continuous measurements (up to 8 years) and remotely piloted aerial system-based maps of cropland and deciduous forest in the Republic of Korea. The dual RSS-GAN enhanced spatial representations in Landsat 8 images and captured seasonal variation in vegetation indices (R2 > 0.95, for the dual RSS-GAN maps vs. in situ data from all sites). Overall, the dual RSS-GAN reduced Landsat 8 vegetation index underestimations compared with in situ measurements; relative bias values of NDVI ranged from β3.2% to 1.2% and β12.4% to β3.7% for the dual RSS-GAN and Landsat 8, respectively. This improvement was caused by spatial enhancement through the dual RSS-GAN, which captured fine-scale information from Planet Fusion. This study presents a new approach for the restoration of hidden sub-pixel spatial information in Landsat images.
Mapping canopy photosynthesis in both high spatial and temporal resolution is essential for carbon cycle monitoring in heterogeneous areas. However, well established satellites in sun-synchronous orbits such as Sentinel-2, Landsat and MODIS can only provide either high spatial or high temporal resolution but not both. Recently established CubeSat satellite constellations have created an opportunity to overcome this resolution trade-off. In particular, Planet Fusion allows full utilization of the CubeSat data resolution and coverage while maintaining high radiometric quality. In Chapter 4, I used the Planet Fusion surface reflectance product to calculate daily, 3-m resolution, gap-free maps of the near-infrared radiation reflected from vegetation (NIRvP). I then evaluated the performance of these NIRvP maps for estimating canopy photosynthesis by comparing with data from a flux tower network in Sacramento-San Joaquin Delta, California, USA. Overall, NIRvP maps captured temporal variations in canopy photosynthesis of individual sites, despite changes in water extent in the wetlands and frequent mowing in the crop fields. When combining data from all sites, however, I found that robust agreement between NIRvP maps and canopy photosynthesis could only be achieved when matching NIRvP maps to the flux tower footprints. In this case of matched footprints, NIRvP maps showed considerably better performance than in situ NIRvP in estimating canopy photosynthesis both for daily sum and data around the time of satellite overpass (R2 = 0.78 vs. 0.60, for maps vs. in situ for the satellite overpass time case). This difference in performance was mostly due to the higher degree of consistency in slopes of NIRvP-canopy photosynthesis relationships across the study sites for flux tower footprint-matched maps. Our results show the importance of matching satellite observations to the flux tower footprint and demonstrate the potential of CubeSat constellation imagery to monitor canopy photosynthesis remotely at high spatio-temporal resolution.Chapter 1. Introduction 2
1. Background 2
1.1 Daily gap-free surface reflectance using geostationary satellite products 2
1.2 Monitoring past vegetation changes with high-spatial-resolution 3
1.3 High spatiotemporal resolution vegetation photosynthesis maps 4
2. Purpose of Research 4
Chapter 2. Generating daily gap-filled BRDF adjusted surface reflectance product at 10 m resolution using geostationary satellite product for monitoring daily canopy photosynthesis 6
1. Introduction 6
2. Methods 11
2.1 Study sites 11
2.2 In situ measurements 13
2.3 Satellite products 14
2.4 Integrated system 17
2.5 Canopy photosynthesis 21
2.6 Evaluation 23
3. Results and discussion 24
3.1 Comparison of STIF NDVI and NIRv with in situ NDVI and NIRv 24
3.2 Comparison of STIF NIRvP with in situ NIRvP 28
4. Conclusion 31
Chapter 3. Super-resolution of historic Landsat imagery using a dual Generative Adversarial Network (GAN) model with CubeSat constellation imagery for monitoring vegetation changes 32
