• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of Life Science
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• v.20 no.9
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• pp.1353-1357
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• 2010

Quantification of the ecosystem respiration is essential in understanding the carbon cycling of natural and disturbed landscapes. Soil respiration and some environmental factors which affect soil respiration were investigated in a Larix leptolepis plantation inKongju, Korea. Soil respiration was measured at midday of the $15^{th}$ and $30^{th}$ day of every month from May to December in a non-cutting area (Control) and a cutting area (Treatment) with IRGA Soil Respiration Analyzer. Throughout the study period, average soil temperature and water content were $23.3{\pm}0.5^{\circ}C$ and $27.76{\pm}7.12%$ for control, and $25.9{\pm}3.1^{\circ}C$ and $24.55{\pm}5.12%$ for treatment, respectively. There was a positive correlation ($R^2$=0.8905) between soil respiration and soil temperature in the study area. However, there was no significant correlation between soil respiration and soil moisture ($R^2$=0.4437). The seasonal soil respiration increased in the summer and decreased in the winter. In August, maximum soil respirations in the control and treatment areas were $0.82{\pm}0.13$ and $1.32{\pm}0.10$ $gCO_2{\cdot}^{-2}{\cdot}r^{-1}$, respectively. Total amounts of $CO_2$ evolution in the control and treatment areas from May to December in 2008 were 2,419.2 and 3,610.8 $CO_2g{\cdot}m^{-2}$, respectively. The amount of soil respiration in the treatment area was 49.3% greater than in the control. Increased soil respiration in the treatment area may be due to increased soil temperature, which drives increased microbial decomposition. According to our present investigation, forest cutting will increase the atmospheric $CO_2$ by increasing soil respiration.

• Korean Journal of Microbiology
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• v.51 no.2
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• pp.97-107
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• 2015

Wetlands constitute a transitional zone between terrestrial and aquatic ecosystems and have unique characteristics such as frequent inundation, inflow of nutrients from terrestrial ecosystems, presence of plants adapted to grow in water, and soil that is occasionally oxygen deficient due to saturation. These characteristics and the presence of vegetation determine physical and chemical properties that affect decomposition rates of organic matter (OM). Decomposition of OM is associated with activities of various extracellular enzymes (EE) produced by bacteria and fungi. Extracellular enzymes convert macromolecules to simple compounds such as labile organic carbon (C), nitrogen (N), phosphorus (P), and sulfur (S) that can be easily taken up by microbes and plants. Therefore, the enzymatic approach is helpful to understand the decomposition rates of OM and nutrient cycling in wetland soils. This paper reviews the physical and biogeochemical factors that regulate extracellular enzyme activities (EEa) in wetland soils, including those of ${\beta}$-glucosidase, ${\beta}$-N-acetylglucosaminidase, phosphatase, arylsulfatase, and phenol oxidase that decompose organic matter and release C, N, P, and S nutrients for microbial and plant growths. Effects of pH, water table, and particle size of OM on EEa were not significantly different among sites, whereas the influence of temperature on EEa varied depending on microbial acclimation to extreme temperatures. Addition of C, N, or P affected EEa differently depending on the nutrient state, C:N ratio, limiting factors, and types of enzymes of wetland soils. Substrate quality influenced EEa more significantly than did other factors. Also, drainage of wetland and increased temperature due to global climate change can stimulate phenol oxidase activity, and anthropogenic N deposition can enhance the hydrolytic EEa; these effects increase OM decomposition rates and emissions of $CO_2$ and $CH_4$ from wetland systems. The researches on the relationship between microbial structures and EE functions, and environmental factors controlling EEa can be helpful to manipulate wetland ecosystems for treating pollutants and to monitor wetland ecosystem services.

• Journal of Korean Society of Forest Science
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• v.102 no.2
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• pp.210-215
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• 2013

Litterfall production represents a major contribution of carbon and nutrient cycling in forest ecosystems. This study was carried out to determine the litterfall production in a broadleaved deciduous forest at the Mt. Keumsan Long Term Ecological Research (LTER) site, Southern Korea. Littefall was collected monthly or bimonthly from the site for 7 years from 2004 to2010. Leaf and reproductive (catkins) litters showed a seasonal variation, but litters of needle, branch, and barks were not changed across the seasons. Annual leaf litter of Quercus serrata and Carpinus laxiflora were significantly different (p<0.05) but that of C. cordata, Chamaecyparis obtusa, and Pinus thunbergii was not significantly changed for 7 years (p>0.05). Annual average litterfall production was 5,223 kg/ha, but annual variations were very large with minimum of 4,110 kg/ha/yr in 2004 and maximum of 6,002 kg/ha/yr in 2007. Total litterfall comprised of 2,323 kg/ha/yr in Q. serrata, 442 kg/ha/yr in C. laxiflora, 157 kg/ha/yr in C. cordata, 131 kg/ha/yr in Acer pseudosieboldianum, 390 kg/ha/yr in other deciduous tree species, 74 kg/ha/yr in P. thunbergii, 37 kg/ha/yr in C. obtusa, 672 kg/ha/yr in branches, 515 kg/ha/yr in miscellaneous, 448 kg/ha/yr in reproductive parts, and 54 kg/ha/yr in barks. respectively. The results indicate that litterfall production of the Mt. Keumsan LTER site was yearly fructurated with the positive linear relationship between leaf or total litterfall and annual mean temperature if no disturbance such as a typoon, and was lower than that of other Korean LTER sites.