• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.340-350
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• 2015

This experiment was carried out to find out the mitigation of greenhouse gases (GHGs) emission and changes of soil carbon contents in the cropland. In order to minimize the soil disturbance, this study was conducted without crop cultivation at the pots treated with different biomass. Different biomass was buried in the soil for 12 months. Decomposition rates of expander rice hull, pig manure compost and carbonized rice hull were 18%, 11~11.5% and 0.5~1.2%, respectively. It was appeared that carbonized rice hull was slightly decomposed. No difference was shown between chemical fertilizer treatment plot and non-application plot. It was appeared that soil carbon content in the non chemical fertilizer application plot was high when compared to its chemical fertilizer. Its content at soil depth of 20 cm more decreased than the upper layer of soil. Accumulative emission of $CO_2$ with different treatments of biomass was highest of 829.0~876.6 g $CO_2m^{-2}$ in the application plot of PMC (Pig Manure Compost) regardless of chemical fertilizer treatment during 16 months of experiment. However, the emission for expander rice hull treatment plot was lowest of 672.3~808.1 g $CO_2m^{-2}$. For application plot of the carbonized rice hull, it was shown that non chemical fertilizer plot, 304.1 mg $N_2Om^{-2}$, was higher than the chemical fertilizer treatment, 271.6 mg $N_2Om^{-2}$. Greenhouse gas emissions in the PMC treatment were highest of 0.94 ton $CO_2eqha^{-1}yr^{-1}$. However, it was estimated to be the lowest in the expander rice hull treatment.

• Korean Journal of Agricultural Science
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• v.46 no.1
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• pp.205-217
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• 2019

The aim of this study was to identify land resources because food production and supply in North Korea have been at risk due to variations in its seasonal climate. More than three-fifths of the soils are locally derived from the weathering of granitic rocks or various kinds of schists developed from crystalline rocks. Well-developed reddish brown soils derived from limestone are found in the North Hwanghae province and in the southern part of the South Pyeongan province. Additionally, a narrow strip of similarly fertile land runs through the eastern seaboard of the Hamgyong and Kangwon Provinces. The loss of clay particles and organic matter are major causes of degradation in the soil physical and chemical properties in North Korea. 75% of the areas converted from forests became croplands, and 69% of the land converted to croplands came from forests. The net forest loss was quite small from the 1990s to the 2000s. However, deforestation in areas with a slightly lower elevation and gentler slope between 1997 and 2014 led to severe soil erosion resulting in a drastic change in the physical and chemical properties of the soil which influenced cropland stability and productivity. Therefore, the drastic changes in land cover as well as in the physical and chemical properties of the soil caused by various geographical features have seriously influenced the productivity of crops in North Korea.

• Journal of Climate Change Research
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• v.6 no.1
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• pp.33-40
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• 2015

Forests are to be recognized as an important carbon sink under the UNFCCC that consist of above- and below-biomass, dead organic matter (DOM) such as dead wood and litter, and soil organic matter (SOM). In order to asses for DOM and SOM, however, it is relevant to land-use change matrices over last 20 years for each land-use category. In this study, a land-use change matrix was produced and its uncertainty was assessed using a point sampling technique with permanent sample plots in national forest inventory at Chungbuk province. With point sampling estimated areas at 2012 year for each land-use category were significantly similar to the true areas by given six land-use categories. Relative standard error in terms of uncertainty of land-use change among land-use categories ranged in 4.3~44.4%, excluding the other land. Forest and cropland covered relatively large areas showed lower uncertainty compared to the other land-use categories. This result showed that selected permanent samples in the NFI are able to support for producing land-use change matrix at a national or province level. If the $6^{th}$ NFI data are fully collected, the uncertainty of estimated area should be improved.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.385-398
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• 2018

Although the importance of developing reliable and systematic GHG inventory has increased, the GIS/RS-based national scale LULUCF (Land Use, Land-Use Change and Forestry) sector analysis is insufficient in the context of the Paris Agreement. In this study, the change in $CO_2$ storage of forest land due to land use change is estimated using two GIS/RS methodologies, Sampling and Wall-to-Wall methods, from 2000 to 2010. Particularly, various imagery with sampling data and land cover maps are used for Sampling and Wall-to-Wall methods, respectively. This land use matrix of these methodologies and the national cadastral statistics are classified by six land-use categories (Forest land, Cropland, Grassland, Wetlands, Settlements, and Other land). The difference of area between the result of Sampling methods and the cadastral statistics decreases as the sample plot distance decreases. However, the difference is not significant under a 2 km sample plot. In the 2000s, the Wall-to-Wall method showed similar results to sampling under a 2 km distance except for the Settlement category. With the Wall-to-Wall method, $CO_2$ storage is higher than that of the Sampling method. Accordingly, the Wall-to-Wall method would be more advantageous than the Sampling method in the presence of sufficient spatial data for GHG inventory assessment. These results can contribute to establish an annual report system of national greenhouse gas inventory in the LULUCF sector.

