• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.6
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• pp.15-34
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• 2021

Syntaxonomy and Synecology on Quercus variabilis forests in Daecheong-dam basin was carried out using the methods of Braun-Blanquet phytosociology. 6 syntaxa classified as species compositions described were Quercus variabilis community, Platycarya strobilacea-Quercus variabilis community(typicum subcommunity, dictamnus dasycarpus subcommunity), Quercetum variabili-serratae, Zelkova serrata-Quercus variabilis community and Dendranthema boreale-Quercus variabilis community. All syntaxa were shown habitat environmental conditions including steep inclination of more than 30°, high rock exposure rate of more than 50% and South-facing slope. These communities excepting Dendranthema boreale-Quercus variabilis community classified as natural vegetation were identified as low emergence rate of annual plants and species compositions composed native species, so it was confirmed that relatively natural succession were proceeding well. Quercetum variabili-serratae and Dendranthema boreale-Quercus variabilis community distributed forested hillslope of open water edge were representative Quercus variabilis syntaxa in Daecheong-dam basin.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.293-302
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• 2023

This study examines and proposes standardization approaches to address the heterogeneous issues of metadata in drone imagery within the agricultural sector. Image metadata comes in various formats depending on different camera manufacturers, with most utilizing EXIF and XMP. The metadata of cameras used in fixed-wing and rotary-wing platforms, along with the metadata requirements in image alignment software, were analyzed for sensors like DJI XT2, MicaSense RedEdge-M, and Sentera Double4K. In the agricultural domain, multispectral imagery is crucial for vegetation analysis, making the provision of such imagery essential. Based on Pix4D SW, a comparative analysis of metadata attributes was performed, and necessary elements were compiled and presented as a proposed standardization (draft) in the form of tag information.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.395-408
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• 2015

High-resolution satellite image mosaics are becoming increasingly important in the field of remote sensing image analysis as an essential image processing to create a large image constructed from several smaller images. In this paper, we present an automatic seamline extraction technique and the procedure to generate a mosaic image by this technique. For more effective seamline extraction in the overlap region of adjacent images, an NDVI-based seamline extraction technique is developed, which takes advantage of the computational time and memory. The Normalized Difference Vegetation Index(NDVI) is an index of plant "greeness" or photosynthetic activity that is employed to extract the initial seamline. The NDVI can divide into manmade region and natural region. The cost image is obtained by the canny edge detector and the buffering technique is used to extract the ranging cost image. The seamline is extracted by applying the Dijkstra algorithm to a cost image generated through the labeling process of the extracted edge information. Histogram matching is also conducted to alleviate radiometric distortion between adjacent images acquired at different time. In the experimental results using the KOMPSAT-2/3 satellite imagery, it is confirmed that the proposed method greatly reduces the visual discontinuity caused by geometric difference of adjacent images and the computation time.

• 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 the Korean Association of Geographic Information Studies
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• v.15 no.4
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• pp.1-14
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• 2012

A river is defined as the watercourse flowing through its channel, and the mapping tasks of a river plays an important role for the research on the topographic changes in the riparian zones and the research on the monitoring of flooding in its floodplain. However, the utilization of the ground surveying technologies is not efficient for the mapping tasks of a river due to the irregular surfaces of the riparian zones and the dynamic changes of water level of a river. Recently, the spatial information data sets are widely used for the coastal mapping tasks due to the acquisition of the topographic information without human accessibility. In this research, we tried to extract a river from the RapidEye imagery by using the ISODATA(Iterative Self_Organizing Data Analysis) classification algorithm with the two different parameters(NIR (Near Infra-Red) band and NDVI(Normalized Difference Vegetation Index)). First, the two different images(the NIR band image and the NDVI image) were generated from the RapidEye imagery. Second, the ISODATA algorithm were applied to each image and each river was generated in each image through the post-processing steps. River boundaries were also extracted from each classified image using the Sobel edge detection algorithm. Ground truths determined by the experienced expert are used for the assessment of the accuracy of an each generated river. Statistical results show that the extracted river using the NIR band has higher accuracies than the extracted river using the NDVI.

