Innovative reconstruction and evaluation of forest refinement datasets by combining multi-source data: A case study of Guangdong Province

Abstract

The acquisition and monitoring of forest cover data are crucial for ecological protection, resource management, and climate change research. However, relying on a single data source provides limited data accuracy and does not adequately capture the forest structure and functional attributes. We combined six commonly land cover datasets and forest age, canopy height, above-ground biomass, and tree species distribution datasets to reconstruct 30 m spatially accurate forest refinement dataset (FRD) for Guangdong Province. In addition, the distribution characteristics of forest structure and function were evaluated using forest morphological spatial pattern analysis. The results show that the overall accuracy of FRD of the Guangdong Province in 2020 reached 86.07 %. Forest types in the Guangdong Province were mainly dominated by evergreen needle-leaf forests. Tsuga chinensis, Red cedar, and Pinus sylvestris were more commonly planted. Older and taller trees were found in northern and eastern Guangdong. In addition, forest above-ground biomass (AGB) was larger in the coastal areas of northern and western Guangdong. The core and perforation had the oldest age and the highest tree height, and the islet had the lowest for all forest structure and function indicators. Based on multi-source datasets, this study contributes to a better understanding of the attributes characterizing the structure and function of forests. The refined dataset and research framework will effectively enhance forest management efficiency and policy making, as well as provide case references for research on climate change response, forest conservation and biodiversity assessment.

Keywords

• Data fusion
• Multi-source data
• Morphological spatial pattern analysis