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    μ‹œκ³΅κ°„ 해상도 ν–₯상을 ν†΅ν•œ 식생 λ³€ν™” λͺ¨λ‹ˆν„°λ§

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    ν•™μœ„λ…Όλ¬Έ(박사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : ν™˜κ²½λŒ€ν•™μ› ν˜‘λ™κ³Όμ • μ‘°κ²½ν•™, 2023. 2. λ₯˜μ˜λ ¬.μœ‘μƒ μƒνƒœκ³„μ—μ„œ λŒ€κΈ°κΆŒκ³Ό μƒλ¬ΌκΆŒμ˜ μƒν˜Έ μž‘μš©μ„ μ΄ν•΄ν•˜κΈ° μœ„ν•΄μ„œλŠ” 식생 λ³€ν™”μ˜ λͺ¨λ‹ˆν„°λ§μ΄ ν•„μš”ν•˜λ‹€. 이 λ•Œ, μœ„μ„±μ˜μƒμ€ μ§€ν‘œλ©΄μ„ κ΄€μΈ‘ν•˜μ—¬ 식생지도λ₯Ό μ œκ³΅ν•  수 μžˆμ§€λ§Œ, μ§€ν‘œλ³€ν™”μ˜ μƒμ„Έν•œ μ •λ³΄λŠ” κ΅¬λ¦„μ΄λ‚˜ μœ„μ„± μ΄λ―Έμ§€μ˜ 곡간 해상도에 μ˜ν•΄ μ œν•œλ˜μ—ˆλ‹€. λ˜ν•œ μœ„μ„±μ˜μƒμ˜ μ‹œκ³΅κ°„ 해상도가 식생지도λ₯Ό ν†΅ν•œ κ΄‘ν•©μ„± λͺ¨λ‹ˆν„°λ§μ— λ―ΈμΉ˜λŠ” 영ν–₯은 μ™„μ „νžˆ λ°ν˜€μ§€μ§€ μ•Šμ•˜λ‹€. λ³Έ λ…Όλ¬Έμ—μ„œλŠ” 고해상도 식생 지도λ₯Ό μΌλ‹¨μœ„λ‘œ μƒμ„±ν•˜κΈ° μœ„μ„± μ˜μƒμ˜ μ‹œκ³΅κ°„ 해상도λ₯Ό ν–₯μƒμ‹œν‚€λŠ” 것을 λͺ©ν‘œλ‘œ ν•˜μ˜€λ‹€. 고해상도 μœ„μ„±μ˜μƒμ„ ν™œμš©ν•œ 식생 λ³€ν™” λͺ¨λ‹ˆν„°λ§μ„ μ‹œκ³΅κ°„μ μœΌλ‘œ ν™•μž₯ν•˜κΈ° μœ„ν•΄ 1) 정지ꢀ도 μœ„μ„±μ„ ν™œμš©ν•œ μ˜μƒμœ΅ν•©μ„ 톡해 μ‹œκ°„ν•΄μƒλ„ ν–₯상, 2) μ λŒ€μ μƒμ„±λ„€νŠΈμ›Œν¬λ₯Ό ν™œμš©ν•œ 곡간해상도 ν–₯상, 3) μ‹œκ³΅κ°„ν•΄μƒλ„κ°€ 높은 μœ„μ„±μ˜μƒμ„ 토지피볡이 κ· μ§ˆν•˜μ§€ μ•Šμ€ κ³΅κ°„μ—μ„œ 식물 κ΄‘ν•©μ„± λͺ¨λ‹ˆν„°λ§μ„ μˆ˜ν–‰ν•˜μ˜€λ‹€. 이처럼, μœ„μ„±κΈ°λ°˜ μ›κ²©νƒμ§€μ—μ„œ μƒˆλ‘œμš΄ 기술이 λ“±μž₯함에 따라 ν˜„μž¬ 및 과거의 μœ„μ„±μ˜μƒμ€ μ‹œκ³΅κ°„ 해상도 μΈ‘λ©΄μ—μ„œ ν–₯μƒλ˜μ–΄ 식생 λ³€ν™”μ˜ λͺ¨λ‹ˆν„°λ§ ν•  수 μžˆλ‹€. 제2μž₯μ—μ„œλŠ” μ •μ§€κΆ€λ„μœ„μ„±μ˜μƒμ„ ν™œμš©ν•˜λŠ” μ‹œκ³΅κ°„ μ˜μƒμœ΅ν•©μœΌλ‘œ μ‹λ¬Όμ˜ 광합성을 λͺ¨λ‹ˆν„°λ§ ν–ˆμ„ λ•Œ, μ‹œκ°„ν•΄μƒλ„κ°€ ν–₯상됨을 λ³΄μ˜€λ‹€. μ‹œκ³΅κ°„ μ˜μƒμœ΅ν•© μ‹œ, ꡬ름탐지, μ–‘λ°©ν–₯ λ°˜μ‚¬ ν•¨μˆ˜ μ‘°μ •, 곡간 등둝, μ‹œκ³΅κ°„ μœ΅ν•©, μ‹œκ³΅κ°„ 결츑치 보완 λ“±μ˜ 과정을 κ±°μΉœλ‹€. 이 μ˜μƒμœ΅ν•© μ‚°μΆœλ¬Όμ€ κ²½μž‘κ΄€λ¦¬ λ“±μœΌλ‘œ 식생 μ§€μˆ˜μ˜ μ—°κ°„ 변동이 큰 두 μž₯μ†Œ(농경지와 λ‚™μ—½μˆ˜λ¦Ό)μ—μ„œ ν‰κ°€ν•˜μ˜€λ‹€. κ·Έ κ²°κ³Ό, μ‹œκ³΅κ°„ μ˜μƒμœ΅ν•© μ‚°μΆœλ¬Όμ€ 결츑치 없이 ν˜„μž₯관츑을 μ˜ˆμΈ‘ν•˜μ˜€λ‹€ (R2 = 0.71, μƒλŒ€ 편ν–₯ = 5.64% 농경지; R2 = 0.79, μƒλŒ€ 편ν–₯ = -13.8%, ν™œμ—½μˆ˜λ¦Ό). μ‹œκ³΅κ°„ μ˜μƒμœ΅ν•©μ€ 식생 μ§€λ„μ˜ μ‹œκ³΅κ°„ 해상도λ₯Ό μ μ§„μ μœΌλ‘œ κ°œμ„ ν•˜μ—¬, 식물 생μž₯κΈ°λ™μ•ˆ μœ„μ„±μ˜μƒμ΄ ν˜„μž₯ 관츑을 κ³Όμ†Œ 평가λ₯Ό μ€„μ˜€λ‹€. μ˜μƒμœ΅ν•©μ€ 높은 μ‹œκ³΅κ°„ ν•΄μƒλ„λ‘œ κ΄‘ν•©μ„± 지도λ₯Ό μΌκ°„κ²©μœΌλ‘œ μƒμ„±ν•˜κΈ°μ— 이λ₯Ό ν™œμš©ν•˜μ—¬ μœ„μ„± μ˜μƒμ˜ μ œν•œλœ μ‹œκ³΅κ°„ ν•΄μƒλ„λ‘œ λ°ν˜€μ§€μ§€ μ•Šμ€ μ‹λ¬Όλ³€ν™”μ˜ 과정을 λ°œκ²¬ν•˜κΈΈ κΈ°λŒ€ν•œλ‹€. μ‹μƒμ˜ 곡간뢄포은 정밀농업과 토지 피볡 λ³€ν™” λͺ¨λ‹ˆν„°λ§μ„ μœ„ν•΄ ν•„μˆ˜μ μ΄λ‹€. 고해상도 μœ„μ„±μ˜μƒμœΌλ‘œ 지ꡬ ν‘œλ©΄μ„ κ΄€μΈ‘ν•˜λŠ” 것을 μš©μ΄ν•˜κ²Œ ν•΄μ‘Œλ‹€. 특히 Planet Fusion은 μ΄ˆμ†Œν˜•μœ„μ„±κ΅° 데이터λ₯Ό μ΅œλŒ€ν•œ ν™œμš©ν•΄ 데이터 결츑이 μ—†λŠ” 3m 곡간 ν•΄μƒλ„μ˜ μ§€ν‘œ ν‘œλ©΄ λ°˜μ‚¬λ„μ΄λ‹€. κ·ΈλŸ¬λ‚˜ κ³Όκ±° μœ„μ„± μ„Όμ„œ(Landsat의 경우 30~60m)의 곡간 ν•΄μƒλ„λŠ” μ‹μƒμ˜ 곡간적 λ³€ν™”λ₯Ό 상세 λΆ„μ„ν•˜λŠ” 것을 μ œν•œν–ˆλ‹€. 제3μž₯μ—μ„œλŠ” Landsat λ°μ΄ν„°μ˜ 곡간 해상도λ₯Ό ν–₯μƒν•˜κΈ° μœ„ν•΄ Planet Fusion 및 Landsat 8 데이터λ₯Ό μ‚¬μš©ν•˜μ—¬ 이쀑 μ λŒ€μ  생성 λ„€νŠΈμ›Œν¬(the dual RSS-GAN)λ₯Ό ν•™μŠ΅μ‹œμΌœ, 고해상도 μ •κ·œν™” 식생 μ§€μˆ˜(NDVI)와 식물 근적외선 λ°˜μ‚¬(NIRv)도λ₯Ό μƒμ„±ν•˜λŠ” ν•œλ‹€. νƒ€μ›ŒκΈ°λ°˜ ν˜„μž₯ μ‹μƒμ§€μˆ˜(μ΅œλŒ€ 8λ…„)와 λ“œλ‘ κΈ°λ°˜ μ΄ˆλΆ„κ΄‘μ§€λ„λ‘œ the dual RSS-GAN의 μ„±λŠ₯을 λŒ€ν•œλ―Όκ΅­ λ‚΄ 두 λŒ€μƒμ§€(농경지와 ν™œμ—½μˆ˜λ¦Ό)μ—μ„œ ν‰κ°€ν–ˆλ‹€. The dual RSS-GAN은 Landsat 8 μ˜μƒμ˜ 곡간해상도λ₯Ό ν–₯μƒμ‹œμΌœ 곡간 ν‘œν˜„μ„ λ³΄μ™„ν•˜κ³  식생 μ§€μˆ˜μ˜ κ³„μ ˆμ  λ³€ν™”λ₯Ό ν¬μ°©ν–ˆλ‹€(R2> 0.96). 그리고 the dual RSS-GAN은 Landsat 8 식생 μ§€μˆ˜κ°€ ν˜„μž₯에 λΉ„ν•΄ κ³Όμ†Œ ν‰κ°€λ˜λŠ” 것을 μ™„ν™”ν–ˆλ‹€. ν˜„μž₯ 관츑에 λΉ„ν•΄ 이쀑 RSS-GANκ³Ό Landsat 8의 μƒλŒ€ 편ν–₯ κ°’ 각각 -0.8% μ—μ„œ -1.5%, -10.3% μ—μ„œ -4.6% μ˜€λ‹€. μ΄λŸ¬ν•œ κ°œμ„ μ€ Planet Fusion의 곡간정보λ₯Ό 이쀑 RSS-GAN둜 ν•™μŠ΅ν•˜μ˜€κΈ°μ— κ°€λŠ₯ν–ˆλ‹€. ν—€λ‹Ή 연ꡬ κ²°κ³ΌλŠ” Landsat μ˜μƒμ˜ 곡간 해상도λ₯Ό ν–₯μƒμ‹œμΌœ μˆ¨κ²¨μ§„ 곡간 정보λ₯Ό μ œκ³΅ν•˜λŠ” μƒˆλ‘œμš΄ μ ‘κ·Ό 방식이닀. κ³ ν•΄μƒλ„μ—μ„œ 식물 κ΄‘ν•©μ„± μ§€λ„λŠ” 토지피볡이 λ³΅μž‘ν•œ κ³΅κ°„μ—μ„œ νƒ„μ†Œ μˆœν™˜ λͺ¨λ‹ˆν„°λ§μ‹œ ν•„μˆ˜μ μ΄λ‹€. κ·ΈλŸ¬λ‚˜ Sentinel-2, Landsat 및 MODIS와 같이 νƒœμ–‘ 동쑰 ꢀ도에 μžˆλŠ” μœ„μ„±μ€ 곡간 해상도가 λ†’κ±°λ‚˜ μ‹œκ°„ 해상도 높은 μœ„μ„±μ˜μƒλ§Œ μ œκ³΅ν•  수 μžˆλ‹€. 