• Ecology and Resilient Infrastructure
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• v.3 no.4
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• pp.231-237
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• 2016

The purpose of this study is to analyze phosphate adsorption characteristic of the filter media applied in water purification technology. And it is also to observe the removal efficiency of the technology that was developed by combining the purification abilities of filter media and the vegetation mat. The filter media, Adphos, is the subject of this study. The result of adsorption test shows that $PO_4{^{3-}-P}$ adsorption characteristics of Adphos is determined by the Langmuir isotherm model fitting and the $q_m$ (maximum adsorption amount) value is calculated as 1.162 mg/g. To verify the removal efficiency of the purification technology, a lab-scale reactor, including a 400 mm depth of filter bed filled by Adphos, was manufactured. Yellow flag Iris (Iris pseudacours L.) was planted on the vegetation bed and the coir-roll was used to fix the root of vegetation. The reactor ran 8 hours per day over 71 days, and the results of filtration experiment show that average removal efficiency of SS, T-N and T-P were calculated as 94%, 41% and 64% respectively. With these results, it was proved that the purification technology is highly effective. And for the long-time use, a maintenance guide is also required.

• Korean Journal of Remote Sensing
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• v.35 no.2
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• pp.229-239
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• 2019

All vegetation colonies have layered structure. This layer is called 'forest vertical structure.' Nowadays it is considered as an important indicator to estimate forest's vital condition, diversity and environmental effect of forest. So forest vertical structure should be surveyed. However, vertical structure is a kind of inner structure, so forest surveys are generally conducted through field surveys, a traditional forest inventory method which costs plenty of time and budget. Therefore, in this study, we propose a useful method to classify the vertical structure of forests using remote sensing aerial photographs and machine learning capable of mass data mining in order to reduce time and budget for forest vertical structure investigation. We classified it as SVM (Support Vector Machine) using RGB airborne photos and LiDAR (Light Detection and Ranging) DSM (Digital Surface Model) DTM (Digital Terrain Model). Accuracy based on pixel count is 66.22% when compared to field survey results. It is concluded that classification accuracy of layer classification is relatively high for single-layer and multi-layer classification, but it was concluded that it is difficult in multi-layer classification. The results of this study are expected to further develop the field of machine learning research on vegetation structure by collecting various vegetation data and image data in the future.

• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.4
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• pp.82-89
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• 1997

A physically-based soil-water erosion model with simple hydrology and Rose & Hairsine's erosion concept is described, and was implemented in the form of computer program. The model derived from the concept of stream power(Bagnold, 1977) considers settling velocity characteristics of the soil and distinguishes between the processes of entrainment and re-entrainment. It deals separately with rill flow and sheet flow, handles vegetation in terms of soil contact cover, and has the ability to simulate soil movement on nonuniform slopes. The model predicted sediment concentrations reasonably with the results of Mclsaac et al. (1990). It showed a capability to quantitatively predict the movement of soil on uniform and nonuniform slopes. Among the model parameters, soil depositability $({\phi})$ was the most sensitive from the sensitivity analysis.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.310-313
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• 2004

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from TM satellite images; and the vegetation index value from SPOT satellite images. Landslide hazardous area were analysed and mapped using the landslide-occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better prediction accuracy than probabilistic model.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.33-36
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• 2008

This paper presents soil moisture retrieval from measured polarimetric backscattering coefficients of a vegetated surface. Based on the analysis of the quite complicate first-order radiative transfer scattering model for vegetated surfaces, a simplified scattering model is proposed for an inversion algorithm. Extraction of the surface-scatter component from the total scattering of a vegetation canopy is addressed using the simplified model, and also using the three-component decomposition technique. The backscattering coefficients are measured with a polarimetric L-band scatterometer during two months. At the same time, the biomasses, leaf moisture contents, and soil moisture contents are also measured. Then the measurement data are used to estimate the model parameters for vv-, hh-, and vh-polarizations. The scattering model for tall-grass-covered surfaces is inverted to retrieve the soil moisture content from the measurements using a genetic algorithm. The retrieved soil moisture contents agree quite well with the in-situ measured soil moisture data.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.52 no.4
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• pp.393-402
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• 2007

Remote sensing data can be integrated into crop models, making simulation improved. A crop model that uses remote sensing data was evaluated for its capability, which was performed through comparing three different methods of canopy measurement for cotton(Gossypium hirsutum L.). The measurement methods used were leaf area index(LAI), hand-held remotely sensed perpendicular vegetation index(PVI), and satellite remotely sensed PVI. Simulated values of cotton growth and lint yield showed reasonable agreement with the corresponding measurements when canopy measurements of LAI and hand-held remotely sensed PVI were used for model calibration. Meanwhile, simulated lint yields involving the satellite remotely sensed PVI were in rough agreement with the measured lint yields. We believe this matter could be improved by using remote sensing data obtained from finer resolution sensors. The model not only has simple input requirements but also is easy to use. It promises to expand its applicability to other regions for crop production, and to be applicable to regional crop growth monitoring and yield mapping projects.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.413-413
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• 2023

