• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.305-311
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• 2006

Although there have been several attempts to estimate forest LAI using optical remote sensor data, there are still not enough evidences whether the NDVI is effective to estimate forest LAI, particularly in fully closed canopy situation. In this study, we have conducted a simple correlation analysis between LAI and spectral reflectance at two different settings: 1) laboratory spectral measurements on the multiple-layers of leaf samples and 2) Landsat ETM+ reflectance in the close canopy forest stands with fieldmeasured LAI. In both cases, the correlation coefficients between LAI and spectral reflectance were higher in short-wave infrared (SWIR) and visible wavelength regions. Although the near-IR reflectance showed positive correlations with LAI, the correlations strength is weaker than in SWIR and visible region. The higher correlations were found with the spectral reflectance data measured on the simulated vegetation samples than with the ETM+ reflectance on the actual forests. In addition, there was no significant correlation between the forest.LAI and NDVI, in particular when the LAI values were larger than three. The SWIR reflectance may be important factor to improve the potential of optical remote sensor data to estimate forest LAI in close canopy situation.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.7-10
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• 2005

Plant water deficiency during drought season causes physiological stress and can be a critical indicator of forest fire vulnerability. In this study, we attempt to analyze the spectral characteristics of water stressed vegetation by using the laboratory measurement on leaf samples and the canopy reflectance spectra extracted from satellite hyperspectral image data. Leaf-level reflectance spectra were measured by varying moisture content using a portable spectro-radiometer. Canopy reflectance spectra of sample forest stands of two primary species (pine and oak) located in central part of the Korean peninsula were extracted from EO-l Hyperion imaging spectrometer data obtained during the drought season in 2001 and the normal precipitation year in 2002. The preliminary analysis on the reflectance spectra shows that the spectral characteristics of leaf samples are not compatible with the ones obtained from canopy level. Although moisture content of vegetation can be influential to the radiant flux reflected from leaf-level, it may not be very straightforward to obtain the spectral characteristics that are directly related to the level of canopy moisture content. Canopy spectra form forest stands can be varied by structural variables (such as LAt, percent coverage, and biomass) other than canopy moisture content.

• Agricultural and Biosystems Engineering
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• v.6 no.1
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• pp.1-7
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• 2005

Assessing health condition of crop in the field is one of core operation in precision fanning. A sensing system was proposed to remotely detect the crop health condition in terms of SP AD readings directly related to chlorophyll contents of crop using a multispectral camera equipped on ground-based platform. Since the image taken by a camera was sensitive to changes in ambient light intensity, it was needed to convert gray scale image data into reflectance, an index to indicate the reflection characteristics of target crop. A reference reflectance panel consisting of four pieces of sub-panels with different reflectance was developed for a dynamic calibration, by which a calibration equation was updated for every crop image captured by the camera. The system performance was evaluated in a field by investigating the relationship between com canopy reflectance and SP AD values. The validation tests revealed that the com canopy reflectance induced from Green band in the multispectral camera had the most significant correlation with SPAD values $(r^2=0.75)$ and NIR band could be used to filter out unwanted non-crop features such as soil background and empty space in a crop canopy. This research confirmed that it was technically feasible to develop a ground-based remote sensing system for assessing crop health condition.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.325-328
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• 2007

We discuss the Normalized Difference Vegetation Index (NDVI) of the forest canopy and floor separately based on airborne spectral reflectance measurements and simultaneous airborne land surface images acquired around Yakutsk, Siberia in 2000. The aerial land surface images were visually classified into four forest types: no-green canopy and snow floor (Type-1), green canopy and snow floor (Type-2), no-green canopy and no-snow floor (Type-3), and green canopy and no-snow floor (Type-4). The mean NDVI was calculated for these four types. Although Type-2 had green canopy, the NDVI was rather small (0.17) because of high reflection from the snow cover on the floor. Type-3, which had no green canopy, indicated considerably large NDVI (0.45) due to the greenness of the floor. Type-4 had the largest NDVI (0.75) because of the greenness of both the canopy and floor. These results reveal that the NDVI depends considerably on forest floor greenness and snow cover in addition to canopy greenness.