졜근 λ°œμ‚¬λœ μ΄ˆμ†Œν˜•μœ„μ„±κ΅°μ€ μ΄λŸ¬ν•œ 해상도 ν•œκ³„μ„ 극볡할 수 μžˆλ‹€. 특히 Planet Fusion은 μ΄ˆμ†Œν˜•μœ„μ„± 자료의 μ‹œκ³΅κ°„ ν•΄μƒλ„λ‘œ μ§€ν‘œλ©΄μ„ κ΄€μΈ‘ν•  수 μžˆλ‹€. 4μž₯μ—μ„œ, Planet Fusion μ§€ν‘œλ°˜μ‚¬λ„λ₯Ό μ΄μš©ν•˜μ—¬ μ‹μƒμ—μ„œ λ°˜μ‚¬λœ 근적외선 볡사(NIRvP)λ₯Ό 3m 해상도 지도λ₯Ό μΌκ°„κ²©μœΌλ‘œ μƒμ„±ν–ˆλ‹€. 그런 λ‹€μŒ λ―Έκ΅­ μΊ˜λ¦¬ν¬λ‹ˆμ•„μ£Ό μƒˆν¬λΌλ©˜ν† -μƒŒ ν˜Έμ•„ν‚¨ λΈνƒ€μ˜ ν”ŒλŸ­μŠ€ νƒ€μ›Œ λ„€νŠΈμ›Œν¬ 데이터와 λΉ„κ΅ν•˜μ—¬ 식물 광합성을 μΆ”μ •ν•˜κΈ° μœ„ν•œ NIRvP μ§€λ„μ˜ μ„±λŠ₯을 ν‰κ°€ν•˜μ˜€λ‹€. μ „μ²΄μ μœΌλ‘œ NIRvP μ§€λ„λŠ” μŠ΅μ§€μ˜ μž¦μ€ μˆ˜μœ„ 변화에도 λΆˆκ΅¬ν•˜κ³  κ°œλ³„ λŒ€μƒμ§€μ˜ 식물 κ΄‘ν•©μ„±μ˜ μ‹œκ°„μ  λ³€ν™”λ₯Ό ν¬μ°©ν•˜μ˜€λ‹€. κ·ΈλŸ¬λ‚˜ λŒ€μƒμ§€ 전체에 λŒ€ν•œ NIRvP 지도와 식물 κ΄‘ν•©μ„± μ‚¬μ΄μ˜ κ΄€κ³„λŠ” NIRvP 지도λ₯Ό ν”ŒλŸ­μŠ€ νƒ€μ›Œ κ΄€μΈ‘λ²”μœ„μ™€ μΌμΉ˜μ‹œν‚¬ λ•Œλ§Œ 높은 상관관계λ₯Ό λ³΄μ˜€λ‹€. κ΄€μΈ‘λ²”μœ„λ₯Ό μΌμΉ˜μ‹œν‚¬ 경우, NIRvP μ§€λ„λŠ” 식물 광합성을 μΆ”μ •ν•˜λŠ” 데 μžˆμ–΄ ν˜„μž₯ NIRvP보닀 μš°μˆ˜ν•œ μ„±λŠ₯을 λ³΄μ˜€λ‹€. μ΄λŸ¬ν•œ μ„±λŠ₯ μ°¨μ΄λŠ” ν”ŒλŸ­μŠ€ νƒ€μ›Œ κ΄€μΈ‘λ²”μœ„λ₯Ό μΌμΉ˜μ‹œν‚¬ λ•Œ, 연ꡬ λŒ€μƒμ§€ κ°„μ˜ NIRvP-식물 κ΄‘ν•©μ„± κ΄€κ³„μ˜ κΈ°μšΈκΈ°κ°€ 일관성을 λ³΄μ˜€κΈ° λ•Œλ¬Έμ΄λ‹€. λ³Έ 연ꡬ κ²°κ³ΌλŠ” μœ„μ„± 관츑을 ν”ŒλŸ­μŠ€ νƒ€μ›Œ κ΄€μΈ‘λ²”μœ„μ™€ μΌμΉ˜μ‹œν‚€λŠ” κ²ƒμ˜ μ€‘μš”μ„±μ„ 보여주고 높은 μ‹œκ³΅κ°„ ν•΄μƒλ„λ‘œ 식물 광합성을 μ›κ²©μœΌλ‘œ λͺ¨λ‹ˆν„°λ§ν•˜λŠ” μ΄ˆμ†Œν˜•μœ„μ„±κ΅° 자료의 잠재λ ₯을 보여쀀닀.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., 30–60 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 βˆ’3.2% to 1.2% and βˆ’12.4% to βˆ’3.7% 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

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    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

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    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 ‘ ν‘œλ©΄ 원격 μ„Όμ‹± μ‹œμŠ€ν…œλ“€μ„ μ΄μš©ν•œ 지속적 식물 κ³„μ ˆ 및 νƒœμ–‘ μœ λ„ μ—½λ‘μ†Œ ν˜•κ΄‘λ¬Όμ§ˆ κ΄€μΈ‘