Wetlands are a critical component of the global carbon cycle and are essential in mitigating climate change. Accurately quantifying wetland carbon emissions is crucial for understanding and predicting the impact of wetlands on the global carbon budget. The uncertainty quantifying carbon in wetlands may comes from the ecosystem's hydrological, biochemical, and microbiological variability. The Community Land Model is a sophisticated and flexible land surface model that offers several configuration options such as energy and water fluxes, vegetation dynamics, and biogeochemical cycling, necessitating careful consideration for the alternative configurations before model implementation to develop a practical model framework. We conducted a systematic literature review, analyzing the alternatives, focusing on the carbon stock pools configurations and the parameters with significant sensitivity for carbon quantification in wetlands. In addition, we evaluated the feasibility and availability of in situ observation data necessary for validating the different alternatives. This analysis identified the most suitable option for our study site, the Binbong Wetland, in Korea.

• Korean Journal of Remote Sensing
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• v.37 no.6_3
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• pp.1975-1984
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• 2021

Urban expansion results in raising the temperature in the city, which can cause social, economic and physical damage. In order to prevent the urban heat island and reduce the urban land surface temperature, it is important to quantify the cooling effect of the features of the urban space. Therefore, in order to understand the relationship between each object of land cover and the land surface temperature in Seoul, the land cover map was classified into 6 classes. And the correlation and multiple regression analysis between land surface temperature and the area of objects, perimeter/area, and normalized difference vegetation index was analyzed. As a result of the analysis, the normalized difference vegetation index showed a high correlation with the land surface temperature. Also, in multiple regression analysis, the normalized difference vegetation index exerted a higher influence on the land surface temperature prediction than other coefficients. However, the explanatory power of the derived models as a result of multiple regression analysis was low. In the future, if continuous monitoring is performed using high-resolution MIR Image from KOMPSAT-3A, it will be possible to improve the explanatory power of the model. By utilizing the relationship between such various land cover types considering vegetation vitality of green areas with that of land surface temperature within urban spaces for urban planning, it is expected to contribute in reducing the land surface temperature in urban spaces.

• Journal of Korean Society of Forest Science
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• v.100 no.2
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• pp.240-245
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• 2011

Vegetation changes were studied for 16 yr in clearcut logged Pinus densiflora forests in the southern Gangwon-do province in Korea by applying chronosequence approach. Ambient temperature and relative humidity, Detrended Correspondence Analysis (DCA), Multiple Responses Permutation Procedure (MRPP), Indicator Species Analysis (ISPAN) were used to examine successional trajectory and compositional changes. After clearcutting, canopy openness was increased abruptly at three folds (1yr 68.3% and R1 23.0%) and then decreased, but relative moisture was slightly decreased (6%) compare to control site. In the result of DCA, right after clear cutting, vegetation composition was developed heterogeneously compared to control sites, and then approached to control sites within 16 years. Based on MRPP, species composition of each developmental stages (1yr, 3yr, 10yr and 16yr) revealed signigicant differences to that of control vegetation (R1, R3, R10 and R16). Indicator species in 1yr and 3yr samples included various woody species rather than herbaceous species, but in 10yr and 16yr, herbaceous were more abundant. Earlier succession of pine forests likely can explain to Initial Floristic Composition (IFC) Model.

• Korean Journal of Soil Science and Fertilizer
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• v.50 no.4
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• pp.306-317
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• 2017

Unmanned aerial vehicles (UAVs) became popular platforms for the collection of remotely sensed data in the last years. This study deals with the monitoring of multi-temporal onion growth with very high resolution by means of low-cost equipment. The concept of the monitoring was estimation of multi-temporal onion growth using normalized difference vegetation index (NDVI) and meteorological factors. For this study, UAV imagery was taken on the Changnyeong, Hapcheon and Muan regions eight times from early February to late June during the onion growing season. In precision agriculture frequent remote sensing on such scales during the vegetation period provided important spatial information on the crop status. Meanwhile, four plant growth parameters, plant height (P.H.), leaf number (L.N.), plant diameter (P.D.) and fresh weight (F.W.) were measured for about three hundred plants (twenty plants per plot) for each field campaign. Three meteorological factors included average temperature, rainfall and irradiation over an entire onion growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, $NDVI_{UAV}$ and rainfall in the model explain 88% and 68% of the P.H. and F.W. with a root mean square error (RMSE) of 7.29 cm and 59.47 g, respectively. And $NDVI_{UAV}$ in the model explain 43% of the L.N. with a RMSE of 0.96. These lead to the result that the characteristics of variations in onion growth according to $NDVI_{UAV}$ and other meteorological factors were well reflected in the model.