• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.370-375
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• 1998

Spectral reflectance signatures of upland crops at OSMI bands were collected and evaluated for the feasibility of crop discrimination knowledge-based on crop calendar. Effective bands and their ratio values for discriminating corn from two other legumes were defined with OSMI equivalent bands and their ratio values. June 22 among measurements dates was the best date for corn discrimination from two other legumes, peanut and soybean, because all OSMI equivalent bands and their ratio values in June 22 were highly significant for corn separability. Phenological growth stage of a silage corn (rs510) could be estimated as a function of spectral reflectance signatures in vegetative stage. Five growth stage prediction models were generated by the SAS procedures REG and STEPWISE with OSMI equivalent bands and their ratio values in vegetative stage.

• Journal of Forest and Environmental Science
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• v.29 no.2
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• pp.131-137
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• 2013

This study attempts to classify forest species using hyperspectral data for supporting resources management. The primary dataset used was AISA sensor. The sensor was mounted onboard the NOMAD GAF-27 aircraft at 2,000 m altitude creating a 2 m spatial resolution on the ground. Pre-processing was carried out with CALIGEO software, which automatically corrects for both geometric and radiometric distortions of the raw image data. The radiance data set was then converted to at-sensor reflectance derived from the FODIS sensor. Spectral Angle Mapper (SAM) technique was used for image classification. The spectra libraries for tree species were established after confirming the appropriate match between field spectra and pixel spectra. Results showed that the highest spectral signature in NIR range were Kembang Semangkok (Scaphium macropodum), followed by Meranti Sarang Punai (Shorea parvifolia) and Chengal (Neobalanocarpus hemii). Meanwhile, the lowest spectral response were Kasai (Pometia pinnata), Kelat (Eugenia spp.) and Merawan (Hopea beccariana), respectively. The overall accuracy obtained was 79%. Although the accuracy of SAM techniques is below the expectation level, SAM classifier was able to classify tropical tree species. In future it is believe that the most effective way of ground data collection is to use the ground object that has the strongest response to sensor for more significant tree signatures.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.425-427
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• 2004

Motivation of this study is based on these two aspects: geologic uses of ASTER and application scheme of Spectral Mixture Analysis. This study aims at geologic mapping for mineral exploration using ASTER and LANDSAT 7 ETM+ at Mongolian plateau region by SMA. After basic pre-processing such as the normalization, geometric corrections and calibration of reflectance, related to endmembers selection and spectral signature deviation, both methods using spectral library and using PPI(Pixel Purity Index) are performed and compared on a given task. Based on these schemes, SMA is performed using LANDSAT 7 ETM+ and ASTER image. As the results, fraction map showing geologic rock types are enough to meet purposes such as geologic mapping and mineral potential mapping in the case of both uses of these different types of remotely sensed images. It concluded that this approach based on SMA with LANDSAT and ASTER is regarded as one of effective schemes for geologic remote sensing.

• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.83-94
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• 1998

To have a basic idea on the spectral reflectance signature in paddy rice canopy, we measured spectral reflectance from paddy rice canopy(Ilpumbyeo) using spectroradiometer (GER Inc. SFOV : 0.35~2.50 ${\mu}{\textrm}{m}$) in situ weekly or biweekly from transplanting to ripening stage. Spectral reflectance of the visible range (0.4~0.7 ${\mu}{\textrm}{m}$) was decreased to below 5% and then slightly increased again after heading stage in rice canopy. Meanwhile spectral reflectance of the near-infrared range (0.7~1.1 ${\mu}{\textrm}{m}$) was increased to 40~50% and then decreased a great deal after panicle initiation stage in rice canopy. Landsat TM equivalent band set ($\bar{p}$$_{TMi}$) was created by averaging spectral reflectance values to the real TM bands. Correlation analysis between the rice crop variables (LAI, total dry matter) and TM equivalent band set ($\bar{p}$$_{TMi}$) showed that LAI and total dry matter of rice were highly correlated with visible bands such as $\bar{p}$$_{TM1}$, $\bar{p}$$_{TM2}$, and $\bar{p}$$_{TM3}$. Ratio values ($\bar{p}$$_{TMi}$/$\bar{p}$$_{TMi}$) such as $\bar{p}$$_{TM4}$/$\bar{p}$$_{TM2}$ were also highly correlated with rice crop variables such as LAI and total dry matter.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2004.03a
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• pp.493-498
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• 2004

