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    ํƒ„์†Œ ์ค‘๋ฆฝ ๋„์‹œ๋ฅผ ์œ„ํ•œ ์ด์งˆ์  ๋„์‹œ ํ”ผ๋ณต ๋‚ด ํ† ์–‘ ์œ ๊ธฐ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰ ํ‰๊ฐ€

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ™˜๊ฒฝ๋Œ€ํ•™์› ํ˜‘๋™๊ณผ์ • ์กฐ๊ฒฝํ•™, 2021.8. ๋ฐฐ์ง€ํ™˜.Soils hold the largest organic carbon in urban ecosystem. Quantifying urban soil organic carbon (SOC) stocks is a preliminary step for carbon neutral strategy in urban ecosystem. The impacts of urbanization on SOC stocks have gained attention from policy makers as well as land managers. However, urban SOC stocks are often omitted from estimates of carbon budgets in urban area due to the small sample size and high degree of variability under heterogeneous land-cover types. In this dissertation, I aimed to find the spatial and vertical variations of urban SOC stocks. Understanding how SOC stocks in heterogeneous land-cover types is essential to assess urban carbon storage; however, the spatial and vertical distributions of SOC stocks have been poorly characterized. Intensive urbanization, and underground development in particular, leads to spatial and vertical heterogeneity of SOC. The magnitude and origin of SOC under different urban setting have not been well explored. In Chapter 2, I quantified SOC stocks to a 5 m depth beneath impervious surfaces and adjacent vegetative surfaces at three housing complexes in Seoul Special City, Republic of Korea. The objectives of Chapter 2 were (1) to quantify the spatial and vertical distribution of SOC stocks beneath impervious surfaces and vegetative surfaces; (2) to investigate the key factors that control the spatial and vertical distribution of SOC stocks; and (3) to understand how anthropogenic factors affect SOC stocks in heterogeneous urban setting. In the top 1 m of the profile, SOC stocks under vegetative surfaces were three times greater than those under impervious surfaces. However, we discovered that unexpectedly high SOC stocks appeared in deeper soil layers under both surface types, which led to comparable SOC stocks at a depth of 5 m beneath the impervious surface (16.9 ยฑ 1.9 kgC mโˆ’2) and at the vegetative surface (22.3 ยฑ 2.2 kgC mโˆ’2). Consequently, the ratio of SOC stocks at depths of 1 m to 5 m were 16% in impervious surfaces and 34% in vegetative surfaces, suggesting conventional soil sampling at 1 m depth could miss large SOC. Stable isotope data (ฮด13C and ฮด15N) combined with historical aerial photographs revealed that cropland that existed until the 1970s formed the high SOC cultural layer in deeper soils. Our results highlight that deep soils under impervious surfaces could be overlooked carbon hotspots in urban ecosystems. We believe this finding could help city planners and policy makers to assess regional carbon budgets and to reduce carbon footprint by recycling the deep SOC excavated from various construction projects towards sustainable urban development. Landscape fragmentation has created large areas of forest edge. Understanding how SOC stocks within forest edges respond to fragmentation is essential to assess carbon budgets; however, the causes and magnitude of edge effects on SOC stocks have been poorly