1
2
Appendix: Analysis of driving mechanisms for LUCC
3
Selection of variables for LUCC
4
Elevation and slope, which were derived from a regional digital elevation model (DEM), were chosen
5
to describe the general configuration of the surface. Climatic factors, especially precipitation and
6
temperature, have a large impact on terrestrial vegetation and human land-use activities. The climatic
7
records from weather stations around the Dongjiang River watershed from 1987 to 2010 were used to
8
evaluate the influence of rainfall and temperature on LUCC. Aside from natural factors, human
9
activities have also greatly influenced LUCC (Su et al. 2011; Weng 2002). Thus, population density
10
and per capita GDP data were used to evaluate the influence of population mobility and socioeconomic
11
growth on LUCC. In addition, proximity to national highways, provincial highways, cities, and rivers
12
are four factors that contribute to the impact of human accessibility on LUCC (Patarasuk and Binford
13
2012). Hence, four variables (e.g., distance to national highway, distance to provincial highway,
14
distance to city and distance to river) were chosen so that the impact of these factors on land use/land
15
cover changes could be determined ( see in Table 1).
16
Acquisitions of variables for LUCC
17
The detailed extraction process of the selected driving forces was as follows. The grid maps of rainfall
18
and temperature for the four periods (1994–1999, 1999–2004, 2004–2009, 1994–2009), derived from
19
11 meteorological stations (Figure 1), were generated using a Kriging method. Elevation and slope
20
were derived from a DEM. The vector map of national and provincial highways in 2005 was obtained
21
from the traffic map of Guangdong Province at a scale of 1:500,000. The vector map of the river and
22
city were selected from the DEM and local administrative maps, respectively. The four distance
23
variables (distance to national highways, distance to provincial highways, distance to rivers, and
24
distance to cities) were calculated by a GIS spatial analysis method. The vector maps of the mean
25
values of population and GDP for the four periods, derived from statistical yearbooks of 17 local
26
counties, were attached to the corresponding map data of the 17 local counties using ArcGIS 9.3.
27
The RDA model establishment
28
In this case, RDA was used as an explanatory model to quantitatively determine the relationships
29
between the dominant components of LUCC and biophysical and socio-economic driving forces in
30
each of the grid cells, using climate, landform, population, GDP, distance to highways, distance to
31
rivers, and distance to cities as explanatory variables. The response variable was swap changes of each
32
LULC type as a result of map overlays in the four periods (1994–1999, 1999–2004, 2004–2009 and
33
1994–2009). According to Pontius et al (2004), the amount of swap changes for each LULC type was
34
twice as much as the minimum of the gain and loss. Therefore, maps of the dominant LUCC for the
35
four periods were selected and generated from the gross gain and loss of each kind of LULC type
36
(Table 2).
37
Before applying RDA, the ten variables for each study period (1994–1999, 1999–2004, 2004–2009
38
and 1994–2009) were converted into raster data at a resolution of 30×30 m. The study region was
39
divided into 1215 zones with an area of 5×5 km, which was subsequently used as the base statistical
40
unit. The formula of thee response variables (ΔLij) is calculated using Eq. (1):
𝐿𝑘𝑗
41
∆𝐿𝑗 =
42
where ΔLj is the area of swap change of LUCC and j is between 1 and 7 (1: cropland, 2: garden land, 3:
43
grassland, 4: woodland, 5: built-up land, 6: wetland and 7: bare land). Lkj is the swap change area of the
44
typej and it was located in zonek (k = 1, 2, 3, ……, 1215). Tj is the total area of typej in the study region.
45
Before statistical analysis, all of the dependent and independent variables were arcsine-square root and
46
“min-max” normalized, respectively.