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    ν•™μœ„λ…Όλ¬Έ(박사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : ν™˜κ²½λŒ€ν•™μ› ν˜‘λ™κ³Όμ • μ‘°κ²½ν•™, 2022.2. λ₯˜μ˜λ ¬.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.식물 κ³„μ ˆ 및 μ‹μƒμ˜ 생리학적, ꡬ쑰적인 λ³€ν™”λ₯Ό μ§€μ†μ μœΌλ‘œ λͺ¨λ‹ˆν„°λ§ ν•˜λŠ” 것은 μœ‘μƒμƒνƒœκ³„μ™€ λŒ€κΈ°κΆŒ μ‚¬μ΄μ˜ μ—λ„ˆμ§€, νƒ„μ†Œ μˆœν™˜ λ“±μ˜ ν”Όλ“œλ°±μ„ μ΄ν•΄ν•˜λŠ”λ° ν•„μˆ˜μ μ΄λ‹€. 이λ₯Ό κ΄€μΈ‘ν•˜κΈ° μœ„ν•˜μ—¬ μœ„μ„±μ—μ„œ κ΄€μΈ‘λœ μ •κ·œν™” 식생 μ§€μˆ˜ (NDVI) νƒœμ–‘ μœ λ„ μ—½λ‘μ†Œ ν˜•κ΄‘λ¬Όμ§ˆ (SIF)λŠ” λŒ€μ€‘μ μœΌλ‘œ μ‚¬μš©λ˜κ³  μžˆλ‹€. κ·ΈλŸ¬λ‚˜, 우주 μœ„μ„± 기반의 μžλ£ŒλŠ” λ‹€μŒκ³Ό 같은 ν•œκ³„μ λ“€μ΄ μ‘΄μž¬ν•œλ‹€. 1) μ•„μ§κΉŒμ§€ κ³ ν•΄μƒλ„μ˜ μœ„μ„± 기반 SIF μžλ£ŒλŠ” μ—†κ³ , 2) μœ„μ„± μžλ£Œλ“€μ€ λŒ€κΈ°μ˜ 질 (예, ꡬ름)에 영ν–₯을 λ°›μ•„, 흐린 λ‚ μ˜ μ‹μƒμ˜ 생리학적, ꡬ쑰적 λ³€ν™”λ₯Ό νƒμ§€ν•˜κΈ° νž˜λ“€λ‹€. λ˜ν•œ, 3) μœ„μ„± μ΄λ―Έμ§€λŠ” 상뢀 식생과 ν•˜λΆ€ 식생이 ν˜Όν•©λ˜μ–΄ μ„žμΈ μ‹ ν˜Έλ₯Ό νƒμ§€ν•˜κΈ° λ•Œλ¬Έμ—, 각 측의 식물 κ³„μ ˆμ„ 각각 μ—°κ΅¬ν•˜κΈ°μ— 어렀움이 μžˆλ‹€. κ·ΈλŸ¬λ―€λ‘œ, μœ„μ„±μ—μ„œ νƒμ§€ν•œ μ‹ ν˜Έλ₯Ό ν‰κ°€ν•˜κ³ , μ‹μƒμ˜ 생리학적, ꡬ쑰적 λ³€ν™”λ₯Ό 보닀 μ •ν™•νžˆ μ΄ν•΄ν•˜κΈ° μœ„ν•΄μ„œλŠ” κ·Όμ ‘ ν‘œλ©΄ 원격 μ„Όμ‹± μ‹œμŠ€ν…œμ„ μ΄μš©ν•œ μ‹€μΈ‘ 자료 기반의 연ꡬ듀이 μš”κ΅¬λœλ‹€. λ³Έ ν•™μœ„λ…Όλ¬Έμ˜ μ£Ό λͺ©μ μ€ κ·Όμ ‘ ν‘œλ©΄ 원격 μ„Όμ‹± μ‹œμŠ€ν…œμ„ μ΄μš©ν•˜μ—¬ 식물 κ³„μ ˆ 및 SIFλ₯Ό ν˜„μž₯μ—μ„œ μ§€μ†μ μœΌλ‘œ μ‹€μΈ‘ν•˜κ³ , μœ„μ„± μ˜μƒ 기반의 연ꡬ가 κ°–κ³  μžˆλŠ” ν•œκ³„μ  및 κΆκΈˆμ¦λ“€μ„ ν•΄κ²° 및 λ³΄μ™„ν•˜λŠ” 것이닀. 이 λͺ©μ μ„ λ‹¬μ„±ν•˜κΈ° μœ„ν•˜μ—¬, μ•„λž˜μ™€ 같은 세가지 Chapter: 1) SIFλ₯Ό κ΄€μΈ‘ν•˜κΈ° μœ„ν•œ ν•„ν„° 기반의 μ €λ ΄ν•œ κ·Όμ ‘ ν‘œλ©΄ μ„Όμ‹± μ‹œμŠ€ν…œ 개발, 2)μ˜¨λŒ€ μΉ¨μ—½μˆ˜λ¦Όμ—μ„œμ˜ 연속적인 SIF κ΄€μΈ‘, 3)μœ„μ„± 기반의 식물 κ³„μ ˆκ³Ό μ‹€μΈ‘ν•œ λ‹€μΈ΅ μ‹μƒμ˜ 식물 κ³„μ ˆ λΉ„κ΅λ‘œ κ΅¬μ„±ν•˜κ³ , 이λ₯Ό μ§„ν–‰ν•˜μ˜€λ‹€. SIFλŠ” μ‹μƒμ˜ ꡬ쑰적, 생리학적 λ³€ν™”λ₯Ό μ΄ν•΄ν•˜κ³ , μΆ”μ •ν•˜λŠ” 인자둜 μ‚¬μš©λ  수 μžˆμ–΄, SIFλ₯Ό ν˜„μž₯μ—μ„œ κ΄€μΈ‘ν•˜κΈ° μœ„ν•œ λ‹€μ–‘ν•œ κ·Όμ ‘ ν‘œλ©΄ 원격 μ„Όμ‹± μ‹œμŠ€ν…œλ“€μ΄ 졜근 μ œμ‹œλ˜μ–΄ 였고 μžˆλ‹€. κ·ΈλŸ¬λ‚˜, μ•„μ§κΉŒμ§€ SIFλ₯Ό κ΄€μΈ‘ν•˜κΈ° μœ„ν•œ μƒμ—…μ μœΌλ‘œ μœ ν†΅λ˜λŠ” κ΄€μΈ‘ μ‹œμŠ€ν…œμ€ ν˜„μ €νžˆ λΆ€μ‘±ν•˜λ©°, λΆ„κ΄‘κ³„μ˜ ꡬ쑰적 νŠΉμ„±μƒ ν˜„μž₯μ—μ„œ κ΄€μΈ‘ μ‹œμŠ€ν…œμ„ 보정 및 κ΄€λ¦¬ν•˜κΈ°κ°€ μ–΄λ €μ›Œ 높은 질의 SIFλ₯Ό μ·¨λ“ν•˜λŠ” 것은 맀우 도전 적인 뢄야이닀. λ³Έ ν•™μœ„ λ…Όλ¬Έμ˜ Chapter 2μ—μ„œλŠ” SIFλ₯Ό ν˜„μž₯μ—μ„œ 보닀 μ†μ‰½κ²Œ κ΄€μΈ‘ν•˜κΈ° μœ„ν•œ ν•„ν„° 기반의 κ·Όμ ‘ ν‘œλ©΄ μ„Όμ‹± μ‹œμŠ€ν…œ(Smart Surface Sensing System, 4S-SIF)을 κ°œλ°œν•˜μ˜€λ‹€. μ„Όμ„œλŠ” λŒ€μ—­ ν•„ν„°λ“€κ³Ό ν¬ν† λ‹€μ΄μ˜€λ“œκ°€ κ²°ν•©λ˜μ–΄ 있으며, μ„œλ³΄ λͺ¨ν„°λ₯Ό μ‚¬μš©ν•˜μ—¬ λŒ€μ—­ ν•„ν„° 및 κ΄€μΈ‘ λ°©ν–₯을 μžλ™μ μœΌλ‘œ λ³€κ²½ν•¨μœΌλ‘œμ¨, ν•œ 개의 ν¬ν† λ‹€μ΄μ˜€λ“œκ°€ 3개의 파μž₯ λ²”μœ„(757, 760, 770 nm)의 λΉ› 및 νƒœμ–‘μœΌλ‘œλΆ€ν„° μž…μ‚¬λ˜λŠ” κ΄‘λŸ‰κ³Ό μ‹μƒμœΌλ‘œ λ°˜μ‚¬/방좜된 κ΄‘λŸ‰μ„ κ΄€μΈ‘ν•  수 μžˆλ„λ‘ κ³ μ•ˆλ˜μ—ˆλ‹€. ν¬ν† λ‹€μ΄μ˜€λ“œλ‘œλΆ€ν„° μΈμ‹λœ 디지털 수치 값은 μƒμ—…μ μœΌλ‘œ νŒλ§€λ˜λŠ” μ΄ˆκ³ ν•΄μƒλ„ 뢄광계(QE Pro, Ocean Insight)와 λšœλ ·ν•œ μ„ ν˜• 관계λ₯Ό λ³΄μ΄λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€ (R2 > 0.99). μΆ”κ°€μ μœΌλ‘œ, 4S-SIFμ—μ„œ κ΄€μΈ‘λœ SIF와 μ΄ˆκ³ ν•΄μƒλ„ 뢄광계λ₯Ό μ΄μš©ν•˜μ—¬ μΆ”μΆœν•œ SIFκ°€ μ„ ν˜•μ μΈ 관계λ₯Ό μ΄λ£¨λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€. μ‹μƒμ˜ 생리학적 λ³€ν™”λ₯Ό μΌμœΌν‚€λŠ” ν™”ν•™ 물질인 DCMU(3-(3, 4-dichlorophenyl)-1, 1-dimethyurea)을 μ²˜λ¦¬ν–ˆμŒμ—λ„ λΆˆκ΅¬ν•˜κ³ , μ‚°μΆœλœ SIF듀은 μ„ ν˜• 관계λ₯Ό λ³΄μ˜€λ‹€. λ³Έ μ„Όμ„œλŠ” κΈ°μ‘΄ μ‹œμŠ€ν…œλ“€μ— λΉ„ν•΄ μ‚¬μš©ν•˜κΈ° 쉽고 κ°„λ‹¨ν•˜λ©°, μ €λ ΄ν•˜κΈ° λ•Œλ¬Έμ— λ‹€μ–‘ν•œ μ‹œκ³΅κ°„μ  μŠ€μΌ€μΌμ˜ SIFλ₯Ό κ΄€μΈ‘ν•  수 μžˆλ‹€λŠ” μž₯점이 μžˆλ‹€. μœ„μ„± 기반의 SIFλ₯Ό μ΄μš©ν•˜μ—¬ 총일차생산성(gross primary productivity, GPP)을 μΆ”μ •ν•˜λŠ” μ—°κ΅¬λŠ” 졜근 νƒ„μ†Œ μˆœν™˜ 연ꡬ λΆ„μ•Όμ—μ„œ 각광받고 μžˆλŠ” 연ꡬ μ£Όμ œμ΄λ‹€. κ·ΈλŸ¬λ‚˜, SIF와 GPP의 κ΄€κ³„λŠ” μ—¬μ „νžˆ λ§Žμ€ λΆˆν™•μ‹€μ„±μ„ μ§€λ‹ˆκ³  μžˆλŠ”λ°, μ΄λŠ” SIF-GPP 관계λ₯Ό μ‘°μ ˆν•˜λŠ” μ‹μƒμ˜ ꡬ쑰적 및 생리학적 μš”μΈμ„ λ”°λ‘œ λΆ„λ¦¬ν•˜μ—¬ κ³ μ°°ν•œ 연ꡬ듀이 λΆ€μ‘±ν•˜κΈ° λ•Œλ¬Έμ΄λ‹€. λ³Έ ν•™μœ„ λ…Όλ¬Έμ˜ Chapter 3μ—μ„œλŠ” μ§€μ†μ μœΌλ‘œ SIF, GPP, 흑수된 κ΄‘ν•©μ„±μœ νš¨λ³΅μ‚¬λŸ‰ (absorbed photosynthetically active radiation, APAR), 그리고 