• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.120-131
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• 2020

Water body detection using remote sensing based on machine interpretation of satellite image is efficient for managing water resource, drought and flood monitoring. In this study, water body detection with SAR satellite image based on machine learning was performed. However, non water body area can be misclassified to water body because of shadow effect or objects that have similar scattering characteristic comparing to water body, such as roads. To decrease misclassifying, 8 combination of morphology open filtered band, DEM band, curvature band and Cosmo-SkyMed SAR satellite image band about Mokpo region were trained to semantic segmentation machine learning models, respectively. For 8 case of machine learning models, global accuracy that is final test result was computed. Furthermore, concordance rate between landcover data of Mokpo region was calculated. In conclusion, combination of SAR satellite image, morphology open filtered band, DEM band and curvature band showed best result in global accuracy and concordance rate with landcover data. In that case, global accuracy was 95.07% and concordance rate with landcover data was 89.93%.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.22 no.6
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• pp.115-124
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• 2019

In a climate change environment where heat damage and drought occur during a rainy season such as in 2018, a vegetation-based LID system that enables disaster prevention as well as environment improvement is suggested in lieu of an installation-type LID system that is limited to the prevention of floods. However, the quantification of its performance as against construction cost is limited. This study aims to present an experiment environment and evaluation method on quantitative performance, which is required in order to disseminate the vegetation-based LID system. To this end, a 3^rd quartile huff time distribution mass curve was generated for 20-year frequency, 60-minute probable rainfall of 68mm/hr in Cheonan, and effluent was analyzed by recreating artificial rainfall. In order to assess the reliability of the rainfall event simulator, 10 repeat tests were conducted at one-minute intervals for 20 minutes with minimum rainfall intensity of 22.29mm/hr and the maximum rainfall intensity of 140.69mm/hr from the calculated probable rainfall. Effective rainfall as against influent flow was 21.83mm/hr (sd=0.17~1.36, n=20) on average at the minimum rainfall intensity and 142.27mm/hr (sd=1.02~3.25, n=20) on average at the maximum rainfall intensity. In artificial rainfall recreation experiments repeated for three times, the most frequent quartile was found to be the third quartile, which is around 40 minutes after beginning the experiment. The peak flow was observed 70 minutes after beginning the experiment in the experiment zone and after 50 minutes in the control zone. While the control zone recorded the maximum runoff intensity of 2.26mm/min(sd=0.25) 50 minutes after beginning the experiment, the experiment zone recorded the maximum runoff intensity of 0.77mm/min (sd=0.15) 70 minutes after beginning the experiment, which is 20 minutes later than the control zone. Also, the maximum runoff intensity of the experiment zone was 79.6% lower than that of the control zone, which confirmed that vegetation unit-type LID system had rainfall runoff reduction and delay effects. Based on the above findings, the reliability of a lab-level rainfall simulator for monitoring the vegetation-based LID system was reviewed, and maximum runoff intensity reduction and runoff time delay were confirmed. As a result, the study presented a performance evaluation method that can be applied to the pre-design of the vegetation-based LID system for rainfall events on a location before construction.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1612-1612
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• 2001

