Sensing the multi-scale landscape functions heterogeneity by big geodata from parcel to urban agglomerations-a case of the Greater Bay Area, China

Multi-scale landscape functions play a critical role in revealing intricate functional structures within large regions. However, previous studies on landscape functions have predominantly focused on a single macro or micro scale, impeding a holistic multi-scale understanding of the spatial distribution and heterogeneity of landscape functions. To address this gap, this study proposes a framework leveraging the power of big geodata to mine multi-scale landscape functions from parcel to entire urban agglomerations, as well as non-administrative divisions. Our study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. Firstly, we integrated multi-source big geodata to derive parcel-scale landscape functions. Subsequently, we employed the Normalized Revealed Comparative Advantage index to derive landscape functions at broader scales, including towns, counties and cities. The effectiveness of our approach is validated through in-field investigations and comparisons with established policy planning positions. The outcomes not only offer distinctive planning insights at various scales but also highlight the versatility of big geodata in extracting landscape functions across scales. This study demonstrates that big geodata is adept at uncovering multi-scale landscape functions irrespective of administrative boundaries, providing valuable insights for fostering multi-scale regional coordinated development.