The objective of the present study is to compare various conventional and recently evolved satellite image-processing techniques and to ascertain the best possible technique that can identify and position of aquaculture farms accurately in and around the Younggwang coastal area. Several conventional techniques performed to extract such information fiom the Landsat-TM imagery do not seem to yield better information about the aquaculture farms, and lead to misclassification. The large errors between the actual and extracted aquaculture farm information are due to existence of spectral confusion and inadequate spatial resolution of the sensor. This leads to possible occurrence of mixture pixels or 'mixels' of the source of errors in the classification techniques. Understanding the confusing and mixture pixel problems requires the development of efficient methods that can enable more reliable extraction of aquaculture farm information. Thus, the more recently evolved methods such as the step-by-step partial spectral end-member extraction and linear spectral unmixing methods are introduced. The farmer one assumes that an end-member, which is often referred to as 'spectrally pure signature' of a target feature, does not appear to be a spectrally pure form, but always mix with the other features at certain proportions. The assumption of the linear spectral unmxing is that the measured reflectance of a pixel is the linear sum of the reflectance of the mixture components that make up that pixel. The classification accuracy of the step-by-step partial end-member extraction improved significantly compared to that obtained from the traditional supervised classifiers. However, this method did not distinguish the aquaculture ponds and non-aquaculture ponds within the region of the aquaculture farming areas. In contrast, the linear spectral unmixing model produced a set of fraction images for the aquaculture, water and soil. Of these, the aquaculture fraction yields good estimates about the proportion of the aquaculture farm in each pixel. The acquired proportion was compared with the values of NDVI and both are positively correlated (R$^2$ =0.91), indicating the reliability of the sub-pixel classification.ixel classification.

• Korean Journal of Soil Science and Fertilizer
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• v.33 no.4
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• pp.242-246
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• 2000

The Near Infrared Reflectance Spectroscopy(NIR) was used to evaluate nutritional diagnosis for rapid leaf analysis method, 177 'Fuji' apple and 130 'Campbell Early' grape leaves were measured by Near Infrared reflectance spectra in the NIR region(1,100~2.500nm). Total nitrogen content was measured by kjelldhal distillation, after salycilic acid-sulfuric acid digestion. An empirical equation to predict total nitrogen content from its spectral signature was developed by adapting the Near Infrared Reflectance Spectroscopy analysis(NIRa) technique and the results were apple-0.965(R). 0.086(SEC), grape-0.926(R), 0.152(SEC). Standard Error of Prediction(SEP) of NIRa for predicting the total nitrogen of apple and grape leaves was 0.360 and 0.210, respectively. It was concluded that Near infrared reflectance spectroscopy analysis is promising method for rapid analysis of apple and grape leaves.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.274-274
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• 2002

Damage to forest trees, caused by wildfire, changes their spectral reflectance signature. This factor led to the initiation of a research project at the Remote Sensing & GIS Laboratory, Kookmin University, to determine if multispectral data acquired by IKONOS could provide fire scar and bum severity mapping. This paper will present detail mapping of burned areas in the eastern coast of Korea with IKONOS imagery. In addition, a single post-burn Landsat-7 ETM＋ data was used to compare with IKONOS, the study area. Burn severity map based on IKONOS image was found to be affected by strong topographic illumination effects in the mountain forest. But it has better the delineation of the bum-scarred area. In this study the NDVI was analyzed for geometric illumination conditions influenced by topography(slop, aspect and elevation) and shadow(solar elevation and azimuth angle).

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.6
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• pp.577-585
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• 2010

This paper is a part of developing a software that predicts the infrared signal emitted from a ground object by considering solar irradiation. The radiance emitted from a surface can be calculated by using the temperature and optical characteristics of the surface object. The bidirectional reflectance distribution function (BRDF) is defined as the ratio of reflected radiance to incident irradiance. It is a very important surface reflection property that decides the reflected radiance from the object. In this paper, the spectral radiance received by a remote sensor over the mid-wave infrared(MWIR), and the long-wave infrared(LWIR) regions are computed and compared each other for several different materials. The results show that the optical surface properties such as the BRDF and the emissivity of the object surface can play a major role in generating the infrared signatures of various objects, and the largest infrared signal may reach up to 10 times the smallest one when the infrared signals obtained from a flat plate with different surface conditions under the sun light.