• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.503-514
• /
• 2021

Rice production with adequate level of area is important for decision making of rice supply and demand policy. It is essential to grasp rice cultivation areas in advance for estimating rice production of the year. This study was carried out to classify paddy rice cultivation in Gimje-si using sentinel-1 SAR (synthetic aperture radar) and UAV imagery in early July. Time-series Sentinel-1A and 1B images acquired from early May to early July were processed to convert into sigma naught (dB) images using SNAP (SeNtinel application platform, Version 8.0) toolbox provided by European Space Agency. Farm map and parcel map, which are spatial data of vector polygon, were used to stratify paddy field population for classifying rice paddy cultivation. To distinguish paddy rice from other crops grown in the paddy fields, we used the decision tree method using threshold levels and random forest model. Random forest model, trained by mainly rice cultivation area and rice and soybean cultivation area in UAV image area, showed the best performance as overall accuracy 89.9%, Kappa coefficient 0.774. Through this, we were able to confirm the possibility of early estimation of rice cultivation area in Gimje-si using UAV image.

• Korean Journal of Remote Sensing
• /
• v.33 no.5_2
• /
• pp.677-687
• /
• 2017

In this paper, we propose the use of hierarchical classification for winter crop mapping based on satellite imagery. A hierarchical classification is a classifier that maps input data into defined subsumptive output categories. This classification method can reduce mixed pixel effects and improve classification performance. The methodology are illustrated focus on winter cropsin Gimje city, Jeonbuk with Landsat-8 imagery. First, agriculture fields were extracted from Landsat-8 imagery using Smart Farm Map. And then winter crop fields were extracted from agriculture fields using temporal Normalized Difference Vegetation Index (NDVI). Finally, winter crop fields were then classified into wheat, barley, IRG, whole crop barley and mixed crop fields using signature from Unmanned Aerial Vehicle (UAV). The results indicate that hierarchical classifier could effectively identify winter crop fields with an overall classification accuracy of 98.99%. Thus, it is expected that the proposed classification method would be effectively used for crop mapping.

• Journal of Korean Society for Geospatial Information Science
• /
• v.1 no.1 s.1
• /
• pp.181-192
• /
• 1993

Image is composed of the digital numbers including information on natural phenomena, their condition and the kind of objects. Digital numbers change in geometric correction(that is preprocessing). This change of digital numbers gave an effect on results of land-cover classification. We intend to know the influence of resampling as classifying land-cover using the image reconstructed by geometric correction in this paper. Chun-cheon basin was selected the study area having most variable land-cover pattern in North-Han river valley and made on use of RESTEC data resampled in preprocessing. Land-cover is classified as six classes of LEVEL I using maximum likelyhood classification method. We classified land-cover using the image resampled by two methods in this study. Bilinear interpolation method was most accurate in five classes except bear-land in the result of comparing each class with topographic map. We should choose the method of resampling according to the class in which we put the importance in the image resampling of geometric correction. And if we use four-season's image, we may classify more accurately in case of the confusion in case of the confusion in borders of rice field and farm.

• Journal of the Korean Solar Energy Society
• /
• v.31 no.6
• /
• pp.8-22
• /
• 2011

Investigations on modeling methods of a CFD wind resource prediction program, WindSim for a ccurate predictions of wind speeds were performed with the field measurements. Meteorological Masts having heights of 40m and 50m were installed at two different sites in complex terrain. The wind speeds and direction were monitored from sensors installed on the masts and recorded for one year. Modeling parameters of WindSim input variables for accurate predictions of wind speeds were investigated by performing cross predictions of wind speeds at the masts using the measured data. Four parameters that most affect the wind speed prediction in WindSim including the size of a topographical map, cell sizes in x and y direction, height distribution factors, and the roughness lengths were studied to find out more suitable input parameters for better wind speed predictions. The parameters were then applied to WindSim to predict the wind speed of another location in complex terrain in Korea for validation. The predicted annual wind speeds were compared with the averaged measured data for one year from meteorological masts installed for this study, and the errors were within 6.9%. The results of the proposed practical study are believed to be very useful to give guidelines to wind engineers for more accurate prediction results and time-saving in predicting wind speed of complex terrain that will be used to predict annual energy production of a virtual wind farm in complex terrain.