ν΄λ‘œλ‘œν•„κ³Ό μΉ΄λ‘œν‹°λ…Έμ΄λ“œμ˜ λΉ„μœ¨ 인자 (chlorophyll: carotenoid index, CCI)λ₯Ό μ˜¨λŒ€μΉ¨μ—½μˆ˜λ¦Όμ—μ„œ μ—°μ†μ μœΌλ‘œ κ΄€μΈ‘ν•˜μ˜€λ‹€. SIF-GPP κ΄€κ³„μ˜ ꡬ체적인 λ©”μ»€λ‹ˆμ¦˜ 관계λ₯Ό 밝히기 μœ„ν•˜μ—¬, κ΄‘ 이용효율 (light use efficiency, LUE_p), μ—½λ‘μ†Œ ν˜•κ΄‘ μˆ˜λ“λ₯  (chlorophyll fluorescence yield, Ξ¦_F) 그리고 SIF κ΄‘μžκ°€ κ΅°λ½μœΌλ‘œλΆ€ν„° λ°©μΆœλ˜λŠ” λΉ„μœ¨ (escape fraction, f_esc)을 각각 λ„μΆœν•˜κ³  νƒκ΅¬ν•˜μ˜€λ‹€. SIF와 GPP의 κ΄€κ³„λŠ” λšœλ ·ν•œ λΉ„ μ„ ν˜•μ μΈ 관계λ₯Ό λ³΄μ΄λŠ” 것을 ν™•μΈν–ˆμœΌλ©°(R2 = 0.91 with a hyperbolic regression function, daily), 일주기 λ‹¨μœ„μ—μ„œ SIFλŠ” APAR에 λŒ€ν•΄ μ„ ν˜•μ μ΄μ—ˆμ§€λ§Œ GPPλŠ” APAR에 λŒ€ν•΄ λšœλ ·ν•œ 포화 관계λ₯Ό λ³΄μ΄λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€. μΆ”κ°€μ μœΌλ‘œ LUE_p 와 Ξ¦_F κ°€ λŒ€κΈ° μ˜¨λ„μ— 따라 λ°˜μ‘ν•˜λŠ” 정도가 λ‹€λ₯Έ 것을 ν™•μΈν•˜μ˜€λ‹€. LUE_pλŠ” 높은 μ˜¨λ„μ—μ„œ 포화 λ˜μ—ˆμ§€λ§Œ, Ξ¦_FλŠ” 포화 νŒ¨ν„΄μ„ 확인할 수 μ—†μ—ˆλ‹€. λ˜ν•œ, ꡰ락 μˆ˜μ€€μ—μ„œμ˜ CCI와 Ξ¦_Fκ°€ λšœλ ·ν•œ 상관 관계λ₯Ό λ³΄μ˜€λ‹€(R2 = 0.84). μ΄λŠ” Ξ¦_Fκ°€ μ—½λ‘μ†Œ μƒ‰μ†Œμ— 영ν–₯을 LUE_p에 λΉ„ν•΄ 더 κ°•ν•œ 관계가 μžˆμ„ 수 μžˆμŒμ„ μ‹œμ‚¬ν•œλ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ, f_escκ°€ νƒœμ–‘κ΄‘μ˜ μ‚°λž€λœ 정도에 따라 λ°˜μ‘μ„ ν•˜μ—¬, Ξ¦_F와 LUE_p의 κ°•ν•œ 상관 관계λ₯Ό ν˜•μ„±ν•˜λŠ”λ° κΈ°μ—¬ν•˜λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€. μ΄λŸ¬ν•œ λ°œκ²¬μ€ μ˜¨λŒ€ μΉ¨μ—½μˆ˜λ¦Όμ—μ„œ ꡰ락 μˆ˜μ€€μ˜ SIF-GPP관계λ₯Ό 생리학적 및 ꡬ쑰적 μΈ‘λ©΄μ—μ„œ μ΄ν•΄ν•˜κ³  규λͺ…ν•˜λŠ”λ° 큰 도움이 될 것이닀. 식물 κ³„μ ˆμ€ 식생이 철을 따라 주기적으둜 λ‚˜νƒ€λ‚΄λŠ” λ³€ν™”λ₯Ό κ΄€μΈ‘ν•˜λŠ” λ°˜μ‘μ΄λ‹€. 식물 κ³„μ ˆμ€ μœ‘μƒμƒνƒœκ³„μ™€ λŒ€κΈ°κΆŒ μ‚¬μ΄μ˜ 물질 μˆœν™˜μ„ μ΄ν•΄ν•˜λŠ”λ° 맀우 μ€‘μš”ν•˜λ‹€. μœ„μ„± 기반의 NDVIλŠ” 식물 κ³„μ ˆμ„ νƒμ§€ν•˜κ³  μ—°κ΅¬ν•˜λŠ”λ° κ°€μž₯ λŒ€μ€‘μ μœΌλ‘œ μ‚¬μš©λœλ‹€. κ·ΈλŸ¬λ‚˜, ν™œμ—½μˆ˜λ¦Όμ—μ„œμ˜ μœ„μ„± NDVI 기반의 κ°œμ—½ μ‹œκΈ° 및 μ„±μˆ™ μ‹œκΈ°κ°€ μ‹€μ œ μ–΄λŠ μ‹œμ μ„ νƒμ§€ν•˜λŠ”μ§€λŠ” λΆˆλΆ„λͺ…ν•˜λ‹€. μ‹€μ œ ν™œμ—½μˆ˜λ¦Όμ€ λ‹€μΈ΅ 식생 ꡬ쑰의 μ‚Όμ°¨μ›μœΌλ‘œ 이루어져 μžˆλŠ” 반면, μœ„μ„± μ˜μƒμ€ λ‹€μΈ΅ μ‹μƒμ˜ μ‹ ν˜Έκ°€ μ„žμ—¬ μžˆλŠ” μ΄μ°¨μ›μ˜ 결과물이기 λ•Œλ¬Έμ΄λ‹€. λ”°λΌμ„œ, μœ„μ„± NDVI 기반의 식물 κ³„μ ˆμ΄ λ‹€μΈ΅ 식생 ꡬ쑰λ₯Ό 이루고 μžˆλŠ” ν™œμ—½μˆ˜λ¦Όμ—μ„œ μ‹€μ œ ν˜„μž₯ κ΄€μΈ‘κ³Ό λΉ„κ΅ν•˜μ˜€μ„ λ•Œ μ–΄λŠ μ‹œμ μ„ νƒμ§€ν•˜λŠ”μ§€μ— λŒ€ν•œ ꢁ금증이 λ‚¨λŠ”λ‹€. λ³Έ ν•™μœ„ λ…Όλ¬Έμ˜ Chapter 4μ—μ„œλŠ” μ§€μ†μ μœΌλ‘œ 8λ…„ λ™μ•ˆ ν™œμ—½μˆ˜λ¦Όλ‚΄μ˜ λ‹€μΈ΅ μ‹μƒμ˜ 식물 κ³„μ ˆμ„ κ·Όμ ‘ ν‘œλ©΄ 원격 μ„Όμ‹± μ‹œμŠ€ν…œμ„ μ΄μš©ν•˜μ—¬ κ΄€μΈ‘ν•˜κ³ , μœ„μ„± NDVI 기반의 식물 κ³„μ ˆκ³Ό λΉ„κ΅ν•˜μ˜€λ‹€. 닀채널 뢄광계λ₯Ό 상뢀 μ‹μƒμ˜ μœ„μ™€ μ•„λž˜μ— μ„€μΉ˜ν•¨μœΌλ‘œμ¨, 상뢀 식생과 ν•˜λΆ€ μ‹μƒμ˜ 식물 κ³„μ ˆμ„ 각각 μ—°μ†μ μœΌλ‘œ κ΄€μΈ‘ν•˜μ˜€λ‹€. 식물 κ³„μ ˆμ„ νƒμ§€ν•˜κΈ° μœ„ν•˜μ—¬ κ°€μž₯ 많이 μ‚¬μš©λ˜λŠ” 방법인 1) μ—­μΉ˜λ₯Ό μ΄μš©ν•˜λŠ” 방법과 2) μ΄κ³„λ„ν•¨μˆ˜λ₯Ό μ΄μš©ν•˜λŠ” 방법을 μ‚¬μš©ν•˜μ—¬ κ°œμ—½ μ‹œκΈ° 및 μ„±μˆ™ μ‹œκΈ°λ₯Ό κ³„μ‚°ν•˜κ³  이λ₯Ό λ‹€μΈ΅ μ‹μƒμ˜ 식물 κ³„μ ˆκ³Ό λΉ„κ΅ν•˜μ˜€λ‹€. λ³Έ 연ꡬ κ²°κ³Ό, 첫번째둜, ꡰ락의 μƒμΈ΅λΆ€μ—μ„œ μ‹€μΈ‘ν•œ NDVI와 μœ„μ„± 기반의 NDVIκ°€ κ°•ν•œ μ„ ν˜• 관계λ₯Ό λ³΄μ΄λŠ” 것을 ν™•μΈν–ˆλ‹€ (R2=0.95 λŠ” MODIS μ˜μƒλ“€ 및 R2= 0.85 λŠ” Landsat8). λ‘λ²ˆμ§Έλ‘œ, μ΄κ³„λ„ν•¨μˆ˜ 방법과 10%의 μ—­μΉ˜ 값을 μ΄μš©ν•œ 방법이 λΉ„μŠ·ν•œ κ°œμ—½ μ‹œκΈ°λ₯Ό μΆ”μ •ν•˜λŠ” 것을 ν™•μΈν•˜μ˜€μœΌλ©°, ν•˜λΆ€ μ‹μƒμ˜ κ°œμ—½ μ‹œκΈ°μ™€ λΉ„μŠ·ν•œ μ‹œκΈ°μž„μ„ ν™•μΈν•˜μ˜€λ‹€. μ„Έλ²ˆμ§Έλ‘œ, μ΄κ³„λ„ν•¨μˆ˜ 방법과 90%의 μ—­μΉ˜ 값을 μ΄μš©ν•œ 방법이 λΉ„μŠ·ν•œ μ„±μˆ™ μ‹œκΈ°λ₯Ό μ‚°μΆœν•˜μ˜€μœΌλ©°, μ΄λŠ” 상뢀 μ‹μƒμ˜ μ„±μˆ™ μ‹œκΈ°μ™€ λΉ„μŠ·ν•˜μ˜€λ‹€. μΆ”κ°€μ μœΌλ‘œ 상뢀 μ‹μƒμ˜ κ°œμ—½ μ‹œκΈ°μ™€ ν•˜λΆ€ μ‹μƒμ˜ κ°œμ—½ μ‹œκΈ°κ°€ μ˜¨λ„μ™€ λ°˜μ‘ν•˜λŠ” 정도가 λšœλ ·ν•˜κ²Œ 차이가 λ‚˜λŠ” 것을 확인할 수 μžˆμ—ˆλ‹€. 상뢀 μ‹μƒμ˜ κ°œμ—½ μ‹œκΈ°λŠ” 적산 생μž₯ μ˜¨λ„ 일수 (AGDD)와 κ°•ν•œ 상관성을 λ³΄μ˜€κ³ , ν•˜λΆ€ μ‹μƒμ˜ κ°œμ—½ μ‹œκΈ°λŠ” AGDD와 연관성을 κ°–κ³  μžˆμ„ 뿐만 μ•„λ‹ˆλΌ μΆ”μœ„ 일수(NCD)에도 λ―Όκ°ν•˜κ²Œ λ°˜μ‘ν•˜λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€. μ΄λŸ¬ν•œ κ²°κ³ΌλŠ” μœ„μ„± NDVI 기반의 κ°œμ—½ μ‹œκΈ°λŠ” ν•˜λΆ€ μ‹μƒμ˜ κ°œμ—½ μ‹œκΈ°μ™€ 연관성이 λ†’κ³ , μ„±μˆ™ μ‹œκΈ°λŠ” 상뢀 μ‹μƒμ˜ μ„±μˆ™ μ‹œκΈ°μ™€ λΉ„μŠ·ν•˜λ‹€λŠ” 것을 μ˜λ―Έν•œλ‹€. λ˜ν•œ, 상뢀 식생과 ν•˜λΆ€ 식생이 μ˜¨λ„μ— λ‹€λ₯Έ 