• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Advances in Energy Research
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• v.5 no.3
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• pp.207-217
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• 2017

Energy consumption in agriculture is responsible for greenhouse gas emission but it can be reduced after efficient utilization of energy inputs. Therefore, the present study aims for the estimation of energy efficiency and extent of greenhouse gas reduction after benchmarking of inefficient farms in off-season tomato in Punjab province of Pakistan. Primary data were collected from 70 farmers with simple random sampling. By using data envelopment analysis, the average value of technical, pure technical and scale efficiency was 0.80, 0.92 and 0.87, respectively while increasing, constant and decreasing return to scale was observed in 33, 26 and 11 farmers, respectively. Total input energy was reduced by $12,688.91MJ\;ha^{-1}$ (13.89%) if inefficient farms used the energy inputs according to recommendations or benchmarking. A major portion of energy saving comes from fertilizers (68.79%) followed by diesel (15.70%), chemicals (5.91%), machinery (4.37%) and water (4.00%). Total greenhouse gases reduction was $499.17kg\;CO_2\;eq.ha^{-1}$ (14.57%) as a result of improvement in energy efficiency or benchmarking of inefficient farms. Agricultural extension staff should visit the vegetable farms on regular basis and give necessary information about efficient utilization of energy inputs. The government should create awareness about the optimum use of input through seminars and pamphlets.

• Journal of Korean Society on Water Environment
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• v.25 no.3
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• pp.351-362
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• 2009

The Soil and Water Assessment Tool (SWAT) model, semi-distributed model, first divides the watershed into multiple subwatersheds, and then extracts the basic computation element, called the Hydrologic Response Unit (HRU). In the process of HRU generation, the spatial information of land use and soil maps within each subwatershed is lost. The SWAT model estimates the HRU topographic data based on the average slope of each subwatershed, and then use this topographic datum for all HRUs within the subwatershed. To improve the SWAT capabilities for various watershed scenarios, the Spatially Distributed-HRU (SD-HRU) pre-processor module was developed in this study to simulate site-specific topographic data. The SD-HRU was applied to the Hae-an watershed, where field slope lengths and slopes are measured for all agricultural fields. The analysis revealed that the SD-HRU pre-processor module needs to be applied in SWAT sediment simulation for accurate analysis of soil erosion and sediment behaviors. If the SD-HRU pre-processor module is not applied in SWAT runs, the other SWAT factors may be over or under estimated, resulting in errors in physical and empirical computation modules although the SWAT estimated flow and sediment values match the measured data reasonably well.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.3
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• pp.65-74
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• 2012

In this study, three different unit hydrograph methods (NRCS, Snyder and Clark) in the HEC-HMS were compared to find better fit with the observed data in the Namgang-Dam watershed. The Sancheong, Shinan, and Changchon in Namgang-Dam watershed were selected as the study watersheds. The input data for HEC-HMS were calculated land use, digital elevation map, stream, and watershed map provided by WAter Management Information System (WAMIS). Sixty six storms from 2004 to 2011 were selected for model calibration and validation. Three unit hydrograph methods were compared with the observed data in terms of simulated runoff volume, and peak runoff for the selected storms. The results showed that the coefficient of determination ($R^2$) for the peak runoff was 0.8295~0.9999 and root mean square error (RMSE) was 0.029~0.086 mm/day for calibration stages. In the model validation, $R^2$ for the peak runoff was 0.9061~0.9916 and RMSE was 0.030~0.088 mm/day which were more accurate than calibrated data. Analysis of variance showed that there was no significant difference among the three unit hydrograph methods.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1586-1590
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• 2006

In order to calculate the actual erosion according to the universal soil loss equation (USLE) and to estimate the impact of land use on soil erosion in Saemangeum, it is important to know the C-factor. Based on the USLE crop-growth stages, the cover-management C-factors were calculated for the main crop and crop rotation systems by National Institute of Agricultural Science and Technology. Combining this result with statistical data about crop cultivation area and crop rotation systems, C-factors of each administrative district in Saemangeum watershed were calculated. The range of C-factors were between 0.28 and 0.35. High C-factor value was obtained with Gimje (C = 0.35) and small C-factor values were found in Wanju (C = 0.28) and Jeongeup (C = 0.29). With this result, calculated annual soil loss was 2,804,483 ton per year. Because of the lack of sufficient statistical data about crop rotation systems, further studies are required on collecting field survey data.

• Journal of Korean Society of Rural Planning
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• v.22 no.1
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• pp.13-23
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• 2016

There is a need to analyze unconfined groundwater behavior since the demand of groundwater use has been increasing. While unconfined groundwater temperature is tend to be affected by air temperature, it is hard to find an empirical study in South Korea. In this research, we try to determine the relationship between daily average air temperature and daily average groundwater temperature by time-sequential analysis of groundwater monitoring wells in Galshin basin in Yesan-Gun, Chungcheongnam-Do. In addition, models to estimate groundwater temperature from air temperature were developed. In this research 101-day moving average method with measured air temperature is used to estimate groundwater temperature. To verify the developed model, estimated values of average groundwater temperature with 101 moving average are compared to the measured data from September 10 2007 to September 9 2008. And, Nash-Stucliff Efficiency and Coefficient of Determination were 0.970 and 0.976, therefore it was concluded that the model allowing groundwater temperature estimation from air temperature is with reasonable applicability.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.6
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• pp.439-446
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• 2009