1. Introduction 32
2. Methods 38
2.1 Real-ESRGAN model 38
2.2 Study sites 40
2.3 In situ measurements 42
2.4 Vegetation index 44
2.5 Satellite data 45
2.6 Planet Fusion 48
2.7 Dual RSS-GAN via fine-tuned Real-ESRGAN 49
2.8 Evaluation 54
3. Results 57
3.1 Comparison of NDVI and NIRv maps from Planet Fusion, Sentinel 2 NBAR, and Landsat 8 NBAR data with in situ NDVI and NIRv 57
3.2 Comparison of dual RSS-SRGAN model results with Landsat 8 NDVI and NIRv 60
3.3 Comparison of dual RSS-GAN model results with respect to in situ time-series NDVI and NIRv 63
3.4 Comparison of the dual RSS-GAN model with NDVI and NIRv maps derived from RPAS 66
4. Discussion 70
4.1 Monitoring changes in terrestrial vegetation using the dual RSS-GAN model 70
4.2 CubeSat data in the dual RSS-GAN model 72
4.3 Perspectives and limitations 73
5. Conclusion 78
Appendices 79
Supplementary material 82
Chapter 4. Matching high resolution satellite data and flux tower footprints improves their agreement in photosynthesis estimates 85
1. Introduction 85
2. Methods 89
2.1 Study sites 89
2.2 In situ measurements 92
2.3 Planet Fusion NIRvP 94
2.4 Flux footprint model 98
2.5 Evaluation 98
3. Results 105
3.1 Comparison of Planet Fusion NIRv and NIRvP with in situ NIRv and NIRvP 105
3.2 Comparison of instantaneous Planet Fusion NIRv and NIRvP with against tower GPP estimates 108
3.3 Daily GPP estimation from Planet Fusion -derived NIRvP 114
4. Discussion 118
4.1 Flux tower footprint matching and effects of spatial and temporal resolution on GPP estimation 118
4.2 Roles of radiation component in GPP mapping 123
4.3 Limitations and perspectives 126
5. Conclusion 133
Appendix 135
Supplementary Materials 144
Chapter 5. Conclusion 153
Bibliography 155
Abstract in Korea 199
Acknowledgements 202λ°
Chapter 34 - Biocompatibility of nanocellulose: Emerging biomedical applications
Nanocellulose already proved to be a highly relevant material for biomedical
applications, ensued by its outstanding mechanical properties and, more importantly, its biocompatibility. Nevertheless, despite their previous intensive
research, a notable number of emerging applications are still being developed.
Interestingly, this drive is not solely based on the nanocellulose features, but also
heavily dependent on sustainability. The three core nanocelluloses encompass
cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial nanocellulose (BNC). All these different types of nanocellulose display highly interesting biomedical properties per se, after modification and when used in
composite formulations. Novel applications that use nanocellulose includewell-known areas, namely, wound dressings, implants, indwelling medical
devices, scaffolds, and novel printed scaffolds. Their cytotoxicity and biocompatibility using recent methodologies are thoroughly analyzed to reinforce their
near future applicability. By analyzing the pristine core nanocellulose, none
display cytotoxicity. However, CNF has the highest potential to fail long-term
biocompatibility since it tends to trigger inflammation. On the other hand, neverdried BNC displays a remarkable biocompatibility. Despite this, all nanocelluloses clearly represent a flag bearer of future superior biomaterials, being
elite materials in the urgent replacement of our petrochemical dependence
Examining Ecosystem Drought Responses Using Remote Sensing and Flux Tower Observations
Indiana University-Purdue University Indianapolis (IUPUI)Water is fundamental for plant growth, and vegetation response to water availability influences water, carbon, and energy exchanges between land and atmosphere. Vegetation plays the most active role in water and carbon cycle of various ecosystems. Therefore, comprehensive evaluation of drought impact on vegetation productivity will play a critical role for better understanding the global water cycle under future climate conditions.
