Jin-Ki Park

Development of Korean Paddy Rice Yield Prediction Model (KRPM) using Meteorological Element and MODIS NDVI

Food policy is considered as the most basic and central issue for all countries, while making efforts to keep each country`s food sovereignty and enhance food self-sufficiency. In the case of Korea where the staple food is rice, the rice yield prediction is regarded as a very important task to cope with unstable food supply at a national level. In this study, Korean paddy Rice yield Prediction Model (KRPM) developed to predict the paddy rice yield using meteorological element and MODIS NDVI. A multiple linear regression analysis was carried out by using the NDVI extracted from satellite image. Six meteorological elements include average temperature; maximum temperature; minimum temperature; rainfall; accumulated rainfall and duration of sunshine. Concerning the evaluation for the applicability of the KRPM, the accuracy assessment was carried out through correlation analysis between predicted and provided data by the National Statistical Office of paddy rice yield in 2011. The 2011 predicted yield of paddy rice by KRPM was 505 kg/10a at whole country level and 487 kg/10a by agroclimatic zones using stepwise regression while the predicted value by KOrea Statistical Information Service was 532 kg/10a. The characteristics of changes in paddy rice yield according to NDVI and other meteorological elements were well reflected by the KRPM.

Evaluating the spatial and temporal solar energy potential in South Korea

Recent issues of climate changes and natural disasters have brought many changes in world energy utilization. Especially due to the Japans earthquake and tsunami, potential of nuclear power have made negative. And thus many countries are looking for a new renewable energy that can replace. Of which solar energy has emerged as a useful alternative. Under these circumstances, it is highly desirable that detailed information about the availability of solar radiation on the surface is essential for the optimum design and study of solar energy systems. And its components at a given location are very essential. Hence the solar radiation data is one of the key parameters required to be monitored at any meteorological station. But solar radiation measurements are not easily available due to the cost and maintenance requirements of the measuring equipment. Therefore, solar resource modeling or mapping is one of the essential tools for proper design, planning, maintenance and pricing of solar energy system. In this study, the feasibility of a regression model using image fusion for the prediction of solar energy potential in Republic of Korea was investigated. Meteorological and geographical data of 22 cities in South Korea for period of 10 years (2001–2011) were used. Meteorological and geographical data (latitude, longitude, altitude, month, sunshine duration, temperature, and relative humidity) were used as inputs to the model, while the regional solar radiation was used as the output of the model. The model for evaluating the spatial and temporal solar radiation was executed for South Korea. The annual mean solar radiation estimates in South Korea vary from a minimum of 5.48 MJ/m2/day to a maximum of 19.51 MJ/m2/day. Our proposed annual mean solar radiation is 13.5 MJ/m2/day. These compare favorably with the observed data as expected. This study has shown that a simple method can accurately predict solar radiation potential in South Korea.

Daily global solar radiation estimate in the South Korea based on geostationary satellite remote sensing

Available solar radiation is an important crop growth monitoring and water balance that determines South Koreas crop productivity but has been poorly characterized. This paper studied the method of estimates for the available solar energy and crop growth monitoring based on data of Satellite imagery and meteorological observed data between 1982 and 2005 in South Korea. The objectives of this study are: a) assess of daily and monthly global radiation which most closely associated with rice growth; b) estimates for the available solar energy of the South Korea. Results from the study not only monitor the solar radiation in investigation area, but also illustrate the powerful potential to provide information about solar radiation based geostationary satellite imagery data.

Quantification of the Relationship Between Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) in Arable Land

The survey of Landsat satellite image is effective in the continuous monitoring of a vast area during long periods of time. It is increasingly being used to derive and analyze spatial distribution data of both the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) that are major indicators for an analysis of vegetation-environment. Likewise, NDVI and LST are essential in order to detect, as well as to monitor, the environmental changes in arable land. Therefore, the relationship between NDVI and LST should be quantified for the accuracy improvement of agricultural statistical data based on Remote Sensing. This study has intended to analyze the characteristics of NDVI and LST using Landsat imagery of arable land in Cheongju City, to quantify the relationship between NDVI and LST. The results indicated that time seasonal change of raster data for four times of the highest group of LST and the lowest group of vegetation located in the Cheongju city, Chungcheongbuk-do, Korea, are observed and analyzed their correlations for the change detection of land cover. This experiment, based on proposed algorithms, detected a strong and proportional correlation relationship between the highest group of LST and the lowest group of vegetation index which exceeded R=(+)0.9. Therefore, the proposed Correlation Analysis Model between the highest group of LST and the lowest group of vegetation index will be able to give proof of an effective suitability to the land cover change detection and monitoring.

Integrated model for predicting rice yield with climate change

Abstract Rice is the chief agricultural product and one of the primary food source. For this reason, it is of pivotal importance for worldwide economy and development. Therefore, in a decision-support-system both for the farmers and in the planning and management of the country’s economy, forecasting yield is vital. However, crop yield, which is a dependent of the soil-bio-atmospheric system, is difficult to represent in statistical language. This paper describes a novel approach for predicting rice yield using artificial neural network, spatial interpolation, remote sensing and GIS methods. Herein, the variation in the yield is attributed to climatic parameters and crop health, and the normalized difference vegetation index from MODIS is used as an indicator of plant health and growth. Due importance was given to scaling up the input parameters using spatial interpolation and GIS and minimising the sources of error in every step of the modelling. The low percentage error (2.91) and high correlation (0.76) signifies the robust performance of the proposed model. This simple but effective approach is then used to estimate the influence of climate change on South Korean rice production. As proposed in the RCP8.5 scenario, an upswing in temperature may increase the rice yield throughout South Korea.

