• Korean Journal of Environmental Agriculture
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• v.28 no.1
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• pp.1-8
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• 2009

This study was aimed at assess environmental conservation functions by analyzing the change of land use areas in agricultural fields between 1999 and 2006, and comparing land surface temperature distribution between 1994 and 2006 in Yongin city. Land use maps of Yongin city were obtained from soil maps for 1999, Quickbird satellite images(less than 1 m) and parcel map for 2006. The land use area for Yongin city was in the order of forest > paddy field > upland > residence & building in 1999, and forest > residence & building > paddy field > upland in 2006. Decrease of paddy and upland fields reduced 34% and 41% of the capability of agricultural multifunctionality as to environment including flood control, groundwater recharge, and air cooling. Land surface temperature(LST) was derived from Landsat TM thermal infrared band acquired in September of 1994 and 2006 and classified into three grades. The results impplied that green vegetation in agricultural field and forest play an important role to reduce land surface temperature in warm season.

• Korean Journal of Agricultural and Forest Meteorology
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• v.17 no.3
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• pp.217-226
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• 2015

To investigate the distributions of BVOCs (Biogenic Volatile Organic Compounds) from mountain near mega city and their role in forest atmospheric, BVOCs and their oxidized species were measured at a 41 m tower in Mt. Taehwa during May, June and August 2013. A proton transfer reaction-mass spectrometer (PTR-MS) was used to quantify isoprene and monoterpenes. In conjunction with BVOCs, $O_3$, meteorological parameters, PAR (Photosynthetically Active Radiation) and LAI (Leaf Area Index) were measured. The average concentrations of isoprene and monoterpenes were 0.71 ppbv and 0.17 ppbv, respectively. BVOCs showed higher concentrations in the early summer (June) compared to the late summer (August). Isoprene started increasing at 2 PM and reached the maximum concentration around 5 PM. In contrast, monoterpenes concentrations began to increase 4 PM and stayed high at night. The $O_3$ maximum was generally found at 3 PM and remained high until 5 PM or later, which was concurrent with the enhancement of $O_3$. The concentrations of BVOCs were higher below canopy (18 m) than above canopy, which indicated these species were produced by trees. At night, monoterpenes concentrations were negatively correlated with these of $O_3$ below canopy. Using MEGAN (Model of Emissions of Gases and Aerosols from Nature), the emissions of isoprene and monoterpenes were estimated at 1.1 ton/year and 0.9 ton/year, respectively at Mt. Taehwa.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.327-336
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• 2019

Soybean is one of the most important crops of which the grains contain high protein content and has been consumed in various forms of food. Soybean plants are generally cultivated on the field and their yield and quality are strongly affected by climate change. Recently, the abnormal climate conditions, including heat wave and heavy rainfall, frequently occurs which would increase the risk of the farm management. The real-time assessment techniques for quality and growth of soybean would reduce the losses of the crop in terms of quantity and quality. The objective of this work was to develop a simple model to estimate the growth of soybean plant using a multispectral sensor mounted on a rotor-wing unmanned aerial vehicle(UAV). The soybean growth model was developed by using simple linear regression analysis with three phenotypic data (fresh weight, dry weight, leaf area index) and two types of vegetation indices (VIs). It was found that the accuracy and precision of LAI model using GNDVI (R²= 0.789, RMSE=0.73 ㎡/㎡, RE=34.91%) was greater than those of the model using NDVI (R²= 0.587, RMSE=1.01 ㎡/㎡, RE=48.98%). The accuracy and precision based on the simple ratio indices were better than those based on the normalized vegetation indices, such as RRVI (R²= 0.760, RMSE=0.78 ㎡/㎡, RE=37.26%) and GRVI (R²= 0.828, RMSE=0.66 ㎡/㎡, RE=31.59%). The outcome of this study could aid the production of soybeans with high and uniform quality when a variable rate fertilization system is introduced to cope with the adverse climate conditions.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.312-326
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• 2020

Generation of weather forecasts at 100 m resolution through a statistical downscaling process was implemented by Korea Meteorological Administration Post- Processing (KMAPP) system. The KMAPP data started to be used in various industries such as hydrologic, agricultural, and renewable energy, sports, etc. Cheorwon area and Jeonbuk area have horizontal planes in a relatively wide range in Korea, where there are many complex mountainous areas. Cheorwon, which has a large number of in-situ and remotely sensed phenological data over large-scale rice paddy cultivation areas, is considered as an appropriate area for verifying KMAPP prediction performance in agricultural areas. In this study, the performance of predicting KMAPP temperature changes according to ecological changes in agricultural areas in Cheorwon was compared and verified using KMA and National Center for AgroMeteorology (NCAM) observations. Also, during the heat wave in Jeonbuk Province, solar radiation forecast was verified using Automated Synoptic Observing System (ASOS) data to review the usefulness of KMAPP forecast data as input data for application models such as livestock heat stress models. Although there is a limit to the need for more cases to be collected and selected, the improvement in post-harvest temperature forecasting performance in agricultural areas over ordinary residential areas has led to indirect guesses of the biophysical and phenological effects on forecasting accuracy. In the case of solar radiation prediction, it is expected that KMAPP data will be used in the application model as detailed regional forecast data, as it tends to be consistent with observed values, although errors are inevitable due to human activity in agricultural land and data unit conversion.