• Journal of Wetlands Research
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• v.10 no.1
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• pp.39-47
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• 2008

An eco-washland is increasingly getting attentions as a new alternative plan for management of water resources because of role as flooding control and ecological park without social and ecological side effects. However, there is a lack of study regarding technological development and ecological guidelines to create eco-washland. This study was carried out to suggest ecological guidelines necessary for engineering process to create eco-washland. A study site was the lower reach of Seokjang stream connecting to Yimjin river, a candidate of new eco-washland, and Kumsa area in Namhan river was selected as a reference site. The analysis of ecological characteristics focused on vegetation, fishes, and birds. Major vegetation communities, composed of dominant species such as Salix koreensis, Salix gracylistyla, Miscanthus sacchariflorus, Phragmites communis, etc., formed physical conditions along with other land uses including open water, sandbar, cultivated land, etc. Dominant species of fishes were some species belonging to Cyprinidae and Acheilognathinae, and in case of birds Anser albifrons, Anas platyrhychos, Anas poecilorhyncha belonging to waterbirds and Paradoxornis webbinanus dependent to forest edge were dominantly distributed. The results showed that complex wetland types associated with partially deep water and upland was the optimal eco-hydrological condition of washland. Cyprinidae and Acheilognathinae in case of fish and Egretta spp. (spring season) and Anas spp. (winter season) in case of bird were selected as target species for the these wetland types. Finally, a detail planning criteria to create habitats of these target species were discussed in terms of spawning, breeding, feeding, resting, refuge, nesting, etc.

• Journal of Biosystems Engineering
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• v.43 no.2
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• pp.148-159
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• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• The Korean Journal of Ecology
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• v.24 no.2
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• pp.101-109
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• 2001

We created an environmental forest on the basis of ecological design around the incineration plant of Jindo Engineering and Construction Co., Ltd., which is located in Jeongwang-dong, Siheung-si, Kyunggi-do. To get ecological information of this site, physico-chemical properties of soil on salt marsh, which is located close to the syudy site and of forest soil transported from other sites for ecological restoration were analyzed. Texture of salt marsh and transported soils were loam and sandy loam, respectively. pH, organic matter, T-N, available P, and exchangeable K and Na contents of salt marsh and transported forest soils were 6.7 and 5.4, 4.1 and 0.4%, 1.0 and 0.3mg/g, 46.7 and 6.8ppm, 521 and 207ppm, and 3.8 and 0.5mg/g, respectively. Introduced plants were selected among the dominant species of forests and the species composing the potential natural vegetation around the present study site. Those plants were selected again by considering the tolerances to air pollution and to salt, and their availability. Selected trees were Pinus thunbergii, Sophora japonica, Celtis sinensis, Quercus aliena, Q. serrata, Q. dentata, and Q. acutissima. Selected sub-trees were Albizzia julibrissin, Koelreuteria poniculata, and Styrax japonica and shrubs were Rhododendron yedoense var. poukhanense, R. mucronulatum, Callicarpa japonica, Euonymus alatus, E. japonica, and R. schlippenbachii. On the other hand, introduction of herbs was not considered except for Liriope platyphylla, which was ornamentally planted in one site. Planting bed of mound type was adopted to provide the fine drainage system. Mound was designed to furnish litter, A, B, and C layers simuating the profile of forest soil. Slope of mound was mulched by rice straw of 2cm in thickness to prevent for sliding of litter and soil in cases of strong wind or heavy rain. Height of mound was designed to secure more than 1 m by combining A and B layers. Narrow zones, in which mound with stable slope degree cannot be prepared, was designed to equip the standard soil depth with the introduction of stone for supporting. On the other hand, plants with shallow root system were arranged in some zones, in which satisfactory soil depth cannot be ensured. Plants were arranged in the order of tree, sub-tree, and shrub from center to edge on the mound to make a mature forest of a dome shape in the future. Dispersion of plants was designed to be random pattern rather than clumped one. Problems on creation of the environmental forest by such ecological design were found to be management or inspection by non-specialized project operators and inspecting officers, and regulations for construction without ecological background. Alternative plans to solve such problems were suggested.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.823-833
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• 2020

In order to stably produce crops, there is an increasing demand for effective crop monitoring techniques in domestic agricultural areas. In this manuscript, a cultivation area extraction method by using deep learning model is developed, and then, applied to satellite imagery. Training dataset for crop cultivation areas were generated using RapidEye satellite images that include blue, green, red, red-edge, and NIR bands useful for vegetation and environmental analysis, and using this, we tried to estimate the crop cultivation area of onion and garlic by deep learning model. In order to training the model, atmospheric-corrected RapidEye satellite images were used, and then, a deep learning model using FC-DenseNet, which is one of the representative deep learning models for semantic segmentation, was created. The final crop cultivation area was determined as object-based data through combination with cadastral maps. As a result of the experiment, it was confirmed that the FC-DenseNet model learned using atmospheric-corrected training data can effectively detect crop cultivation areas.

• 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.