민감성을 κ°–κ³  μžˆμ–΄, μœ„μ„±μ—μ„œ μ‚°μΆœλœ 식물 κ³„μ ˆμ„ μ΄μš©ν•˜μ—¬ κΈ°ν›„λ³€ν™”λ₯Ό μ΄ν•΄ν•˜κ³ μž ν•  λ•Œ, μ–΄λ–€ 측의 식생이 μœ„μ„± μ˜μƒμ— 주된 영ν–₯을 λ―ΈμΉ˜λŠ”μ§€ κ³ λ €ν•΄μ•Ό ν•œλ‹€λŠ” 것을 μ‹œμ‚¬ν•œλ‹€. μœ„μ„±μ€ 넓은 μ§€μ—­μ˜ λ³€ν™”λ₯Ό μ†μ‰½κ²Œ λͺ¨λ‹ˆν„°λ§ν•  수 μžˆμ–΄ λ§Žμ€ κ°€λŠ₯성을 κ°–κ³  μžˆλŠ” λ„κ΅¬μ΄μ§€λ§Œ, 보닀 μ •ν™•ν•œ μœ„μ„± κ΄€μΈ‘ 값을 μ΄ν•΄ν•˜κΈ° μœ„ν•΄μ„œλŠ” ν˜„μž₯μ—μ„œ κ΄€μΈ‘λœ 자료λ₯Ό 기반으둜 ν•œ 검증이 μš”κ΅¬λœλ‹€. λ³Έ ν•™μœ„ λ…Όλ¬Έμ—μ„œλŠ” 1) κ·Όμ ‘ ν‘œλ©΄ μ„Όμ‹± μ‹œμŠ€ν…œμ„ 개발, 2) κ·Όμ ‘ ν‘œλ©΄ μ„Όμ‹± μ‹œμŠ€ν…œμ„ ν™œμš©ν•œ μ‹μƒμ˜ 생리학적 ꡬ쑰적 λ³€ν™”μ˜ 지속적인 κ΄€μΈ‘, 3) λ‹€μΈ΅ 식생 κ΅¬μ‘°μ—μ„œ κ΄€μΈ‘λ˜λŠ” 식물 κ³„μ ˆ 및 μœ„μ„±μ—μ„œ μΆ”μ •λœ 식물 κ³„μ ˆμ˜ μ—°κ΄€μ„± 평가λ₯Ό μˆ˜ν–‰ν•˜μ˜€λ‹€. κ°œλ°œν•œ κ·Όμ ‘ ν‘œλ©΄ μ„Όμ„œλŠ” 상업 μ„Όμ„œλ“€κ³Ό λΉ„κ΅ν–ˆμ„ λ•Œ, κ°€κ²©μ μœΌλ‘œ μ €λ ΄ν•˜κ³  손 μ‰½κ²Œ μ‚¬μš©ν•  수 μžˆμ—ˆμœΌλ©°, μ„±λŠ₯μ μœΌλ‘œλ„ 뢀쑱함이 μ—†μ—ˆλ‹€. κ·Όμ ‘ ν‘œλ©΄ μ„Όμ‹± μ‹œμŠ€ν…œμ„ μ΄μš©ν•˜μ—¬ SIFλ₯Ό μ˜¨λŒ€ μΉ¨μ—½μˆ˜λ¦Όμ—μ„œ μ§€μ†μ μœΌλ‘œ κ΄€μΈ‘ν•œ κ²°κ³Ό, 총일차생산성과 SIFλŠ” λΉ„μ„ ν˜• 관계λ₯Ό κ°–λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€. μ΄λŠ” λ§Žμ€ μ„ ν–‰ μ—°κ΅¬λ“€μ—μ„œ λ°œν‘œν•œ μœ„μ„± 기반의 SIF와 GPPκ°€ μ„ ν˜•μ μΈ 관계λ₯Ό λ³΄μΈλ‹€λŠ” κ²ƒκ³ΌλŠ” λ‹€μ†Œ μƒλ°˜λœ 결과이닀. λ‹€μΈ‘ μ‹μƒμ˜ λ΄„μ²  식물 κ³„μ ˆμ„ μ—°μ†μ μœΌλ‘œ κ΄€μΈ‘ν•˜κ³ , μœ„μ„± 기반의 식물 κ³„μ ˆκ³Ό λΉ„κ΅ν‰κ°€ν•œ μ—°κ΅¬μ—μ„œλŠ” μœ„μ„± 기반의 κ°œμ—½ μ‹œκΈ°λŠ” ν•˜λΆ€ 식생에 영ν–₯을 주둜 λ°›κ³ , μ„±μˆ™ μ‹œκΈ°λŠ” 상뢀 μ‹μƒμ˜ μ‹œκΈ°μ™€ λΉ„μŠ·ν•œ 것을 ν™•μΈν•˜μ˜€λ‹€. 즉, κ·Όμ ‘ ν‘œλ©΄ μ„Όμ‹± μ‹œμŠ€ν…œμ„ μ΄μš©ν•˜μ—¬ ν˜„μž₯μ—μ„œ μ‹€μΈ‘ν•œ κ²°κ³ΌλŠ” μœ„μ„± μ˜μƒμ„ ν™œμš©ν•œ μ—°κ΅¬λ“€κ³ΌλŠ” λ‹€λ₯Έ κ²°κ³Όλ₯Ό 보일 μˆ˜λ„ 있으며, μœ„μ„± μ˜μƒμ„ 평가 및 μ΄ν•΄ν•˜λŠ”λ° μ‚¬μš©λ  수 μžˆλ‹€. λ”°λΌμ„œ, 보닀 μ •ν™•ν•œ μ‹μƒμ˜ ꡬ쑰적, 생리학적 λ©”μ»€λ‹ˆμ¦˜μ„ μ΄ν•΄ν•˜κΈ° μœ„ν•΄μ„œλŠ” κ·Όμ ‘ ν‘œλ©΄ 센싱을 ν™œμš©ν•œ ν˜„μž₯μ—μ„œ κ΅¬μΆ•ν•œ 자료 기반의 더 λ§Žμ€ 연ꡬ듀이 ν•„μš”ν•˜λ‹€λŠ” 것을 μ‹œμ‚¬ν•œλ‹€.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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