Coupling coordination analysis of industrial mining land, landscape pattern and carbon storage in a mining city: a case study of Ordos, China

Figures & data

Figure 1. Overview map of Ordos.

(China map vector boundary from the Ministry of Natural Resources: GS(2020)4619, no modification from the base map boundary)

Figure 2. Map of plus model driving factors.

Table 1. The data sources used in this research.

Data Type	Data Name	Data source	Spatial Resolution
LULC	LULC	Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (https://www.resdc.cn) (Xu et al., 2018)	30m
Natural Environment	Average temperature in 2020	NOAA (https://gis.ncdc.noaa.gov)	—
	Average precipitation in 2020	NOAA (https://gis.ncdc.noaa.gov)	—
	DEM	SRTM30	30m
	Soil Types	Resource and Environmental Science and Data Center, Chinese Academy of Sciences (https://www.resdc.cn)	1km
	NDVI	National Ecological Data Center Resource Sharing Service Platform (http://www.nesdc.org.cn) (Yang et al., 2019)	30m
	Expressway	Open Street Map(https://www.openstreetmap.org)	—
	First, second and third class roads	Open Street Map(https://www.openstreetmap.org)	—
	Railroad	Sky Map (https://www.tianditu.gov.cn)	—
	Water system	Sky Map (https://www.tianditu.gov.cn)	—
	Night lighting	NPP-VllRS	750m
Socio- economic	Population	World Pop(https://hub.worldpop.org)	1km
	GDP	The dataset produced by Zhao et al., (2017)	1km

Figure 3. Technology road map.

Table 2. Carbon density of land use types in Ordos (t/hm²).

Land Use Type	AGC	BGC	SOC	DOC
Cropland	42.49	8.07	100.38	4.25
Forest	36.69	14.68	144.64	3.67
Grassland	1.67	8.66	92.51	0.17
Water	3.00	5.87	49.08	0.30
Urban Rural Land	8.60	0	144.04	0.86
Industrial Mining Land	0	0	0	0
Barren Land	0.56	3.09	29.08	0.06

Table 3. Table of ecosystem service values per unit area by category in Ordos (CNY/hm²).

Primary Classification	Secondary Classification	Cropland	Forest	Grassland	Water	Urban Rural Land	Industrial Mining land	Barren land
Supply Services	Food production	1,799.82	534.65	370.55	1693.95	0.00	0.00	529.36
	Raw material supply	846.98	1,228.12	545.24	487.01	0.00	0.00	550.53
	Water supply	42.35	635.23	301.74	17,553.58	0.00	0.00	21.17
Reconciliation Services	Gas regulation	1,418.69	4,039.02	1,916.28	1,630.43	0.00	−42.35	137.63
	Climate regulation	762.28	12,085.29	5,065.98	4,848.94	0.00	0.00	105.87
	Waste treatment	211.74	3,541.42	1,672.78	11,751.80	0.00	−211.74	434.08
	Water regulation	571.71	7,908.64	3,710.81	216,487.14	0.00	−63.52	254.09
Support Services	Erosion control	2,180.96	4,917.76	2,334.48	1,969.22	0.00	−42.35	158.81
	Nutrient cycling	254.09	375.85	179.98	148.22	0.00	0.00	10.59
	Biological control	275.27	4,478.39	2,122.73	5,399.47	0.00	−42.35	148.22
Cultural Services	Aesthetic landscape	127.05	1,963.93	936.97	4,001.96	0.00	−21.17	63.52

Table 4. Selection of the landscape pattern index and its description.