The production of grain for export and domestic use is one of Australia's most important agricultural industries, and the NIR technique has been used extensively over many years for the routine monitoring of grain quality, particularly moisture and protein content. Because most Australian grain is intended for human food production, the determinants of grain quality for livestock feed, apart from protein, have been largely ignored. However the increasing use of grain for feeding to pigs, poultry, beef cattle and dairy cows has led to an important national research project entitled “Premium Grains for Livestock”. Two of the objectives of this project are to determine the compositional and functional characteristics of grains which influence their nutritional quality for the various classes of livestock, and to adopt rapid and objective analytical tests for these quality criteria. NIR has been used in this project firstly to identify a set of grain samples from a large population of breeders' lines which showed a wide spectral variation, and hence a potentially wide variation in nutritional value. The selected samples were not only subjected to an extensive array of chemical, physical and in vitro analyses, but also were grown out to produce sufficient quantities of grain to feed to animals in vivo studies. Additional grains were also strategically selected from farms in order to include the effect of weather damage, such as rain, drought and frost. In this study to date, NIR calibrations have been derived or attempted, on both ground and whole grains, for in vivo dry matter digestibility (DMD), pepsin-cellulase dry matter disappearance, protein, fat, acid detergent fibre, neutral detergent fibre, starch, in sacco DMD and in vitro assays to simulate starch digestion in the lumen and small intestine. Results so far indicate high calibration accuracy for chemical components (SECV 0.3 to 2.6%) and very promising statistics for in vivo DMD (SECV 1.8, $R^2$ 0.93, SD 7.0, range 61.9 to 92.3, n=60). There appears to be some potential for NIR to estimate some in vitro properties, depending upon the accuracy of reference methods and appropriate sample populations. Current work is in progress to extend the range of grains with in vivo DMD values (a very laborious and expensive process) and to increase the robustness of the various NIR calibrations, with the aim of implementing uniform testing procedures for nutritional value of grains throughout Australia.

• Journal of Korean Society of Forest Science
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• v.103 no.4
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• pp.703-708
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• 2014

This study was conducted to investigate effects of soil amendments on survival rate and growth of seedlings in a semi-arid region, Mongolia. 2-year-old Populus sibirica and Ulmus pumila seedlings were planted in alkaline sandy soils and treated with 2 levels of nitrogen, elemental sulfur, artificial moisture retention and converted loess. After 4 months, the seedling survival rate of both species decreased as the amount of nitrogen increased. Nitrogen has been generally known to increase seedling survival rate and growth by supplying nutrients, however, reduced survival rate in this study might be affected by consequential increase in soil osmotic pressure, which was caused by excessive nitrogen fertilization. The root collar diameter (RCD) growth of both species increased significantly by the treatment of converted loess, and only RCD growth of P. sibirica increased by the artificial moisture retention treatment. Although P. sibirica is drought-tolerant, it is in the group of Populus spp. which requires a high capacity of available water. Conversely, the elemental sulfur treatment showed no effect on survival rate and RCD growth for both species due to the low oxidation of sulfur in arid soils. The extended monitoring of seedling growth and soil characteristics is required to elucidate the long-term effects of soil amendments in the semi-arid region, and the further studies are also needed to examine the appropriate amount of fertilizers for both species.

• Journal of Bio-Environment Control
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• v.23 no.1
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• pp.50-55
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• 2014

This study was conducted to investigate the responses of red pepper (Hongjinju) leaves under water stress. Hyperspectral short wave infrared (SWIR, 1000~1800 nm) reflectance imaging techniques were used to acquire the spectral images for the red pepper leaves with and without water stress. The acquired spectral data were analyzed with a multivariate analysis method of ANOVA (analysis of variance). The ANOVA model suggested that 1449 nm wavebands was the most effective to determine the stress responses of the red pepper leaves exposed to the water deficiency. The waveband of 1449 nm was closely related to the water absorption band. The processed spectral image of 1449 nm could separate the non-stress, moderate stress (-20 kPa), and severe stress (-50 kPa) groups of red pepper leaves distinctively. Results demonstrated that hyperspectral imaging technique can be applied to monitoring the stress responses of red pepper leaves which are an indicator of physiological and biochemical changes under water deficiency.

