Optimal Sampling Intensity in South Korea for a Land-Use Change Matrix Using Point Sampling
To report changes in land use, the forestry sector, and land-use change matrix (LUCM), monitoring is necessary in South Korea to adequately respond to the Post-2020 climate regime. To calculate the greenhouse gas statistics observing the principle of transparency required by the Climate Change Conve...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/10/7/677 |
| _version_ | 1797528621717389312 |
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| author | Ga-Hyun Moon Jong-Su Yim Na-Hyun Moon |
| author_facet | Ga-Hyun Moon Jong-Su Yim Na-Hyun Moon |
| author_sort | Ga-Hyun Moon |
| collection | DOAJ |
| description | To report changes in land use, the forestry sector, and land-use change matrix (LUCM), monitoring is necessary in South Korea to adequately respond to the Post-2020 climate regime. To calculate the greenhouse gas statistics observing the principle of transparency required by the Climate Change Convention, a consistent nationwide land-use classification and LUCM are required. However, in South Korea, land-use information is available from the 5th National Forest Inventory conducted in 2006 onwards; therefore, developing methods to determine historical LUCM information, including the base year required by the Intergovernmnetal Panel on Climate Change (IPCC), is essential. To determine the optimal sampling intensity for measuring systematic land-use changes and to estimate the corresponding area of land-use categories for previously unmeasured years, seven intensities—2 × 2 km to 8 × 8 km—were tested using the areas of the 3rd and 4th aerial photographs in time series for forestland, cropland, grassland, wetland, and settlements, according to their standard deviations and estimates of uncertainty. Analyses of statistical accuracy, statistical efficiency, economic efficiency, and convenience showed that a sampling intensity of 4 × 4 km was ideal. Additionally, the categorized areas of unmeasured land-use years were calculated through linear interpolation and extrapolation. Our LUCM can be utilized for developing a national greenhouse gas inventory. |
| first_indexed | 2024-03-10T10:00:58Z |
| format | Article |
| id | doaj.art-b514e494a29649178f0a6bb4adbe7c7e |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-10T10:00:58Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-b514e494a29649178f0a6bb4adbe7c7e2023-11-22T01:57:50ZengMDPI AGLand2073-445X2021-06-0110767710.3390/land10070677Optimal Sampling Intensity in South Korea for a Land-Use Change Matrix Using Point SamplingGa-Hyun Moon0Jong-Su Yim1Na-Hyun Moon2Forest ICT Research Center, National Institute of Forest Science, Seoul 02455, KoreaForest ICT Research Center, National Institute of Forest Science, Seoul 02455, KoreaDepartment of Forest, Environment, and System, Kookmin University, Seoul 02707, KoreaTo report changes in land use, the forestry sector, and land-use change matrix (LUCM), monitoring is necessary in South Korea to adequately respond to the Post-2020 climate regime. To calculate the greenhouse gas statistics observing the principle of transparency required by the Climate Change Convention, a consistent nationwide land-use classification and LUCM are required. However, in South Korea, land-use information is available from the 5th National Forest Inventory conducted in 2006 onwards; therefore, developing methods to determine historical LUCM information, including the base year required by the Intergovernmnetal Panel on Climate Change (IPCC), is essential. To determine the optimal sampling intensity for measuring systematic land-use changes and to estimate the corresponding area of land-use categories for previously unmeasured years, seven intensities—2 × 2 km to 8 × 8 km—were tested using the areas of the 3rd and 4th aerial photographs in time series for forestland, cropland, grassland, wetland, and settlements, according to their standard deviations and estimates of uncertainty. Analyses of statistical accuracy, statistical efficiency, economic efficiency, and convenience showed that a sampling intensity of 4 × 4 km was ideal. Additionally, the categorized areas of unmeasured land-use years were calculated through linear interpolation and extrapolation. Our LUCM can be utilized for developing a national greenhouse gas inventory.https://www.mdpi.com/2073-445X/10/7/677land-useland-use change and forestry (LULUCF)land-use change matrix (LUCM)sampling intensityuncertainty assessmentpoint sampling |
| spellingShingle | Ga-Hyun Moon Jong-Su Yim Na-Hyun Moon Optimal Sampling Intensity in South Korea for a Land-Use Change Matrix Using Point Sampling Land land-use land-use change and forestry (LULUCF) land-use change matrix (LUCM) sampling intensity uncertainty assessment point sampling |
| title | Optimal Sampling Intensity in South Korea for a Land-Use Change Matrix Using Point Sampling |
| title_full | Optimal Sampling Intensity in South Korea for a Land-Use Change Matrix Using Point Sampling |
| title_fullStr | Optimal Sampling Intensity in South Korea for a Land-Use Change Matrix Using Point Sampling |
| title_full_unstemmed | Optimal Sampling Intensity in South Korea for a Land-Use Change Matrix Using Point Sampling |
| title_short | Optimal Sampling Intensity in South Korea for a Land-Use Change Matrix Using Point Sampling |
| title_sort | optimal sampling intensity in south korea for a land use change matrix using point sampling |
| topic | land-use land-use change and forestry (LULUCF) land-use change matrix (LUCM) sampling intensity uncertainty assessment point sampling |
| url | https://www.mdpi.com/2073-445X/10/7/677 |
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