• Korean Journal of Remote Sensing
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• v.31 no.1
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• pp.11-20
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• 2015

This study aimed to investigate the canopy growth conditions and the accuracy of phenological stages of paddy rice using ground-based remote sensing data. Plant growth variables including Leaf Area Index (LAI) and canopy reflectance of paddy rice were measured at the experimental fields of Chonnam National University, Gwangju, Republic of Korea during the crop seasons of 2011, 2012, and 2013. LAI values were also determined based on correlations with Vegetation Indices (VIs) obtained from the canopy reflectance. Three phenological stages (tillering, booting, and grain filling) of paddy rice could be identified using VIs and a spatial index (NIR versus red). We found that exponential relationships could be applied between LAI and the VIs of interest. This information, as well as the relationships between LAI and VIs obtained in the present study, could be used to estimate and monitor the relative growth and development of rice canopies during the growing season.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.2
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• pp.92-103
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• 2003

Research and technological advances in the field of remote sensing have greatly enhanced the ability to detect and quantify physical and biological stresses that affect the productivity of agricultural crops. Reflectance in specific visible and near-infrared regions of the electromagnetic spectrum have proved useful in detection of nutrient deficiencies. Especially crop canopy sensors as a ground remote sensing measure the amount of light reflected from nearby surfaces such as leaf tissue or soil and is in contrast to aircraft or satellite platforms that generate photographs or various types of digital images. Multi-spectral vegetation indices derived from crop canopy reflectance in relatively wide wave band can be used to monitor the growth response of plants in relation to environmental factors. The normalized difference vegetation index (NDVI), where NDVI = (NIR-Red)/(NIR+Red), was originally proposed as a means of estimating green biomass. The basis of this relationship is the strong absorption (low reflectance) of red light by chlorophyll and low absorption (high reflectance and transmittance) in the near infrared (NIR) by green leaves. Thereafter many researchers have proposed the other indices for assessing crop vegetation due to confounding soil background effects in the measurement. The green normalized difference vegetation index (GNDVI), where the green band is substituted for the red band in the NDVI equation, was proved to be more useful for assessing canopy variation in green crop biomass related to nitrogen fertility in soils. Consequently ground remote sensing as a non destructive real-time assessment of nitrogen status in plant was thought to be useful tool for site specific crop nitrogen management providing both spatial and temporal information.

• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.1055-1066
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• 2018

Vegetation indices on the basis of optical characteristics of vegetation can represent various conditions such as canopy biomass and physiological activity. Those have been mostly developed with the large-scaled applications of multi-band optical sensors on-board satellites. However, the sensitivity of vegetation indices for detecting vegetation features will be different depending on the spatial scales. Therefore, in this study, the investigation of photochemical reflectance index (PRI), known as one of useful vegetation indices for detecting photosynthetic ability and vegetation stress, under the three spatial scales was conducted using multi-spectral camera installed in unmanned aerial vehicle (UAV),field spectrometer, and leaf reflectometer. In the leaf scale, diurnal PRI had minimum values at different local-time according to the compass direction of leaf face. It meant that each leaf in some moment had the different degree of light use efficiency (LUE). In early growth stage of crop, $PRI_{leaf}$ was higher than $PRI_{stands}$ and $PRI_{canopy}$ because the leaf scale is completely not governed by the vegetation cover fraction.In the stands and canopy scales, PRI showed a large spatial variability unlike normalized difference vegetation index (NDVI). However, the bias for the relationship between $PRI_{stands}$ and $PRI_{canopy}$ is lower than that in $NDVI_{stands}$ and $NDVI_{canopy}$. Our results will help to understand and utilize PRIs observed at different spatial scales.