• Korean Journal of Soil Science and Fertilizer
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• v.39 no.2
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• pp.109-115
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• 2006

Understanding characteristics of claypan soils has long been an issue for researchers and farmers because the high-clay subsoil has a pronounced effect on grain crop productivity. The claypan restricts water infiltration and storage within the crop root zone, but these effects are not uniform within fields. Conventional techniques of identifying claypan soil characteristics require manual probing and analysis which can be quite expensive; an expense most farmers are unwilling to pay. On the other hand, farmers would be very interested if this information could be obtained with easy-to-use field sensors. Two examples of sensors that show promise for helping in claypan soil characterization are soil profile strength sensing and bulk soil apparent electrical conductivity (ECa). Little has been reported on claypan soils relating the combined information from these two sensors with grain crop yield. The objective of this research was to identify the relationships of sensed profile soil strength and soil EC with nine years of crop yield (maize and soybean) from a claypan soil field in central Missouri. A multiple-probe (five probes on 19-cm spacing) cone penetrometer was used to measure soil strength and an electromagnetic induction sensor was used to measure soil EC at 55 grid site locations within a 4-ha research field. Crop yields were obtained using a combine equipped with a yield monitoring system. Soil strength at the 15 to 45 cm soil depth were significantly correlated to crop yield and ECa. Estimated crop yields from apparent electrical conductivity and soil strength were validated with an independent data set. Using measurements from these two sensors, standard error rates for estimating yield ranged from 9 to 16%. In conclusion, these results showed that the sensed profile soil strength and soil EC could be used as a measure of the soil productivity for grain crop production.

• Journal of Korea Water Resources Association
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• v.49 no.5
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• pp.423-429
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• 2016

This study aims at assessing the quality of the Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture products onboard GCOM-W1 satellite based on Land Parameter Retrieval Model (LPRM) soil moisture retrieval algorithm with field measurements in South Korea from March to September, 2014. Results of mean bias and root mean square error between AMSR2 LPRM soil moisture products (X-band) and ground measurements showed reasonable value of 0.03 and 0.16. Also, the maximum of the Pearson correlation coefficients was 0.67, which showed good agreement in terms of temporal variability with ground measurements. By comparing AMSR2 soil moisture with in-situ measurement according to the overpass time and band frequency, X-band products on the ascending time outperformed than those of C1-band and C2-band. Furthermore, this study offers an insight into the applicability of the AMSR2 soil moisture products for monitoring various natural disasters at a large scale such as drought and flood.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Korean Journal of Environmental Biology
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• v.38 no.1
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• pp.30-39
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• 2020

The changes in fish stock and biomass before and after fishway renovation located in a Korean estuary were studied and fluctuations in the economic value of the fish resources were estimated. The target fishway located in the east coast area in Korea was renovated in 2014 from the small fish ladder to the ice-harbor fishway. Monitoring was continued for five consecutive years after the renovation(2015 to 2019). Since the renovation of the fish passage, the economic values increased with increases in the fishery resources, except for in 2016 when the drought impact was severe. The yearly average incremental increase in the five years after the renovation was about 227%. The increase in economic value is believed to be due to the increased population of migratory fish as a result of habitat expansion. The exponential rise model showed an increase in economic value with increasing fishery resources (R²=0.896). The model coefficient contributing to economic analysis was 0.582 and the maximum economic value after the renovation was estimated at about 30.4 million. The economic value would be a useful index for quantitative comparison in terms of ecosystem services before and after renovation.

• Journal of Wetlands Research
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• v.21 no.3
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• pp.191-198
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• 2019

High impervious surfaces increase the surface runoff during rainfall and reduces the underground infiltration thereby leading to water cycle distortion. The distortion of water cycle causes various urban environmental problems such as urban flooding, drought, water pollutant due to non-point pollution runoff, and water ecosystem damage. Climate change intensified seasonal biases in urban rainfall and affected urban microclimate, thereby increasing the intensity and frequency of urban floods and droughts. Low impact development(LID) technology has been applied to various purposes as a technique to reduce urban environmental problems caused by water by restoring the natural water cycle in the city. This study evaluated the contribution of hydrologic characteristics and water cycle recovery after LID application using long-term monitoring results of various LID technology applied in urban areas. Based on the results, the high retention and infiltration rate of the LID facility was found to contribute significantly to peak flow reduction and runoff delay during rainfall. The average runoff reduction effect was more than 60% at the LID facility. The surface area of the LID facility area ratio(SA/CA) was evaluated as an important factor affecting peak flow reduction and runoff delay effect.