Using ZY1-02D satellite hyperspectral remote sensing to monitor landscape diversity and its spatial scaling change in the Yellow River Estuary

Abstract

Monitoring and assessing wetland diversity is crucial for its accurate preservation. Hyperspectral satellites have been proven effective for detailed investigations of plant diversity in many places. However, it's unclear whether spectral diversity invert landscape diversity, and whether the inversion accuracy varies with spatial scale. In this study, the ZY1-02D hyperspectral remote sensing images of the Yellow River Estuary were supervised and classified by the support vector machine. Then, the landscape diversity indices (i.e., community richness, Shannon-Wiener index, Simpson index, and Pielou index) and spectral diversity indices (i.e., coefficient of variation, convex hull volume, and eight vegetation indices) were calculated. A random forest model was used to predict landscape diversity by using spectral diversity. The spatial scale relationship between spectral diversity and landscape diversity were explored lastly. Our results showed that the overall accuracy of plant community classification in the Yellow River Estuary was 91.53 %, with a Kappa coefficient of 0.90. Spectral diversity had the best inversion accuracy on the Shannon-Wiener index (14 ∼ 57 %, average = 38 %), while the intermediate on Pielou index (3 ∼ 56 %, average = 30 %) and community richness (2 ∼ 48 %, average = 30 %), but the lowest on the Simpson index (2 ∼ 43 %, average = 16 %). The inversion accuracy of landscape diversity index increased first and then stabilized with the increase of scales, reaching stability at a sampling size of 2880 m × 2880 m. Our results indicated that ZY1-02D hyperspectral data can be used to monitor spatial changes of landscape diversity in wetland systems. However, its accuracy is affected by diversity index type and spatial scaling effects. Our findings provide a new perspective for the conservation and management of large-scale wetland landscape diversity.

Keywords

• Hyperspectral remote sensing
• Landscape diversity
• Spectral diversity
• Random forest
• Scaling effect
• Wetlands