Review related spatial data and literature
The spatial data for determining the spatial boundaries and constructing the LUC matrix were analyzed by dividing the data based on the content, time series, and spatial data types (Table 4). The cadastral map used in the study consisted of 28 categories according to South Korea's cadastral status, including forests, fields, paddies, and roads, and its also functioned as a basic topographic map with necessary data for urban planning and farmland management. This map shows the boundaries and ownership of land parcels that separate adjacent land plots and contains both spatial information (shape, area, boundary, and location) and non-spatial information (land use, value, and tenure) encoded in the text on attribute tables [20]. The cadastral map is useful for estimating the spatial area of changes in detailed land categories when estimating the GHG inventory. However, because the greenhouse gas removal and emission source information is not included in the map, it is necessary to construct activity data for estimating the biomass change in land use for 20 years presented by the IPCC. The use district map shows spatial data that divides the land into urban areas, management areas, agricultural and forestry areas, and natural environment conservation areas, and includes land use regulations for land management. The spatial boundaries of forest land, cropland, and settlements and information on land use plans can be determined; however, it is difficult to estimate the current land use status in detail because the map has set the content and range of regulations for land use planning and management. The Digital Forest Type map shows the forest distribution in South Korea and includes the forest type, species, and age for estimating the GHG inventory.[21] but does not contain information on other categories. The land cover map shows the current state of the ground surface determined by analyzing remote sensing images [22]. However, it is difficult to estimate the land management status according to land use because information on only the land cover state is provided. A smart farm map is a digital map of cropland based on high-resolution aerial images on the spatial area but contains no data on the past. Therefore, the spatial boundary of each category was determined using cadastral map to ensure that the spatial boundary was consistent with other categories.
Table 4. Spatial data review for constructing land use, land-use change matrix, and activity data
Data name
|
Description
|
Time series coverage
|
Spatial resolution and data type
|
Cadastral map
|
Map prepared by dividing the national land into 28 categories according to land-use purpose and status
|
1970s–present
(renewed monthly)
|
Vector
|
Use district map
|
Areas determined by urban management plans do not overlap with economical and efficient land use and promote public welfare by limiting land use
|
2005–present
(renewed monthly)
|
Vector
|
Ortho-images
|
Data produced through orthometric correction using aerial photographs of South Korea
|
2002–present
(renewed every 2 years)
|
Raster with 12 cm (urban area), 25 cm (others)
|
Digital forest type map
(1:25,000 scale)
|
Map of forest information on National Forest Resource Survey
|
1st (1971–1974),
2nd (1978–1980),
3rd (1986–1992),
4th (1996–2005),
5th (2006–2010)
|
Vector
|
Digital forest type map
(1:5,000 scale)
|
Map of detailed forest information using digital aerial photographs, ortho-images, and 1:25,000 Forest Type Map
|
2009–2013
|
Vector
|
Forest aerial photographs
|
Black-and-white aerial images constructed over 4 periods in South Korean territory
|
1st (1971–1974),
2nd (1978–1980),
3rd (1986–1992),
4th (1996–2005)
|
Raster with 0.8 m
|
Land cover map
(1:50,000 scale)
|
Spatial data represented by classifying land cover types into 7 categories
|
Construction (1998), Advanced (2000), Actualizing (2010), Update (2019)
|
Vector
|
Land cover map
(1:25,000 scale)
|
Spatial data represented by classifying land cover types into 22 categories
|
Construction (2004),
1st update (2007),
2nd update (2009),
3rd update (2013),
update (2018)
|
Vector
|
Land cover map
(1:5,000 scale)
|
Spatial data represented by classifying land cover types into 41 categories
|
constructed by region (2010–2016)
|
Vector
|
Smart farm map
|
Map of agricultural land constructed using high-resolution aerial images
|
2014–2018
|
Vector
|
Use of spatial data
The spatial boundaries were set using available cadastral maps while considering the definition, consistency, and time series between land-use categories. Specific categories in the cadastral map were classified according to those in the LULUCF (Table 5). Settlements were determined to be comprised of 19 categories, not including ‘other’ categories.
