Scale effect of coastal landscape pattern stability and driving forces: a case study of Guangdong Province, China
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Abstract: The long-term dynamic evolution and underlying mechanisms of coastal landscape pattern stability, driven by strong anthropogenic interference and consequently climate change, are topics of major interest in national and international scientific research. Guangdong Province, located in southeastern China, has been undergoing rapid urbanization over several decades. In this study, we quantitatively determined the scale threshold characteristics of coastal landscape pattern stability in Guangdong Province, from the dual perspective of spatial heterogeneity and spatial autocorrelation. An analysis of the spatiotemporal evolution of the coastal landscape was conducted after the optical scale was determined. Then, we applied the geodetector statistical method to quantitatively explore the mechanisms underlying coastal landscape pattern stability. Based on the inflection point of landscape metrics and the maximum value of the Moran Ⅰ index, the optimal scale for analyzing coastal landscape pattern stability in Guangdong Province was 240 m × 240 m. Within the past several decades, coastal landscape pattern stability increased slightly and then decreased, with a turning point around 2005. The most significant variations in coastal landscape pattern stability were observed in the transition zone of rural-urban expansion. A q-statistics analysis showed that the explanatory power of paired factors was greater than that of a single driving factor; the paired factors with the greatest impact on coastal landscape pattern stability in Guangdong Province were the change in gross industrial output and change in average annual precipitation from 2010 to 2015, based on a q value of 0.604. These results will contribute to future efforts to achieve sustainable coastal development and provide a scientific basis and technical support for the rational planning and utilization of resources in large estuarine areas, including marine disaster prevention and seawall ecological restoration.
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Figure 1. Location of the coastal zone in Guangdong Province, China. ZJ: Zhanjiang City; MM: Maoming City; YJ: Yanjiang City; JM: Jiangmen City; ZH: Zhuhai City; ZS: Zhongshan City; GZ: Guangzhou City; DG: Dongguan City; SZ: Shenzhen City; HZ: Huizhou City; SW: Shanwei City; JY: Jieyang City; ST: Shantou City; CZ: Chaozhou City. Drawing from Minstry of Natural Resources of China (http://bzdt.eh.mnr.gov.cn/). Approval number: GS(2020)no.4619.
Figure 2. Sensitivity analysis of four indices to classification accuracy. NDWI: normalized difference water index; NDVI: normalized difference vegetation index; EVI: enhanced vegetation index; MVI: mangrove vegetation index.
Figure 3. Seven landscape-level pattern metrics response curves to progressively increasing grain size. PD: patch density; MPS: mean patch size; TECI: total edge contrast index, MPI: mean proximity index, AI: aggregation index, CONTAG: contagion; SHDI: Shannon’s diversity index; p < 0.01.
Figure 4. Spatiotemporal patterns of urbanization in the coastal zone of Guangdong Province as described by seven landscape-level pattern metrics at the optimal analysis scale. PD: patch density; TECI: total edge contrast index; MPS: mean patch size; MPI: mean proximity index; AI: aggregation index; CONTAG: contagion; SHDI: Shannon’s diversity index.
Figure 5. Spatiotemporal patterns of coastal landscape stability in Guangdong Province from 1985 to 2020. WCZ: western coastal zone of Guangdong Province; MCZ: middle coastal zone of Guangdong Province; ECZ: eastern coastal zone of Guangdong Province.
Figure 6. Changes in coastal landscape pattern stability in Guangdong Province from 1985 to 2020. WCZ: western coastal zone of Guangdong Province; MCZ: middle coastal zone of Guangdong Province; ECZ: eastern coastal zone of Guangdong Province. Landscape stability decreased: the annual mean stability metric belowed –0.05; landscape stability increased: the annual mean stability metric begonded 0.05; landscape relative stability: the annual mean stability metric ranged from –0.05 to 0.05.
Figure 7. q values of influential factors (X1−X8) from 1985 to 2020. X1: Change in population density; X2: change in the agricultural output value; X3: change in the gross industrial output value; X4: change in the road network density; X5: DEM, digital elevation model; X6: slope; X7: average annual temperature change; X8: average annual precipitation change. p < 0.01.
Figure 8. Interaction detection results of potential influencing factors (X1−X8). X1: change in population density; X2: change in the agricultural output value; X3: change in the gross industrial output value; X4: change in the road network density; X5: DEM, digital elevation model; X6: slope; X7: average annual temperature change; X8: average annual precipitation change.
Table 1. Remote sensing data used in this study
Year Data Sensor Number
of scenesMonth of acquisition Cloud cover Spatial resolution and
revisit timePath and row 1985 Landsat-5 TM/ETM+ thematic mapper/enhanced thematic mapper plus 16 from June to September in each typical year <5% 30 m × 30 m, 16 d paths: 119–124, rows: 43–46 1990 Landsat-5 TM/ETM+ thematic mapper/enhanced thematic mapper plus 16 from June to September in each typical year <5% 30 m × 30 m, 16 d paths: 119–124, rows: 43–46 1995 Landsat-5 TM/ETM+ thematic mapper/enhanced thematic mapper plus 15 from June to September in each typical year <5% 30 m × 30 m, 16 d paths: 119–124, rows: 43–46 2000 Landsat-5 TM/ETM+ thematic mapper/enhanced thematic mapper plus 16 from June to September in each typical year <5% 30 m × 30 m, 16 d paths: 119–124, rows: 43–46 2005 Landsat-5 TM/ETM+ thematic mapper/enhanced thematic mapper plus 16 from June to September in each typical year <5% 30 m × 30 m, 16 d paths: 119–124, rows: 43–46 2010 Landsat-5 TM/ETM+ thematic mapper/enhanced thematic mapper plus 16 from June to September in each typical year <5% 30 m × 30 m, 16 d paths: 119–124, rows: 43–46 2015 Landsat-8
OLIoperational land imager 15 from June to September in each typical year <5% 30 m × 30 m, 16 d paths: 119–124, rows: 43–46 2020 Landsat-8
OLIoperational land imager 15 from June to September in each typical year <5% 30 m × 30 m, 16 d paths: 119–124, rows: 43–46 Note: Total number of scenes is 125. 
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Table 2. User’s accuracy, total accuracy and Kappa coefficients for the landscape classification result
Calendar (year) User’s accuracy/% Total accuracy/% Kappa coefficient Impervious water surface Water Forest Farmland Mangrove forest Other 1985 83.33 85.67 85.00 82.67 81.33 80.33 83.06 0.826 1990 84.67 87.00 86.00 83.33 82.33 82.00 84.22 0.842 1995 86.00 88.33 87.00 85.00 84.00 82.33 85.41 0.851 2000 87.00 89.33 88.67 87.00 84.67 82.67 86.56 0.854 2005 88.33 90.67 90.00 87.00 86.67 84.67 87.89 0.865 2010 89.33 91.33 90.67 88.33 87.00 85.33 88.67 0.876 2015 91.00 92.33 91.33 89.00 87.67 86.00 89.56 0.888 2020 91.67 93.00 92.00 89.33 88.33 86.67 90.17 0.898
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Table 3. Types of potential factors driving landscape pattern stability
Criteria Interaction type q(X1 ∩ X2) < Min(q(X1), q(X2)) nonlinear weakening Min(q(X1), q(X2)) < q(X1 ∩ X2) < Max(q( X1), q( X2)) single-factor nonlinear attenuation q(X1 ∩ X2) > Max(q(X1), q(X2)) two-factor interaction enhancement q(X1 ∩ X2) = q(X1) + q(X2) independence q(X1 ∩ X2) > q(X1) + q(X2) nonlinear enhancement
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