• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.483-497
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• 2016

Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of barley and wheat growth prediction equation by using UAV derived vegetation index. UAV imagery was taken on the test plots six times from late February to late June during the barley and wheat growing season. The field spectral reflectance during growing period for the 5 variety (Keunal-bori, Huinchalssal-bori, Saechalssal-bori, Keumkang and Jopum) were measured using ground spectroradiometer and three growth parameters, including plant height, shoot dry weight and number of tiller were investigated for each ground survey. Among the 6 Vegetation Indices (VI), the RVI, NDVI, NGRDI and GLI between measured and image derived showed high relationship with the coefficient of determination respectively. Using the field investigation data, the vegetation indices regression curves were derived, and the growth parameters were tried to compare with the VIs value.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.637-648
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• 2021

In order to support the establishment of a farming plan, it is important to preemptively evaluate crop changes and to provide precise information. Therefore, it is necessary to provide customized information suitable for decision-making by farming stage through scientific and continuous monitoring using drones. This study was carried out to support the establishment of the farming plan for ground vegetable. The cultivation management map of each information was obtained from preliminary study. Three cultivation management maps include 'field emergence map', 'stress map' and 'productivity map' reflected spatial variation in the plantation by providing information in units of plants based on 3-dimensions. Application fields of the cultivation management map can be summarized as follows: detect miss-planted, replanting decision, fertilization, weeding, pest control, irrigation schedule, market quality evaluation, harvest schedule, etc.

• 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)

• Proceedings of the KSRS Conference
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• v.2
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• pp.860-863
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• 2006

Epipolar images have to be generated to stereo display aerial images or satellite images. Pushbroom sensor is used to acquire high resolution satellite images. These satellite images have curvilinear epipolar lines unlike the epipolar lines of frame images, which are straight lines. The aforementioned fact makes it difficult to generate epipolar images for pushbroom satellite images. If we assume a linear transition of the sensor having constant speed and attitude during image acquisition, we can generate epipolar images based on parallel projection model (2D Affine model). Recent high resolution images are provided with RPC values so that we can exploit these values to generate epipolar images without using ground control points and tie point. This paper provides a procedure based on the parallel projection model for generating epipolar images directly from a stereo IKONOS images, and experimental results.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.150-152
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• 2003

Laser scanned lidar data record 3D surface information in detail. Exploring valuable spatial information from lidar data is a prerequisite task for its applications, such as DEM generation and 3D building model reconstruction. However, the inherent spatial information is implicit in the abundant, densely and randomly distributed point cloud. This paper proposes a novel method to organize point cloud data, so that further analysis or feature extraction can proceed based on a well organized data model. The principle of the proposed algorithm is to segment point cloud into 3D planes. A split and merge segmentation based on the octree structure is developed for the implementation. Some practical airborne and ground lidar data are tested for demonstration and discussion. We expect this data organization could provide a stepping stone for extracting spatial information from lidar data.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.230-230
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• 2016

Hydrological modeling is a very complex task dealing with multi-source of data, but it can be potentially benefited from recent improvements and developments in remote sensing. The estimation of actual land surface evapotranspiration (ET), an important variable in water management, has become possible based entirely on satellite data. This study adopted a Surface Energy Balance Algorithm for Land (SEBAL) with the use of MODerate Resolution Imaging Spectrometer (MODIS) satellite products. The SEBAL model is one of the commonly used approach for the ET estimation. A primary advantage of the SEBAL model is rather its minimum requirement for ground-based weather data. The MODIS provides ET (MOD16) product that is based on the Penman-Monteith equation. This study aims to further develop the SEBAL model by employing a more rigorous parameterization scheme including the estimation of uncertainty associated with parameter and model selection in regression model. Finally, the proposed model is compared with the existing approaches and comprehensive discussion is then provided.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.421-434
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• 2012

