• Journal of the Korean Society of Environmental Restoration Technology
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• v.8 no.2
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• pp.33-40
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• 2005

The objective of this research was to find an indirect method to estimate spectral reflectance and NDVI(Normalized Difference Vegetation Index) efficiently, using the spectroradiometer and NOAA AVHRR satellite data. For collecting RS base data, used spectro-radiometer that measures reflection characteristics between 300~1,100nm was used and measured the reflection of vegetation from paddy rice during the growing season at Chungbuk national university's farm in 2002. The feasibility of detecting the temporal variation in the spectral reflectance and NDVI in paddy rice were conducted on eight growth stages. AVHRR data were collected in eight different months over a one year period in 2002. The results were compared with those obtained by analyzing NDVI characteristics. The spectral reflectance and NDVI of paddy rice have a great effect on the growth condition. Considerably, NDVI was increased by developing muscle fiber tissue at the near infrared wavelength until the Booting stage. Then the NDVI increased until the Maturity stage and then decreased until harvest. The highest month was at July and the lower month was at March. The difference NDVI analysis using March and another months data was conducted, the results were provided information on the growth condition of crops.

• Journal of Astronomy and Space Sciences
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• v.29 no.4
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• pp.407-411
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• 2012

Space-borne remote sensing is an effective and inexpensive way to identify crop fields and detect the crop condition. We examined the multi-temporal spectral characteristics of rice fields in South Korea to detect their phenological development and condition. These rice fields are compact, small-scale parcels of land. For the analysis, moderate resolution imaging spectroradiometer (MODIS) and RapidEye images acquired in 2011 were used. The annual spectral tendencies of different crop types could be detected using MODIS data because of its high temporal resolution, despite its relatively low spatial resolution. A comparison between MODIS and RapidEye showed that the spectral characteristics changed with the spatial resolution. The vegetation index (VI) derived from MODIS revealed more moderate values among different land-cover types than the index derived from RapidEye. Additionally, an analysis of various VIs using RapidEye satellite data showed that the VI adopting the red edge band reflected crop conditions better than the traditionally used normalized difference VI.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.1
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• pp.97-110
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• 2021

Vegetation index information is an important figure that is used in many fields such as landscape architecture, urban planning, and environment. Vegetation may vary slightly in vegetation vitality depending on photosynthesis and chlorophyll content. In this study, a range of vegetation worth preserving in the Taehwa River water system was determined, and hyperspectral images of drones were acquired (August, October), and the results were presented through DVI(Normalized Defference Vegetation Index), EVI(Enhanced Vegetation Index), PRI(Photochemical Reflectance Index), ARI (Anthocyanin Reflectance Index) index analysis. In addition, field spectral data and VRS-GPS(Virtual Reference System-GPS) surveys were performed to ensure the quality and location accuracy of the spectral band. As a result of the analysis, NDVI and EVI showed low vegetation vitality in October, -0.165 and -0.085, respectively, and PRI and ARI increased to 0.011 and 7.588 in October, respectively. For general vegetation vitality, it was suggested that NDVI and EVI analysis were effectively performed, and PRI and ARI were thought to be effective in analyzing detailed characteristics of plants by spectral band. It is expected that it can be widely used for park design and landscape information modeling by using drone image information construction and vegetation information.

• Journal of the Korean Geosynthetics Society
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• v.20 no.4
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• pp.141-149
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• 2021

In this study, an laboratory experiment was performed for prediction technology of water content in the ground using hyperspectral information. And the spectral reflectance with a specific wavelength band was obtained according to the fine and water content. Through it, the spectral information was normalized with the spectral index of the existing literature, and the relationship with the fine and water contents and the reliability of the prediction technology were analyzed. As a result of analysis, the spectral reflectance is decreased when the water and fine contents are increased under the high water contents. In addition, the reliability of prediction technology of water content was evaluated by examining 7 different spectral index calculation methods. Among them, DVI showed relatively high prediction reliability and was superior to other calculation methods in terms of sensitivity.