Table 5. The spatial boundary of land use, land-use change, and forestry (LULUCF) sector using a cadastral map in South Korea.
|
Categories in LULUCF
|
Categories in cadastral map
|
Forest land
|
Forest land
|
|
Cropland
|
Field paddy field, orchard
|
|
Grassland
|
Ranch
|
|
Wetlands
|
River, ditch, reservoir, fish farm
|
|
Settlements
|
Mineral spring site, salt farm, site, factory site, school site, parking zone, gas station, storage site, road, railroad, embankment, waterways, park, physical site, amusement park, religion site, historic site, graveyard, miscellaneous land
|
|
Other land
|
-
|
|
Constructing land-use change matrix and activity data
Of the 37 Annex I parties, 15 countries constructed matrices using sampling methods, 14 countries used both sampling and wall-to-wall methods, and other activity data were determined using existing national statistics [23]. In the wall-to-wall method, a theme map is constructed for the LUC using remote-sensing data based on the time series of a theme map or combination with other data. The sampling method directly estimates land use and land-use changes through the repetitive sampling of different areas, obtained through field surveys or remote sensing data. In South Korea, Park et al. [24,25] and Yu et al. [26] attempted to construct a LUC matrix using remote sensing data based on sample points using a random sampling method. Park et al.[27] analyzed the advantages and disadvantages of applying the sampling and wall-to-wall methods to forest lands in South Korea. As the sampling method detects land-use changes based on the sample points, the time-series conversion can be identified easily, whereas it is difficult to determine the boundaries of each category. In contrast, the wall-to-wall method reveals differences in the spatial boundaries between land cover and land use. By combining the results of these prior studies, the sample area was extracted using the cadastral map as primary data, and activity data were obtained by combining the wall-to-wall and sampling methods to construct a LUC matrix for the whole sample area. To construct a LUC matrix according to the land-use status between 2000 and 2019, particularly settlements, the 32,071 sampling area (grid) was extracted using a systematic sampling method by 10% of the total settlements grid (Fig 3).
The sampling ratio for each province was calculated to estimate the total land converted area using the data derived from the sample area (Table 6).
Table 6. Ratio of sampling area to total current settlements areas.
Division
|
Total settlements area (ha)
|
Settlements in sampling area (ha)
|
Sampling ratio (%)
|
Gangwon-do
|
78,072.89
|
7,463.95
|
10.46
|
Gyeonggi-do
|
201,263.03
|
24,786.09
|
8.12
|
Gyeongsangnam-do
|
103,032.89
|
9,692.65
|
10.63
|
Gyeongsangbuk-do
|
118,482.20
|
11,812.78
|
10.03
|
Gwangju-si
|
15,932.51
|
1,625.77
|
9.80
|
Daegu-si
|
23,378.41
|
2,289.76
|
10.21
|
Daejeon-si
|
15,296.09
|
1,514.46
|
10.10
|
Busan-si
|
27,748.82
|
2,627.73
|
10.56
|
Seoul-si
|
38,659.02
|
3,920.79
|
9.86
|
Sejong-si
|
7,598.85
|
762.17
|
9.97
|
Ulsan-si
|
19,574.74
|
1,880.38
|
10.41
|
Incheon-si
|
37,359.91
|
3,139.49
|
11.90
|
Jeollanam-do
|
121,483.69
|
11,023.93
|
11.02
|
Jeollabuk-do
|
80,440.75
|
7,832.59
|
10.27
|
Chungcheongnam-do
|
97,995.02
|
9,597.95
|
10.21
|
Chungcheongbuk-do
|
68,026.56
|
6,843.72
|
9.94
|
Jeju-si
|
27,308.21
|
2,568.98
|
10.63
|
Total
|
1,081,653.59
|
105,630.23
|
10.24
|
We constructed a settlement LUC matrix such as forest land converted to settlements (FS), cropland converted to settlements (CS), and grassland converted to settlements (GS). For wetlands, it appeared that no areas were converted to settlements during the study period, and thus these data were excluded from analysis. The SS and LS were 878,393.17 ha (81.21%) and 203,260.42 ha (18.79%), respectively, at the national level. Gyeonggi-do is the largest SS area (19,927.00 ha), followed by Jeollanam-do (8,914.11 ha), Gyeongsangbuk-do (8,852.8 ha), Gyeongsangnam-do (8,264.92 ha), and Chungcheongnam-do (7,419.17ha). Seoul-si (97.37%) showed the highest percentage of SS, followed by Incheon-si (92.06%), Jeollabuk-do (87.17%), Chungcheongbuk-do (86.61%), and Gyeongsangnam-do (85.27%) (Table 7).
Table 7. Settlements remaining settlements in South Korea.
