• Journal of the Korean Society of Environmental Restoration Technology
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• v.15 no.1
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• pp.141-154
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• 2012

This study was launched to verify the effective composition of plant species and its management program most suited for the dredged soiled area near Nakdong River Site 14. The improvement methods of planting base and the composition of plants such as silver grass, reed, and some colonies in aesthetic effect were studied. To search the management methods to decrease the confining pressure risen from the burried seeds which would consequently harm the previously seeded plants, experimental construction process was measured on the site. The purpose of this experiment was to figure out which part of the improvement on the plant base has the most significant effect for the revegetation of infertile, dredged soil, to verify the easily seeded, developing plants among seeded plants, and finally, to find the restoration model using plants near the dredged soil around riverside. 8 seeded plants and 23 invaded species were appeared which among the emerged plants, development of Aster yomena MAKINO, Lotus corniculatus var. japonica Regel, Trifolium repens L, and Dianthus longicalyx Miq were proved to be brought up well. Difference risen from the seed composition were not noticeable until 150day since the germination was proceeded mainly by Aster yomena MAKINO. The experimental plot with dredging sand+organic fertilizer method of construction and dredging sand+soil conditioner method showed most development while the effect of the plot with only the soil base of dredging sand stayed low. Another important method for the management of infertile, dredged soil base would be the removal of disturbing species which the experiment showed the tied relationship between the removal of disturbing species and development of seeding plants. Although this study was carried out focused on the Nakdong River Project, the study suggests the general management program that the removal of disturbing species such as Humulus japonicus Sieboid & Zucc. and Pueraria lobata (Willd.) Ohwi in times around rainy season(60days after seedling) would be effective for the easy growth of revegetation plants.

• Journal of Wetlands Research
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• v.20 no.1
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• pp.35-42
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• 2018

This study presents the flow response and habitat region of the aquatic plants in the urban streams. Phragmites japonica, Phragmites communis, Miscanthus sacchariflorus, Persicaria blumei and Persicaria thunbergii were selected as for typical plants. Flow response and habitat region were determined by flow velocity/depth and vegetation growth. Stages for flow response of the aquatic plants were classified into stable, recovered, damaged and swept away. Criteria between the recovered and damaged stage was determined by the bending angle of $30{\sim}50^{\circ}$. Capability against flow was high in the order of Phragmites japonica, Phragmites communis, Miscanthus sacchariflorus, Persicaria blumei and Persicaria thunbergi. Phragmites japonica and Phragmites communis were capable of coping with flow depth 0.9 m, flow velocity 1.5 m/s and with flow depth 1.0 m, flow velocity 0.9 m/s, respectively. Miscanthus sacchariflorus was capable within the region of flow depth 1.0 m and flow velocity 0.6 m/s. Persicaria blumei and Persicaria thunbergii were less capable than the other aquatic plants and were vulnerable exceeding the water depth of 1.0 m. Habitat regions by the flow response of each plants were suggested.

• Korean Journal of Ecology and Environment
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• v.41 no.1
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• pp.19-25
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• 2008

To evaluate the functions of vegetation mat of artificial floating island (AFI) installed in Lake Paldang, nutrients, such as total phosphorus (TP), dissolved inorganic phosphorus (DIP), total nitrogen (TN) and nitrate $(NO_3)$ and microbial factors such as total bacterial numbers, active bacterial numbers and exoenzymatic activities of $\beta$-glucosidase and phosphatase in pore water of medium and bulk lake water were analyzed. The concentration of TN and $NO_3$ in pore water ranged from 4.4 to 7.5mg $L^{-1}$ from 1.2 to 3.8mg $L^{-1}$ respectively, which were ca. 2 times higher than those of lake water. The ranges of TP and DIP of were $1.4\sim4.1mg\;L^{-1}$ and $0.003\sim0.137mg\;L^{-1}$ in pore water of media which were $4\sim25$ and 5 times higher than those of lake water, respectively. The numbers of total bacteria and active bacteria in pore waterwere about 10 times higher than those of laker water. Also, both phosphatase and $\beta$-glucosidase activities of pore water were on an average 10 times higher than those of lake water. These results suggest that the bacteria were playing important role for nutrients concentrating and cycling in media of artificial floating island. And the medium of artificial floating island contained newly created microbial ecosystem, which is responsible for sustaining the growth of macrophytes and the creation of new aquatic ecosystem.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1669-1683
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• 2021

