• 농업과학연구
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• 제47권4호
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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.

• 한국기후변화학회지
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• 제8권4호
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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.

• 한국기후변화학회지
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• 제9권4호
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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.

• 한국기후변화학회지
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• 제6권1호
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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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• 제33권4호
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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.

• 환경영향평가
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• 제4권1호
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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.

• 농촌계획
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• 제17권3호
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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.

• 대한원격탐사학회지
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• 제35권6_2호
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• pp.1053-1065
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• 2019

신기후체제에 대응하여 정확한 탄소흡수 및 배출량을 산정하기 위해 토지이용 범주별 통계량 산출은 활동자료로서 매우 중요한 자료이다. 본 연구는 효과적인 토지이용 범주별 판독을 위하여 산림항공사진(이하 FAP)에 딥러닝모델을 적용하여 토지이용 범주별 자동화 판독 분류를 한 후 샘플링기법을 통해 국가단위 통계량을 산출하였다. 딥러닝모델에 적용한 데이터세트(이하, DS)는 국가산림자원조사 고정표본점 위치 기반 FAP의 이미지를 추출하여 훈련데이터세트(이하, 훈련DS)와 시험데이터세트(이하, 시험 DS)로 구분하였다. 훈련 DS는 토지이용 범주별 정의에 따라 이미지별 레이블을 부여하였으며, 딥러닝모델을 학습하고 검증하였다. 검증 시 모델의 학습정확도는 학습 횟수 1500회에서 정확도가 약 89%로 가장 높았다. 학습된 딥러닝모델을 시험DS에 적용한 결과, 이미지 레이블의 판독 분류정확도는 약 90%로 높았다. 샘플링기법을 통해 범주별 분류 결과에 대해 면적을 추정하여 국가통계와 비교한 결과 정합성 또한 높아 향후 LULUCF(Land Use, Land Use Change, Forestry)분야 국가 온실가스 인벤토리 보고서의 활동자료로 활용하기에 충분하다고 판단된다.

• 임산에너지
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• 제20권1호
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• pp.53-61
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• 2001

본 논문에서는 토지이용 및 임업부문에서의 온실가스 배출통계에 있어 국제기준이 되는 수정된 IPCC 가이드라인을 토대로 우리에게 적합한 산출방법을 개발하고, 이에 의해 1998US도 기준 온실가스 흡수/배출 현황을 제시하였다. 먼저 산림 및 목질계 바이오매스 변화 범주에 있어 생장에 의한 이산화탄소 흡수량은 11,911×10³탄소톤이고 벌채에 의한 배출량은 824×10³탄소톤으로서 순흡수량은 11,087×10³탄소톤었다. 두 번째로 산림의 타용도 전용시 목재를 반출하고 현지에 남아 썩게되는 바이오매스에 의한 온실가스 순배출은 82×10³탄소톤으로 미미한 수준이다. 마지막으로 토지이용변화 및 토지관리에 따른 토양에서의 총 순배출량은 1,057×10³탄소톤이며, 그 중 토지이용체계변화에 따른 무기토양의 순탄소량 변화가 1,025×10³탄소톤으로 대부분을 차지하고, 나머지 32×10³탄소톤만이 농경지에서의 석회시용에 따른 배출이었다. 결과적으로 1998년도 토지이용변화 및 임업부문의 온실가스 수지는 흡수 11,911×10³탄소톤, 배출 1,963×10³탄소톤로서 순흡수 9,948×10³탄소톤이었으며, 이는 같은 해 우리나라의 에너지 연소에 따른 배출량 103,601×10³탄소톤의 9.6%에 해당한다.

• 한국환경복원기술학회지
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• 제17권6호
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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.