characterized. The goal of Chapter 3 is to assess the edge effects on SOC stocks in fragmented urban-rural forests. Here, I quantified the edge effects on SOC stocks along an urban-rural gradient from three fragmented urban forests to a large patch of rural forest. The SOC stocks within 20 m of the rural forest edge (1.86 kgC m-2) is on average 80% lower than the interiors of rural forest (10.47 kgC m-2). We found that biotic factors, including annual litterfall mass (R2 > 0.94), peak leaf area index (R2 > 0.92), and fine-root mass density (R2 > 0.77), explained the spatial variation in SOC stocks within the rural forest. In urban forests, human activities at forest edges led to contrasting edge effects on SOC stocks, for instance, the SOC stocks at the east edges (4.74 kgC m-2) were over 63% greater than at the west edges (2.9 kgC m-2) explained by the adjacent land uses (e.g., paved roads vs. non-paved soils) and in-situ litterfall management. We also found significant differences in summer soil temperature (โˆ†TS > 2.8โ„ƒ) and soil moisture (โˆ†VWC > 0.05 m3 m-3) between the east and west forest edges. Our results reveal that the factors responsible for the edge effects on SOC stocks in rural forests are biotic factors, while heterogeneous human activities at the local scale lead to complex edge effects on urban forest SOC stocks. Urban soil is a heterogeneous mixture of various parent materials and significantly affected by anthropogenic activities. Improving our understanding of the relationships between the pattern of land use and the SOC stocks requires large amounts of timely and cost efficient SOC analysis, which is difficult to obtain with routine chemical analysis. In Chapter 4, I evaluated a predictive model for SOC based on hyperspectral reflectance dataset in urban soils using ASD-FieldSpec, then used partial least squares (PLS) regression to establish the predictive models for SOC in urban soils. A total of 136 samples were collected and the SOC and ฮด13C values of topsoil (0-20 cm) were measured under different land cover types. The SOC stocks varied between 0.33 kg m-2 to 12.51 kg m-2. The ฮด13C data varied between -30.18โ€ฐ to -17.17โ€ฐ. The average SOC and ฮด13C data showed a clear vegetation-dependent pattern. The PLSR model achieved acceptable results with coe๏ฌƒcient of determination (R2) and root mean square error (RMSE) of calibration set for SOC (Rยฒ= 0.83; RMSE = 1.6%). The leave one out cross validation procedure confirmed the robust performance of PLS model. The results indicated that 1) the SOC can be estimated with reasonable accuracy across heterogeneous urban land-cover types based solely on the hyperspectral reflectance spectroscopy, and 2) the strategy has the potential of upscaling for city scale assessments of SOC stocks.