In-situ meteorological measurements and the eddy covariance flux tower network, which provide meteorological data, and estimates of ecosystem productivity and respiration are remarkable tools to assess the impacts of drought on ecosystem carbon and water cycles. In regions with limited in-situ observations, remote sensing can be a very useful tool to monitor ecosystem drought status since it provides continuous observations of relevant variables linked to ecosystem function and the hydrologic cycle. However, the detailed understanding of ecosystem responses to drought is still lacking and it is challenging to quantify the impacts of drought on ecosystem carbon balance and several factors hinder our explicit understanding of the complex drought impacts. This dissertation addressed drought monitoring, ecosystem drought responses, trends of vegetation water constraint based on in-situ metrological observations, flux tower and multi-sensor remote sensing observations. This dissertation first developed a new integrated drought index applicable across diverse climate regions based on in-situ meteorological observations and multi-sensor remote sensing data, and another integrated drought index applicable across diverse climate regions only based on multi-sensor remote sensing data. The dissertation also evaluated the applicability of new satellite dataset (e.g., solar induced fluorescence, SIF) for responding to meteorological drought. Results show that satellite SIF data could have the potential to reflect meteorological drought, but the application should be limited to dry regions. The work in this dissertation also accessed changes in water constraint on global vegetation productivity, and quantified different drought dimensions on ecosystem productivity and respiration. Results indicate that a significant increase in vegetation water constraint over the last 30 years. The results highlighted the need for a more explicit consideration of the influence of water constraints on regional and global vegetation under a warming climate
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°
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This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling
Remote Sensing of Precipitation: Part II
Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earthβs atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products
Advances in Ecohydrology for Water Resources Optimization in Arid and Semi-arid Areas
This Special Issue (SI) aims to investigate the relationships between hydrological and ecological processes and how these interactions can contribute to the optimization of water resources in arid and semi-arid areas. This SI collected 10 original contributions on sustainable land management and the optimization of water resources in fragile environments that are at elevated risk due to climate change. The topics mainly concern transpiration, evapotranspiration, groundwater recharge, deep percolation, and related issues. The collection of manuscripts presented in this SI represents a contribution of knowledge in ecohydrology
Modelling uncertainty in global natural gas resources and markets
This thesis explores uncertainties in global gas resources and markets. Whilst global gas consumption growth has been strong in recent years, the need to shift the global energy system to net zero has raised significant questions about the role of gas in this transition. In addition to the global context of addressing climate change, gas markets have undergone significant shifts in recent years, driven by a multitude of factors including, but by no means limited to, rapid production growth from unconventional gas in the United States, rapidly increasing demand in China, and increased competition along the gas supply chain. This thesis finds that if global temperatures are to be kept towards-1.5oC, then gas consumption needs to peak now. Significant regional variations were found in this result, with European and North American demand declining from the present day, whilst key Asian markets (including China) see consumption growth to 2035 but then rapid decline. Therefore, several transition risks are identified in this work. This also has implications for price formation across different import markets, with a significant result from this thesis suggesting that prices in major Asian (e.g. China and Japan) markets converge, whilst European prices remain consistently lower. To generate novel insights in this thesis, two models were used, which have been soft linked to generate consistency of inputs and outputs. The existing TIMES Integrated Assessment Model at UCL (TIAM-UCL) provides long-term insights into the role of gas in the wider energy system, particularly under ambitious decarbonisation pathways. Additionally, a new global GAs Production, Trade and Annual Pricing model (GAPTAP), provides a novel representation of gas markets, with insights into regional price formation mechanisms, investment in new fields, the role of associated gas on price formation mechanisms, and variations in government revenues from gas production
Sustainable Organic Agriculture for Developing Agribusiness Sector
Developing sustainable organic agriculture and resilient agribusiness sector is fundamental, keeping in mind the value of the opportunity presented by the growing demand for healthy and safe food globally, with the expectation for the global population to reach 9.8 billion by 2050, and 11 billion by 2100.Lately, the main threats in Europe, and worldwide, are the increasingly dynamic climate change and economic factors related to currency fluctuations. While the current environmental policy provides several mechanisms to support agribusinesses in mitigating organic food for daily increasing human population and stability of the currency, it does not contemplate the relative readiness of individuals and businesses to act correctly.Organic farming is the practice that relies more on using sustainable methods to cultivate crops and produce food animals, avoiding chemicals and dietary synthetic drug inputs that do not belong to the natural ecosystem. Organic agriculture can also contribute to meaningful socioeconomic, ecologically sustainable development, and significantly in the development of the agribusiness sector, especially in developing countries
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