• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.699-717
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• 2021

South Korea is pushing for the advancement of crop production technology to achieve food self-sufficiency and meet the demand for safe food. A medium-sized satellite for agriculture is being launched in 2023 with the aim of collecting and providing information on agriculture, not only in Korea but also in neighboring countries. The satellite is to be equipped with various sensors, though reference data for ground information are lacking. Hyperspectral remote sensing combined with 1st derivative is an efficient tool for the identification of agricultural crops. In our study, we develop a system for hyperspectral analysis of the ground-based reflectance spectrum, which is monitored seven times during the cultivation period of three soybean crops using a PSR-2500 hyperspectral sensor. In the reflection spectrum of soybean canopy, wavelength variations correspond with stages of soybean growths. The spectral reflection characteristics of soybeans can be divided according to growth into the vegetative (V)stage and the reproductive (R)stage. As a result of the first derivative analysis of the spectral reflection characteristics, it is possible to identify the characteristics of each wavelength band. Using our developed monitoring system, we observed that the near-infrared (NIR) variation was largest during the vegetative (V1-V3) stage, followed by a similar variation pattern in the order of red-edge and visible. In the reproductive stage (R1-R8), the effect of the shape and color of the soybean leaf was reflected, and the pattern is different from that in the vegetative (V) stage. At the R1 to R6 stages, the variation in NIR was the largest, and red-edge and green showed similar variation patterns, but red showed little change. In particular, the reflectance characteristics of the R1 stage provides information that could help us distinguish between the three varieties of soybean that were studied. In the R7-R8 stage, close to the harvest period, the red-edge and NIR variation patterns and the visible variation patterns changed. These results are interpreted as a result of the large effects of pigments such as chlorophyll for each of the three soybean varieties, as well as from the formation and color of the leaf and stem. The results obtained in this study provide useful information that helps us to determine the wavelength width and range of the optimal band for monitoring and acquiring vegetation information on crops using satellites and unmanned aerial vehicles (UAVs)

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.235-243
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• 2023

Due to the revision of the River Act and the enactment of the Act on the Investigation, Planning, and Management of Water Resources, a regular bed change survey has become mandatory and a system is being prepared such that local governments can manage water resources in a planned manner. Since the topography of a bed cannot be measured directly, it is indirectly measured via contact-type depth measurements such as level survey or using an echo sounder, which features a low spatial resolution and does not allow continuous surveying owing to constraints in data acquisition. Therefore, a depth measurement method using remote sensing-LiDAR or hyperspectral imaging-has recently been developed, which allows a wider area survey than the contact-type method as it acquires hyperspectral images from a lightweight hyperspectral sensor mounted on a frequently operating drone and by applying the optimal bandwidth ratio search algorithm to estimate the depth. In the existing hyperspectral remote sensing technique, specific physical quantities are analyzed after matching the hyperspectral image acquired by the drone's path to the image of a surface unit. Previous studies focus primarily on the application of this technology to measure the bathymetry of sandy rivers, whereas bed materials are rarely evaluated. In this study, the existing hyperspectral image-based water depth estimation technique is applied to rivers with vegetation, whereas spatio-temporal hyperspectral imaging and cross-sectional hyperspectral imaging are performed for two cases in the same area before and after vegetation is removed. The result shows that the water depth estimation in the absence of vegetation is more accurate, and in the presence of vegetation, the water depth is estimated by recognizing the height of vegetation as the bottom. In addition, highly accurate water depth estimation is achieved not only in conventional cross-sectional hyperspectral imaging, but also in spatio-temporal hyperspectral imaging. As such, the possibility of monitoring bed fluctuations (water depth fluctuation) using spatio-temporal hyperspectral imaging is confirmed.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.48-63
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• 2015

In this study, we introduce various hyperspectral data processing techniques for the monitoring of shallow and coastal waters to enlarge the application range and to improve the accuracy of the end results in Korea. Unlike land, more accurate atmospheric correction is needed in coastal region showing relatively low reflectance in visible wavelengths. Sun-glint which occurs due to a geometry of sun-sea surface-sensor is another issue for the data processing in the ocean application of hyperspectal imagery. After the preprocessing of the hyperspectral data, a semi-analytical algorithm based on a radiative transfer model and a spectral library can be used for bathymetry mapping in coastal area, type classification and status monitoring of benthos or substrate classification. In general, semi-analytical algorithms using spectral information obtained from hyperspectral imagey shows higher accuracy than an empirical method using multispectral data. The water depth and quality are constraint factors in the ocean application of optical data. Although a radiative transfer model suggests the theoretical limit of about 25m in depth for bathymetry and bottom classification, hyperspectral data have been used practically at depths of up to 10 m in shallow and coastal waters. It means we have to focus on the maximum depth of water and water quality conditions that affect the coastal applicability of hyperspectral data, and to define the spectral library of coastal waters to classify the types of benthos and substrates.

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.213-222
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• 2008

Although hyperspectral sensing data have shown great potential to derive various surface information that is not usually available from conventional multispectral image, the acquisition of proper hyperspectral image data are often limited. To use ground-based hyperspectral camera image for remote sensing studies, radiometric calibration should be prerequisite. The objective of this study is to develop radiometric calibration procedure to convert image digital number (DN) value to surface reflectance for the 120 bands ground-based hyperspectral camera. Hyperspectral image and spectral measurements were simultaneously obtained from the experimental target that includes 22 different surface materials of diverse spectral characteristics at wavelength range between 400 to 900 nm. Calibration coefficients to convert image DN value to at-sensor radiance were initially derived from the regression equations between the sample image and spectral measurements using ASD spectroradiometer. Assuming that there is no atmospheric effects when the image acquisition and spectral measurements were made at very close distance in ground, we were also able to derive calibration coefficients that directly transform DN value to surface reflectance. However, these coefficients for deriving reflectance values should not be applied when the camera is used for aerial image that contains significant effect from atmosphere and further atmospheric correction procedure is required in such case.