๊ธฐํ›„ ๋ณ€ํ™” ๋Œ€์‘์ฑ…์œผ๋กœ ํƒ„์†Œ์ค‘๋ฆฝ ๋„์‹œ์˜ ์ค‘์š”์„ฑ์ด ์ฆ๊ฐ€ํ•˜๋ฉฐ, ๋„์‹œ ํ† ์–‘ ํƒ„์†Œ ํ‰๊ฐ€ ๋ฐ ๊ด€๋ฆฌ์˜ ์ค‘์š”์„ฑ ์—ญ์‹œ ๊ฐ•์กฐ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์€ ๋„์‹œํ™” ๊ณผ์ •์—์„œ ์ˆ˜๋ฐ˜๋˜๋Š” ์„ธ ์ข…๋ฅ˜์˜ ํ† ์–‘ ํ™˜๊ฒฝ ๋ณ€ํ™” ์š”์ธ: 1) ์ง€ํ•˜ ๊ฐœ๋ฐœ, 2) ๋…น์ง€ ํŒŒํŽธํ™”, 3) ๋„์‹œ ํŒฝ์ฐฝ์ด ํ† ์–‘ ํƒ„์†Œ ๋ถ„ํฌ์— ์–ด๋–ค ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”์ง€ ์ •๋Ÿ‰์ ์œผ๋กœ ํ‰๊ฐ€ํ•จ๊ณผ ๋™์‹œ์— ํšจ์œจ์  ๋„์‹œ ํ† ์–‘ ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•œ ๋ถ„๊ด‘ ๊ธฐ๋ฐ˜ ํ† ์–‘ ํƒ„์†Œ ์˜ˆ์ธก ์‹œ์Šคํ…œ์˜ ์ž ์žฌ๋ ฅ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋„์‹œํ™”์— ๋”ฐ๋ฅธ ํ† ์ง€ ํ”ผ๋ณต ๋ณ€ํ™”๋Š” ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ ๋ถ„ํฌ์˜ ์ˆ˜ํ‰ ๋ฐ ์ˆ˜์ง์  ๊ต๋ž€์„ ๋™๋ฐ˜ํ•œ๋‹ค. Chapter 2 ์—์„œ๋Š” ๋„์‹œ ์ง€ํ•˜ ๊ฐœ๋ฐœ์ด ์•ผ๊ธฐํ•˜๋Š” ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ ๋ถ„ํฌ์˜ ์ˆ˜์ง ๊ต๋ž€์„ ๋ถˆํˆฌ์ˆ˜์ธต ๋ฐ ๋„์‹œ ๋…น์ง€๋Œ€์—์„œ 5 m ๊นŠ์ด๊นŒ์ง€ ๋น„๊ต ํ‰๊ฐ€ํ•˜์˜€๋‹ค. Chapter 3 ์—์„œ๋Š” ๋„์‹œ ๊ฐœ๋ฐœ๋กœ ํŒŒํŽธํ™”๋œ ๋…น์ง€ ์ถ•์„ ์„ ์ •ํ•˜์—ฌ, ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์˜ ๊ฐ€์žฅ์ž๋ฆฌ ํšจ๊ณผ๋ฅผ ๋„์‹œ ๋…น์ง€ ๋ฐ ๋„์‹œ ์‚ฐ๋ฆผ ๋ถ€์ง€์—์„œ ๋น„๊ต ๋ถ„์„ํ•˜์˜€๋‹ค. Chapter 4 ์—์„œ๋Š” ํŒฝ์ฐฝํ•˜๋Š” ๋„์‹œ ์† ํšจ์œจ์  ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ ํ‰๊ฐ€ ๋ฐ ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•ด, ์ดˆ๋ถ„๊ด‘ ๋ฐ˜์‚ฌ์ •๋ณด๋ฅผ ํ™œ์šฉํ•œ ํ† ์–‘ ํƒ„์†Œ ์˜ˆ์ธก ๋ชจ๋ธ์„ ๊ฐœ๋ฐœ ๋ฐ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋„์‹œ ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰ ํ‰๊ฐ€๋Š” ๋„์‹œ ๋…น์ง€ ํ‘œํ† ์˜ ํƒ„์†Œ ์ €์žฅ๋Šฅ์— ์ง‘์ค‘๋˜์–ด ์™”์œผ๋ฉฐ, ๋„์‹œ๋ฅผ ๋Œ€ํ‘œํ•˜๋Š” ํ† ์ง€ ํ”ผ๋ณต ์ค‘ ํ•˜๋‚˜์ธ ๋ถˆํˆฌ์ˆ˜์ธต ์•„๋ž˜ ํ† ์–‘ ํƒ„์†Œ ํ‰๊ฐ€๋Š” ๊ธฐ์กด ๋„์‹œ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„์ ์œผ๋กœ ์ธ์‹๋˜์–ด์™”๋‹ค. ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์˜ Chapter 2์—์„œ๋Š” ์„œ์šธ ๊ฐ•๋‚จ์˜ ์•„ํŒŒํŠธ ๋‹จ์ง€๋“ค์„ ๋Œ€์ƒ์œผ๋กœ, ์กฐ๊ฒฝ์ง€ ๋ฐ ๋ถˆํˆฌ์ˆ˜์ธต ์•„๋ž˜์˜ ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์„ 5 m ๊นŠ์ด๊นŒ์ง€ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ๋„์‹œ ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์„ 5 m ๊นŠ์ด๋กœ ํ‰๊ฐ€ํ•œ ๊ฒฐ๊ณผ, ๋ถˆํˆฌ์ˆ˜์ธต(16.9 ยฑ 1.9 kgC mโˆ’2)๊ณผ ์กฐ๊ฒฝ์ง€(22.3 ยฑ 2.2 kgC mโˆ’2)์—์„œ ํฐ ์ฐจ์ด๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์—†์—ˆ๋‹ค. 1 m ๊นŠ์ด ๋‚ด ํ† ์–‘ ํƒ„์†Œ๊ฐ€ 5 m ์ด ๊นŠ์ด์—์„œ ์ฐจ์ง€ํ•˜๋Š” ๋น„์ค‘ ์—ญ์‹œ ์กฐ๊ฒฝ์ง€์—์„œ 34%, ๋ถˆํˆฌ์ˆ˜์ธต ๋ถ€์ง€์—์„œ 16%์ธ ๊ฒƒ์œผ๋กœ ํ‰๊ฐ€๋˜์—ˆ๋‹ค. ๋„์‹œ ์‹ฌํ† ์ธต์— ์ถ•์ ๋œ ์ƒ๋‹น๋Ÿ‰์˜ ํ† ์–‘ ํƒ„์†Œ ๊ธฐ์›์€ ์•ˆ์ •์„ฑ ํƒ„์†Œ-์งˆ์†Œ ๋™์œ„์›์†Œ(ฮด13C, ฮด15N) ๋ถ„์„ ๊ฒฐ๊ณผ, ๊ณผ๊ฑฐ ๋Œ€์ƒ์ง€์˜ ์ฃผ๋œ ์ธ๊ฐ„ํ™œ๋™์ธ ๋†๊ฒฝ ํ™œ๋™์˜ ์˜ํ–ฅ์œผ๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค. ์ฆ‰, ์•„ํŒŒํŠธ ๊ฐœ๋ฐœ ๋ถ€์ง€ ๋‚ด ๊ณผ๊ฑฐ ๋†๊ฒฝ์ง€ ํ† ์–‘์ด ์„ฑํ† ์™€ ๊ฐ™์€ ์ธ์œ„์  ๊ต๋ž€์œผ๋กœ 2-3 m ๊นŠ์ด์— ์žฅ๊ธฐ๊ฐ„ ์ €์žฅ๋˜์–ด ๋ฌธํ™”์ ์ธต์œผ๋กœ ์˜ค๋Š˜๋‚  ์กด์žฌํ•œ ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ๊ธฐ์กด ๋„์‹œ ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์ด ๊ณผ์†Œํ‰๊ฐ€๋œ ๊ฐ€๋Šฅ์„ฑ๊ณผ ๋”๋ถˆ์–ด ๋„์‹œ ๊ฐœ๋ฐœ ์—ญ์‚ฌ์™€ ์ˆ˜์ง์  ํ† ์–‘ ๊ต๋ž€์ด ๋„์‹œ ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Šฅ์— ์ค‘์š”ํ•œ ์š”์†Œ์ž„์„ ์‹œ์‚ฌํ•œ๋‹ค. ํ•œ์ •๋œ ํ† ์ง€ ํ™œ์šฉ์„ ๊ทน๋Œ€ํ™”ํ•˜๊ธฐ ์œ„ํ•œ ์ง€ํ•˜ ๊ฐœ๋ฐœ์ด ๊ฐ€์†ํ™”๋˜๋Š” ํ˜„๋Œ€ ๋„์‹œ์—์„œ ๋ฐ˜์ถœ๋œ ์‹ฌํ†  ๋‚ด ๋†’์€ ์œ ๊ธฐ ํƒ„์†Œ๋Š” ๊ธฐํ›„ ๋ณ€ํ™” ์† ํƒ„์†Œ ์ค‘๋ฆฝ ์ •์ฑ…์„ ์œ„ํ•œ ํƒ„์†Œ ์ž์› ๋ฐ ์กฐ๊ฒฝ ์†Œ์žฌ๋กœ ์žฌ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋„์‹œํ™”์— ๋”ฐ๋ฅธ ๋…น์ง€ ํŒŒํŽธํ™”๋Š” ๋…น์ง€ ๊ฐ€์žฅ์ž๋ฆฌ ๋ฉด์ ์„ ๊ธ‰๊ฒฉํžˆ ์ฆ๊ฐ€์‹œํ‚จ๋‹ค. ๋…น์ง€ ๊ฐ€์žฅ์ž๋ฆฌ์™€ ์ค‘์‹ฌ๋ถ€ ์‚ฌ์ด ํ™˜๊ฒฝ ์กฐ๊ฑด์˜ ์ฐจ์ด๋Š” ์ƒ๋ฌผ์ , ๋น„์ƒ๋ฌผ์  ๊ฐ€์žฅ์ž๋ฆฌ ํšจ๊ณผ๋ฅผ ๋™๋ฐ˜ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ ๋ถ„ํฌ์˜ ๊ฐ€์žฅ์ž๋ฆฌ ํšจ๊ณผ๋Š” ๊ทธ ์›์ธ๊ณผ ๊ทœ๋ชจ ์ธก๋ฉด์—์„œ ์‚ฌ๋ก€ ์—ฐ๊ตฌ๊ฐ€ ๋ถ€์กฑํ•˜๋‹ค. Chapter 3์—์„œ๋Š” ํŒŒํŽธํ™”๋œ ๋„์‹œ ๋…น์ง€ ์ถ•์„ ๋Œ€์ƒ์ง€๋กœ, ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์˜ ๊ฐ€์žฅ์ž๋ฆฌ ํšจ๊ณผ๋ฅผ ๋„์‹œ ์ˆฒ๊ณผ ์‚ฐ๋ฆผ์ง€์—ญ์—์„œ ๋น„๊ต ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์‚ฐ๋ฆผ ๊ฐ€์žฅ์ž๋ฆฌ๋กœ๋ถ€ํ„ฐ 20 m ๊ฑฐ๋ฆฌ๋‚ด ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰(1.86 kgC m-2)์€ ์‚ฐ๋ฆผ ์ค‘์‹ฌ๋ถ€(10.47 kgC m-2)์™€ ๋น„๊ตํ•˜์—ฌ 80% ๋‚ฎ์€ ๊ฒƒ์œผ๋กœ ํ‰๊ฐ€๋˜์—ˆ๋‹ค. ์‚ฐ๋ฆผ ๊ฐ€์žฅ์ž๋ฆฌ์˜ ๋‚ฎ์€ ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์€ ํ† ์–‘ ํƒ„์†Œ ๊ธฐ์›์ธ ์‹์ƒ ๋ฏธ์„ธ ๋ฟŒ๋ฆฌ ํ•จ๋Ÿ‰(R2 > 0.77), ์—ฐ๊ฐ„ ๋‚™์—ฝ๋Ÿ‰(R2 > 0.94), ์—ฝ๋ฉด์  ์ง€์ˆ˜(R2 > 0.92)์™€ ๊ฐ™์€ ์ƒ๋ฌผ์  ์š”์†Œ ๊ฐ์†Œ๋กœ ์„ค๋ช…๋˜์—ˆ๋‹ค. ๋ฐ˜๋ฉด ๋„์‹œ ์ˆฒ์˜ ๊ฒฝ์šฐ, ๊ฐ€์žฅ์ž๋ฆฌ ์ธ์ ‘ ์ด์งˆ์  ์ธ๊ฐ„ํ™œ๋™์ด ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์— ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ฒƒ์œผ๋กœ ๋ถ„์„๋˜์—ˆ๋‹ค. ๊ทธ ์˜ˆ๋กœ, ๋„์‹œ ์ˆฒ์˜ ๋™์ชฝ(4.74 kgC m-2) ๋ฐ ์„œ์ชฝ(2.9 kgC m-2) ๊ฐ€์žฅ์ž๋ฆฌ ๋…น์ง€๋Š” ๊ฐ€์žฅ์ž๋ฆฌ ๊ตฌ์กฐ์™€ ๋‚™์—ฝ๊ด€๋ฆฌ์˜ ์ฐจ์ด๋กœ, ๋น„๋ก ๋™์ผํ•œ ์‹œ๊ธฐ์— ํŒŒํŽธํ™”๋˜์—ˆ๋”๋ผ๋„, ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์˜ ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. ๋˜ํ•œ, ์‹์ƒ ์ƒ์œก ๊ธฐ๊ฐ„ ๋‚ด ํ‘œํ† ์˜ ์˜จ๋„(โˆ†TS > 2.8โ„ƒ)์™€ ์Šต๋„(โˆ†VWC > 0.05 m3 m-3) ์—ญ์‹œ ๋„์‹œ ๋…น์ง€์˜ ๋™์ชฝ-์„œ์ชฝ ๊ฐ€์žฅ์ž๋ฆฌ์—์„œ ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๋ฅผ ๋ณด์˜€๋‹ค. ์ด๋Š” ๋„์‹œ ๊ฐ€์žฅ์ž๋ฆฌ ๋…น์ง€๋Š” ์ƒ๋ฌผ์  ์š”์†Œ ๋ฟ ์•„๋‹ˆ๋ผ ๋งˆ์ฃผํ•œ ์ธ์ ‘ ํ† ์ง€ ํ”ผ๋ณต์˜ ์œ ํ˜•๊ณผ ์ธ๊ฐ„ ํ™œ๋™์ด ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์˜ ์ด์งˆ์  ๊ฐ€์žฅ์ž๋ฆฌ ํšจ๊ณผ๋ฅผ ์œ ๋„ํ•  ์ˆ˜ ์žˆ์Œ์„ ์˜๋ฏธํ•œ๋‹ค. ๋”๋ถˆ์–ด, ๋‚™์—ฝ ์กด์น˜์™€ ๊ฐ™์€ ํ† ์–‘ ํƒ„์†Œ์› ๊ด€๋ฆฌ๋ฅผ ํ†ตํ•ด, ๋‚ฎ๊ฒŒ ํ‰๊ฐ€๋œ ๊ฐ€์žฅ์ž๋ฆฌ ๋…น์ง€๋‚ด ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์ด ํ–ฅํ›„ ๊ฐœ์„ ์˜ ์—ฌ์ง€๊ฐ€ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋„์‹œ ํ† ์–‘์˜ ์œ ๊ธฐ ํƒ„์†Œ๋Š” ๋‹ค์–‘ํ•œ ์ธ๊ฐ„ํ™œ๋™๊ณผ ์ด์งˆ์  ๋„์‹œ ๋ชจ์งˆ์—์„œ ๊ธฐ์›ํ•œ๋‹ค. ๋„์‹œ ํ† ์–‘ ๊ด€๋ฆฌ ์ธก๋ฉด์—์„œ ์œ ๊ธฐ ํƒ„์†Œ ์ €์žฅ ๋ฐ ๊ธฐ์› ํ‰๊ฐ€๋Š” ๊ธฐํ›„ ๋ณ€ํ™” ์† ๊ทธ ์ค‘์š”์„ฑ์ด ๊ฐ•์กฐ๋˜๋Š” ๋ฐ˜๋ฉด, ๊ธฐ์กด ํ™”ํ•™์  ๋ถ„์„๋ฒ•์€ ๋น„์šฉ๊ณผ ์‹œ๊ฐ„ ์ธก๋ฉด์—์„œ ๊ฐ€์†๋˜๋Š” ๋„์‹œ ํŒฝ์ฐฝ์— ๋Œ€์ฒ˜ํ•˜๊ธฐ ์–ด๋ ต๋‹ค๋Š” ํ•œ๊ณ„์ ์„ ๊ฐ–๋Š”๋‹ค. Chapter 4 ์—์„œ๋Š” ๋ถ„๊ด‘๊ณ„๋กœ ์ทจ๋“ํ•œ ๋„์‹œ ํ† ์–‘ ์ดˆ๋ถ„๊ด‘ ๋ฐ˜์‚ฌ ์ •๋ณด(350-2,500 nm)๋ฅผ ํ™œ์šฉํ•˜์—ฌ, ๋„์‹œ ๊ณต์›๋‚ด ์—ฌ์„ฏ ์ข…๋ฅ˜ ํ† ์ง€ ํ”ผ๋ณต(ํ˜ผํšจ๋ฆผ, ์นจ์—ฝ์ˆ˜๋ฆผ, ํ™œ์—ฝ์ˆ˜๋ฆผ, ์ž”๋””, ์Šต์ง€, ๋‚˜์ง€) ์กฐ๊ฑด์˜ ํ† ์–‘ ์œ ๊ธฐ ํƒ„์†Œ ํ•จ๋Ÿ‰์„ ์˜ˆ์ธกํ•˜๋Š” ๋ถ„๊ด‘๊ธฐ๋ฐ˜ ํ† ์–‘ ํƒ„์†Œ ์˜ˆ์ธก ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ด์งˆ์  ๋„์‹œ ํ”ผ๋ณต ๋‚ด ํ† ์–‘ ํƒ„์†Œ ๊ธฐ์› ์ถ”์ ์„ ์œ„ํ•ด, ์•ˆ์ •์„ฑ ํƒ„์†Œ ๋™์œ„์›์†Œ(ฮด13C) ์—ญ์‹œ ์—ฌ์„ฏ ์ข…๋ฅ˜์˜ ํ† ์ง€ ํ”ผ๋ณต ์กฐ๊ฑด์—์„œ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ด 136๊ฐœ์˜ ํ‘œํ†  ์ƒ˜ํ”Œ์„ ๋ฐ”ํƒ•์œผ๋กœ LOOCV ๊ต์ฐจ๊ฒ€์ฆ(leave-one-out cross-validation)์„ ์‹ค์‹œํ•˜์˜€์œผ๋ฉฐ, PLS(partial least squares) ํšŒ๊ท€๋ถ„์„๋ฒ•์„ ํ†ตํ•ด ๊ธฐ์กด ํ™”ํ•™์  ๋ถ„์„๋ฒ•๊ณผ ๋ถ„๊ด‘๊ธฐ๋ฐ˜ ํ‰๊ฐ€๋ฒ•์„ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ, ์œ ๊ธฐ ํƒ„์†Œ(Rยฒ = 0.83; RMSE = 1.6%) ์ถ”์ •์—์„œ ์œ ์˜๋ฏธํ•œ ์˜ˆ์ธก ์ •ํ™•๋„๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๋ถ„๊ด‘ ๊ธฐ๋ฐ˜ ํ† ์–‘ ์œ ๊ธฐ ํƒ„์†Œ ์˜ˆ์ธก์˜ ์ค‘์š” ํŒŒ์žฅ๋Œ€(1 > VIP; variable importance in projection)๋Š” ๊ฐ€์‹œ๊ด‘์„ (400~700 nm) ๋ฐ ๊ทผ์ ์™ธ์„ (2200~2300 nm) ์˜์—ญ ์ผ๋ถ€๊ฐ€ ์ฃผ์š”ํ•จ์ด ํ‰๊ฐ€๋˜์—ˆ๋‹ค. ์ด์™€ ๊ฐ™์€ ๊ฒฐ๊ณผ๋Š” ๋ถ„๊ด‘ ๋ฐ˜์‚ฌ์ •๋ณด๋ฅผ ํ†ตํ•œ ๋„์‹œ ํ† ์–‘ ํƒ„์†Œ ์˜ˆ์ธก์ด ๋†’์€ ์ž ์žฌ๋ ฅ์„ ๊ฐ–๊ณ  ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋„์‹œ ๊ณต์›๋‚ด ์—ฌ์„ฏ ํ† ์ง€ ํ”ผ๋ณต ์ค‘, ์Šต์ง€ ํ† ์–‘ ํƒ„์†Œ ์ถ”์ •์น˜๊ฐ€ ํ™”ํ•™์  ๋ถ„์„๋ฒ• ๋Œ€๋น„ ์ €ํ‰๊ฐ€ ๋˜์—ˆ์œผ๋ฉฐ, ์ด๋Š” ์Šต์ง€ ํ† ์–‘์˜ ๋„“์€ ํƒ„์†Œ ํ•จ๋Ÿ‰ ๋ฒ”์œ„ ๋ฐ ์ˆ˜์ƒํƒœ๊ณ„ ๊ธฐ์› ์ด์งˆ์  ํƒ„์†Œ์›์˜ ์˜ํ–ฅ์œผ๋กœ ์ถ”์ •๋œ๋‹ค. ์ด๋Ÿฌํ•œ ํ•œ๊ณ„์ ์€ ์Šต์ง€์™€ ๊ฐ™์€ ํŠน์ • ํ† ์ง€ ํ”ผ๋ณต์˜ ๊ฐœ๋ณ„ ๋ถ„๊ด‘ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ๊ตฌ์ถ• ๋ฐ ์ถ”์ • ๋ชจ๋ธ ๊ฐœ๋ฐœ๋กœ ๋ณด์™„๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ ๋„์ถœ๋œ ๋„์‹œ ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ ํŠน์„ฑ์˜ ๊ณตํ†ต ํ•จ์˜๋Š” 1) ๊ณผ๊ฑฐ ๋ฐ ์˜ค๋Š˜๋‚ ์˜ ์ธ๊ฐ„ํ™œ๋™์ด ๋„์‹œ ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰ ๋ณ€ํ™”๋ฅผ ์ฃผ๋„ํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ, 2) ๋„์‹œ ์ƒํƒœ๊ณ„ ํŠน์œ ์˜ ์ˆ˜ํ‰ ๋ฐ ์ˆ˜์ง์  ํ† ์–‘ ํƒ„์†Œ ๋ถ„ํฌ ํ˜•ํƒœ๊ฐ€ ์กด์žฌํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ์ด๋Ÿฌํ•œ ๋„์‹œ ํŠน์œ ์˜ ํ† ์–‘ ํƒ„์†Œ ๋ถ„ํฌ๋Š” 3) ๋ถ„๊ด‘ ๊ธฐ๋ฐ˜ ์˜ˆ์ธก ์‹œ์Šคํ…œ์„ ํ†ตํ•ด ๋„์‹œ ๊ทœ๋ชจ๋กœ ํ‰๊ฐ€๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋Š” ํƒ„์†Œ์ค‘๋ฆฝ ๋„์‹œ ์ •์ฑ… ์ˆ˜๋ฆฝ์˜ ๊ธฐ์ดˆ์ž๋ฃŒ๋กœ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์„ ๊ฒƒ์ด๋‹ค.Chapter 1. Introduction 2 1. Background 2 1.1. Urbanization and the cultural layers of SOC stocks 2 1.2. Edge effects on SOC stocks in fragmented urban landscpae 3 1.3. Landscape-scale assessments of SOC via hyperspectral data 3 2. Purpose 4 Chapter 2. High soil organic carbon stocks under impervious surfaces contributed by urban deep cultural layers 8 1. Introduction 8 2. Methods and materials 9 2.1 Site description 9 2.2 Data collection 11 2.3. Data processing 11 2.4. Statistical analyses 12 3. Results 13 3.1. Vertical dsitributions of soil bulk density, SOC concentration and fine roots 13 3.2. Vertical distributions of SOC stocks 15 3.3. Depth profiles of soil carbon and nitrogen isotopes 16 4. Discussion 17 4.1 What controls vertical heterogeneity of urban SOC stocks? 17 4.2 How does the impervious surfaces affect the urban SOC stocks? 20 4.3 How can deep SOC data be used for sustainable urban development? 22 5. Conclusions 23 Chapter 3. The magnitude and causes of edge effects on soil organic carbon stocks along an urban-rural gradient 24 1. Introduction 24 2. Methods and Materials 27 2.1. Site description 27 2.2. Data collection 29 2.3. Data processing 31 2.4. Statistical analyses 32 3. Results 33 3.1. Soil organic carbon (SOC) stocks along an urban-rural gradientโ€ฆ 33 3.2. The spatiotemporal variation of abiotic factors across urban and rural forests 34 3.3. Relationships between spatial variation of biotic factors and soil organic carbon stocks 36 4. Discussion 39 4.1. Magnitude and causes of edge effects on SOC stocks across urban and rural forests 39 4.2. Effects of anthropogenic activities on urban