• Journal of Korea Society of Waste Management
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• v.35 no.8
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• pp.777-784
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• 2018

Since climate change increases the risk of extreme rainfall events, concerns on flood management have also increased. In order to rapidly recover from flood damages and prevent secondary damages, fast collection and treatment of flood debris are necessary. Therefore, a quick and precise estimation of flood debris generation is a crucial procedure in disaster management. Despite the importance of debris estimation, methodologies have not been well established. Given the intrinsic heterogeneity of flood debris from local conditions, a regional-scale model can increase the accuracy of the estimation. The objectives of this study are 1) to identify significant damage variables to predict the flood debris generation, 2) to ascertain the difference in the coefficients, and 3) to evaluate the accuracy of the debris estimation model. The scope of this work is flood events in Ulsan city region during 2008-2016. According to the correlation test and multicollinearity test, the number of damaged buildings, area of damaged cropland, and length of damaged roads were derived as significant parameters. Key parameters seems to be strongly dependent on regional conditions and not only selected parameters but also coefficients in this study were different from those in previous studies. The debris estimation in this study has better accuracy than previous models in nationwide scale. It can be said that the development of a regional-scale flood debris estimation model will enhance the accuracy of the prediction.

• Journal of Korean Society of Rural Planning
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• v.27 no.4
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• pp.33-41
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• 2021

Ammonia emission from the agricultural sector contributes almost 78% of total ammonia emission in Korea. The current ammonia emission estimation method from fertilizer application has high uncertainty and needs to be improved. In this study, we propose an improvement method for estimating the amount of ammonia emission from agricultural land with improved spatiotemporal resolution using Farm Manager Registration Information System and criteria for the fertilizer. We calculated ammonia emissions by utilizing the 2020 cultivation area provided by Farm Manager Registration Information System for 55 kinds of upland crops cultivated in the field area of the farmland. As a result, soybeans were the most cultivated field crop in 2020, and the area of cultivated land was surveyed at about 77,021 ha, followed by sweet potatoes 22,057 ha, garlic 20004 ha, potatoes 17,512 ha, and corn 16,636 ha. The month with the highest ammonia emissions throughout the year was calculated by emitting 590.01 ton yr-1 in May, followed by 486.55 ton yr-1 in March. Hallim-eup in Jeju showed the highest ammonia emission at 117.50 ton yr-1.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.329-329
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• 2022

Accurate characterization of terrestrial precipitation variation from high spatial resolution satellite sensors is beneficial for urban hydrology and microscale agriculture modeling, as well as natural disasters (e.g., urban flooding) early warning. However, the widely-used top-down approach for precipitation retrieval from microwave satellites is limited in several hydrological and agricultural applications due to their coarse spatial resolution. In this research, we aim to apply a novel bottom-up method, the parameterized SM2RAIN, where precipitation can be estimated from soil moisture signals based on an inversion of water balance model, to generate high spatial resolution terrestrial precipitation estimates at 0.01º grid (roughly 1-km) from the C-band SAR Sentinel-1. This product was then tested against a common reanalysis-based precipitation data and a domestic rain gauge network from the Korean Meteorological Administration (KMA) over central South Korea, since a clear difference between climatic types (coasts and mainlands) and land covers (croplands and mixed forests) was reported in this area. The results showed that seasonal precipitation variability strongly affected the SM2RAIN performances, and the product derived from separated parameters (rainy and non-rainy seasons) outperformed that estimated considering the entire year. In addition, the product retrieved over the mainland mixed forest region showed slightly superior performance compared to that over the coastal cropland region, suggesting that the 6-day time resolution of S1 data is suitable for capturing the stable precipitation pattern in mainland mixed forests rather than the highly variable precipitation pattern in coastal croplands. Future studies suggest comparing this product to the traditional top-down products, as well as evaluating their integration for enhancing high spatial resolution precipitation over entire South Korea.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.507-519
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• 2023

