Temporal–Spatial Distribution of Ecosystem Health and Its Response to Human Interference Based on Different Terrain Gradients: A Case Study in Gannan, China

Abstract

The exploitation, utilization, and protection of land resources are some of the great social problems during the process of rapid urbanization in China. The status of land use directly affects ecosystem health (ESH). The evaluation of ESH and the spatial correlations between urbanization caused by human interference help us to analyze the influence of urbanization on ecosystems and also provide new insight into reasonable and scientific resource management. In this study, we evaluated the ESH of Gannan, in Jiangxi Province, China, based on ecosystem service values (ESV) and selected a series of indicators to detect the impact of urbanization on ecosystem health in 1990, 1995, 2000, 2005, 2010. and 2015. Remote sensing (RS) and the Geographic Information System (GIS) were used as processing tools to calculate basic data and to map the results based on different terrain gradients. The results show that ecosystem health suffered a downward trend from 1990 to 2015. Especially, the area proportion at an unhealthy level and average health (ave-health) level increased prominently, and the area of a well state decreased. Further, the results indicate that urbanization had a negative impact on ESH. The degree of a negative correlation increases with the process of urban sprawl. In addition, we found that from 1990 to 2015, the area proportion of a degraded level and unhealthy level was the highest on the first terrain gradient, and as the terrain gradient increased, this area proportion also decreased. However, the high interference region occupies a higher proportion in the lower terrain gradient. Consequently, the results could reveal the impact of urbanization on ecosystem health and could provide an even more effective service for a sustainable development.

Ecosystem Health Assessment of World Natural Heritage Sites Based on Remote Sensing and Field Sampling Verification: Bayanbulak as Case Study

“…However, the northern part of the property zone exhibited a decline in ecosystem health in 2011, and the declined areas were distributed in the restricted and exhibition zones within the property zone. A possible explanation is that those areas are close to Bayanbulak town and vulnerable to tourism and grazing activities, which would decrease the ecosystem health [75]. Jia et al (2011) and Qian et al (2016) also found the low ecosystem health level areas were easier to be affected by human activities in Sanjiangyuan and Dongting Lake wetland, respectively [41,76].…”

Spatial Response of Ecosystem Service Value to Urbanization in Fragile Vegetation Areas Based on Terrain Gradient

“…Through the investigation focusing on different ecosystem types on all levels of TGs, we found that the distribution of Shannan’s ecosystem types is significantly different in different TGs, mainly forest and grassland, and the areas of these two types increased at all TGs. The area of farmland decreases with the increase in the TGs; it reduced mainly at the third, fourth and fifth TGs, probably due to its higher elevation and slope restricting the development of agriculture [ 48 ]. The decreases in forest area are found with TGs increasing, which is different from the previous study concerning low-altitude regions, including Anhui province, Gannan region in Jiangxi province and Huailai County in Hebei province.…”

“…To explore the space association and agglomeration model between ecosystem service value and urbanization in Shannan, we conducted a spatial correlation analysis. Moran’s I index shows the spatial association put forward by Moran (1950), which shows the similarity of the spatially adjoining regional unit value [ 48 ]. This research explored the spatial association with global bivariate Moran’s I index and local bivariate Moran’s I index in Geo Da spatial analysis software.…”

Assessing the Terrain Gradient Effect of Landscape Ecological Risk in the Dianchi Lake Basin of China Using Geo-Information Tupu Method

“…In order to better reflect the distribution pattern of LER on different terrain gradients, we merged the DEM and slope in the ArcGIS10.7 software to establish the terrain niche index (TNI) [ 31 ] ( Figure 2 ). As a comprehensive terrain factor, it can make up for the non-obvious changes in low terrain gradient caused by high elevation, small slope, or low elevation and large slope, and effectively reflect the spatial differentiation of terrain gradient [ 43 ]. The calculation [ 31 ] is as follows: where TNI represents the terrain niche index; and , respectively, represent the altitude value of any point and its average altitude value around the neighborhood in the research around; and are the slope value of any point and its average slope value around the neighborhood in the study area, respectively.…”