• 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.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.5
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• pp.409-421
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• 2017

Recently Unmanned Aerial Vehicle (UAV) technology offers new opportunities for assessing crop growth condition using UAV imagery. The objective of this study was to select optimal vegetation indices and regression model for estimating of rice growth using UAV images. This study was conducted using a fixed-wing UAV (Model : Ebee) with Cannon S110 and Cannon IXUS camera during farming season in 2016 on the experiment field of National Institute of Crop Science. Before heading stage of rice, there were strong relationships between rice growth parameters (plant height, dry weight and LAI (Leaf Area Index)) and NDVI (Normalized Difference Vegetation Index) using natural exponential function ($R{\geq}0.97$). After heading stage, there were strong relationships between rice dry weight and NDVI, gNDVI (green NDVI), RVI (Ratio Vegetation Index), CI-G (Chlorophyll Index-Green) using quadratic function ($R{\leq}-0.98$). There were no apparent relationships between rice growth parameters and vegetation indices using only Red-Green-Blue band images.

• Journal of The Korean Society of Grassland and Forage Science
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• v.44 no.3
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• pp.165-172
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• 2024

Italian ryegrass (IRG) has become a vital forage crop due to its increasing cultivation area and its role in enhancing forage self-sufficiency. However, its production is susceptible to environmental factors such as climate change and drought, necessitating precise yield prediction technologies. This study aimed to assess the growth characteristics of IRG and predict dry matter yield (DMY) using vegetation indices derived from unmanned aerial vehicle (UAV)-based remote sensing. The Green Leaf Index (GLI), normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), and optimized soil-adjusted vegetation index (OSAVI) were employed to develop DMY estimation models. Among the indices, GLI demonstrated the highest correlation with DMY (R² = 0.971). The results revealed that GLI-based UAV observations can serve as reliable tools for estimating forage yield under varying environmental conditions. Additionally, post-winter vegetation coverage in the study area was assessed using GLI, and 54% coverage was observed in March 2023. This study assesses that UAV-based remote sensing can provide high-precision predictions of crop yield, thus contributing to the stabilization of forage production under climate variability.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.3
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• pp.75-82
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• 2011

This study refers to the derivation of simple crop yield prediction equation by using KOMPSAT-2 derived vegetation index. For a 1.25 ha small farm area located in the middle part of South Korea, the KOMPSAT-2 panchromatic and multi-spectral images of 31th August 2008, 17th November 2008, and 10th September 2009 were used. The field spectral reflectance during growing period for the 6 crops (rice, potato, corn, red pepper, garlic, and bean) were measured using ground spectroradiometer and the yield was investigated. Among the 6 vegetation indices (VI), the NDVI and ARVI between measured and image derived showed high relationship with the coefficient of determination of 0.85 and 0.95 respectively. Using the 3 years field data, the NDVI and ARVI regression curves were derived, and the yields were tried to compare with the maximum VIs value.

• Proceedings of the Korean Society of Crop Science Conference
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• 1987.06a
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• pp.42-43
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• 1987

• Korean Journal of Weed Science
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• v.32 no.1
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• pp.1-9
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• 2012

This study was conducted to survey vegetation changes and soil characteristics in Saemangeum new reclaimed tidal land. Soil salinity in border area to tidal land was 22.3 dS $m^{-1}$ but showed 1.1~3.44 dS $m^{-1}$ over the distance of 2 km from border line. The vascular plants in survey sites were recorded as total 26 taxa in 6 families. The frequency of species appearance of Aster tripolium, A. subulatus var. sandwicensis were highest by 61.5 and that of Phragmites communis, Puccinellia nipponica were 53.8. The almost vegetations occurred in the patch which range of soil salinity 14 dS $m^{-1}$ were halophytes as Salicornia europaea, Suaeda asparagoides, S. japonica. As lowed soil salinity as 6.7 dS $m^{-1}$, mixed vegetation of halophytes with P. communis, P. nipponica, Carex pumila were occurred. Dominant species in the range of 3.0 dS $m^{-1}$ area were A. subulatus var. sandwicensis, P. communis, Echinochloa spp., Zoysia sinica and Conyza canadensis. Biomass production was the highest in the area of dominant vegetation with P. communis, and mixed zone with P. communis and Aeschynomene indica are followed. The correlation between vegetation biomass and soil salinity, soil pH and dominance index of vegetation were negative. But that of vegetation biomass and soil organic content were positive.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1559-1572
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• 2021

The response of vegetation under the crop stress condition was evaluated using structural, biochemical, and physiological vegetation indices based on unmanned aerial vehicle (UAV) images and field-spectrometer data. A high concentration of herbicide was sprayed at the different growth stages of garlic to process crop stress, the above ground dry matter of garlic at experimental area (EA) decreased about 46.2~84.5% compared to that at control area. The structural vegetation indices clearly responded to these crop damages. Spectral reflectance at near-infrared wavelength consistently decreased at EA. Most biochemical vegetation indices reflected the crop stress conditions, but the meaning of physiological vegetation indices is not clear due to the effect of vinyl mulching. The difference of the decreasing ratio of vegetation indices after the herbicide spray was 2.3% averagely in the case of structural vegetation indices and 1.3~4.1% in the case of normalization-based vegetation indices. These results meant that appropriate vegetation indices should be utilized depending on the types of crop stress and the cultivation environment and the normalization-based vegetation indices measured from the different spatial scale has the minimized difference.

• Weed & Turfgrass Science
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• v.2 no.3
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• pp.260-266
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• 2013

This study was conducted to investigate changes of vegetation and soil characteristics to tidal land of Saemangeum reclaimed land from 2010 to 2012. Soil salinity was 0.16-22.3 dS $m^{-1}$ in the first survey, while the three years later, it was decreased to 0.12-4.22 dS $m^{-1}$. Vegetations were classified as 6 families and 26 species but it was increased to 8 families and 31 species after three years. Numbers of average species in survey site were increased from 7.1 species to 10.6 species. Numbers of vegetation were increased at each survey sites except for survey site 7 : there was decreased halophyte according to decrease in the soil salinity. Biomass production was increased at low production site, and showed highest production in area of dominant vegetation as Phragmites communis. Simpson's dominance ratio(SDR) of main vegetation as Phragmites communis, Calamagrostis epigeios were increased but Suaeda maritima, Salicornia europaea, Puccinellia nipponica and Zoysia sinica were decreased.

• 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.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.665-673
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• 2010

The role of remote sensing in phenological studies is increasingly regarded as a key in understanding large area seasonal phenomena. This paper describes the application of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for vegetation classification using seasonal variation patterns. The vegetation seasonal variation phase of Seoul and provinces in Korea was inferred using 8 day composite MODIS NDVI (Normalized Difference Vegetation Index) dataset of 2006. The seasonal vegetation classification approach is performed with reclassification of 4 categories as urban, crop land, broad-leaf and needle-leaf forest area. The BISE (Best Index Slope Extraction) filtering algorithm was applied for a smoothing processing of MODIS NDVI time series data and fuzzy classification method was used for vegetation classification. The overall accuracy of classification was 77.5% and the kappa coefficient was 0.61%, thus suggesting overall high classification accuracy.