• Korean Journal of Soil Science and Fertilizer
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• v.48 no.5
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• pp.535-541
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• 2015

Korean soil classification system is based on the US soil taxonomy. This study aimed to understand and inform the soil taxonomy of 2010 and 2014 US soil classification systems. Ingwan series belonged to anthropogenic soil was classified to coarse loamy over sandy, mixed, mesic family of Aquic Udorthents based on the soil taxonomy of 2010 and coarse loamy over sandy, mixed, mesic family of Anthroportic Udorthents based on the soil taxonomy of 2014. An anthropogenic soil is increasing in Korea. Considering the domestic relationship depending on US soil taxonomy, it is important to be well-informed of the taxonomy and apply the updated taxonomy system properly to the domestic soil classification. This study of defining the anthropogenic soil will provide useful information on soil management and its utilization.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.274-277
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• 2007

This study is to estimate the spatial distribution of soil loss using the land use data produced from QuickBird satellite imagery. For a small agricultural watershed (1.16 $km^2$) located in the upstream of Gyeongan-cheon watershed, a precise agricultural land use map were prepared using QuickBird satellite image of April 5 of 2003. RUSLE (Revised Universal Soil Loss Equation) was adopted for soil loss estimation. The data (DEM, soil and land use) for the RUSLE were prepared for 5 m and 30 m spatial resolution. The results were compared with each other and the result of 30 m Landsat land use data.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.236-245
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• 2000

The feasibility of precision farming for small sized fields was studied by determining fertilizing amount of nitrogenous and calcareous to a cite specific region. A detailed soil survey at three experimental fields of 672㎡, 300㎡ and 140㎡ revealed a considerable spatial variation of the pH and organic matter(OM) levels. Soil organic matter was measured using Walkley-Black method and soil pH was measured with a pH sensor. Soil sample was obtained by Grid Node Sampling Method. The soil sampling depth was 10 - 20 cm from the soil surface. To display soil nutrient variation, a soil map was made using Geographic Information System (GIS) software. In soil mapping, soil data between nodes was interpolated using Inverse Distance Weighting (IDW) method. The variation was about 1 - 1.8 in pH value and 1.4 -7 % in OM content. Fertilizing Amount of nitrogenous and calcareous was determined by the fertilizing equation which was proposed by National Institute of Agricultural Science and Technology.(NIAST). The variation of fertilizing amount was about 3 - 11 kg/10a in nitrogenous and 70 - 140 kg/10a in calcareous. The results showed a feasibility of precision fertilizing for small size fields.

• Journal of Biosystems Engineering
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• v.27 no.1
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• pp.39-44
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• 2002

In order to obtain the information of bio-environment control, the thermal characteristics of soil in the greenhouse heated by the heat pump and latent heat storage system were experimentally analyzed. The experimental systems were composed of the greenhouse with a heat pump and a latent heat storage system (system I), the greenhouse with a heat pump (system II), the greenhouse with a latent heat storage system (system III), and the greenhouse without auxiliary heating system (system IV). The thermal characteristics experimentally analyzed in each system were temperature of soil layers, soil heat storage and release, soil heat capacity and soil heat storage ratio. The results could be summarized as follows. 1. Time to reach the highest temperature at 20cm deep in soil layers of the crop routs in case of system I was shown to be delayed by 6 hours in comparison to the time of the highest temperature at the soil surface. 2. In the clear winter days, the stored heat capacity values fur the system I and the system II were shown to be 22.3% and 11.0% higher than the released heat capacity respectively, and the stored heat capacity values for the system III and the system IV were shown to be 6.2% and 29.6% lower than the released heat capacity respectively This confirms that the system I provided the best heat storage effect. j. The heat quantity values stored or released were shown to be highest at 5 cm depth of soil layers. And it was reduced with increasing of depth of soil layers until 20 cm and was not changed under the soil layer of 20 cm depth. 4. The heat absorption rates of soil, the ratio between supplied and stored heat energy, fur both the system I and system II were lower than 23%.

• Korean Journal of Soil Science and Fertilizer
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• v.46 no.6
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• pp.536-541
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• 2013

In Soil Taxonomy system, anthropogenic soils are still classified as Entisols since the International Classification Committee for Anthropogenic Soils is in the process of classifying anthropogenic soils as new orders. In reality, it is difficult to characterize anthropogenic soils because Soil Taxonomy (ST) system does not distinguish between natural and anthropogenic Entisols. On the other hand, World Reference Base for soil resources (WRB) considers human impacts on soils and contains an independent category of anthropogenic soils, which makes easier to understand anthropogenic soil characteristics than Soil Taxonomy system. A remodeled paddy field (Gasan) was selected to classify by ST and WRB. Soil samples were taken to analyze chemical and physical properties. Based on the results of the analyses, the ST system classified Gasan as coarse loamy, mixed, mesic, Aquic Udorthents while the WRB did as Stagnic Urbic Technosols (Oxyaquic, Arenic). As a conclusion, the WRB classification information of the anthropogenic provides more detail characteristics of the anthropogenic soils.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.426-439
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• 2018

