Research on accurate and effective identification of ecosystem surface based on human footprint index

Abstract

Ecosystem surface plays an important role in maintaining ecological balance and supporting economic development. Accurate and effective identification of ecosystem surface with different ecological uses allows for better categorization and management and contributes to the orderly restoration of ecosystems. In this study, the Human Footprint Index (HFI) is constructed by comprehensively considering the factors of biodiversity, human activity impacts, and geometric features, and combined with the multi-factor knowledge constraints such as changes in land cover. We carry out accurate and effective identification and analysis of ecosystem surfaces, using Changzhou City’s tree cover as a test area. The results show that: (1) The degree of tree cover affected by human activities in Changzhou City varies greatly, and Gross Domestic Product (GDP) and elevation are the main factors affecting the level of the HFI. (2) The distribution of native ecosystem surface and artificial ecosystem surface are uneven, with more than 95% of native ecosystem surfaces distributed in the peripheral areas of the city with higher elevations and farther away from the city center. Artificial ecosystem surfaces are more likely to be found in areas with high GDP and frequent human activities, mainly in urban centers with a large proportion of buildings and along roads. (3) The overall identification accuracy of the ecosystem surface is 83.20%, which is higher in the urban center area with a large proportion of buildings and slightly lower in the area with a large proportion of agricultural production land. The results of the study provide a method for the objective, accurate and effective identification of ecosystem surface, which can improve the enthusiasm of local governments in fulfilling their ecological protection duties and contribute to the efficient achievement of the goal of ecological civilization construction.

Keywords

• Ecosystem surface
• Human footprint index
• Habitat quality
• Multi-factor knowledge
• Tree cover