• 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.

• Journal of Korean Society of Forest Science
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• v.98 no.3
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• pp.311-318
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• 2009

This study purposed to construct supply and demand potential map of forest bioenergy with a GIS-based decision support system. The target areas of this study were a part of the forests in Yongdong region, Gangwondo, and most types of forests were pinus densiflora, pinus koreaiansis, and Oak. Data about forest type, age classes, the number of households, regional silviculture planning was stored in GIS to define the potential areas for supplying potential bioenergy from the forests, and to assess biomass available for a household. Theoretical potential biomass energy based on silviculture plan was estimated in average 3,144 Tcal, and this quantity will be enough to supply the quantity of demand of households in that area. However, if it assumed that average collecting rates of Kangwon province were 10%, the available quantity of biomass will be between 6% and 15% of demand. If the collecting rates were 60%, the supply of biomass could exceed the quantity of demand in certain cities.

• Korean Journal of Remote Sensing
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• v.28 no.6
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• pp.661-670
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• 2012

Urban forests provide great ecosystem services to population in metropolitan areas even though they occupy little green space in a huge gray landscape. Unfortunately, urbanization inherently results in threatening the green infrastructure, and the recent urbanization trends drew great attention of scientists and policy makers on how to preserve or restore green infrastructure in metropolitan area. For this reason, mapping the spatial distribution of the green infrastructure is important in urban environments since the resulting map helps us identify hot green spots and set up long term plan on how to preserve or restore green infrastructure in urban environments. As a preliminary step for mapping green infrastructure utilizing multi-source remote sensing data in urban environments, the objective of this study is to map vegetation volume by fusing LiDAR and multispectral data in urban environments. Multispectral imageries are used to identify the two dimensional distribution of green infrastructure, while LiDAR data are utilized to characterize the vertical structure of the identified green structure. Vegetation volume was calculated over the metropolitan Chicago city area, and the vegetation volume was summarized over 16 NLCD classes. The experimental results indicated that vegetation volume varies greatly even in the same land cover class, and traditional land cover map based above ground biomass estimation approach may introduce bias in the estimation results.

• Journal of Korean Society of Forest Science
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• v.96 no.5
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• pp.591-601
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• 2007

Spatial information on forest biomass is an important factor to evaluate the capability of forest as a carbon sequestrator and is a core independent variable required to drive models which describe ecological processes such as carbon budget, hydrological budget, and energy flow. The objective of this study is to understand the relationship between satellite image and field data, and to quantitatively estimate and map the spatial distribution of forest biomass. Landsat Enhanced Thematic Mapper (ETM+) derived vegetation indices and field survey data were applied to estimate the biomass distribution of mountainous forest located in Gwangneung Experimental Forest (230 ha). Field survey data collected from the ground plots were used as the dependent variable, forest biomass, while satellite image reflectance data (Band 1~5 and Band 7), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and RVI (Ratio Vegetation Index) were used as the independent variables. The mean and total biomass of Gwangneung catchment area were estimated to be about 229.5 ton/ha and $52.8{\times}10^3$ tons respectively. Regression analysis revealed significant relationships between the measured biomass and Landsat derived variables in both of deciduous forest ($R^2=0.76$, P < 0.05) and coniferous forest ($R^2=0.75$, P < 0.05). However, there still exist many uncertainties in the estimation of forest ecosystem parameters based on vegetation remote sensing. Developing remote sensing techniques with adequate filed survey data over a long period are expected to increase the estimation accuracy of spatial information of the forest ecosystem.

• 한국신재생에너지학회:학술대회논문집
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• 2006.11a
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• pp.149-152
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• 2006

New and renewable energy information becomes one of the greatest issues because of serious environment problems and limited fossil resources However, There are few system to manage and utilize new and renewable energy information efficient. Therefore this study establish the GIS based Resource Map System to save and analyze new and renewable energy Informal ion about solar energy, wind power, small hydro, biomass, and geothermal. This Resource Map System is composed of the management system, practical system, field system and Web-service system. This System can Provide var ious spatial analysis tools such as data searching, treating thematic maps, evaluating location requirements for energy facilities.

