摘要:量化城市乔木树种多样性是定量研究其生态系统服务的前提和基础。目前，城市树种多样性水平的定量研究以地面调查方法为主，存在效率低、成本高的问题。针对上述问题，首先验证了光谱变异性假说和生产力假说在城市中的适用性，进而提出了快速量化城市树种多样性水平的方法。该方法基于神经网络刻画了乔木斑块多样性与光谱异质性之间的关系，能够较准确地反演城市乔木的香浓维纳指数和辛普森指数。为城市乔木树种多样性的调查提供了新的思路，并将为定量研究城市乔木多样性的生态系统服务提供基础数据。



Abstract:Urban tree diversity is responsible for many ecosystem services. Quantifying the level and spatial pattern of tree diversity is crucial for the assessment of ecosystem services. However, previous quantitative studies on urban tree diversity are mainly based on field surveys, which are highly expensive and restricted by poor efficiency. In this study, we firstly tested the applicability of Spectral Variation Hypothesis (SVH) and Productivity Hypothesis in urban areas. Then, we developed a new approach for quickly and efficiently quantifying tree diversity using remote sensing imagery. This new approach described the relationship between urban tree diversity and spectral heterogeneity based on Neural Network Model, which could be used to calculate the Shannon-Wiener index and the Simpson index of urban trees. This study provides a new direction for studying urban tree diversity, and our results are critical basic data for studying ecosystem services of urban tree diversity.





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