Optimal Sampling Intensity in South Korea for a Land-Use Change Matrix Using Point Sampling

Abstract

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.

A Declining Trend in China’s Future Cropland-N2O Emissions Due to Reduced Cropland Area

“…Furthermore, the predicted cropland area decreases below the least cultivated land area (LCLA) target (Text S7) after 2058 using the conventional method, whereas this phenomenon does not occur in the DLUCP model (Figure S17b). The LCLA target is another regulation besides PBF, which was put forward in the National Land Use Planning Outline to ensure food security in China, which cannot be disrupted unless of necessary construction requirements. ,, Linear extrapolation was used to predict the changes of the LCLA target because it is widely used in land-use change prediction studies, which is convenient and free of using too many data. − The improvement of the DLUCP model compared to the conventional method could be explained by the fact that the PBF constraint in the DLUCP model was refined to each SCU rather than using only the overall PBF constraint in the conventional method. Thus, all the SCUs would satisfy the requirement of the PBF constraint, and the predicted results would be more realistic.…”