• New & Renewable Energy
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• v.2 no.4 s.8
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• pp.27-32
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• 2006

New and renewable energy information becomes one of the greatest issues because of serious environment problems and limited fossil resources. However, There are few system to manage and utilize new and renewable energy information efficiently. Therefore this study establish the GIS based Resource Map System to save and analyze new and renewable energy information about solar energy, wind power, small hydro, biomass, and geothermal. This Resource Map System is composed of the management system, practical system, field system, and Web-service system. This System can provide various spatial analysis tools such as data searching, creating thematic maps, evaluating location requirements for energy facilities.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.4
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• pp.13-20
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• 2013

Due to the global warming, international agreements have been propelled by industrialized countries. These days, there are various studies and projects to reduce the carbon emission quantity in South Korea, because South Korea is a strong candidate for a newly industrialized nation by Kyoto Protocol. Therefore, this study arranges plans to create various thematic map by producing database that can manage various datum based on grid spatial objects to manage quantity of forest biomass and carbon dioxide. Moreover, this study designs a system to create forest biomass by using the best method of calculation with LiDAR data and KOMPSAT-2 satellite images. In addition, this study designs a biomass monitoring system for public institutions to register biomass, suggesting actual plans to extract, manage, and utilized forest biomass.

• Journal of Korean Society of Forest Science
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• v.98 no.4
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• pp.409-416
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• 2009

Forest biomass estimation is essential for greenhouse gas inventories and terrestrial carbon accounting. Remote sensing allows for estimating forest biomass over a large area. This study was conducted to estimate forest biomass and to produce a forest biomass map for Muju county using forest biomass conversion table developed by field plot data from the 5th National Forest Inventory and Landsat TM-5. Correlation analysis was carried out to select suitable independent variables for developing regression models. It was resulted that the height class, crown closure density, and age class were highly correlated with forest biomass. Six regression models were used with the combination of these three stand variables and verified by validation statistics such as root mean square error (RMSE) and mean bias. It was found that a regression model with crown closure density and height class (Model V) was better than others for estimating forest biomass. A biomass conversion table by model V was produced and then used for estimating forest biomass in the study site. The total forest biomass of the Muju county was estimated about 8.8 million ton, or 128.3 ton/ha by the conversion table.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.12
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• pp.1699-1705
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• 2011

Magnesium ammonium phosphate (MAP) was recovered from swine wastewater and the feasibility of reutilizing it as a slowly-releasing fertilizer was evaluated. Maize growth was investigated with normal and high application rates of MAP and a fused super phosphate (FSP) fertilizer. A total of 5 treatments ($T_0$ = control, $T_1$ = MAP based on 30 kg P $ha^{-1}$, $T_2$ = FSP based on 30 kg P $ha^{-1}$+urea equivalent to nitrogen of MAP applied in $T_1$, $T_3$ = MAP based on 40 kg P $ha^{-1}$, $T_4$ = FSP based on 40 kg P $ha^{-1}$+urea equivalent to nitrogen of MAP applied in $T_3$) were arranged with 3 replications. In the case of height and circumference, significant differences were found between controls and treated maize plants (p<0.01). However, no statistical differences were found between MAP- and FSP-urea treated maize. Leaf area and green biomass yield were significantly (p<0.01) higher in the treated group than control. Leaf area was also found significantly higher (p<0.01) in the higher MAP- treated group (2,374 $cm^2$ $plant^{-1}$) than other treatments. $N_2O$ emission was found to be lower in MAP treated soil than that from FSP-urea treated soil, which might be due to the slow releasing pattern of MAP. It could be assumed from the results that MAP would be an eco-friendly sustainable fertilizer source for crop production.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.63-71
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• 2013

This study calculated vegetation indexes such as SR, NDVI, SAVI, and LAI to figure out correlations regarding vegetation by using high resolution KOMPSAT-2 images and LANDSAT images based on the forest biomass distribution map that utilized field survey data, satellite images and LiDAR data and then analyzed correlations between their values and forest biomass. The analysis results reveal that the vegetation indexes of high resolution KOMPSAT-2 images had higher correlations than those of LANDSAT images and that NDVI recorded high correlations among the vegetation indexes. In addition, the study analyzed the characteristics of hyperspectral images by using the COMIS of STSAT-3 and Hyperion images of a similar sensor, EO-1, and further the usability of biomass estimation in hyperspectral images by comparing vegetation index, which had relatively high correlations with biomass, with the vegetation indexes of LANDSAT with the same GSD conditions.