Zy1-02d Satellite Hyperspectral Remote Sensing: Spatial Scaling Analysis and Monitoring of Landscape Wetland Diversity

Monitoring and assessing wetland diversity is crucial for its accurate preservation. Hyperspectral satellites have been proven effective for detailed investigations of plant diversity in large areas. However, it's unclear if spectral diversity can represent landscape diversity or if the inversion accuracy changes with spatial scale. In this study, we utilized the support vector machine method for supervised classification of ZY1-02D hyperspectral remote sensing images in the Yellow River Estuary. Subsequently, landscape diversity indices (community richness, Shannon-Wiener index, Simpson index, and Pielou index) and spectral diversity indices (coefficient of variation, convex hull volume, and eight vegetation indices) were calculated for its coastal wetlands. We then used a random forest model to predict landscape diversity based on spectral diversity. Finally, we explored the spatial scale relationship between spectral diversity and landscape diversity. The results showed that the overall accuracy of wetland classification in the Yellow River Estuary was 91.53%, with a Kappa coefficient of 0.90. Spectral diversity had the best inversion effect on the Shannon-Wiener index, with a maximum inversion accuracy of 57%, followed by the Pielou index (56%), community richness (48%), and finally the Simpson index (43%). The inversion accuracy of each landscape diversity index increased first and then stabilized with scale, reaching stability at a plot size of 2880×2880 m. The results of this study indicate that ZY1-02D hyperspectral data can monitor the spatial pattern changes of landscape diversity in the Yellow River Estuary. However, the accuracy is affected by the type of diversity index and spatial scale effects. The findings of this study provide a new perspective for the conservation and management of large-scale wetland landscape diversity.