Mapping the Spatiotemporal Pattern of Sandy Island Ecosystem Health during the Last Decades Based on Remote Sensing

Abstract

Sandy islands suffer multiple external disturbances and thus experience drastic temporal ecological changes, and the two parts, that is, the states of essential components (Part 1) and the resilience under multiple disturbances (Part 2), are both indispensable to the sandy island ecosystem health. In this study, a model for the sandy island ecosystem health was established by integrating the two parts. In Part 1, the states were measured following the framework of vegetation, soil, and landscape, and a total of 12 factors in the three components were adopted. In Part 2, three typical disturbances, namely, geomorphological change, soil salinization, and human influence, were identified, and the resilience across different time intervals was measured by clarifying the intrinsic correlations between the components and disturbances. A sandy island ecosystem health index (SIEHI) was proposed based on the two parts, and Chongming Island was selected as the study area to demonstrate the model. The results indicated that the SIEHI continuously increased from 1988 to 2017, denoting the good effects of “Eco-Island construction” on Chongming Island. In different components and factors, the vegetation and soil components contributed more than the landscape component to the sandy island ecosystem health, and vegetation quality and soil carbon/nitrogen were the factors that made the most contributions. In different disturbances, the human influence played a major role in driving the spatiotemporal variations of the sandy island ecosystem health. Farming and building construction contributed the most and accounted for 37.12% and 35.59% of the total human influence, respectively, while traffic development exerted the highest influence per area. Then, influence coefficients of different human activities on the sandy island were determined, and measures for different functional zones were proposed for balancing the protection and development and achieving the sandy island ecosystem-based management.

Long-Term Dynamics of Sandy Vegetation and Land in North China

“…Duan et al [18] utilized high-resolution temporal continuous MODIS remote sensing data to focus on the dynamic changes in Horqin Sandy Land from 2000 to 2015. Various studies have demonstrated that the remote sensing index serves as an important indicator for monitoring dynamic changes in sandy lands through remote sensing [6,12,13,16,[19][20][21][22][23][24][25][26][27], and remote sensing data monitoring methods have become the primary approach for monitoring the characteristics of sandy land changes. However, owing to the influence of NDVI changes on the FVC, which is affected by meteorological conditions, there may be some randomness in the dynamic characteristics of sand changes during isolated years (such as a sudden increase in precipitation in the sand area that year, leading to a contraction in the area of sand monitored by the FVC).…”