• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.933-940
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• 2020

Land-use change matrix data is important for calculating the LULUCF (land use, land use change and forestry) sector of the national greenhouse gas inventory. In this study, land cover changes in 2004 and 2019 were compared using the Wall-to-Wall technique with a land cover map of Sejong City from the Ministry of Environment. Sejong City was classified into six land use classes according to the Intergovernmental Panel on Climate Change (IPCC) guidelines: Forest land, crop land, grassland, wetland, settlement and other land. The coordinate system of the land cover maps of 2004 and 2019 were harmonized and the land use was reclassified. The results indicate that during the 15 years from 2004 to 2019 forestlands and croplands decreased from 50.4% (234.2 ㎢) and 34.6% (161.0 ㎢) to 43.4% (201.7 ㎢) and 20.7% (96.2 ㎢), respectively, while Settlement and Other land area increased significantly from 8.9% (41.1 ㎢) and 1.4% (6.9 ㎢) to 35.6% (119.0 ㎢) and 6.5% (30.3 ㎢). 79.㎢ of cropland area (96.2 ㎢) in 2019 was maintained as cropland, and 8.8 ㎢, 1.7 ㎢, 0.5 ㎢, 5.4 ㎢, and 0.4 ㎢ were converted from forestland, grassland, wetland, and settlement, respectively. This research, however, is subject to several limitations. The uncertainty of the land use change matrix when using the wall-to-wall technique depends on the accuracy of the utilized land cover map. Also, the land cover maps have different resolutions and different classification criteria for each production period. Despite these limitations, creating a land use change matrix using the Wall-to-Wall technique with a Land cover map has great advantages of saving time and money.

• Journal of Climate Change Research
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• v.8 no.4
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• pp.329-337
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• 2017

In this study, land-use changes from 1990 to 2010 in Jeju Island by different approaches were produced and compared to suggest a more efficient approach. In a sample-based method, land-use changes were analyzed with different sampling intensities of 2 km and 4 km grids, which were distributed by the fifth National Forest Inventory (NFI5), and their uncertainty was assessed. When comparing the uncertainty for different sampling intensities, the one with the grid of 2 km provided more precise information; ranged from 6.6 to 31.3% of the relative standard error for remaining land-use categories for 20 years. On the other hand, land-cover maps by a wall-to-wall approach were produced by using time-series Landsat imageries. Forest land increased from 34,194 ha to 44,154 ha for 20 years, where about 69% of total forest land were remained as forest land and 19% and 8% within forest lands were converted to grassland and cropland, respectively. In the case of grassland, only about 40% of which were remained as grassland and most of the area were converted to forest land and cropland. When comparing land-cover area by land-use categories with land-use statistics, forest areas were underestimated while areas of cropland and grassland were overestimated. In order to analyze land use change, it is necessary to establish a clear and consistent definition on the six land use classification.

• Journal of Climate Change Research
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• v.9 no.4
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• pp.385-398
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• 2018

Although the importance of developing reliable and systematic GHG inventory has increased, the GIS/RS-based national scale LULUCF (Land Use, Land-Use Change and Forestry) sector analysis is insufficient in the context of the Paris Agreement. In this study, the change in $CO_2$ storage of forest land due to land use change is estimated using two GIS/RS methodologies, Sampling and Wall-to-Wall methods, from 2000 to 2010. Particularly, various imagery with sampling data and land cover maps are used for Sampling and Wall-to-Wall methods, respectively. This land use matrix of these methodologies and the national cadastral statistics are classified by six land-use categories (Forest land, Cropland, Grassland, Wetlands, Settlements, and Other land). The difference of area between the result of Sampling methods and the cadastral statistics decreases as the sample plot distance decreases. However, the difference is not significant under a 2 km sample plot. In the 2000s, the Wall-to-Wall method showed similar results to sampling under a 2 km distance except for the Settlement category. With the Wall-to-Wall method, $CO_2$ storage is higher than that of the Sampling method. Accordingly, the Wall-to-Wall method would be more advantageous than the Sampling method in the presence of sufficient spatial data for GHG inventory assessment. These results can contribute to establish an annual report system of national greenhouse gas inventory in the LULUCF sector.

• Journal of Climate Change Research
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• v.6 no.1
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• pp.33-40
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• 2015

Forests are to be recognized as an important carbon sink under the UNFCCC that consist of above- and below-biomass, dead organic matter (DOM) such as dead wood and litter, and soil organic matter (SOM). In order to asses for DOM and SOM, however, it is relevant to land-use change matrices over last 20 years for each land-use category. In this study, a land-use change matrix was produced and its uncertainty was assessed using a point sampling technique with permanent sample plots in national forest inventory at Chungbuk province. With point sampling estimated areas at 2012 year for each land-use category were significantly similar to the true areas by given six land-use categories. Relative standard error in terms of uncertainty of land-use change among land-use categories ranged in 4.3~44.4%, excluding the other land. Forest and cropland covered relatively large areas showed lower uncertainty compared to the other land-use categories. This result showed that selected permanent samples in the NFI are able to support for producing land-use change matrix at a national or province level. If the $6^{th}$ NFI data are fully collected, the uncertainty of estimated area should be improved.