𝑇𝑗
(1)
47
RDA results were presented graphically on the biplots, on which swap changes for each kind of
48
LUCC and driving forces were shown with arrows and their abbreviated names; the former were
49
presented in blue and the latter were indicated in red. The length of the arrows indicated the
50
significance of the correlations between the distribution of the changed land use types and the driving
51
forces. A longer arrow indicated a stronger correlation between them (Sadyś et al. 2015). The vector of
52
relationships (directly proportional or inversely proportional) was interpreted based on the position of
53
the changed land use types relative to the end of the arrows. If the land use types were close to the end
54
of the arrow, the correlation was positive. If they were at the opposite side from the driving forces, the
55
correlation was negative (Sadyś et al. 2015).
56
57
58
59
Table A1 Transition matrix of land use/land cover change in the Dongjiang River watershed from 1994
to 2009
Periods
1994–1999
60
61
Types
Crop
Garden
Grass
Wood
Built-up
Wet
Bare
Total
1999–2004
Crop
Garden
Grass
Wood
Built-up
Wet
Bare
Total
2004–2009
Crop
Garden
Grass
Wood
Built-up
Wet
Bare
Total
Crop
Garden
Grass
Wood
1994–2009
Built-up
Wet
Bare
Total
Note: units (km2)
Crop
Garden
Grass
Wood
Built-up
Wet
Bare
Total
1138.0
30.5
29.0
173.3
215.6
0.1
571.0
2157.3
590.8
515.5
471.2
574.1
293.9
58.9
204.5
2709.1
695.6
729.9
291.8
740.2
358.0
24.0
138.7
2978.9
669.5
332.9
439.6
946.1
259.3
26.2
304.9
2978.9
505.0
769.8
1341.3
1200.5
19.9
0.0
41.1
3877.6
480.2
986.6
891.2
964.3
327.2
28.7
160.0
3838.9
105.6
285.3
88.7
237.1
77.7
4.2
8.7
807.4
70.7
261.3
139.3
181.4
51.2
2.6
100.9
807.4
163.1
1177.7
1182.3
615.4
95.1
0.0
221.8
3455.4
205.7
288.5
277.0
452.3
170.1
6.8
69.3
1470.0
631.5
1018.3
357.9
1170.3
355.3
10.3
67.3
3611.3
571.2
644.2
746.3
1050.0
205.5
13.7
380.1
3611.3
33.4
2153.9
219.6
11649.7
34.5
6.0
2.1
14099.1
303.5
1729.3
1511.1
11506.5
319.3
24.9
78.7
15477.7
767.4
1403.9
544.8
12597.7
535.1
41.0
128.0
16021.0
550.9
2720.3
1323.2
10963.1
303.9
19.5
137.3
16021.0
265.7
158.2
65.8
167.4
1411.3
12.2
32.3
2112.9
442.7
252.9
228.5
441.0
826.7
51.3
100.3
2343.9
347.4
321.8
144.9
611.3
929.1
224.0
63.8
2642.9
374.2
260.7
151.6
599.9
903.4
119.4
233.4
2642.9
2.8
0.8
0.0
12.4
64.6
594.5
0.0
675.0
45.9
9.7
19.5
35.6
122.3
492.3
21.0
746.3
30.8
17.3
4.2
14.5
26.8
427.3
5.9
526.8
12.9
12.7
5.8
14.0
54.0
415.5
11.9
526.8
195.8
0.3
7.1
73.3
12.9
0.5
391.1
681.0
88.1
94.9
56.5
122.7
50.5
12.0
47.2
472.0
130.4
61.9
37.6
103.4
61.7
15.4
59.5
469.9
54.2
58.7
39.3
135.5
74.9
16.4
90.9
469.9
2303.8
4291.1
2845.1
13891.9
1853.9
613.2
1259.3
–
2157.3
3877.6
3455.4
14099.1
2112.9
675.0
681.0
–
2709.1
3838.9
1470.0
15477.7
2343.9
746.3
472.0
–
2303.8
4291.1
2845.1
13891.9
1853.9
613.2
1259.3
–