SOC stocks 40 5. Conclusion 42 Chapter 4. Spatial variations of soil organic carbon under diverse land cover types: pplication of laboratory-based hyperspectral reflectance spectroscopy 43 1. Introduction 43 2. Materials and Methods 44 2.1. Site description and Data collection 44 2.2. Data processing 45 2.3. Statistical analyses 46 3. Results and discussion 47 3.1. Spatial variations of SOC concentration and ฮด13C among different land cover types 47 3.2. Assessments of urban SOC concentration using reflectance spectroscopy 49 4. Conclusions 50 References 52 Chapter 5. Conclusion 64 Abstract in Korean 66๋ฐ•

    CEA ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค๋ฅผ ํ™œ์šฉํ•œ ํ‰ํ˜• ์œ ๋™ CFD ์ฝ”๋“œ ๊ฐœ๋ฐœ ๋ฐ SiC ์ฝ”ํŒ… ๋…ธ์ฆ์˜ ์‚ญ๋งˆ๋Ÿ‰ ์˜ˆ์ธก

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€, 2017. 8. ๊น€๊ทœํ™.This study was conducted with the aim such as development of ablation model of SiC due to combustion gas inside nozzle, prediction on ablation of SiC coating nozzle, development of ablation analysis code. For this purpose, equilibrium flow analysis code using CEA database and a structural temperature distribution analysis code had been developed and the reliability of the code had been secured by carrying out the validation process. These codes were coupled by transferring the wall heat flux and wall temperature as boundary conditions to each other, and the flow/structure integrated analysis code was developed. In order to develop the ablation model, the active and passive oxidation characteristics of SiC materials was investigated. The shear erosion model was developed to simulate mechanical ablation and the melting model was developed to simulate thermochemical ablation. The amount of ablation over time was predicted and compared with the measured amount of erosion depth of nozzle test delivered from the Agency for Defense Development.1. INTRODUCTION 1 2. EQUILIBRIUM FLOW CODE DEVELOPMENT 3 2.1. Flow type of a chemically reacting gas 3 2.2. Governing equations for equilibrium flow 4 2.3. Non-dimensionalization of governing equations 6 2.4. Numerical method 8 2.4.1. AUSMPW+ 8 2.4.2. Time integration method : LU-SGS 10 2.4.3. Time integration method : Dual time stepping 12 2.5. Equilibrium flow analysis using CEA database 13 2.5.1. CEA (Chemical Equilibrium with Applications) 14 2.5.2. Free energy minimization method 16 2.5.3. Thermodynamic properties and transfer coefficient of CEA 18 2.5.3.1. Thermodynamic properties of CEA 18 2.5.3.2. Transport coefficients of CEA 18 2.5.4. Equivalent gamma using CEA database 21 2.5.5. Utilization of CEA database 23 2.5.6. CEA output verification 27 2.5.6.1. Monotonic function 27 2.5.6.2. Comparison of CEA GUI program output values and literature values 28 2.5.6.3. Comparison of CEA GUI program output values and output values of bilinear interpolation function using table 29 2.5.7. Precise verification of output values of bilinear interpolation function using table and table significant digits improvement 30 2.5.8. Generalization of Equilibrium Flow Codes 35 3. FLOW/STRUCTURE INTEGRATED ANALYSIS CODE DEVELOPMENT 36 3.1. Flow/structure integrated analysis code outline 36 3.2. Algorithm of flow and structure analysis