Index Type	Landscape Pattern Index	Formula	Meaning
Fragmentation Index	Number of Patches NP	$NP=n$	Denotes the total number of all patches in the landscape n
	Patches Density PD	$PD=n/A$	Denotes the percentage of total patch area to total landscape area, A is the total landscape area
	Landscape Shape Index LSI	$\mathit{LSI}=\frac{e_i}{\min e_i}$	Landscape Shape Index is one of the indicators of aggregation, the more discrete the patch type, the larger the LSI value
Diversity Index	Shannon’s Diversity Index SHDI	$\mathit{SHDI}=-{\sum }_{i=1}^m{P}_{i}ln(P_i)$	Reflecting landscape heterogeneity and diversity, where $P_i$ is the proportion of type i in the landscape
	Shannon’s Evenness Index SHEI	$\mathit{SHEI}=-{\sum }_{k=1}^m{P}_{k}ln(P_k)/\mathit{lnn}$	Reflects landscape uniformity and dominance
Aggregation Index	Aggregation Index AI	$AI=\frac{g_{ii}}{\max g_{ii}}\times 100$	Describe the spatial distribution characteristics of different plaque types and their mixing status, reflecting the trend of spatial aggregation of plaque types

Table 5. Classification level of coupling coordination degree.

Coupling coordination degree	Degree of coordination	Coupling coordination degree	Degree of coordination
0 < DLMC≦0.09	Extreme Disorder	0.49 < DLMC≦0.59	Barely Coordination
0.09 < DLMC≦0.19	Severe Disorder	0.59 < DLMC≦0.69	Primary Coordination
0.19 < DLMC≦0.29	Moderate Disorder	0.69 < DLMC≦0.79	Intermediate Coordination
0.29 < DLMC≦0.39	Mild Disorder	0.79 < DLMC≦0.89	Good Coordination
0.39 < DLMC≦0.49	On the Verge of Disorder	0.89 < DLMC≦1	High Quality Coordination

Figure 4. Land use map of Ordos from 2000 to 2020.

Table 6. Area and proportion of land use types in Ordos by period from 2000 to 2020 (km²).

Land Use Type	2000		2010		2020
	Area	Proportion	Area	Proportion	Area	Proportion
Cropland	4,499.55	5.17%	4,941.93	5.68%	4,418.67	5.08%
Forest	1,692.34	1.94%	1,626.30	1.87%	2,169.64	2.49%
Grassland	53,120.84	61.04%	53,596.26	61.59%	52,033.40	59.79%
Water	2,096.39	2.41%	1,875.16	2.15%	2,042.26	2.35%
urban rural Land	762.12	0.88%	902.01	1.04%	890.91	1.02%
Industrial Mining Land	38.68	0.04%	332.31	0.38%	955.63	1.10%
Barren Land	24,816.07	28.52%	23,752.02	27.29%	24,513.09	28.17%

Table 7. Land use transfer matrix in Ordos from 2000 to 2020 (km²).

2000	2020
	Cropland	Forest	Grassland	Water	Urban Rural Land	Industrial Mining Land	Barren Land
Cropland	3,876.09	117.39	281.31	67.27	37.07	82.30	38.03
Forest	30.27	1,359.54	188.60	13.25	11.15	50.62	38.89
Grassland	407.49	418.61	49,870.21	113.79	114.60	597.23	1,597.64
Water	36.18	18.71	147.27	1,750.69	1.84	24.87	116.27
Urban Rural Land	15.85	1.82	22.11	2.44	710.13	3.98	5.78
Industrial Mining Land	0.42	0	0.49	0.42	0.13	36.99	0.23
Barren Land	52.37	253.57	1,523.38	94.40	15.98	159.63	22,716.25

Figure 5. Distribution of carbon storage in Ordos from 2000 to 2020.

Table 8. Land use carbon storage in Ordos from 2000 to 2020 (10⁶t).

Land Use Type	2000		2010		2020
	Carbon Storage	Proportion	Carbon Storage	Proportion	Carbon Storage	Proportion
Cropland	69.83	9.26%	76.69	10.08%	68.57	9.13%
Forest	33.79	4.48%	32.47	4.27%	43.32	5.77%
Grassland	547.20	72.59%	552.11	72.53%	536.00	71.35%
Water	12.21	1.62%	10.92	1.43%	11.90	1.58%
Urban Rural Land	9.41	1.25%	11.14	1.46%	11.00	1.46%
Industrial Mining Land	0	0.00%	0	0.00%	0	0.00%
Barren Land	81.37	10.79%	77.88	10.23%	80.38	10.70%

Figure 6. Land use distribution of three predicted scenarios in Ordos.