• Korean Journal of Agricultural Science
• /
• v.47 no.2
• /
• pp.205-217
• /
• 2020

In this study, a deep learning-based tillage boundary detection method for autonomous tillage by a tractor was developed, which consisted of image cropping, object classification, area segmentation, and boundary detection methods. Full HD (1920 × 1080) images were obtained using a RGB camera installed on the hood of a tractor and were cropped to 112 × 112 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the path boundary was detected using a probability map, which was generated by the integration of softmax outputs. The results show that the F1-score of the classification was approximately 0.91, and it had a similar performance as the deep learning-based classification task in the agriculture field. The path boundary was determined with edge detection and the Hough transform, and it was compared to the actual path boundary. The average lateral error was approximately 11.4 cm, and the average angle error was approximately 8.9°. The proposed technique can perform as well as other approaches; however, it only needs low cost memory to execute the process unlike other deep learning-based approaches. It is possible that an autonomous farm robot can be easily developed with this proposed technique using a simple hardware configuration.

• Journal of Environmental Impact Assessment
• /
• v.20 no.3
• /
• pp.313-324
• /
• 2011

The purpose of this study is to present the raw data for establishing the plan of top soil conservation in soil environment and preventing the soil loss by establishing the potential amount of soil loss using RUSLE. The results are as follows. To apply the RUSLE model, we calculated the potential amount of soil loss by using 5 factors; rainfall erosion factor(R), topographical factor(LS), soil erosion factor(K), land cover factor(C) and erosion control factor(P). The assessment map of soil loss was drawn up by classifying 5 grades. According to the soil loss estimation by the RUSLE, it showed that approximately 83.9% of the study area had relatively lower possibility of soil loss which was the 1 ton/ha in annual soil loss. Whereas, the 7.0% of the study area was defined as high risk area which was the 10 ton/ha in annual. Therefore, this area was needed that there was environment-friendly construction of farm land, improvement of cultivation environment and so forth. In future, if we will analyze the amount of soil loss of Gyeongju national park and Hyeongsan river watershed, we will offer the help to establishing the conservation plan of soil environment in Gyeongsangbuk-do.

• Journal of Biosystems Engineering
• /
• v.41 no.4
• /
• pp.304-312
• /
• 2016

Purpose: Soil strength has been measured using a cone penetrometer, which is making it difficult to obtain the spatial data required for precision agriculture. Our objectives were to evaluate real-time horizontal soil strength (RHSS) to measure soil strength in real time while moving across the field. Using the RHSS data, the tillage depth was determined, and the power consumption of a tractor and rotavators were compared. Methods: The horizontal soil-strength index (HSSI) obtained by the RHSS was compared with the cone index (CI), which was measured using a cone penetrometer. Comparison analysis in accordance with the measurement depth that increased at 5-cm interval was conducted using kriged maps at six sensing depths. For tillage control and evaluation of the power consumption, the system was installed with a potentiometer for tillage depth, a torque sensor from the rear axle, and a power take-off (PTO) shaft. Results: The HSSI was lower than the CI, but they were the same at 54.81% of the total grids for the 5-cm depth and at 3.85% for the 10-cm depth. In accordance with the recommended tillage map, tillage operations between 0 and 15 cm left 2.3% and 7% residue cover on the soil, and that between 20 and 10 cm covered a wider utilization of 3% and 18.4%, respectively. When the tillage depth was 15 cm, the comparison result of the power requirements between the PTO and rear axle in terms of control performance revealed that the maximum power requirements of the axle and PTO were 44.63 and 23.24 kW, respectively. Conclusions: An HSSI measurement system was evaluated by comparison with the conventional soil strength measurement system (CI) and applied to a tractor to compare the tillage power consumption. Further study is needed on its application to various farm works using a tractor for precision agriculture.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.37 no.11
• /
• pp.1121-1130
• /
• 2009