Exploratory data analysis and modelling solar radiation and associated environmental phenomenon

Though, solar radiation data is not available in maximum weather stations, it is an important parameters for biogeochemical models hence there is an increasing demands of available energy data. The seasonal model described here can be used for a rough estimation of maximum available solar energy in nearby areas of the measuring station in clear sky condition with minimum calculation and efforts. Other than that it is found that sunshine duration is the best for estimating solar radiation. Prevalent cloud condition over Korean peninsula makes sunshine fraction most important factor determining available solar energy than other. The variation of residuals in the seasonal model (Fig. 5) also indicates the strong dependency of radiation data on sunshine fraction. Still, many weather stations does not have sunshine duration data whereas temperature is one of the mostly and easily available parameters that can be used for estimating solar radiation. Cloudiness data is also measured in many weather stations and found to be a good estimator for solar radiation, better than temperature. Therefore, low validation error and high regression coefficient indicate that all these parameters can be used for successful solar radiation modelling.

Estimating distribution of precision solar radiation using unmanned aerial vehicle

The aim of this study was to perform a precise spatial distribution of solar radiation estimation using UAV as a way to use solar energy efficiently. Fixed wing UAV was used to acquire image for a precisely topographical map. The whole work can be divided into two step. In first step, images acquired by UAV are used to build a digital surface model based on point cloud method. In the next step, calculated slope and angle data is used to model clear sky solar radiation. The solar radiation data for 2015 was taken from Korean Meteorological Administration (KMA). The highest daily solar radiation appears in May of 22.7 MJ/m2, and the lowest daily solar radiation in May appears 15.7MJ/m2. And the range of daily solar radiation appears in December is 2.9 MJ/m2 to 7.1 MJ/m2. These results may be useful providing input to agricultural and ecosystem that are dependent on solar radiation, such as engineers, designers, developers, and so forth, during the selection location process for solar installation.

Application of Agricultural Subsidy Inspection Using UAV Image

The most important parameters, should be considered during application of remote sensing techniques in agricultural sector, is to acquire image data in appropriate moment in accordance with the growth of the crop. Unmanned Aerial Vehicles (UAVs) have several advantages over conventional remote sensing techniques. They can acquire high-resolution images quickly and repeatedly with a comparatively lower flight altitude i.e. 80~400m nullifying the effect of extreme weather and cloud. This study discussed the use of low cost-effective UAV based remote sensing application in inspection of agricultural subsidy. The study area includes 129.1km2 of Miwon town. UAV images acquired 41 times from July 17 to August 10, 2015 for 7 days. The UAV images identify a significant amount of incorrect applications for agricultural subsidy, almost 29.6% (559 of 1,889). Surveying with UAV for agricultural payment instead of field stuff can reduce the time as much as 76.7 % and increase the effectiveness of inspection methods.

Evaluation of surface heat flux based on satellite remote sensing and field measurement data

The objective of this study was to evaluate surface heat fluxes based on satellite remote sensing data and field measurement data. Surface heat fluxes were estimated around the city of Cheongju, South Korea using Landsat 5 TM data and modeler of ERDAS Imagine. Landsat data was acquired on 6 August, 2006 (path 115, row 35). Field measurement was conducted in the paddy field and upland. And it was collected the same day with acquisition of Landsat imagery using net radiometer (Q-7.1, Campbell Scientific Inc.) and geothermometer (MF-180M, EKO Instruments Trading Co.). The result of this study was that net radiation at the surface was 350 ~ 670 W/m2. The percentage of surface heat fluxes were approximately 30% from sensible heat flux to the air, approximately 10% from soil heat flux, and 60% from latent heat flux. The surface heat flux in paddy field data was similar to Landsat data. The upland data was much alike, but they do have dissimilarities because the upland was too small area, considering Landsat image resolution. The results suggest that the methodology was feasible to estimate surface heat fluxes with reasonable accuracy over study areas.

The effects of urban stream improving the thermal environment in urban area

Urban areas create distinctive urban climates by Urban Heat Island (UHI) that is the temperature increase in urban areas compared to that in surrounding rural areas and is caused by number of factors, such as land use / land cover (LULC) change, concentration of population and increase anthropogenic heat. In general, the study of thermal environment in urban area focused on UHI intensity and phenomenon. Recently, climate improvement has been studied using water and green belt of urban, as interest in UHI phenomenon mitigation or enhancement has been increased. Therefore in this study, effects of urban stream on urban thermal environment were analyzed using remotely sensed data. The Landsat 7 ETM+ data acquired on 6 September 2009 were utilized to derive the surface Temperature (Ts) and surface energy balance using Surface Energy Balance Algorithms for Land (SEBAL) (Bastiaanssen et al., 1998). The surface energy budget consists of net radiation at the surface (Rn), sensible heat flux to the air (H), latent heat flux (LE) and soil heat flux (G). The net radiation flux is computed by subtracting all outgoing radiant fluxes (K↑: shortwave outgoing, L↑ longwave outgoing) from all incoming radiant fluxes (K↓ shortwave incoming, L↓: longwave incoming). This is given in the surface energy budget equation: Rn = H + LE + G = K↓ - K↑ + L↓ - L↑. The result indicates that the Ts of urban stream are1 °C lower than circumjacent urban area, LE flux of urban stream is higher than surrounding urban area. However, land covers of streamside and around stream with concrete, asphalt and barren belt are comprised of hot spot zone that deteriorates urban thermal environment. And urban stream does perform a role of cool spot zone that improves urban thermal environment.