The ecosystem services offered by pollinators are vital for supporting agriculture and ecosystem functioning, with bees standing out as especially valuable contributors among these insects. Threats such as habitat fragmentation, intensive agriculture, and climate change are contributing to the decline of natural bee populations. Remote sensing could be a useful tool to identify sites of high diversity before investing into more expensive field survey. In this study, the ability of Unoccupied Aerial Vehicles (UAV) images to estimate biodiversity at a local scale has been assessed while testing the concept of the Height Variation Hypothesis (HVH). This hypothesis states that the higher the vegetation height heterogeneity (HH) measured by remote sensing information, the higher the vegetation vertical complexity and the associated species diversity. In this study, the concept has been further developed to understand if vegetation HH can also be considered a proxy for bee diversity and abundance. We tested this approach in 30 grasslands in the South of the Netherlands, where an intensive field data campaign (collection of flower and bee diversity and abundance) was carried out in 2021, along with a UAV campaign (collection of true color-RGB-images at high spatial resolution). Canopy Height Models (CHM) of the grasslands were derived using the photogrammetry technique "Structure from Motion" (SfM) with horizontal resolution (spatial) of 10 cm, 25 cm, and 50 cm. The accuracy of the CHM derived from UAV photogrammetry was assessed by comparing them through linear regression against local CHM LiDAR (Light Detection and Ranging) data derived from an Airborne Laser Scanner campaign completed in 2020/2021, yielding an [Formula: see text] of 0.71. Subsequently, the HH assessed on the CHMs at the three spatial resolutions, using four different heterogeneity indices (Rao's Q, Coefficient of Variation, Berger-Parker index, and Simpson's D index), was correlated with the ground-based flower and bee diversity and bee abundance data. The Rao's Q index was the most effective heterogeneity index, reaching high correlations with the ground-based data (0.44 for flower diversity, 0.47 for bee diversity, and 0.34 for bee abundance). Interestingly, the correlations were not significantly influenced by the spatial resolution of the CHM derived from UAV photogrammetry. Our results suggest that vegetation height heterogeneity can be used as a proxy for large-scale, standardized, and cost-effective inference of flower diversity and habitat quality for bees.

BIOME Lab Department of Biological Geological and Environmental Sciences Alma Mater Studiorum University of Bologna via Irnerio 42 40126 Bologna Italy

Department of Environmental Science and Policy University of Milan Milan Italy

Department of Remote Sensing Helmholtz Centre for Environmental Research UFZ Permoserstr 15 04318 Leipzig Germany

Department of Spatial Sciences Faculty of Environmental Sciences Czech University of Life Sciences Prague Kamýcka 129 Praha Suchdol 16500 Czech Republic

Eurac Research Inst for Alpine Environment Bolzano Italy

Faculty of Agricultural Environmental and Food Sciences Free University of Bolzano Bozen Piazza Universitá Universitätsplatz 1 39100 Bolzano Bozen Italy

German Centre for Integrative Biodiversity Research Halle Jena Leipzig Leipzig Germany

Laboratory of Geo Information Science and Remote Sensing Wageningen University and Research P O Box 47 6700 AA Wageningen The Netherlands

Plant Ecology and Nature Conservation Group Wageningen University Droevendaalsesteeg 3a Wageningen 6708PB The Netherlands

Remote Sensing Centre for Earth System Research Leipzig University Leipzig Germany

Visual art FEIMC Bolzano Italy

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