AI intelligent agriculture and digital agriculture are important for the science of agriculture. Leaf chlorophyll contents(LCC) are one of the most important indicators to determine the growth status of vegetable crops. In this study, a support vector machine (SVM) regression model was produced using an unmanned aerial vehicle-based RGB camera and a multispectral (MSP) sensor for onions and garlic, and the LCC estimation applicability of the RGB camera was reviewed by comparing it with the MSP sensor. As a result of this study, the RGB-based LCC model showed lower results than the MSP-based LCC model with an average R² of 0.09, RMSE 18.66, and nRMSE 3.46%. However, the difference in accuracy between the two sensors was not large, and the accuracy did not drop significantly when compared with previous studies using various sensors and algorithms. In addition, the RGB-based LCC model reflects the field LCC trend well when compared with the actual measured value, but it tends to be underestimated at high chlorophyll concentrations. It was possible to confirm the applicability of the LCC estimation with RGB considering the economic feasibility and versatility of the RGB camera. The results obtained from this study are expected to be usefully utilized in digital agriculture as AI intelligent agriculture technology that applies artificial intelligence and big data convergence technology.

• Korean Journal of Environment and Ecology
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• v.33 no.1
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• pp.86-106
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• 2019

We investigated the environmental conditions of natural habitats of T. nuda. The species was found on rocky northern hills ($60{\sim}90^{\circ}$) near the stream where the sea level ranges 95~145m. The average annual temperature of the habitats was lower than other places of South Korea. The differences of the lowest and the highest of the year was significantly huge than any other places. Plants were growing at the edge of stream that water reached but not submerged. Most of plants were found in North, Northeast or Northwest. It is suggested that these species require moist and low sunlight for growth. The common vegetation along with the T. nuda includes Mukdenia rossii, Selaginella rossii, Calamagrostis epigeios, and Rhododendron yedoense f. poukhanense. The dominance values and sociability of T. nuda were below 3 in all studied habitats and the variance of the number of individuals among the habitats was very high. As the optimum habitats for the T. nuda are decreasing due to the extreme precipitation patterns. It is also expected that the number of T. nuda will be decreased in the future. Therefore restoration activity in situ or ex situ must be conducted to conserve this valuable plant species.

• 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)

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.225-236
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• 2022

Urbanization due to population growth and regional development can cause various environmental problems, such as the urban heat island phenomenon. A planned city is considered an appropriate study site to analyze changes in urban climate caused by rapid urbanization in a short-term period. In this study, changes in land cover and surface heat island phenomenon were analyzed according to the development plan in Sejong City from 2013 to 2020 using Landsat-8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite imagery. The surface temperature was calculated in consideration of the thermal infrared band value provided by the satellite image and the emissivity, and based on this the surface heat island effect intensity and Urban Thermal Field Variance Index (UTFVI) change analysis were performed. The level-2 land cover map provided by the Ministry of Environment was used to confirm the change in land cover as the development progressed and the difference in the surface heat island intensity by each land cover. As a result of the analysis, it was confirmed that the urbanized area increased by 15% and the vegetation decreased by more than 28%. Expansion and intensification of the heat island phenomenon due to urban development were observed, and it was confirmed that the ecological level of the area where the heat island phenomenon occurred was very low. Therefore, It can suggest the need for a policy to improve the residential environment according to the quantitative change of the thermal environment due to rapid urbanization.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.597-608
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• 2022

