Biodiversity Monitoring in Urban Community Gardens Using Proximal Sensing and Drone Remote Sensing

Abstract

In urban community gardens, artificially managed ground cover types, including vegetative components and non-vegetative ground components, are both critical to ecological functioning. Yet, how these non-vegetative components influence spectral diversity in ways that are different from natural systems has not been addressed. This study investigated the potential of combining spectral and structural diversity variables, corresponding to the Spectral Variation and Height Variation Hypotheses, respectively, to monitor plant and ground cover diversity. These variables were derived from in-situ hyperspectral measurements, drone-based multispectral imagery and three-dimensional canopy height models. We examined four biodiversity variables including plant species richness, total plant abundances, ground cover entropy, and ground cover richness, across five urban community gardens over two years. Spectral diversity was calculated based on the Coefficient of Variation (CV), Spectral Angle Mapper (SAM), and Shannon’s Entropy (Entropy) indices at multiple spectral ranges. Structural diversity variables, including canopy height variation and image texture features. Our results showed that RedEdge and Near-infrared (NIR) bands effectively captured compositional variation in ground cover, while visible wavelengths better reflected subtle differences in vegetative components. Texture features and height-based structural variables provided valuable insights into canopy complexity, particularly improving predictions of plant abundance and ground cover entropy. Finally, we found that integrating spectral and structural diversity variables further enhanced predictive performance due to considering canopy biochemical and structural differences. This multi-metric approach outperformed single-source analyses, underscoring the value of combining complementary remote sensing data for better interpreting urban garden biodiversity. Our findings highlight the importance of characterizing canopy structural heterogeneity in advancing biodiversity monitoring within these complex urban ecosystems.

Yasamin Afrasiabian

PhD student

My research interests include remote sensing, particularly hyperspectral UAV and satellite imaging, and machine-learning methods for ecosystem-biodiversity characterisation, precision agriculture, and hydrological analysis.

Prof. Dr. Kang Yu

Professor of Precision Agriculture

My research interests include precision crop farming, hyperspectral remote sensing, and AI in agriculture.