Table 9. Area of different land use types in 2030 under different development scenarios and their changes compared to 2020 in Ordos (km²).

Year	Scenario	Cropland	Forest	Grassland	Water	Urban Rural Land	Industrial Mining Land	Barren Land
2020	——	4,418.67	2,169.64	52,033.4	2,042.26	890.91	955.63	24,513.09
2030	Q1	4,042.59	2,660.06	50,861.55	2,180.71	885.6	1,118.61	25,274.48
	Q2	4,197.74	2,660.06	52,317.18	2,180.71	960.26	955.63	23,752.02
	Q3	4,392.71	2,660.06	52,033.4	2,042.26	1,024.54	1,118.61	23,752.02
Amount of Change Between 2020- 2030	Q1	−376.08	490.42	−1,171.85	138.45	−5.31	162.98	761.39
	Q2	−220.93	490.42	283.78	138.45	69.35	0	−761.07
	Q3	−25.96	490.42	0	0	133.63	162.98	−761.07

Figure 7. Carbon storage distribution of three predicted scenarios in Ordos.

Table 10. Carbon storage of different land use types in 2030 under different development scenarios and their changes compared to 2020 in Ordos (10⁶t).

Year	Scenario	Cropland	Forest	Grassland	Water	Urban Rural Land	Industrial Mining Land	Barren Land
2020	——	68.57	43.32	536.00	11.90	11.00	0	80.38
2030	Q1	62.74	53.12	523.92	12.70	10.94	0	82.88
	Q2	65.14	53.12	538.92	12.70	11.86	0	77.88
	Q3	68.17	53.12	536.00	11.90	12.65	0	77.88
Amount of Change Between 2020-2030	Q1	−5.83	9.80	−12.08	0.80	−0.06	0	2.5
	Q2	−3.43	9.80	2.92	0.80	0.86	0	−2.5
	Q3	−0.40	9.80	0	0	1.65	0	−2.5

Table 11. Coupling coordination degree of industrial mining land, landscape pattern and carbon storage.

Year	Landscape Index	Industrial Mining Land (km^2)	Carbon Storage (10^6t)	Coupling Coordination	Coordination Index
2000	0.764	38.68	753.81	0.950	High Quality Coordination
2010	0.759	332.31	761.21	0.927	High Quality Coordination
2020	0.855	955.63	751.17	0.859	Good Coordination
Q1	0.236	1,118.61	746.30	0.667	Primary Coordination
Q2	0.591	955.63	759.62	0.813	Good Coordination
Q3	0.738	1,118.61	759.72	0.815	Good Coordination

Xu L, He N, Yu G. 2019. A dataset of carbon density in Chinese terrestrial ecosystems (2010s). China Sci Data. 4(1):90–96. doi:10.11922/sciencedb.603.

 Google Scholar

Yang J, Dong J, Xiao X, Dai J, Wu C, Xia J, Zhao G, Zhao M, Li Z, Zhang Y, et al. 2019. Divergent shifts in peak photosynthesis timing of temperate and alpine grasslands in China. Remote Sens Environ. 233:111395. doi:10.1016/j.rse.2019.111395.

 Web of Science ®Google Scholar

Zhao N, Liu Y, Cao G, Samson EL, Zhang J. 2017. Forecasting China’s GDP at the pixel level using nighttime lights time series and population images. GISci Remote Sens. 54(3):407–425. doi:10.1080/15481603.2016.1276705.

 Web of Science ®Google Scholar

Data availability statement

The data is available from the authors upon reasonable request.