• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.201-207
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• 2020

In this study, the practicality of unmanned aerial vehicle photography information was identified. Therefore, a total of four consecutive surveys were conducted on the field-level survey areas among the areas subject to photography using unmanned aerial vehicles, and the changes in crop conditions were analyzed using pictures of unmanned aerial vehicles taken during each survey. It is appropriate to collect and utilize photographic information by directly taking pictures of the survey area according to the time of the on-site survey using unmanned aerial vehicles in the field layer, which is an area where many changes in topography, crop vegetation, and crop types are expected. And it turned out that it was appropriate to utilize satellite images in consideration of economic and efficient aspects in relatively unchanged rice paddies and facilities. If the survey area is well equipped with systems for crop cultivation, deep learning can be utilized in real time by utilizing libraries after obtaining photographic data for a certain area using unmanned aircraft in the future. Through this process, it is believed that it can be used to analyze the overall crop and shipment volume by identifying the crop status and surveying the quantity per unit area.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2009.04a
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• pp.271-272
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• 2009

우리나라의 농업통계조사방법은 과거의 조사방법을 그대로 사용하고 있어 비표본오차발생 가능성이 높은 상황이다. 따라서 작물재배면적의 통계조사에 위성영상을 활용함으로써 조사 및 통계자료의 제공에 효율성 및 정확성을 높이는 것이 필요하다. 이를 위해서 UN, 미국, 캐나다, 유럽 등을 중심으로 위성영상을 사용한 경지면적조사, 재배면적조사, 작황예측분석 등 농업통계의 생산 사례를 살펴보았다. 이후 국내 활용동향을 분석하여 원격탐사기법을 활용한 작물재배면적 파악의 개선전략을 제시하였다.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2018.06a
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• pp.111-111
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• 2018

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.117-120
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• 2009

본 연구는 디자인패턴 설계유형에 따라 Mobile 장치를 이용하여 현장의 농작물 중 벼의 생육 정보 및 생산 정보와 GPS를 이용하여 농작물의 위치정보를 서버에 있는 DB에 사용자의 권한별로 삽입/삭제/수정/검색을 할 수 있고, GIS를 이용하여 사용자의 위치 및 벼의 위치 정보를 수치지도 상에 보여 주는 시스템을 구축한다.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.4
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• pp.277-283
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• 2018

The crop growth model has been widely used for climate change impact assessment. Crop growth model require genetic coefficients for simulating growth and yield. In order to determine the genetic coefficients, regional growth monitoring data or yield trial data of crops has been used to calibrate crop growth model. The aim of this study is to verify that yield trial data of corn is appropriate to calibrate genetic coefficients of CERES-MAIZE. Field experiment sites were Suwon, Jinju, Daegu and Changwon. The distance from the weather station to the experimental field were from 1.3km to 27km. Genetic coefficients calibrated by yield trial data showed good performance in silking day. The genetic coefficients associated with silking are determined only by temperature. In CERES-MAIZE model, precipitation or irrigation does not have a significant effect on phenology related genetic coefficients. Although the effective distance of the temperature could vary depending on the terrain, reliable genetic coefficients were obtained in this study even when a weather observation site was within a maximum of 27 km. Therefore, it is possible to estimate the genetic coefficients by yield trial data in study area. However, the yield-related genetic coefficients did not show good results. These results were caused by simulating the water stress without accurate information on irrigation or rainfall. The yield trial reports have not had accurate information on irrigation timing and volume. In order to obtain significant precipitation data, the distance between experimental field and weather station should be closer to that of the temperature measurement. However, the experimental fields in this study was not close enough to the weather station. Therefore, When determining the genetic coefficients of regional corn yield trial data, it may be appropriate to calibrate only genetic coefficients related to phenology.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.2
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• pp.68-78
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• 2020

Crop models have been used to predict vegetable crop yield, which would have a considerable economic impact on consumers as well as producers. A small number of models have been developed to estimate growth and yield of vegetables due to limited availability of growth observation data in high-quality. In this study, we aimed to analyze the protocols designed for collection of the observation data for major vegetable crops including cabbage, radish, garlic, onion and pepper. We also designed the protocols suitable for development and verification of a vegetable crop growth model. In particular, different measures were proposed to improve the existing protocol used by Statistics Korea (KOSTAT) and Rural Development Administration (RDA), which would enhance reliability of parameter estimation for the crop model. It would be advantageous to select sampling sites in areas where reliable weather observation data can be obtained because crop models quantify the response of crop growth to given weather conditions. It is recommended to choose multiple sampling sites where climate conditions would differ. It is crucial to collect time series data for comparison between observed and simulated crop growth and yield. A crop model can be developed to predict actual yield rather than attainable yield using data for crop damage caused by diseases and pests as well as weather anomalies. A bigdata platform where the observation data are to be shared would facilitate the development of crop models for vegetable crops.