Purpose: To continuously monitor soil and climatic properties, a data acquisition system (DAQ) was developed and tested in plum farms (Gyewol-ri and Haechang-ri, Suncheon, Korea). Methods: The DAQ consisted of a Raspberry-Pi processor, a modem, and an ADC board with multiple sensors (soil moisture content (SEN0193), soil temperature (DS18B20), climatic temperature and humidity (DHT22), and rainfall gauge (TR-525M)). In the laboratory, various tests were conducted to calibrate SEN0193 at different soil moistures, soil temperatures, depths, and bulk densities. For performance comparison of the SEN0193 sensor, two commercial moisture sensors (SMS-BTA and WT-1000B) were tested in the field. The collected field data in Raspberry-Pi were transmitted and stored on a web server database through a commercial communications wireless network. Results: In laboratory tests, it was found that the SEN0193 sensor voltage reading increased significantly with an increase in soil bulk density. A linear calibration equation was developed between voltage and soil moisture content depending on the farm soil bulk density. In field tests, the SEN0193 sensor showed linearity (R = 0.76 and 0.73) between output voltage and moisture content; however, the other two sensors showed no linearity, indicating that site-specific calibration is important for accurate sensing. In the long-term monitoring results, it was observed that the measured climate temperature was almost the same as website information. Soil temperature information was higher than the values measured by DS18B20 during spring and summer. However, the local rainfall measured using TR 525M was significantly different from the values on the website. Conclusion: Based on the test results obtained using the developed monitoring system, it is thought that the measurement of various parameters using one device would be helpful in monitoring plum growth. Field data from the local farm monitoring system can be coupled with website information from the weather station and used more efficiently.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.3
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• pp.153-162
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• 2015

Land information is important for the international agricultural companies. This study investigated the agriculture and soil information in Sri Lanka. This study is the results from investigation of soil properties and agricultural properties determined by the Soil Taxonomy classification system for the soils in Sri Lanka. The order of the main agricultural imports in Sri Lanka was wheat > refined Sugar > dry Onion > Rice > Lentils. The climate of Sri Lanka is divided into three climatic zones. There are a wet zone, an intermediate zone, and a dry zone. Rainfall of the wet zone was $3,000-5,000mm\;year^{-1}$. The rainfall of the dry zone was less than $1,000m^{-1}$. The intermediate zone was in the middle area. Soil series of Sri Lanka were 109 in total. Detailed information of soil series was: 6 of soil Orders, 15 of Suborders, 39 of Great groups, and 56 of Subgroups. Soil texture of topsoil was much more coarse, but subsoil was gravelly coarse soil. Soil of Sri Lanka was classified as a Soil Order. The orders were Entisols > Alfisols > Ultisols > Inceptisols > Histosols > Vertisols.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.497-500
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• 2005

Research and development of remote sensing technique is necessary so that more accurate and extensive information may be obtained. To achieve this goal, the synthesized technique which integrates the high resolution optic and SAR image, and topographical information was examined to investigate the quantitative/qualitative characteristics of the Earth's surface environment. For this purpose, high-precision DEMs of Jeju-Island was generated and data fusion algorithm was developed in order to integrate the multi-spectral optic and polarimetric SAR image. Three dimensional land-cover and two dimensional soil moisture maps were generated conclusively so as to investigate the Earth's surface environments and extract the geophysical parameters.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.129-139
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• 2008

This paper proposes a Ubiquitous Greenhouse Management System (UGMS) based on USN(Ubiquitous Sensor Network) which can be real-time monitoring and controlling of greenhouse's facilities by collecting environment and soil information with environment and soil sensors, and CCTV camera. The existing systems were controlled simply by temperature. Also, it was possible to monitor only at control room in a greenhouse. For solving problems of the exiting system, our system can remotely monitor and control greenhouse by considering environment information. The detail components are as follows. The system includes the sensor manager and the CCTV manager to gather and manage greenhouse information with soil and the environment sensors, and camera. Also the system has the greenhouse database storing greenhouse information and the greenhouse server transmitting greenhouse information to the GUI and controlling greenhouse. Finally, the GUI showing greenhouse condition to users exists in our system. To verify the executability of the UGMS, after developing the greenhouse model, we confirmed that our system could monitor and control the greenhouse condition at remote GUI by applying the UGMS's components to the model.

• Korean Journal of Remote Sensing
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• v.2 no.1
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• pp.23-33
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• 1986

The Universal Soil Loss Equation (USLE) has been applied to the microcomputer based Geographic Information System (GIS) data planes to model a soil erosion map for a county. The conventional method applied by US Soil conservation Service (SCS) has been tedious and time consuming process on a mainframe computer which yields a multisectioned, hard to interprete, line printer map of the each county's soil loss. The new approach proved to be an economical and efficient tool for the natural resource managers in their decision malting in land conservation practice. They can simulate the variety of conservation practices and assess the cost and benefit without physically implementing the conservation measures.7he new approach also can produce all the other graphical and statistical reports.