Multi-temporal optical images have been utilized for time-series monitoring of croplands. However, the presence of clouds imposes limitations on image availability, often requiring a cloud removal procedure. This study assesses the applicability of various machine learning algorithms for effective cloud removal in optical imagery. We conducted comparative experiments by focusing on two key variables that significantly influence the predictive performance of machine learning algorithms: (1) land-cover types of training data and (2) temporal variability of land-cover types. Three machine learning algorithms, including Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF), were employed for the experiments using simulated cloudy images in paddy fields of Gunsan. GPR and SVM exhibited superior prediction accuracy when the training data had the same land-cover types as the cloud region, and GPR showed the best stability with respect to sampling fluctuations. In addition, RF was the least affected by the land-cover types and temporal variations of training data. These results indicate that GPR is recommended when the land-cover type and spectral characteristics of the training data are the same as those of the cloud region. On the other hand, RF should be applied when it is difficult to obtain training data with the same land-cover types as the cloud region. Therefore, the land-cover types in cloud areas should be taken into account for extracting informative training data along with selecting the optimal machine learning algorithm.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.4
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• pp.475-483
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• 2012

Recently, agricultural lands decrease sharply, which was caused by urbanization, land consolidation, road construction, and innovation city construction, etc. In particular, Goyang, Chenan and Wonju city were had severe land use change. Therefore, we analyzed changes of land use, soil properties, and soil information in order to provide the basic soil information and soil management practice in these cities. The results are summarized as follows. The area of crop cultivated land in Korea (2011) was reduced to 17.3ha compared to ones from the previous year (2009). The paddy field decreased by 24.2 ha but, upland field increased by 7.0 ha. The reasons for the reduction of the paddy field were converting paddy field to upland (20.7 ha) > public facilities (3.2) ${\geq}$ building (3.2) > idle land (1.3) > and others (0.9). Other reasons for reduction in the upland field were switching upland to paddy field, (20.7 ha) > land developed (4.5) > and restoration (0.3) respectively. The main reason of converting paddy field to upland was changing from rice to more profitable upland or greenhouse crops. The cropland area (paddy fields, upland, orchard) of Goyang, Cheonan, and Wonju city were reduced to 1,466 ha, 9,708 ha and 6,980 ha respectively. The ratio of cropland area in each city was reduced by 45~25% dramatically compared to upland soil survey project in Korea (1995~1999). These data were compared with MiFAFF statistics data to use for land use cover map of Ministry of environment. But they were differences significantly. Therefore, intensive investigation should be advised throughout the utilization plan. The paddy fields located in small valley in Wonju city were changed into upland or orchard. The drainage classes of soil have been deteriorated because the flows of water were intercepted by road construction and other disturbance to water flows. In particular, paddy fields have been changed to not only upland, orchard, greenhouse cultivation but also to fallow and soil dressing on paddy in Wonju city. The soil suitability classes of paddy field in Wonju innovation city were the 3rd grade for 70.8% of the area and the 4th grade for 29.2%. The soil suitability classes of upland was the 4th grade for 88.7% of the area. Fortunately, good soil suitability classes were not belong to innovation city in Wonju. So, the good farm land should be conserved and revise the related law.

• Journal of Wetlands Research
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• v.7 no.1
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• pp.107-117
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• 2005

Soil erosion is influenced from a variety of factors such as rainfall distribution, soil type, land use, etc. This paper is aimed at analyzing the soil erosion hazard zone in cropland. RUSLE was used for an analysis of soil erosion amount, and for the spatial data of basin, soil erosion amount was calculated by extracting the respect topography space related factors of RUSLE using DEM, Landuse, Soil map as base map. This paper is targeting at the watershed of Gyeongan stream in Gyeonggi-do The result of an analysis of soil erosion amount showed that soil erosion occurred in the order of crop field(1210) planting area, orchard(1220), non-adjusted paddy fields(1120), and adjusted paddy fields(1110), and also the average soil erosion in these planting areas has the most amount in crop field planting area. As a result of analysis on soil erosion hazard zone of farm land by classifying it into 5 classes using the result of that result of analysis on the amount of soil erosion, in case of Class 5 in which the hazard of soil erosion is the highest, approximately 72.5ha that corresponds to 2.4% of the total farm land was decided as erosion hazard zone. For this erosion hazard zone, it was analyzed that dry field crop planting area was 72.4ha and orchard was 0.1ha, and Class 5 hazard zone did not appear in other farming areas. Also, it showed that Class II(1~50ton/ha/yr) area had the most ratio of the entire farm land, i.e., 70.2%, regardless of land use state. According to the result of analysis on soil erosion hazard zone of farm land by classifying it into 5 classes, the Class V has the highest soil erosion hazard, approximately 72.5ha that corresponds to 2.4% of the total farm land was estimated as an erosion hazard zone. This erosion hazard shows 72.4ha in dry field crop planting area, 0.1ha in an orchard, but the highest hazard zone, the Class V was not shown in other farming areas. Also, it showed that Class II area had the most ratio of the entire farm land, i.e., 70.2%, regardless of land use state.