Unit: ha (%)
|
Division
|
Settlements remaining settlements
|
Sampling area
|
Total area
|
Ratio
|
Gangwon-do
|
5,857.72
|
61,268.73
|
78.48
|
Gyeonggi-do
|
19,927.00
|
161,803.71
|
80.39
|
Gyeongsangnam-do
|
8,264.92
|
87,860.59
|
85.27
|
Gyeongsangbuk-do
|
8,852.80
|
88,792.70
|
74.94
|
Gwangju-si
|
1,224.92
|
12,005.86
|
75.35
|
Daegu-si
|
1,860.79
|
18,997.21
|
81.26
|
Daejeon-si
|
1,273.90
|
12,866.10
|
84.11
|
Busan-si
|
2,117.94
|
22,363.92
|
80.59
|
Seoul-si
|
3,817.61
|
37,641.90
|
97.37
|
Sejong-si
|
441.73
|
4,404.42
|
57.96
|
Ulsan-si
|
1,370.07
|
14,262.17
|
72.86
|
Incheon-si
|
2,890.37
|
34,395.03
|
92.06
|
Jeollanam-do
|
8,914.11
|
98,238.14
|
80.87
|
Jeollabuk-do
|
6,828.44
|
70,123.75
|
87.17
|
Chungcheongnam-do
|
7,419.17
|
75,751.69
|
77.30
|
Chungcheongbuk-do
|
5,926.87
|
58,916.68
|
86.61
|
Jeju-si
|
1,758.85
|
18,700.58
|
68.48
|
Total
|
84,994.26
|
878,393.17
|
81.21
|
Gyeonggi-do is the largest LS area (39,459.32 ha), followed by Gyeongsangbuk-do (29,689.50 ha), Jeollanam-do (23,245.55 ha), Chungcheongnam-do (22,243.33 ha), and Gangwon-do (16,804.16 ha). Sejong-si (42.04%) showed the highest percentage of LS (%), followed by Jeju-si (31.52%), Ulsan-si (27.14%), Gyeongsangbuk-do (25.06%), Chungcheongnam-do (22.70%), and Gyeonggi-do (19.61%). The national average estimated that 18.79% of the LS. In addition, according to the LULUCF sector, CS (84,401.37 ha, 7.80%) was estimated to account for the largest area, followed by FS (74,502.57 ha, 6.89%) and GS (44,356.48 ha, 4.10%) (Table 8).
Table 8. Land converted to settlement areas in South Korea
Unit: ha (%)
|
Division
|
Land converted to settlements
(A+B+C)
|
Forest converted to settlements (A)
|
Cropland converted to settlements (B)
|
Grassland converted to settlements (C)
|
Sampling area
|
Total area (land conversion ratio)
|
Sampling area
|
Total area
(land conversion ratio)
|
Sampling area
|
Total area
(land conversion ratio)
|
Sampling area
|
Total area
(land conversion ratio)
|
Gangwon-do
|
1,606.23
|
16,804.16
(21.52)
|
742.33
|
7,766.11
(9.95)
|
498.24
|
5,212.47
(6.68)
|
365.67
|
3,825.57
(4.90)
|
Gyeonggi-do
|
4,859.09
|
39,459.32
(19.61)
|
1,972.12
|
16,015.03
(7.96)
|
1,823.63
|
14,809.21
(7.36)
|
1,063.34
|
8,635.07
(4.29)
|
Gyeongsangnam-do
|
1,427.73
|
15,172.30
(14.73)
|
548.26
|
5,826.32
(5.65)
|
566.7
|
6,022.25
(5.84)
|
312.77
|
3,323.72
(3.23)
|
Gyeongsangbuk-do
|
2,959.98
|
29,689.50
(25.06)
|
1,084.14
|
10,874.28
(9.18)
|
1,219.67
|
12,233.65
(10.33)