• Agricultural and Biosystems Engineering
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• v.6 no.1
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• pp.8-14
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• 2005

This study was carried out to develop a real-time grouping system of chlorophyll contents of rice crop canopy for precision agriculture. The system measured reflected light energy of a rice canopy on a paddy field from visual to near-infrared range and analyzed the collected information of chlorophyll contents of rice crop canopy with given position data. The four filters, 560 nm $({\pm}10nm)$, 650 nm $({\pm}25nm)$, 700 nm $({\pm}12nm)$, and 850 nm $({\pm}40nm)$, were used for a multiple regression to estimate the chlorophyll contents of rice crop canopy. Every $0.2m^2$ area of the open field was inspected at a distance of 1 m above the rice canopy. According to the results of verification test, the chlorophyll content grouping by a commerical chlorophyll meter (SPAD) and by the developed system showed 58.7% match for five-stage chlorophyll contents of rice crop canopy grouping and 93.5% for the $five{\pm}1-stage$ grouping. In addition, the results showed 63.0% match for three-stage grouping and 100.0% for the $three{\pm}1-stage$ grouping.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.5
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• pp.394-406
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• 2004

A split-plot designed experiment including four rice varieties and 10 nitrogen levels was conducted in 2003 at the Experimental Farm of Seoul National University, Suwon, Korea. Before heading, hyperspectral canopy reflectance (300-1100nm with 1.55nm step) and nine crop variables such as shoot fresh weight (SFW), leaf area index, leaf dry weight, shoot dry weight, leaf N concentration, shoot N concentration, leaf N density, shoot N density and N nutrition index were measured at 54 and 72 days after transplanting. Grain yield, total number of spikelets, number of filled spikelets and 1000-grain weight were measured at harvest. 14,635 narrow-band NDVIs as combinations of reflectances at wavelength ${\lambda}l\;and\;{\lambda}2$ were correlated to the nine crop variables. One NDVI with the highest correlation coefficient with a given crop variable was selected as the NDVI of the best fit for this crop variable. As expected, models to predict crop variables before heading using the NDVI of the best fit had higher $r^2$ (>10\%)$ than those using common broad- band NDVI red or NDVI green. The models with the narrow-band NDVI of the best fit overcame broad- band NDVI saturation at high LAI values as frequently reported. Models using NDVIs of the best fit at booting showed higher predictive capacity for yield and yield component than models using crop variables.

• Korean Journal of Soil Science and Fertilizer
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• v.47 no.3
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• pp.217-224
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• 2014

Ground-based remote sensing can be used as one of the non-destructive, fast, and real-time diagnostic tools for predicting yield, biomass, and nitrogen stress during growing season. The objectives of this study were: 1) to assess biomass and nitrogen (N) status of tobacco (Nicotiana tabacum L.) plants under N stress using ground-based remote sensors; and 2) to evaluate the feasibility of spectral reflectance indices for estimating an application rate of N and predicting yield of tobacco. Dry weight (DW), N content, and N uptake at the 40th and 50th day after transplanting (DAT) were positively correlated with chlorophyll content and normalized difference vegetation indexes (NDVIs) from all sensors (P<0.01). Especially, Green NDVI (GNDVI) by spectroradiometer and Crop Circle-passive sensors were highly correlated with DW, N content and N uptake. The yield of tobacco was positively correlated with canopy reflectance indices measured at each growth stage (P<0.01). The regression of GNDVI by spectroradiometer on yield showed positively quadratic curve and explained about 90% for the variability of measured yield. The sufficiency index (SI) calculated from data/maximum value of GNDVI at the $40^{th}$ DAT ranged from 0.72 to 1.0 and showed the same positively quadratic regression with N application rate explaining 84% for the variability of N rate. These results suggest that use of reflectance indices measured with ground-based remote sensors may assist in determining application rate of fertilizer N at the critical season and estimating yield in mid-season.