• Journal of Forest and Environmental Science
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• v.33 no.4
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• pp.295-304
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• 2017

This study compared and analyzed the construction of a land use change matrix for the Intergovernmental Panel on Climate Change's (IPCC) land use, land use change, and forestry area (LULUCF). We used National Forest Inventory (NFI) permanent sample plots (with a sample intensity of 4 km) and permanent sample plots with 500 m sampling intensity. The land use change matrix was formed using the point sampling method, Level-2 Land Cover Maps, and forest aerial photographs (3rd and 4th series). The land use change matrix using the land cover map indicated that the annual change in area was the highest for forests and cropland; the cropland area decreased over time. We evaluated the uncertainty of the land use change matrix. Our results indicated that the forest land use, which had the most sampling, had the lowest uncertainty, while the grassland and wetlands had the highest uncertainty and the least sampling. The uncertainty was higher for the 4 km sampling intensity than for the 500 m sampling intensity, which indicates the importance of selecting the appropriate sample size when constructing a national land use change matrix.

• Journal of Environmental Impact Assessment
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• v.4 no.1
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• pp.59-64
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• 1995

Environmental Impact Assesment(EIA) is composed of various procedures, such as screening, scoping, inventory survey, prediction, assessment, mitigation measure, alternative assessment, and post management. Remote sensing introduced lately begins to be applied ecosystem and land use in inventory survey and assessment of EIA. This study explains on land use classification, buffering analysis of residential area, and overlaying analysis of odor predictive data with residential area for application to EIA with remote sensing data. Residential area extracted from land use classification of remote sensing provides effectively buffering analysis of residential area in selection of landfill site with GIS. It could assess also residential effect to an offensive odor by overlaying analysis. Application methods in EIA should be enlarged to assess effectively.

• Journal of Korean Society of Rural Planning
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• v.17 no.3
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• pp.103-111
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• 2011

The purpose of this study is to review foreign scenic inventory map for the systematic management of natural scenic resources. Several foreign cases were surveyed and analyzed to apply the scenery inventory map in domestic, such as Visual Resource Management(VRM) from United States Bureau of Land Management, Scenery Management System(SMS) from USDA Forest Service and Visual Landscape Inventory(VLI) from British Columbia Ministry of Forest's, that were already established scenery inventory maps. The results are as follows. First, the characteristic of Korean landscape is quite a different from those of north american's, which is much smaller and more complex in topography and land use. So, it would be difficult to apply foreign system directly and we need more researches to our own system. The multi-stepped landscape unit system is highly recommended. Second, scenic quality could be estimated by the pre-built database, such as land forms, vegetation, hydrology and land uses. Historical and cultural attributes should be complemented. Third, existing scenic integrity could be grasped by scenic damage, landscape alteration caused by human activities and land exfoliation. Also, subjective evaluation method should be supplemented by objective criteria through further detailed studies. Finally, about landscape view conditions, landscape control points should be surveyed and established in advance, and viewing distance, viewing frequency, amount of observers and public interests should be considered.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1053-1065
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• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.

• Journal of Korea Foresty Energy
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• v.20 no.1
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• pp.53-61
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• 2001

An approach method for the greenhouse gas inventory in land-use change and forestry in Korea based on the 1996 revised IPCC(Intergovernmental Panel on Climate Change) guideline was developed and carbon budget of the year 1998 in this sector was estimated using the developed method as follow. For the category of changes in forests and other woody biomass stocks, carbon removal from the atmosphere by growth was 11,911 thousands TC(tons of carbon), carbon emissions to the atmosphere by harvests was 824 thousands TC, and net carbon removals was, therefore, 11,087 thousands TC, Emissions from decay of biomass remained after conversion of forest land to other land uses was estimated to 82 thousands TC For the category of land-use change and management, carbon emissions in mineral soils from land-use change was 1,025 thousands TC, that from liming of agricultural soils was 32 thousands TC, and total emissions was, therefore, 1,057 thousands TC. In summary, the carbon budget of land-use change and forestry of the year 1988 was as follows; the removal of 11,911 thousands TC, the emissions of 1,963 thousands TC, and the net removal of 9,948 thousands TC which was 9.6% of the emissions of 103,601 thousands TC from energy sector of the same year.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.17 no.6
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• pp.39-50
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• 2014

This study was conducted to establish inventory and propose conservation strategy of 'village wetlands' in Cheonan. As results, the village wetlands are defined as such places as palustrine wetland, village embankment, agricultural reservoir or small reservoirs located in or near the village and related to everyday life or farming. Firstly 791 provisional village wetlands were identified in Cheonan by using Arc-GIS 10.1, then 104 wetlands were defined as village wetlands and listed the inventory of Cheonan Village Wetlands after being validated through their area (greater than $1,000m^2$), satellite images, Korea Land Information System, land use map, land coverage map and field survey. Finally the 49 wetlands were selected for detailed surveying, and function assessment. As the result of the wetland function assessments, 11 wetlands were found to have 'high' wetland function (conservation) 30 wetlands were 'average' (enhancement) and 8 wetlands were 'low' (restoration or enhancement). Enhancing biodiversity and ecosystem services through ecological management of wetlands in Cheonan and connecting with an ecological network were proposed.