|
656.17
|
6,581.57
(5.55)
|
Gwangju-si
|
400.85
|
3,926.65
(24.65)
|
67.76
|
663.81
(4.17)
|
282.92
|
2,771.48
(17.40)
|
50.16
|
491.36
(3.08)
|
Daegu-si
|
428.97
|
4,381.20
(18.74)
|
81.70
|
834.38
(3.57)
|
249.25
|
2,545.63
(10.89)
|
98.03
|
1,001.19
(4.28)
|
Daejeon-si
|
240.56
|
2,429.99
(15.89)
|
83.00
|
838.45
(5.48)
|
122.6
|
1,238.45
(8.10)
|
34.95
|
353.09
(2.31)
|
Busan-si
|
509.79
|
5,384.90
(19.41)
|
106.46
|
1,124.52
(4.05)
|
248.38
|
2,623.65
(9.45)
|
154.95
|
1,636.72
(5.90)
|
Seoul-si
|
103.18
|
1,017.12
(2.63)
|
41.15
|
405.62
(1.05)
|
51.85
|
511.12
(1.32)
|
10.18
|
100.38
(0.26)
|
Sejong-si
|
320.44
|
3,194.43
(42.04)
|
160.96
|
1,604.55
(21.12)
|
111.91
|
1,115.58
(14.68)
|
47.58
|
474.31
(6.24)
|
Ulsan-si
|
510.31
|
5,312.57
(27.14)
|
216.13
|
2,250.03
(11.49)
|
207.17
|
2,156.69
(11.02)
|
87.01
|
905.85
(4.63)
|
Incheon-si
|
249.12
|
2,964.88
(7.94)
|
76.57
|
911.32
(2.44)
|
97.11
|
1,155.73
(3.09)
|
75.44
|
897.83
(2.40)
|
Jeollanam-do
|
2,109.82
|
23,245.55
(19.13)
|
423.47
|
4,665.75
(3.84)
|
1,164.00
|
12,824.73
(10.56)
|
522.34
|
5,755.07
(4.74)
|
Jeollabuk-do
|
1,004.15
|
10,317.00
(12.83)
|
275.72
|
2,832.82
(3.52)
|
609.56
|
6,262.86
(7.79)
|
118.87
|
1,221.32
(1.52)
|
Chungcheongnam-do
|
2,178.78
|
22,243.33
(22.70)
|
735.81
|
7,511.95
(7.67)
|
935.77
|
9,553.40
(9.75)
|
507.19
|
5,177.99
(5.28)
|
Chungcheongbuk-do
|
916.85
|
9,109.88
(13.39)
|
677.31
|
6,729.84
(9.89)
|
46.68
|
463.83
(0.68)
|
192.85
|
1,916.21
(2.82)
|
Jeju-si
|
810.13
|
8,607.63
(31.52)
|
343.32
|
3,647.78
(13.36)
|
273
|
2,900.63
(10.62)
|
193.81
|
2,059.22
(7.54)
|
Total
|
20,635.97
|
203,260.42
(18.79)
|
7,636.21
|
74,502.57
(6.89)
|
8,508.44
|
84,401.37
(7.80)
|
4,491.32
|
44,356.48
(4.10)
|
GHG emission statistics
The GHG inventory was estimated at the Tier 1 and App 3 levels using the removal/emission factors specified in the 2006 IPCC GL [7]. The calculated CO2 emission was 18,942,905.6 tCO2 for the 20-year period from 2000 to 2019, and the annual CO2 emission was 1,262,860.4 tCO2 yr-1 in the same period. At the province level, Gyeonggi-do showed the highest emission (266,149.17 tCO2 yr-1), followed by Gyeongsangbuk-do (184,404.1 tCO2 yr-1), Chungcheongnam-do (129,694.7 tCO2 yr-1), Gangwon-do (126,203.8 tCO2 yr-1), and Chungcheongbuk-do (102,399.0 tCO2 yr-1). CO2 emissions from FS (16,390,566.0 tCO2 yr-1) were highest in the LULUCF sector, followed by those from CS (1,454,516.9 tCO2 yr-1) and GS (1,097,822.8 tCO2 yr-1) (Table 9).
