• Journal of Plant Biology
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• v.12 no.1
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• pp.7-14
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• 1969

Korea has a lots of margin for security of farm land from her coastal region. The area of saline soil may be reached about 10% of present farm land if the reclamation works are finished. This paper was conducted as a part of studying the possibilities of desalination of saline soil through the experiment of some halophytes. The halophytes in this works were Salicornia herbacea L., Suaeda glauca Bunge, chenopodium acuminatum Willd, and Scirpus triquerter L. Of the above halophytes, Salicornia was proved the most effective plant for desalination of saline soil referring to the following results; 1) The seasonal uptake of chloride by Salicornia was the highest of all. However, the general tendencies of all plants showed a decrease on August. 2) Salinity of soil showed the lowest value on the site where Salicornia was grwon densely. Comparing the other sites grouped by age of saline soil with the above site, the salinity of rice-paddy (10 years after reclamation) is similar to those of the site wehre Salicornia were as well as the 50 cm below the surface soil. 3) The maximum water holding capacity of surface soil appeared in the site of Salicornia, but in 50 cm below the surface, the maximum water holding capacity are almost on equat terms having no connection with the age of saline soil. Soil pH, other chemical compositions such as organic matter, magnesium, potassium, phosphorous, and nitrate were determined to elucidate the relationship between the changes of soil properties and chemical uptakes by certain halophytes. It is assumed that the above chemical compositions are frequently affected by the factors such as coastal circulation of salts, exchangeable base, microbial growth, climatic conditions, and irrigation of water.

• Journal of Construction Engineering and Project Management
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• v.2 no.2
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• pp.45-52
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• 2012

This study introduces a methodology to evaluate different types of non-nuclear technologies to see how they are competitive to the nuclear technology for quality control (QC) and quality assurance (QA) in soil condition measurement for highway pavement construction. The non-nuclear methods including the Electrical Density Gauge (EDG) and the Light Weight Deflectometer (LWD) were tested for their performance against a nuclear gauge, and traditional methods were used as baselines. An innovative way of comparing a deflection gauge to a density gauge was introduced. Results showed that the nuclear gauge generally outperformed the non-nuclear gauge in accuracies of soil density and moisture content measurements. Finally, a framework was developed as a guideline for evaluating various types of non-nuclear soil gauges. From other perspectives rather than accuracy, it was concluded that the non-nuclear gauges would be better alternative to the nuclear gauge when the followings are considered: (1) greater life-cycle cost savings; (2) elimination of intense federal regulations and safety/security concerns; and (3) elimination of licensing and intense training.

• Journal of radiological science and technology
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• v.46 no.1
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• pp.37-42
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• 2023

Most of research on environmental radioactivity is conducted in areas near nuclear power plants, so basic data about the distribution of environmental radioactivity in soil in other areas are insufficient. Therefore, in this study, divide into four categories by the land development characteristics of Incheon and the purpose of development, and confirm the stability of the Incheon through soil sample collection and gamma-ray analysis based on ⁴⁰K, ¹³⁷Cs and ²²⁶Ra (²¹⁴Pb, ²¹⁴Bi). The spectrum obtained by measuring for 80,000 seconds by using the HPGe detector was analyzed by Genie 2000 program. Soil radioactivity concentrations in urban parks of Incheon area are generally within a safe range compared to the results of the Nuclear safety and security commission. However, as ¹³⁷Cs was detected in one park, which will require continuous monitoring.

• Journal of Korean Society of Disaster and Security
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• v.10 no.1
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• pp.35-42
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• 2017

This is a comparative study on the characteristics of excavated soils, which is proceeded using soil strength parameter by literature, geotechnical investigation, standard penetration test by drilling, and downhole test by borehole at six sites in urban areas. The results of these site surveys are used as basic data for the evaluation and development of prediction of ground subsidence risk. Geotechnical properties are estimated with the result of standard penetration test-N value and literature. The dynamic geotechnical characteristics are also estimated with top-down seismic exploration at borehole.

• Journal of Korean Society of Disaster and Security
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• v.14 no.3
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• pp.17-27
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• 2021

In this study, classification models were built using machine learning techniques that can classify the soil creep risk into three classes from A to C (A: risk, B: moderate, C: good). A total of six machine learning techniques were used: K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting and then their classification accuracy was analyzed using the nationwide soil creep field survey data in 2019 and 2020. As a result of classification accuracy analysis, all six methods showed excellent accuracy of 0.9 or more. The methods where numerical data were applied for data training showed better performance than the methods based on character data of field survey evaluation table. Moreover, the methods learned with the data group (R1~R4) reflecting the expert opinion had higher accuracy than the field survey evaluation score data group (C1~C4). The machine learning can be used as a tool for prediction of soil creep if high-quality data are continuously secured and updated in the future.