• Journal of the Korean GEO-environmental Society
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• v.19 no.12
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• pp.65-72
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• 2018

The uses of unmanned aerial vehicles (UAV) have been expanding in agriculture surveys, obtaining real time updates of dangerous facilities where human access is difficult, disaster monitoring, and 3D modeling. In reality, there is an upsurge in the application of UAVs in fields like, construction, infrastructure, imaging, surveying, surveillance and transportation. Especially, when the slope failure such as landslide occurs, the uses of UAVs are increasing. Since, the UAVs can fly in three dimensions, they are able to obtain spatial data in places where human access is nearly impossible. Despite of these advantages, however, the uses of UAVs are still limited during slope failure. In order to overcome these limitations, this study computes the soil volume change during slope failure through the computation technique using photogrammetric information obtained from UAV system. Through this study, it was found that photogrammetric information from UAV can be used to acquire information on amount of earthworks required for repair works when slope collapse occurs in mountainous areas, where human access in difficult.

• Korean Journal of Agricultural and Forest Meteorology
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• v.1 no.1
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• pp.12-19
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• 1999

CERES-rice, a rice growth simulation model, was used in conjunction with daily weather data to figure out the spatial variation of the phenology and yields of paddy rice at 168 rice cultivation zone units(CZU) of Kyunggi Province in 1997. Two sets of cultivar specific coefficients, which represent early and mid-season maturing varieties, were derived from field experiments conducted at two crop experiment stations. The minimum data set to run the model for each CZU (daily maximum and minimum temperature, solar irradiance, and rainfall) was obtained by spatial averaging of existing 'Digital Map of Korean Climate'(Shin et al., 1999). Soil characteristics and management information at each CZU were available from the Rural Development Administration. According to a preliminary test using 5 to 9 years field data, trends of the phasic development(heading and physiological maturity), which were obtained from the model adjusted for these coefficients, were in good agreement with the observed data. However, the simulated inter-annual variation was somewhat greater than the reported variation. Rough rice yields of the early maturing cultivar calculated by the model were comparable with the reported data in terms of both absolute value and inter -annual variation. But those of the mid season cultivar showed overestimation. After running the simulation model runs with 1997 weather data for 168 CZU's, rough rice yields of the 168 CZU's calculated by the model were aggregated into corresponding 33 counties by acreage-weighting to facilitate direct comparison with the reported statistics from the Ministry of Agriculture and Forestry. The simulation results were good at 22 out of the 26 counties with reportedly increasing yield trend with respect to the past 9 years average.

• Korean Journal of Remote Sensing
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• v.36 no.6_2
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• pp.1523-1535
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• 2020

In order to produce crop information using remote sensing, we use classification and growth monitoring based on crop phenology. Therefore, time-series satellite images with a short period are required. However, there are limitations to acquiring time-series satellite data, so it is necessary to use fusion with other earth observation satellites. Before fusion of various satellite image data, it is necessary to overcome the inherent difference in radiometric characteristics of satellites. This study performed Korea Multi-Purpose Satellite-3 (KOMPSAT-3) cross calibration with Landsat-8 as the first step for fusion. Top of Atmosphere (TOA) Reflectance was compared by applying Spectral Band Adjustment Factor (SBAF) to each satellite using hyperspectral sensor band aggregation. As a result of cross calibration, KOMPSAT-3 and Landsat-8 satellites showed a difference in reflectance of less than 4% in Blue, Green, and Red bands, and 6% in NIR bands. KOMPSAT-3, without on-board calibrator, idicate lower radiometric stability compared to ladnsat-8. In the future, efforts are needed to produce normalized reflectance data through BRDF (Bidirectional reflectance distribution function) correction and SBAF application for spectral characteristics of agricultural land.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1219-1230
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• 2022

Multi-spectral drones in agricultural observation require quantitative and reliable data based on physical quantities such as radiance or reflectance in crop yield analysis. In the case of remote sensing data for crop monitoring, images taken in the same area over time-series are required. In particular, biophysical data such as leaf area index or chlorophyll are analyzed through time-series data under the same reference, it can be directly analyzed. So, comparable reflectance data are required. Orthoimagery using drone images, the entire image pixel values are distorted or there is a difference in pixel values at the junction boundary, which limits accurate physical quantity estimation. In this study, reflectance and vegetation index based on drone images were calculated according to the correction method of drone images for time-series crop monitoring. comparing the drone reflectance and ground measured data for spectral characteristics analysis.