code coupling 37 3.3. Structural analysis governing equation 38 3.4. Boundary condition of Structure analysis code 42 3.4.1. External boundary surface in contact with air 42 3.4.2. Inner boundary surface 42 4. VALIDATION OF FLOW/STRUCTURE INTEGRATED ANALYSIS CODE 43 4.1. Validation of flow analysis code 43 4.1.1. NASA CDV Nozzle study 44 4.1.2. Validation of hypersonic flow on flat plate 48 4.2. Validation of structure analysis code 51 5. ABLATION MODEL 55 5.1. SiC oxidation 55 5.1.1. Passive oxidation 55 5.1.2. Active oxidation 56 5.1.3. Active to Passive Transition 57 5.2. Mechanical ablation model 61 5.3. Thermochemical ablation model 61 5.3.1. Equilibrium gas kinetics ablation model 61 5.3.2. Melting erosion model 67 6. ANALYSIS OF ADDs NOZZLE ABLATION TEST RESULTS 69 7. PREDICTION OF ABLATION USING INTEGRATED ANALYSIS CODE 71 7.1. Grid generation for flow and structure analysis 71 7.1.1. Flow analysis grid 71 7.1.2. Structure analysis grid and input material properties by position 72 7.2. Structure material properties correction 75 7.3. Nozzle wall heat flux correction 77 7.4. Physical time step setup 83 7.5. Results of flow/structure integrated analysis code and discussion 86 7.5.1. Results of equilibrium flow field analysis 86 7.5.2. Results of structure temperature distribution analysis 89 7.5.3. Wall heat flux, shear stress, and wall temperature distribution near the nozzle throat 90 7.5.4. Prediction of the amount of SiC coating ablation 92 7.5.4.1. Shear erosion prediction 92 7.5.4.2. Melting erosion prediction 93 7.5.4.3. Total erosion and comparison with measured values 98 8. CONCLUSION 102 9. REFERENCES 104 ๊ตญ๋ฌธ ์ดˆ๋ก 108Maste

    ํ† ์ง€ ์ด์šฉ ๋ฐ ํ”ผ๋ณต ๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๋„์‹œ ๊ณต์› ๋‚ด ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์˜ ์‹œ๊ณต๊ฐ„ ๋ณ€์ด ๋ถ„์„: ์„œ์šธ ์ˆฒ ๊ณต์›์„ ๋Œ€์ƒ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ์ƒํƒœ์กฐ๊ฒฝยท์ง€์—ญ์‹œ์Šคํ…œ๊ณตํ•™๋ถ€(์ƒํƒœ์กฐ๊ฒฝํ•™), 2019. 2. ๋ฅ˜์˜๋ ฌ.Urban parks offer valuable ecosystem services to citizens and they have long been recognized for their recreational servicehowever, less attention has been paid to their carbon sequestration value. Here, we report on soil organic carbon (SOC) stocks in an urban park, Seoul Forest Park, which was built in 2004. We had two objectives: (1) to estimate SOC stocks (to a depth of 1 m) in different land-cover types (wetland, forest, lawn, and bare soil) and (2) quantify the change in the SOC concentration in topsoil in different land-use types over a 10 year period (2003โ€“2013). We found a tenfold difference in SOC stocks across the different land-cover types within the park. Wetland soils had the highest stocks of SOC (13.99 ยฑ 1.05 kg mโˆ’2), followed by forest, lawn, and bare soils. We found that a cultural layer that preserved previous land use history located deep in the soil profile substantially increased SOC stocks in the wetland. SOC concentrations in the topsoil were approximately three times higher in 2013 than in 2003 (256 ยฑ 130%). The normalized difference vegetation index (NDVI) derived from MODIS and Landsat satellite images revealed that land-use history, expansion of plant areas and growth of plants could explain the increase in SOC concentrations in topsoil over the 10 year period. These findings imply that urban park soils could act as a carbon sink, and understanding the land-use history and the choice of land-cover types in park planning can substantially influence the carbon budget of urban parks.