Colored Dissolved Organic Matter (CDOM) is one of the important components of optical properties of seawater to determine ecosystem dynamics in a given marine area. The optical characteristics of CDOM may depend on the various ecosystem and environmental variables in the sea and those variables may vary region to region. Therefore, the retrieval algorithm for determining light absorption coefficient of CDOM ($a_{CDOM}$) using satellite remote sensing reflectance ($R_{rs}$) developed from other region may not be directly applicable to the other region, and it must be validated using an in-situ ground-truth observation. We have tested 6 known CDOM algorithms (three Semi-analytical and three Empirical CDOM algorithms) developed from other regions of the world ocean with laboratory determined in-situ values for the East Sea using field data collected during seven oceanographic cruises in the period of 2009~2011. Our field measurements extended from the coastal waters to the open oceanic type CASE-1 Waters. Our study showed that Quasi-Analytical Algorithm (QAA_v5) derived $a_{CDOM}$(412) appears to match in-situ $a_{CDOM}$(412) values statistically. Semi-analytical algorithms appeared to underestimate and empirical ones overestimated $a_{CDOM}$ in the East Sea. $a_{CDOM}$(412) value was found to be relatively high in the relatively high satellite derived-chlorophyll-a area. $a_{CDOM}$(412) value appears to be influenced by the amount of chlorophyll-a in seawater. The outcome of this work may be referenced to develop $a_{CDOM}$ algorithm for the new Korean Geostationary Ocean Color Imager (GOCI).

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.103-119
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• 2020

Recently, the rapid redeposition and erosion of rivers artificially created by climate change and the Four Rivers Restoration Project is questionable. According to the revised law in Korea, the river management agency will periodically carry out bed changes surveys. However, there are technical limitations in contrast to the trend of increasing spatial coverage, density and narrowing of intervals. National organizations are interest in developing innovative bed changessurvey techniquesfor efficiency. Core of bathymetry survey is to measure the depth of rivers under a variety of river conditions, but that is relatively more risky, time-consuming and expensive compared to conventional ground surveys. To overcome the limitations of traditional technology, echo sounder, which has been mainly used for ocean depth surveying, has been applied to rivers. However, due to various technical limitations, it is still difficult to periodically investigate a wide range of areas. Therefore, technique using the remote sensing has been spotlighted as an alternative, especially showing the possibility of depth measurement using spectral characteristics. In this study, we develop and examine a technique that can measure depth of water using reflectance from spectral characteristics. As a result of applying the technique proposed in thisstudy, it was confirmed that the measured depth and the correlation and error corresponding to 0.986 and 0.053 m were measured in the depth range within 0.95 m. In the future, this study could be applied to the measurement of spatial depth if it is applied to the hyperspectral sensor mounted on the drone.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.167-177
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• 2022

In order to obtain satellite image products using the image transmitted to the ground station after capturing the satellite images, many image pre/post-processing steps are involved. During the pre/post-processing, when converting from level 1R images to level 1G images, geometric correction is essential. An interpolation method necessary for geometric correction is inevitably used, and the quality of the level 1G images is determined according to the accuracy of the interpolation method. Also, it is crucial to speed up the interpolation algorithm by the level processor. In this paper, we proposed a lightweight CNN-based interpolation method required for geometric correction when converting from level 1R to level 1G. The proposed method doubles the resolution of satellite images and constructs a deep learning network with a lightweight deep convolutional neural network for fast processing speed. In addition, a feature map fusion method capable of improving the image quality of multispectral (MS) bands using panchromatic (PAN) band information was proposed. The images obtained through the proposed interpolation method improved by about 0.4 dB for the PAN image and about 4.9 dB for the MS image in the quantitative peak signal-to-noise ratio (PSNR) index compared to the existing deep learning-based interpolation methods. In addition, it was confirmed that the time required to acquire an image that is twice the resolution of the 36,500×36,500 input image based on the PAN image size is improved by about 1.6 times compared to the existing deep learning-based interpolation method.

• Korean Journal of Remote Sensing
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• v.22 no.4
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• pp.285-294
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• 2006

A numerical formula that presents relationship between a point of a satellite image and its ground position is called a sensor model. For precise geolocation of satellite images, we need an error-free sensor model. However, the sensor model based on GOES ephemeris data has some error, in particular after Image Motion Compensation (IMC) mechanism has been turned off. To solve this problem, we investigated three sensor models: collinearity model, direct linear transform (DLT) model and orbit-based model. We applied matching between GOES images and global coastline database and used successful results as control points. With control points we improved the initial image geolocation accuracy using the three models. We compared results from three sensor models. As a result, we showed that the orbit-based model is a suitable sensor model for precise geolocation of GOES-9 Images.