In order to investigate the offshore wind resources, the measured data from the QuikSCAT satellite was analyzed from Jan 2000 to Dec 2008. QuikSCAT satellite is a specialized device for a microwave scatterometer that measures near-surface wind speed and direction under all weather and cloud conditions. Wind speed measured at 10 m above from the sea surface was extrapolated to the hub height by using the power law model. It has been found that the high wind energy prevailing in the south sea and the east sea of the Korean peninsula. From the limitation of seawater depth for piling the tower and archipelagic environment around the south sea, the west and the south-west sea are favorable to construct the large scale offshore wind farm, but it needs efficient blade considering relatively low wind speed. Wind map and monthly variation of wind speed and wind rose using wind energy density were investigated at the specified positions.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.53 no.1
• /
• pp.29-36
• /
• 2011

HPAI (High pathogenic avian influenza) which is a disease legally designated as an epidemic generally shows rapid spread of disease resulting in high mortality rate as well as severe economic damages. Because Korea is contiguous with China and southeast Asia where HPAI have occurred frequently, there is a high risk for HPAI outbreak. A prompt treatment against epidemics is most important for prevention of disease spread. The spread of HPAI should be considered by both direct and indirect contact as well as various spread factors including airborne spread. There are high risk of rapid propagation of HPAI flowing through the air because of collective farms mostly in Korea. Field experiments for the mechanism of disease spread have limitations such as unstable weather condition and difficulties in maintaining experimental conditions. In this study, therefore, computational fluid dynamics which has been actively used for mass transfer modeling were adapted. Korea has complex terrains and many livestock farms are located in the mountain regions. GIS numerical map was used to estimate spreads of virus attached aerosol by means of designing three dimensional complicated geometry including farm location, road network, related facilities. This can be used as back data in order to take preventive measures against HPAI occurrence and spread.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.375-375
• /
• 2023

본 연구는 농지를 대상으로 토양수분 및 가뭄 현황을 분석하는 데 그 목적이 있다. 토양수분을 파악하기 위해 Terra MODIS(Moderate Resolution Imaging Spectroradiometer) 위성영상기반의 토양수분 산정모형을 개발하였다. 해당 모형은 MODIS LST(Land Surface Temperature) 및 NDVI(Normalized Difference Deficit Index)를 기반으로 SCS-CN(Soil Conservation Service-Curve Number) 방법에서 착안한 수문학적 개념 5일 선행강우 및 무강우일수를 입력자료로 하며, 토양 종류 및 계절에 따른 토양수분의 특성을 고려하였다. 모형의 개발을 위해 MODIS LST 및 NDVI 영상을 2013년부터 2022년까지 각각 일별 및 16일 단위로 구축하였으며, 동 기간에 대해 전국 88개소의 기상청 종관기상관측소의 강수량 및 LST 자료를 수집하였다. MODIS LST는 실측 LST 자료를 활용해 조건부합성기법을 적용하여 상세화하였고, 수집된 강수량자료는 역거리가중법을 활용해 공간 보간을 수행하였다. 토양특성의 구분은 농촌진흥청에서 정밀토양도를 수집하여 활용하였다. 공간 분포된 토양수분에서 농지에 해당하는 토양수분을 추출하기 위해 스마트팜맵을 구축하고, 농지 속성에 해당하는 위치 정보를 조회 후 이를 시군구별로 평균하여 일별 평균 토양수분값을 산정하였다. 토양수분 기반의 가뭄 현황 분석을 위해 구축된 정밀토양도에서 작물 생장과 관련된 영구위조점 및 포장용수량을 활용해 5단계(정상, 관심, 주의, 경계, 심각)의 가뭄 위험도를 산정하였으며, 실제 가뭄 현황과의 비교를 통해 토양수분기반의 가뭄 위험도의 실효성을 검증하고자 한다.