• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.267-279
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• 2023

This research assessed the feasibility of using high-resolution aerial images and deep learning algorithms for estimating the land-use and land-cover areas at the Approach 3 level, as outlined by the Intergovernmental Panel on Climate Change. The results from different sampling densities of high-resolution (51 cm) aerial images were compared with the land-cover map, provided by the Ministry of Environment, and analyzed to estimate the accuracy of the land-use and land-cover areas. Transfer learning was applied to the VGG16 architecture for the deep learning model, and sampling densities of 4 × 4 km, 2 × 4 km, 2 × 2 km, 1 × 2 km, 1 × 1 km, 500 × 500 m, and 250 × 250 m were used for estimating and evaluating the areas. The overall accuracy and kappa coefficient of the deep learning model were 91.1% and 88.8%, respectively. The F-scores, except for the pasture category, were >90% for all categories, indicating superior accuracy of the model. Chi-square tests of the sampling densities showed no significant difference in the area ratios of the land-cover map provided by the Ministry of Environment among all sampling densities except for 4 × 4 km at a significance level of p = 0.1. As the sampling density increased, the standard error and relative efficiency decreased. The relative standard error decreased to ≤15% for all land-cover categories at 1 × 1 km sampling density. These results indicated that a sampling density more detailed than 1 x 1 km is appropriate for estimating land-cover area at the local level.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.802-811
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• 2023

Marine spatial planning (MSP) is a crucial element for rational allocation and sustainable use of marine areas. Particularly, Fishing Activity Protected Areas constitute essential zones accounting for 45.6% designated for sustainable fishing activities. However, the current assessment of these zones does not adequately consider future demands and potential values, necessitating appropriate evaluation methods and predictive tools for long-term planning. In this study, we selected key fish species (Scomber japonicus, Trichiurus lepturus, Engraulis japonicus, and Larimichthys polyactis) within the Fishing Activity Protected Area to predict their distribution and compare it with the current designated zones for evaluating the ability of the prediction tool. Employing the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report scenarios (SSP1-2.6 and SSP5-8.5), we used species distribution models (such as MaxEnt) to assess the movement and distribution changes of these species owing to future variations. The results indicated a 30-50% increase in the distribution area of S. japonicus, T. lepturus, and L. polyactis, whereas the distribution area of E. japonicus decreased by approximately 6-11%. Based on these results, a species richness map for the four key species was created. Within the marine spatial planning boundaries, the overlap between areas rated "high" in species richness and the Fishing Activity Protected Area was approximately 15%, increasing to 21% under the RCP 2.6 scenario and 34% under the RCP 8.5 scenario. These findings can serve as scientific evidence for future evaluations of use zones or changes in reserve areas. The current and predicted distributions of species owing to climate change can address the limitations of current use zone evaluations and contribute to the development of plans for sustainable and beneficial use of marine resources.

• Journal of Animal Science and Technology
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• v.55 no.1
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• pp.67-74
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• 2013

To know the emission amount of greenhouse gases from bedding materials of cowshed floor, the emission rates of methane ($CH_4$) and nitrous oxide ($N_2O$) gases from a simulated cowshed floor (SCF) with sawdust that manure loading rate into the bedding material could be accurately controlled were assessed in this study. The manure loading rates of Korean beef and Holstein dairy cattle into the SCF of $0.258m^2$ surface area with 10 to 15 cm height sawdust were $1.586kg/m^2/d$ and $3.588kg/m^2/d$, respectively, and those were calculated on the basis of "Standard model for sustainable livestock" and "Data for excretion amount of manure from livestock". All experiments were done in triplicates in three different seasons (May to July, Sep. to Nov., and Feb. to Apr.) using 12 SCFs. The effects of bedding material thickness on $CH_4$ and $N_2O$ emission from SCFs for both Korean beef cattle and Holstein dairy cattle were not statistically significant (p<0.05). Emission amount of $CH_4$ and $N_2O$ per square meter of SCF for Holstein dairy cattle was 7.5 and 1.2 times higher than that of Korean beef cattle, respectively. The yearly $CH_4$ amount per head was 17.7 times higher in Holstein dairy cattle, obtaining 130.4 g/head/year from SCF for Holstein dairy cattle and 7.4 g/head/year from SCF for Korean beef cattle, and $N_2O$ was also 3.8 times higher in Holstein dairy cattle (3,267 g/head/year in Korean beef cattle and 14,719 g/head/year in Holstein dairy cattle). However, the $N_2O$-N per loaded nitrogen into SCF was higher in Korean beef cattle, having 0.2148 and 0.1632 kg $N_2O$-N/kg N in Korean beef cattle and Holstein dairy cattle, respectively, and those values were 3.07 and 2.33 times higher than that of Intergovernmental Panel on Climate Change (IPCC) 2006 guideline (GL) (0.07 kg $N_2O$-N/kg N).