๋ณธ ์—ฐ๊ตฌ๋Š” ์‹œ๋ฏผ๋“ค์„ ์œ„ํ•œ ๋„์‹œ ๊ณต์›์˜ ์ƒํƒœ์  ๊ฐ€์น˜์™€ ๊ธฐ๋Šฅ์˜ ์ค‘์š”์„ฑ์ด ๊ฐ•์กฐ๋˜๋Š” ํ˜„๋Œ€ ์‚ฌํšŒ์—์„œ, ์„œ์šธ์‹œ์˜ ๋Œ€ํ‘œ์  ๋Œ€ํ˜• ๊ณต์›์ธ ์„œ์šธ ์ˆฒ ๊ณต์›์„ ๋Œ€์ƒ์œผ๋กœ ํ† ์–‘ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์˜ ์‹œ๊ณต๊ฐ„์  ๋ณ€ํ™”๋ฅผ ํƒ๊ตฌํ•˜์˜€๋‹ค. ์šฐ์„ , ์„œ์šธ ์ˆฒ ๊ณต์›์ด ์กฐ์„ฑ๋œ 2003๋…„๊ณผ 10๋…„ ํ›„์ธ 2013๋…„์˜ ํ† ์–‘ ๋‚ด ์œ ๊ธฐ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์˜ ๋ณ€ํ™”๋ฅผ ์ •๋Ÿ‰ํ™”ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ 10๋…„ ์‚ฌ์ด 3๋ฐฐ ์ด์ƒ์˜ ํ† ์–‘ ํƒ„์†Œ ์ฆ๊ฐ€๊ฐ€ ํ‘œํ† ์—์„œ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ํ† ์–‘ ํƒ„์†Œ ์ฆ๊ฐ€์˜ ์›์ธ์„ ํŒŒ์•…ํ•˜๊ณ ์ž ๊ณผ๊ฑฐ ํ† ์ง€ ์ด์šฉ ๋ชฉ์ ๊ณผ ์ฆ๊ฐ€ํ•œ ์‹์ƒ์„ MODIS์™€ Landsat ์œ„์„ฑํƒ์‚ฌ ๋ฐ์ดํ„ฐ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋”๋ถˆ์–ด, ์˜ค๋Š˜๋‚ ์˜ ์„œ์šธ ์ˆฒ ๊ณต์›์„ 6 ์ข…๋ฅ˜์˜ ๋Œ€ํ‘œ ์‹์ƒ ํƒ€์ž…(์นจ์—ผ์ˆ˜๋ฆผ, ํ™œ์—ฝ์ˆ˜๋ฆผ, ํ˜ผํšจ๋ฆผ, ์ž”๋””, ์Šต์ง€, ๋‚˜์ง€)์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜์—ฌ, ์ด 1 m ๊นŠ์ด ๋‚ด 4๊ฐœ์˜ ํ† ์–‘์ธต(0-0.1, 0.1-0.3, 0.3-0.7, 0.7-1.0 m)์„ ๋Œ€์ƒ์œผ๋กœ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์˜ ์ˆ˜์ง์  ๋ถ„ํฌ์˜ ์ด์งˆ์„ฑ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ฐ€์žฅ ๋†’์€ ์œ ๊ธฐ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์€ ์Šต์ง€์—์„œ ๋‚˜ํƒ€๋‚ฌ์œผ๋ฉฐ(13.99ยฑ1.05 kg mโˆ’2), ์ˆฒ ์ง€์—ญ, ์ž”๋””, ๋‚˜์ง€ ์ˆœ์œผ๋กœ ์ •๋Ÿ‰์  ์ฐจ์ด๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์ฐจ์ด๋Š” ์‹์ƒ ํƒ€์ž…์— ๋”ฐ๋ฅธ ๋ฟŒ๋ฆฌ์˜ ์˜ํ–ฅ์œผ๋กœ ์ฃผ๋กœ ํ‘œํ† ์—์„œ ๋ฐœ์ƒ๋˜์—ˆ์ง€๋งŒ, ๊ทธ ์ค‘ ์Šต์ง€์˜ ๋†’์€ ํƒ„์†Œ ์ €์žฅ๋Ÿ‰์€ ๊ณผ๊ฑฐ ๊ณต์›์ด ์กฐ์„ฑ๋  ๋•Œ ๋™๋ฐ˜๋œ ์ธ์œ„์  ๊ต๋ž€์— ๋”ฐ๋ฅธ ์‹ฌํ† ์˜ ๋ฌธํ™”์ ์ธต์ด ๊ธฐ์—ฌํ•œ ๊ฒƒ์œผ๋กœ๋„ ํŒŒ์•…๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์ €ํƒ„์†Œ ์‚ฌํšŒ์™€ ํƒ„์†Œ๊ฑฐ๋ž˜์ œ๋„์— ๋Œ€๋น„ํ•˜์—ฌ ๋„์‹œ ๊ณต์›์ด ํƒ„์†Œ๋ฅผ ์–ผ๋งˆ๋‚˜ ์–ด๋””์— ์ €์žฅํ•  ์ˆ˜ ์žˆ๋Š”์ง€๋ฅผ ์ •๋Ÿ‰์ ์œผ๋กœ ํ‰๊ฐ€ํ•œ ๊ฒƒ์— ์˜๋ฏธ๊ฐ€ ์žˆ์œผ๋ฉฐ, ํ–ฅํ›„ ์ถ”๊ฐ€ ๋ถ„์„์„ ํ†ตํ•ด ๋„์‹œ ๋…น์ง€ ๋‚ด ํƒ„์†Œ์˜ ์‹œ๊ณต๊ฐ„ ๋ถ„ํฌ๋ฅผ ํ‰๊ฐ€ํ•  ๊ธฐ์ดˆ ์ž๋ฃŒ๋กœ ์‚ฌ์šฉ๋  ๊ฒƒ์ด๋ผ ๊ธฐ๋Œ€๋œ๋‹ค.Contents Abstract i Contents ii List of tables iv List of figures iv 1. Introduction 1 2. Methods 4 2.1. Site description 4 2.2. Data collection 7 2.3. Data processing 8 2.4. Remote sensing data 9 2.5. Statistical analyses 10 3. Result 11 3.1. Soil organic carbon concentration 11 3.2. Soil bulk density 11 3.3. Soil organic carbon stocks in different land-cover types 12 3.4. Fine root mass density 14 3.5. Changes in the soil organic carbon concentrations in topsoil between 2003 and 2013 14 3.6. Temporal changes in vegetation activity and land-use types 16 4. Discussion 18 4.1. What controls spatial heterogeneity of SOC stocks among different land-cover types? 18 4.2. How does land-use history influence the temporal variations of the SOC concentrations? 22 5. Conclusion 30 References 31 Abstract (Korean) 37 Acknowledgements 38Maste
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