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.399-406
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• 2013

The quality of satellite imagery should be improved and stabilized to satisfy numerous users. The radiometric characteristics of an optical sensor can be a measure of data quality. In this study, a band aggregation technique and spectral response function of hyperspectral images are used to simulate multispectral images. EO-1 Hyperion and Landsat-8 OLI images acquired with about 30 minutes difference in overpass time were exploited to evaluate radiometric coefficients of OLI. Radiance values of the OLI and the simulated OLI were compared over three subsets covered by different land types. As a result, the index of agreement shows over 0.99 for all VNIR bands although there are errors caused by space/time and sensors.

• Journal of Cadastre & Land InformatiX
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• v.44 no.2
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• pp.103-112
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• 2014

The objective of this study is to suggest the possibility on land cover classification using hyperspectal imagery on area which includes lands and waters. After atmospheric correction as a preprocessing work was conducted on hyperspectral imagery acquired by airborne hyperspectral sensor CASI-1500, the effect of atmospheric correction to a few land cover class in before and after atmospheric correction was compared and analyzed. As the result of accuracy of land cover classification by highspectral imagery using reference data as airphoto and digital topographic map, maximum likelihood method represented overall accuracy as 67.0% and minimum distance method showed overall accuracy as 52.4%. Also product accuracy of land cover classification on road, dry field and green house, but that on river, forest, grassland showed low because the area of those was composed of complex object. Therefore, the study needs to select optimal band to classify specific object and to construct spectral library considering spectral characteristics of specific object.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.2
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• pp.107-116
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• 2016

Airborne or spaceborne remote sensing can increase the efficiency of seabed material surveys compared with field surveying using a vessel. For the same seabed material, the optical remote sensing image shows variation in the reflectance depending on the water depth, which is due to the absorption and scattering by the water column. This study suggests a correction procedure to use the hyperspectral image for seabed material analysis. The study is conducted in the coastal area from Sacheonjin Port to Gyungpo Beach in Gangwon-do. The hyperspectral image is acquired using the CASI-1500 sensor. The diffuse attenuation coefficient is estimated for each band through regression models between the water reflectance and depth. Then, the coefficient is applied to each band of the image. As a result, the completely corrected image can be interpreted for a deeper area, although the interpretable area is very shallow without water column correction. Additionally, the water column corrected image shows decreased variation of reflectance with various water depths.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1111-1124
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• 2021

Mudflats are crucial for understanding the ecological structure and biological function of coastal ecosystem because of its high primary production by microalgae. There have been many studies on measuring primary productivity of tidal flats for the estimation of organic carbon abundance, but it is relatively recent that optical remote sensing technique, particularly hyperspectral sensing, was used for it. This study investigates hyperspectral sensing of chlorophyll concentration on a tidal flat surface, which is a key variable in deriving primary productivity. The study site is a mudflat in Geunso bay, South Korea and field campaigns were conducted at ebb tide in April and June 2021. Hyperspectral reflectance of the mudflat surfaces was measured with two types of hyperspectral sensors; TriOS RAMSES (directionalsensor) and the Specim-IQ (camera sensor), and Normal Differenced Vegetation Index (NDVI) and Contiuum Removal Depth (CRD) were used to estimate Chl-a from the optical measurements. The validation performed against independent field measurements of Chl-a showed that both CRD and NDVI can retrieve surface Chl-a with R² around 0.7 for the Chl-a range of 0~150 mg/m² tested in this study.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.375-387
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• 2019

The use of drone-based hyperspectral image offers considerable advantages in high resolution remote sensing applications. The primary objective of this study was to select the optimal bands based on hyperspectral image for the estimation yield of the chinese cabbage. The hyperspectral narrow bands were acquired over 403.36 to 995.19 nm using a 3.97 nm wide, 150 bands, drone-based hyperspectral imaging sensor. Fresh weight data were obtained from 2,031 sample for each field survey. Normalized difference vegetation indices were computed using red, red-edge and near-infrared bands and their relationship with quantitative each fresh weights were established and compared. As a result, predominant proportion of fresh weights are best estimated using data from three narrow bands, in order of importance, centered around 697.29 nm (red band), 717.15 nm (red-edge band) and 808.51 nm (near-infrared band). The study determined three spectral bands that provide optimal chinese cabbage productivity in the visible and near-infrared portion of the spectrum.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.12-25
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• 2021

Land cover maps are used in various fields in urban expansion and development. This study analyzed the amount of land cover change over time using multi-sensor information, focusing on the waterfront area of the Taehwa River. In order to apply high-accuracy aerial hyperspectral images, patterns with Field-spectral were reviewed and compared with time series Digital map. The hyperspectral image was set as 13 land cover grades, and the time series digital map was classified into 7 and the waterfront area was classified into 5-6 grades and analyzed. As a result of analysis of the change in land cover of the digital map from the 1990s to 2010, it was found that forest areas were rapidly decreasing and Farmland and grassland were becoming urban. As for the land cover change(2010~2019) in the waterfront area(set 500m) analyzed through hyperspectral images, it was found that Farmland(1.4㎢), Forest(1.0㎢), and grassland (0.8㎢) were converted into urbanized and dried areas, and urbanization was accelerating around the Taehwa River waterfront. Recently, a lot of research has been conducted on the production of land cover maps using high-precision satellite images and aerial hyperspectral images, so it is expected that more detailed and precise land cover maps can be produced and utilized.