• Journal of Ecology and Environment
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• v.35 no.2
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• pp.91-98
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• 2012

This study was performed in order to suggest the possibility of tree species classification using high-resolution QuickBird-2 images spectral characteristics comparison(digital numbers [DNs]) of tree species, tree species classification, and accuracy verification. In October 2010, the tree species of three conifers and eight broad-leaved trees were examined in the areas studied. The spectral characteristics of each species were observed, and the study area was classified by image classification. The results were as follows: Panchromatic and multi-spectral band 4 was found to be useful for tree species classification. DNs values of conifers were lower than broad-leaved trees. Vegetation indices such as normalized difference vegetation index (NDVI), soil brightness index (SBI), green vegetation index (GVI) and Biband showed similar patterns to band 4 and panchromatic (PAN); Tukey's multiple comparison test was significant among tree species. However, tree species within the same genus, such as $Pinus$ $densiflora-P.$ $rigida$ and $Quercus$ $mongolica-Q.$ $serrata$, showed similar DNs patterns and, therefore, supervised classification results were difficult to distinguish within the same genus; Random selection of validation pixels showed an overall classification accuracy of 74.1% and Kappa coefficient was 70.6%. The classification accuracy of $Pterocarya$ $stenoptera$, 89.5%, was found to be the highest. The classification accuracy of broad-leaved trees was lower than expected, ranging from 47.9% to 88.9%. $P.$ $densiflora-P.$ $rigida$ and $Q.$ $mongolica-Q.$ $serrata$ were classified as the same species because they did not show significant differences in terms of spectral patterns.

• Bulletin of the Korean Mathematical Society
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• v.35 no.4
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• pp.785-790
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• 1998

Using spectral sequences we calculate the hightest non-vanishing index of Tor for modules of finite projective dimension.

• Journal of Photoscience
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• v.7 no.4
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• pp.139-142
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• 2000

To examine applicability of some optical indices from reflectance to estimate photosynthetic light use efficiency, photosynthesis, and narrow band spectral reflectance were simultaneously measured at various intensities of light with mongolian oak leaves. Narrow band of the broad-band NDVI was better than photochemical reflectance index and simple ratio to estimate photosynthetic light use efficiency in this study. Changes in spectral reflectance were detected at several wavelengths (540nm, 690nm, 740nm, and 800nm) associated with physiological status of plant leaves that could be components for new optical indices.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.111-121
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• 2006

The classification using spectral angle is a new approach based on the fact that the spectra of the same type of surface objects in RS data are approximately linearly scaled variations of one another due to atmospheric and topographic effects. There are many researches on the unsupervised classification using spectral angle recently. Nevertheless, there are only a few which consider the characteristics of Hyperspectral data. On this study, we propose the ISOMUSAC(Iterative Self-Organizing Modified Unsupervised Spectral Angle Classification) which can supplement the defects of previous unsupervised spectral angle classification. ISOMUSAC uses the Angle Division for the selection of seed points and calculates the center of clusters using spectral angle. In addition, ISOMUSAC perform the iterative merging and splitting clusters. As a result, the proposed algorithm can reduce the time of processing and generate better classification result than previous unsupervised classification algorithms by visual and quantitative analysis. For the comparison with previous unsupervised spectral angle classification by quantitative analysis, we propose Validity Index using spectral angle.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.42.2-42.2
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• 2013

The F-GAMMA (FERMI-GST AGN Multi-frequency Monitoring Alliance) project is a program for the monthly monitoring of the broad-band spectra of currently about 90 selected Fermi-GST AGNs. F-GAMMA utilizes several facilities in cm, mm, sub-mm, infrared and optical bands, achieving an unprecedented coverage for the study of the spectral evolution of powerful relativistic jets in AGNs. The KVN joined the F-GAMMA project in May 2011, aiming to monitor flux density at 22 and 43 GHz. We present the preliminary results of flux density variability, evolution of spectral index, and modulation index.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.396-402
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• 1999

The aim of this study is to assess the OSMI (Ocean Scanning Multi-spectral Imager), whose central bands are 443nm, 490nm, 510nm, 555nm, 670nm, and 865nm, for agricultural applications. Radiance measurements, used to determine per cent reflectance of canopies and soils, were acquired with spectro-radiometers (Li-1800;330~1,100nm, GER-SFOV;350 ~2,500nm, and MSR-7000; 300~2,500nm) in situ for crops and indoors for soils. OSMI equivalent bands and their ratio values were prepared(20nm interval for bands 1~5; 4nm interval for band 6) by averaging spectral reflectance values to the real OSMI bands and analyzed as to crop growth parameters, leaf area index (LAI), total dry matter, and growth index in crops and physiochemical properties in soils. Spectral variations for each growth stage in rice and for crop discrimination in upland crops were significant statistically. In soils, clay and water content, CEC (Cation Exchange Capacity), free iron oxide, and some cation content were correlated with the OSMI equivalent bands. The result of this study shows OSMI wave bands would be promising for agricultural application in terms of spectral information and resolution.