Remotely sensed data have been used in various fields, such as disasters, agriculture, urban planning, and the military. Recently, the demand for the multitemporal dataset with the high-spatial-resolution has increased. This manuscript proposed an automatic image matching algorithm using a deep learning technique to utilize a multitemporal remotely sensed dataset. The proposed deep learning model was based on High Resolution Net (HRNet), widely used in image segmentation. In this manuscript, denseblock was added to calculate the correlation map between images effectively and to increase learning efficiency. The training of the proposed model was performed using the multitemporal orthophotos of the National Geographic Information Institute (NGII). In order to evaluate the performance of image matching using a deep learning model, a comparative evaluation was performed. As a result of the experiment, the average horizontal error of the proposed algorithm based on 80% of the image matching rate was 3 pixels. At the same time, that of the Zero Normalized Cross-Correlation (ZNCC) was 25 pixels. In particular, it was confirmed that the proposed method is effective even in mountainous and farmland areas where the image changes according to vegetation growth. Therefore, it is expected that the proposed deep learning algorithm can perform relative image registration and image matching of a multitemporal remote sensed dataset.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.765-779
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• 2022

This study evaluates the accuracy in identifying the burned area in South Korea using multi-temporal data from Sentinel-2 MSI and Landsat 8/9 OLI. Spectral indices such as the Difference Normalized Burn Ratio (dNBR), Relative Difference Normalized Burn Ratio (RdNBR), and Burned Area Index (BAI) were used to identify the burned area in the March 2022 forest fire in Uljin. Based on the results of six indices, the accuracy to detect the burned area was assessed for four satellites using Sentinel-2 and Landsat 8/9, respectively. Sentinel-2 and Landsat 8/9 produce images every 16 and 10 days, respectively, although it is difficult to acquire clear images due to clouds. Furthermore, using images taken before and after a forest fire to examine the burned area results in a rapid shift because vegetation growth in South Korea began in April, making it difficult to detect. Because Sentinel-2 and Landsat 8/9 images from February to May are based on the same date, this study is able to compare the indices with a relatively high detection accuracy and gets over the temporal resolution limitation. The results of this study are expected to be applied in the development of new indices to detect burned areas and indices that are optimized to detect South Korean forest fires.

• Korean Journal of Remote Sensing
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• v.39 no.1
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• pp.47-63
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• 2023

Unmanned aerial vehicle (UAV) and sensor technologies are rapidly developing and being usefully utilized for spatial information-based agricultural management and smart agriculture. Until now, there have been many difficulties in obtaining production information in a timely manner for large-scale agriculture on reclaimed land. However, smart agriculture that utilizes sensors, information technology, and UAV technology and can efficiently manage a large amount of farmland with a small number of people is expected to become more common in the near future. In this study, we evaluated the productivity of forage maize grown on reclaimed land using UAV and sensor-based technologies. This study compared the plant height, vegetation cover ratio, fresh biomass, and dry biomass of maize grown on general farmland and reclaimed land in South Korea. A biomass model was constructed based on plant height, cover ratio, and volume-based biomass using UAV-based images and Farm-Map, and related estimates were obtained. The fresh biomass was estimated with a very precise model (R² =0.97, root mean square error [RMSE]=3.18 t/ha, normalized RMSE [nRMSE]=8.08%). The estimated dry biomass had a coefficient of determination of 0.86, an RMSE of 1.51 t/ha, and an nRMSE of 12.61%. The average plant height distribution for each field lot was about 0.91 m for reclaimed land and about 1.89 m for general farmland, which was analyzed to be a difference of about 48%. The average proportion of the maize fraction in each field lot was approximately 65% in reclaimed land and 94% in general farmland, showing a difference of about 29%. The average fresh biomass of each reclaimed land field lot was 10 t/ha, which was about 36% lower than that of general farmland (28.1 t/ha). The average dry biomass in each field lot was about 4.22 t/ha in reclaimed land and about 8 t/ha in general farmland, with the reclaimed land having approximately 53% of the dry biomass of the general farmland. Based on these results, UAV and sensor-based images confirmed that it is possible to accurately analyze agricultural information and crop growth conditions in a large area. It is expected that the technology and methods used in this study will be useful for implementing field-smart agriculture in large reclaimed areas.