• Spatial Information Research
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• v.3 no.1
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• pp.47-53
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• 1995

The weed survey in lowland rice fields through Korea was conducted in 1992 to determine a change of the weed communities based on different regions, soil types, planting methods, and cultural practices. GIS was applied to identify a spatial analysis of predominant weed species in specific region. On behalf of vegetatine analysis such as absolute and relative density, absolute and relative frequency, importance value, and summed dominance ratio(SDR), there was highly dominant with a perennial weed species, Eleocharis kuroguwai Ohwi over whole country. However, in particular it was most predominant at southem area of Gyunggi province in Korea. Thus, rice farmers of this area have to introduce a specific comperhensive control strategy against this predominant weed species.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.108-118
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• 2018

Recently, agricultural sector apply ICT technology such as Smart Farm to pursue innovation in the changing situation that is emerging as the fourth industrial revolution. However, this innovation requires techniques for forecasting and analyzing in various data bases and spatial information provides such infrastructure data. In this study, the cultivation area of Chinese cabbage, radish, garlic, onion, and red pepper were calculated and analyzed by year. The purpose of this analysis is to cope with sudden changes in vegetable crops and changes in cultivated area caused by weather changes to supply and demand of major vegetables and price instability. As a result of this study, spatial information based on time series information of vegetable complex will be used as efficient agricultural environment observation data, as well as interpretation of various spatial ranges such as the estimation of cultivation area using remote sensing.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.419-427
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• 2021

Due to the effects of climate change and the reduction of the labor force due to COVID-19, the crop yield, harvest time, and cultivated area are rapidly changing every year. In order to respond flexibly to this situation, attempts to apply smart farm technology based on ICT (Information and Communication Technology) to individual farms are increasing. On the other hand, various stakeholders are trying to predict the yield of crops using artificial intelligence and IoT technology, but accurate prediction is difficult due to the lack of learning data. In this study, in order to overcome the data collection problem limited to a specific institution, a smart farmer service technology based on community mapping was developed in which farmers directly participate, input and share accurate data to predict production. In the process, analysis was performed on napa cabbage, which is a vegetable with a large price change compared to production.

• Aerospace Engineering and Technology
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• v.10 no.1
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• pp.149-155
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• 2011

A fast-changing agriculture environment induced by global warming and abnormal climate conditions demands scientific systems for monitoring and predicting crop conditions as well as crop yields at national level. Remote sensing opens up a new application field for precision agriculture with the help of commercial use of high resolution optical as well as radar satellite data. In this study, we investigated the multi-temporal spectral characteristics relative to different agricultural land use types in Korea using RapidEye satellite imagery. There were explicit differences between vegetation and non-vegetation land use types. Also, within the vegetation group spectral vegetation indices represented differences in temporal changing trends as to plant species and paddy types.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.86-101
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• 2019

Remote sensing technology has been continuously developed both quantitatively and qualitatively, including platform development, exploration area, and exploration functions. Recently, the use cases and related researches in the agricultural field are increasing. Also, since it is possible to detect and quantify the condition of cropland and establish management plans and policy support for cropland and agricultural environment, it is being studied in various fields such as crop growth abnormality determination and crop estimation based on time series information. The purpose of this study was to analyze the vegetation index for agricultural land reclamation area using a UAV equipped with a multi-spectral sensor. In addition, field surveys were conducted to evaluate the accuracy of vegetation indices calculated from multispectral image data obtained using UAV. The most appropriate vegetation index was derived by evaluating the correlation between vegetation index calculated by field survey and vegetation index calculated from UAV multispectral image, and was used to analyze vegetation index of the entire area.

• Aerospace Engineering and Technology
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• v.10 no.2
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• pp.29-36
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• 2011

The Science and Technology Satellite-3 (STSAT-3) is based on the KITSAT-1, 2, 3 and STSAT-1, 2 which were Korea micro-satellites for the mission of space and earth science. The objectives of the STSAT-3 are to support earth and space sciences in parallel with the demonstration of spacecraft technology. The STSAT-3 carries an infrared (IR) camera for space & earth observation and an imaging spectrometer for earth observation. The IR payload instrument of the STSAT-3, Multi-purpose Infrared Imaging System (MIRIS), will observe the Galactic plane and North/South Ecliptic poles to research the origin of universe. The secondary payload instrument, Compact Imaging Spectrometer (COMIS), images the Earth's surface. The data acquired from COMIS are expected to be used for various application fields such as monitoring of disaster management, water quality studies, and farmland assessment. In this paper we present the operations concept of STSAT-3 which will be launched into a sun-synchronous orbit at a nominal altitude of 600km in late 2012.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.885-894
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• 2006

The purpose of this study is focused on the development of the effective classification technique using ultra multiband of hyperspectral image. This study suggests the classification technique using canonical correlation analysis, one of multivariate statistical analysis in hyperspectral image classification. High accuracy of classification result is expected for this classification technique as the number of bands increase. This technique is compared with Maximum Likelihood Classification(MLC). The hyperspectral image is the EO1-hyperion image acquired on September 2, 2001, and the number of bands for the experiment were chosen at 30, considering the band scope except the thermal band of Landsat TM. We chose the comparing base map as Ground Truth Data. We evaluate the accuracy by comparing this base map with the classification result image and performing overlay analysis visually. The result showed us that in MLC's case, it can't classify except water, and in case of water, it only classifies big lakes. But Canonical Correlation Classification (CCC) classifies the golf lawn exactly, and it classifies the highway line in the urban area well. In case of water, the ponds that are in golf ground area, the ponds in university, and pools are also classified well. As a result, although the training areas are selected without any trial and error, it was possible to get the exact classification result. Also, the ability to distinguish golf lawn from other vegetations in classification classes, and the ability to classify water was better than MLC technique. Conclusively, this CCC technique for hyperspectral image will be very useful for estimating harvest and detecting surface water. In advance, it will do an important role in the construction of GIS database using the spectral high resolution image, hyperspectral data.