Table 9. Carbon dioxide emission from land converted to settlement in 2000–2019 (Tier 1, Approach 3)
Division
|
Forest converted to settlements
|
Cropland converted to settlements
|
Grassland converted to settlements
|
Total
Carbon dioxide emissions
(tCO2)
|
Annual Carbon dioxide emissions
(tCO2yr-1)
|
Carbon emissions (tC)
|
Carbon dioxide emissions
(tCO2)
|
Carbon emissions (tC)
|
Carbon dioxide emissions
(tCO2)
|
Carbon emissions (tC)
|
Carbon dioxide emissions
(tCO2)
|
Gangwon-do
|
465,966.88
|
1,708,545.24
|
24,498.59
|
89,828.17
|
25,822.63
|
94,682.96
|
1,893,056.37
|
126,203.76
|
Gyeonggi-do
|
960,902.02
|
3,523,307.42
|
69,603.29
|
255,212.06
|
58,286.73
|
213,718.03
|
3,992,237.51
|
266,149.17
|
Gyeongsangnam-do
|
349,579.39
|
1,281,791.09
|
28,304.59
|
103,783.49
|
22,435.11
|
82,262.07
|
1,467,836.65
|
97,855.78
|
Gyeongsangbuk-do
|
652,456.69
|
2,392,341.19
|
57,498.14
|
210,826.52
|
44,425.63
|
162,893.97
|
2,766,061.68
|
184,404.11
|
Gwangju-si
|
39,828.59
|
146,038.17
|
13,025.97
|
47,761.90
|
3,316.65
|
12,161.05
|
205,961.12
|
13,730.74
|
Daegu-si
|
50,062.84
|
183,563.75
|
11,964.47
|
43,869.71
|
6,758.04
|
24,779.48
|
252,212.94
|
16,814.20
|
Daejeon-si
|
50,307.07
|
184,459.26
|
5,820.71
|
21,342.61
|
2,383.36
|
8,738.99
|
214,540.86
|
14,302.72
|
Busan-si
|
67,471.36
|
247,394.99
|
12,331.17
|
45,214.31
|
11,047.87
|
40,508.87
|
333,118.17
|
22,207.88
|
Seoul-si
|
24,337.40
|
89,237.13
|
2,402.26
|
8,808.29
|
677.54
|
2,484.30
|
100,529.72
|
6,701.98
|
Sejong-si
|
96,272.87
|
353,000.53
|
5,243.21
|
19,225.10
|
3,201.59
|
11,739.16
|
383,964.79
|
25,597.65
|
Ulsan-si
|
135,001.90
|
495,006.95
|
10,136.42
|
37,166.87
|
6,114.51
|
22,419.89
|
554,593.71
|
36,972.91
|
Incheon-si
|
54,679.05
|
200,489.87
|
5,431.95
|
19,917.14
|
6,060.35
|
22,221.29
|
242,628.30
|
16,175.22
|
Jeollanam-do
|
279,944.78
|
1,026,464.18
|
60,276.25
|
221,012.92
|
38,846.75
|
142,438.09
|
1,389,915.19
|
92,661.01
|
Jeollabuk-do
|
169,969.45
|
623,221.31
|
29,435.45
|
107,930.00
|
8,243.90
|
30,227.62
|
761,378.92
|
50,758.59
|
Chungcheongnam-do
|
450,716.73
|
1,652,628.01
|
44,900.96
|
164,636.85
|
34,951.42
|
128,155.20
|
1,945,420.05
|
129,694.67
|
Chungcheongbuk-do
|
403,790.50
|
1,480,565.18
|
2,180.01
|
7,993.35
|
12,934.41
|
47,426.19
|
1,535,984.72
|
102,398.98
|
Jeju-si
|
218,866.83
|
802,511.70
|
13,632.98
|
49,987.59
|
13,899.73
|
50,965.68
|
903,464.97
|
60,231.00
|
Total
|
4,470,154.35
|
16,390,565.96
|
396,686.43
|
1,454,516.90
|
299,406.22
|
1,097,822.82
|
18,942,905.68
|
1,262,860.38
|
CO2 emissions were greatly affected in the FS. The province with the highest CO2 emissions from FS were Gyeonggi-do (3,523,307.39 tCO2), followed by Gyeongsangbuk-do (2,392,341.19 tCO2), Chungcheongnam-do (1,652,628.01 tCO2), and Chungcheongbuk-do (1,480,565.18 tCO2) (Figure 4a). For CS, Gyeonggi-do (255,212.06 tCO2) showed the highest CO2 emissions, followed by Jeollanam-do (221,012.92 tCO2), Gyeongsangbuk-do (210,826.52 tCO2), and Chungcheongnam-do (164,636.85 tCO2) (Figure 4b). Finally, CO2 emissions in GS were in the order of Gyeonggi-do (213,718.03tCO2), Gyeongsangbuk-do (162,893.97 tCO2), Jeollanam-do (221,012.92 tCO2), and Chungcheongnam-do (164,636.85 tCO2) (Figure 4c). CO2 emissions from Gyeonggi-do and Gyeongsangbuk-do were high in all cases.