• Journal of Korean Society of Disaster and Security
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• v.7 no.2
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• pp.1-8
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• 2014

Smart Pole Measurement System was constructed with not only the core sensors of a GNSS receiver, a TRS sensor and a soil moisture sensor but supplementary installation of power supply and radio communication for monitoring steep slope sites. Also a data processing software for displacement extraction and visualization was developed. Smart Pole Measurement sensor is composed of a GNSS antenna at the top of the pole, a TRS sensor and a gyro sensor vertical below right of the antenna and a soil moisture sensor at the bottom of the pole. The sensor combination extracts not only ground combination in real time but transltion, slide, settlement and soil moisture content. This measuring/monitoring system which cosists of data receiving part, data collection/transfer part and data processing part was built to exercise their functions and then test measuring/monitoring was conducted by introducing artificial displacement and the results were analyzed to evaluate field applicability.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.43-50
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• 2021

In Korea, the frequency of soil disasters has soared recently due to increased torrential rains caused by abnormal weather conditions. In particular, soil generated from mountainous areas is flowing into small rivers along valleys, depositing rivers and adding to flood damage. In order to prevent damage from such soil disasters, it is important to predict sediments and to quantitatively identify bed load. In this work, we conducted an experiment to indirectly measure acoustic sensor-based bed load collision sounds using pipe hydrophones, and compared them with raw data by applying denoising methods to improve the reliability of the measured data. As a result, we derive results in a more clear analysis of bed load estimation by correcting noise when the denoising method is applied to raw data.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.155-164
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• 2023

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.

• Ecology and Resilient Infrastructure
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• v.7 no.2
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• pp.97-113
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• 2020

Soil is the foundation of human life and the basis for food security. Considering this it is prioritized in the UN's Sustainable Development Goals (SDG). Therefore, research on soil resilience in the agricultural environment is crucial for sound and sustainable soil management, especially in highly uncertain and unpredictable conditions. Soil resilience is defined in different ways by several researchers; however, its definition typically includes the concepts of recovery and resistance to stress. The physical, chemical, and biological characteristics of soils that are used to assess the soil resilience, i.e., the response of soil to various types of stress are summarized in this study. In addition, various statistical processing techniques and quantification methods are summarized considering the wide spatial and temporal scope of soil resilience research. Several soil resilience studies typically conduct the following five steps: (1) soil and site selection (2) stress (independent variable) setting (3) soil characteristics and indicator (dependent variable) setting (4) performing various spatiotemporal scale experiments (5) statistical analysis. The previous and present studies present a general introduction of soil resilience, based on which, further practical research considering domestic agricultural environment should be conducted. The extensive range of soil resilience measurements will require collaboration between researchers in various fields.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1185-1193
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• 2023

The increasing interest in soil moisture data from satellite imagery for applications in hydrology, meteorology, and agriculture has led to the development of methods to produce variable-resolution soil moisture maps. Research on accurate soil moisture estimation using satellite imagery is essential for remote sensing applications. The purpose of this study is to generate a soil moisture estimation map for a test area using KOMPSAT-3/3A and KOMPSAT-5 SAR imagery and to quantitatively compare the results with soil moisture data from the Soil Moisture Active Passive (SMAP) mission provided by NASA, with a focus on accuracy validation. In addition, the Korean Environmental Geographic Information Service (EGIS) land cover map was used to determine soil moisture, especially in agricultural and forested regions. The selected test area for this study is the western part of Jeju, South Korea, where input data were available for the soil moisture estimation algorithm based on the Water Cloud Model (WCM). Synthetic Aperture Radar (SAR) imagery from KOMPSAT-5 HV and Sentinel-1 VV were used for soil moisture estimation, while vegetation indices were calculated from the surface reflectance of KOMPSAT-3 imagery. Comparison of the derived soil moisture results with SMAP (L-3) and SMAP (L-4) data by differencing showed a mean difference of 4.13±3.60 p% and 14.24±2.10 p%, respectively, indicating a level of agreement. This research suggests the potential for producing highly accurate and precise soil moisture maps using future South Korean satellite imagery and publicly available data sources, as demonstrated in this study.