## 3 Results
### Links between LDDI, Climatic, and Anthropogenic Factors  
As shown in Figure 3a, temperature positively impacts LDDI in most of the study area except for the western region of GX, the eastern region of GD, FJ, and the eastern region of LN. Similarly, precipitation positively impacts LDDI in areas other than the PRD, YRD, and TW (Figure 3b). However, the observed and fitted results in the relationship between LDDI and climate factors exhibit a low correlation coefficient of less than 0.3 in general, and the overall relationship fails to pass the significance test (Figure 3c). Nevertheless, despite the overall lack of significance in the regression relationship between LDDI and climate factors, some areas, particularly in the northern region of the study area, including SD, HB, northern JS, and eastern LN, exhibit a significant relationship (\(p<0.01\)). In addition, some pixel-scale regions in southern provinces, such as GX, GD, FJ, ZJ, and HN, also show this significant relationship. Figure 2: Temporal evolution of LDD processes in Coastal China from 1985 to 2015. (**a**) Spatial distribution of LDDI multi-year mean values (the built-in subgraph shows the LDDI of each province); (**b**) Theil–Sen slope of LDDI; (**c**) Theil–Sen slope of temperature; (**d**) Theil–Sen slope of precipitation. The black dots denote data points that have achieved statistical significance at a level of 0.05. The trend of LDDI\({}_{\rm HA}\) indicates the degree to which human activities have impacted the LDD process. Spatially, the LDDI\({}_{\rm HA}\) trend increased throughout the study period, indicating that the influence of human activities on the LDD process was becoming increasingly significant (Figure 4a). However, even under similar natural environmental conditions, the LDDI\({}_{\rm HA}\) trends of different provinces showed significant differences, and the spatial heterogeneity of the LDDI\({}_{\rm HA}\) trend was strong. Among them, the LDDI\({}_{\rm HA}\) trend in GX was the strongest, indicating that the LDD process in this area was most affected by human activities relative to climate factors. Approximately 55.48% of the entire study area was affected by increasing human activities, of which about 8.92% had a significantly increasing LDDI\({}_{\rm HA}\) trend (\(p<0.01\)). In 4.29% of the areas, the influence of human activities on LDD decreased, mainly distributed in the YRD, PRD, and Northeast LN. Additionally, about 38.23% of the areas showed no significant change in the LDDI\({}_{\rm HA}\) trend, mainly distributed in FJ, JS, and TW. Figure 4: Temporal and spatial variation characteristics of LDDI\({}_{\rm HA}\). (**a**) Spatial distribution of LDDI\({}_{\rm HA}\) trends (the built-in subgraph shows the LDDI\({}_{\rm HA}\) trend of each province); (**b**) histograms of LDDI\({}_{\rm HA}\) in different periods (the built-in subgraph indicates the trend of region-average LDDI\({}_{\rm HA}\); the scatter is the LDDI\({}_{\rm HA}\) of region-average for each year from 1982 to 2015; the black arrows indicate transition direction; the red and blue lines indicate the multi-year average LDDI\({}_{\rm HA}\) values of the study region in Pre-TP and in Post-TP, respectively). Figure 3: The correlation between climate factors and LDDI in coastal China. (**a**) The regression coefficient of temperature; (**b**) the regression coefficient of precipitation; and (**c**) the significance level. The black dots denote data points that have achieved statistical significance at a level of 0.05. To gain a deeper understanding of the variations in LDDI\({}_{\rm HA}\) during different periods, this study examined the turning points (TPs) in the LDDI\({}_{\rm HA}\) time series. The analysis using BEAST showed that the average LDDI\({}_{\rm HA}\) in the entire study area increased significantly (\(p<0.01\)) during the study period, with a TP around 2008 (probability = 0.24). Accordingly, the LDDI\({}_{\rm HA}\) before (Pre-TP, red line) and after (Post-TP, blue line) the TP were calculated, respectively (Figure 4b).