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<p>Monitoring plant biodiversity is key to understanding resilience in urban ecosystems and how green spaces contribute to ecosystem services (e.g., climate regulation, air quality, and habitat provision). While satellite remote sensing offers valuable data for biodiversity monitoring, most freely available sources lack the combined high spatial, spectral, and temporal resolutions to capture the fine-scale complexity of urban vegetation diversity. Drone-mounted Red-Green-Blue (RGB) sensors offer a potential cost-effective alternative to satellite imagery, and we assessed its effectiveness for estimation of urban plant biodiversity. Our objectives were to assess (1) to what extent RGB spectral indices and texture analysis reflect herbaceous plant taxonomic and structural diversity, and (2) the effects of resolution loss on spectral information and its ability to reflect continuous variation in biodiversity metrics, as well as to distinguish between areas of high and low biodiversity as the spatial resolution was reduced from 1.25&#xa0;mm to 1&#xa0;cm, 10&#xa0;cm, 25&#xa0;cm, 50&#xa0;cm, and 1&#xa0;m. We found that most spectral indices better predicted structural diversity than taxonomic diversity. The best-performing index was the coefficient of variation of the Visible Atmospherically Resistant Index (VARI), showing moderately high correlations with 8 of the 12 structural biodiversity metrics used (0.61 &lt; <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:\rho\:\)</EquationSource> </InlineEquation> &lt; <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\:-\)</EquationSource> </InlineEquation>0.74). Lower resolutions resulted in loss of correlation strength, however, VARI values differentiated between plots with high and low structural diversity at all resolutions. These findings highlight the potential for RGB drones to effectively and affordably monitor urban plant biodiversity, supporting benefits for humans and urban ecosystems.</p>

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Assessing urban herbaceous plant biodiversity using drone remote sensing: the trade-offs of spatial resolution

  • 期刊论文

  • 发表日期：

    2026年1月28日

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  • Cedric Gazal,
  • Angela Kross,
  • Jennifer Donnini,
  • Sarah E. Turner

摘要

Monitoring plant biodiversity is key to understanding resilience in urban ecosystems and how green spaces contribute to ecosystem services (e.g., climate regulation, air quality, and habitat provision). While satellite remote sensing offers valuable data for biodiversity monitoring, most freely available sources lack the combined high spatial, spectral, and temporal resolutions to capture the fine-scale complexity of urban vegetation diversity. Drone-mounted Red-Green-Blue (RGB) sensors offer a potential cost-effective alternative to satellite imagery, and we assessed its effectiveness for estimation of urban plant biodiversity. Our objectives were to assess (1) to what extent RGB spectral indices and texture analysis reflect herbaceous plant taxonomic and structural diversity, and (2) the effects of resolution loss on spectral information and its ability to reflect continuous variation in biodiversity metrics, as well as to distinguish between areas of high and low biodiversity as the spatial resolution was reduced from 1.25 mm to 1 cm, 10 cm, 25 cm, 50 cm, and 1 m. We found that most spectral indices better predicted structural diversity than taxonomic diversity. The best-performing index was the coefficient of variation of the Visible Atmospherically Resistant Index (VARI), showing moderately high correlations with 8 of the 12 structural biodiversity metrics used (0.61 < \(\:\rho\:\) < \(\:-\) 0.74). Lower resolutions resulted in loss of correlation strength, however, VARI values differentiated between plots with high and low structural diversity at all resolutions. These findings highlight the potential for RGB drones to effectively and affordably monitor urban plant biodiversity, supporting benefits for humans and urban ecosystems.