Nitrogen balance in the regional scale which was calculated the difference between nitrogen input and output was estimated to assess the impact of rice cultivation on water environment. Nitrogen balances in Gyeonggi province, where nitrogen concentration in irrigation water was high and in Chungnam province, where nitrogen absorbtion by rice was high, were -5.4 and -8.3 kg $-8.3kg\;ha^{-1}\;yr^{-1}$, respectively. Nitrogen balances of paddy field in Gangwon province, where nitrogen output was small and irrigation water was clean, and in Gyeongnam province, where organic matter content of soil was high and rice yield was low, were 4.9 and $14.0kg\;ha^{-1}\;yr^{-1}$, respectively. Average nitrogen balance and total nitrogen absorption of paddy field in Korea were estimated to $-0.3kg\;ha^{-1}\;yr^{-1}$ and $-3,315Mg\;yr^{-1}$, respectively. When the nitrogen concentration in irrigation water was increased by $1mg \;L^{-1}$, nitrogen balance of rice paddy changed by $-2.91kg\;ha^{-1}\;yr^{-1}$. Also, when nitrogen fertilizer applied was decreased from 110 to $90kg\;ha^{-1}$ and the same harvest was maintained, the nitrogen absorption by rice paddy from irrigation water was estimated to increase by 10,600 Mg per year in Korea. However, in cases, the harvest was reduced to either 90% or 85%, nitrogen balances were changed from -11.7 to -2.3 and $2.4kg\;ha^{-1}$, respectively. These results suggest that the reduction of nitrogen fertilizer use may not always lead to a negative nitrogen balance and sustainable agriculture can achieve by not cutting down the use of fertilizer only but by reduction of fertilizer application concurrently by maintenance of harvest and by utilization of environmental characteristics such as nutrient contents in irrigation water and soils.

• Journal of Korea Water Resources Association
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• v.39 no.5 s.166
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• pp.431-440
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• 2006

Recently, water conflicts are rapidly increasing and more serious. So, it needs to establish the reasonable water allocation rules and criteria. This study aims at performing inter-sectoral water allocation with a focus on economic effciency and social utility. To allocate water among the sectoral water uses, water shortage damage functions were estimated and then converged to the utility functions. Finally, each sectoral water uses are allocated by applying 'law equimarginal utility' to maximize social utility. Also weighting factor which reflects scale and characteristic of water demand in a region was estimated to perform the inter-regional water allocation. The water allocation rule was applied to the future water-deficit situation in Han-river basin. As a result, domestic water use was allocated more sufficient agricultural and industrial water use. Also, the water shortage occurs severely in the rural area like Gangwon-do because of its low urbanization and industrialization. This study suggests a alternative view of the economic water allocation which have difficulty under water supply mechanism in Korea.

• Economic and Environmental Geology
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• v.42 no.4
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• pp.343-356
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• 2009

SCS method was applied to make the assessments of direct runoff according to land use changes in Jeju island. Land uses were obtained from 5 year-period remote sensing time series data from 1975 to 2000 which are provided by Water Management Information System (WAMIS). Hydrologic soil groups were categorized based on soil series of National Academy of Agricultural Sciences (NAAS), and permeable geologic structures such as Sumgol, Gotzawal and so on. The land uses of Jeju island are obviously characterized by urban-agricultural areas increases, and forest areas decrease. According to land use changes, curve number (CN) for Jeju island was consistently increased from 65.3 in 1975 to 69.6 in 2000. From 1975 to 2000, the amount of direct runoff and ratios increased due to CN changes. When the rainfall data in 1995 was applied to each year, the direct runoff amounts were $299.0{\sim}351.6\;mm$, and runoff ratios were $15.1{\sim}17.7%$. In the case of the application of the rainfall data in 2000, the direct runoff amounts were $136.9{\sim}161.5\;mm$, and runoff ratios were $9.7{\sim}11.5%$. Since direct runoff can be closely related to groundwater recharge and sustainable groundwater yield, the groundwater influence caused by land use changes or district exploitations should be considered for the reasonable water management and development in Jeju island.

• Journal of the Korean Institute of Landscape Architecture
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• v.28 no.6
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• pp.114-126
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• 2001

The purpose of this study is to establish the databases of pollutant sources and water quality measurement data by utilizing GIS, and making the user interface for the management of pollutant sources. Yeongsan Estuarine Lake was formed of a huge levee of 4.35 km constructed by an agricultural reclamation project. Water quality of the reservoir has been degraded gradually, which mainly attributes to increase of point and non-point source pollutant loads from the lake's watershed of 33,374.3 $\textrm{km}^2$ into it. Application of GIS to establishment of the database was researched of pint source such as domestic sewage, industrial wastewater, farm wastes, and fishery wastes, and non-pont source such as residence, rice and upland field, and forest runoffs of the watershed of the lake. NT Acr/Info and ArcView were mainly utilized for the database formation. Land use of the watershed using LANDSAT image data was analyzed for non-point source pollutant load estimation. Pollutant loads from the watershed into the reservoir were calculated using the GIS database and BOD, TN, TP load units of point and non-point sources. Total BOD, TN, TP loads into it reached approximately to 141, 715, 2,094 and 4,743 kg/day respectively. The loads can be used as input parameters for water quality predicting model of it. A user-friendly interface program was developed using Dialog Designer and Avenue Script of AcrView, which can perform spatial analysis of point and non-point sources, calculate pollutant inputs from the sources, update attribute data of them, delete and add point sources, identify locations and volumes of water treatment facilities, and examine water quality data of water sampling points.