Comparative assessment of satellite- and drone-based vegetation indices to predict arthropod biomass in shrub-steppes
Arthropod biomass is a key element in ecosystem functionality and a basic food item for many species. It must be estimated through traditional costly field sampling, normally at just a few sampling points. Arthropod biomass and plant productivity should be narrowly related because a large majority o...
| Autores: | , , , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/704360 |
| Acceso en línea: | http://hdl.handle.net/10486/704360 https://dx.doi.org/10.1002/eap.2707 |
| Access Level: | acceso abierto |
| Palabra clave: | Arthropod biomass Coprophagous arthropods Epigeous arthropods Shrub steppe Vegetation index Medio Ambiente |
| Sumario: | Arthropod biomass is a key element in ecosystem functionality and a basic food item for many species. It must be estimated through traditional costly field sampling, normally at just a few sampling points. Arthropod biomass and plant productivity should be narrowly related because a large majority of arthropods are herbivorous, and others depend on these. Quantifying plant productivity with satellite or aerial vehicle imagery is an easy and fast procedure already tested and implemented in agriculture and field ecology. However, the capability of satellite or aerial vehicle imagery for quantifying arthropod biomass and its relationship with plant productivity has been scarcely addressed. Here, we used unmanned aerial vehicle (UAV) and satellite Sentinel-2 (S2) imagery to establish a relationship between plant productivity and arthropod biomass estimated through ground-truth field sampling in shrub steppes. We UAV-sampled seven plots of 47.6–72.3 ha at a 4-cm pixel resolution, subsequently downscaling spatial resolution to 50 cm resolution. In parallel, we used S2 imagery from the same and other dates and locations at 10-m spatial resolution. We related several vegetation indices (VIs) with arthropod biomass (epigeous, coprophagous, and four functional consumer groups: predatory, detritivore, phytophagous, and diverse) estimated at 41–48 sampling stations for UAV flying plots and in 67–79 sampling stations for S2. VIs derived from UAV were consistently and positively related to all arthropod biomass groups. Three out of seven and six out of seven S2-derived VIs were positively related to epigeous and coprophagous arthropod biomass, respectively. The blue normalized difference VI (BNDVI) and enhanced normalized difference VI (ENDVI) showed consistent and positive relationships with arthropod biomass, regardless of the arthropod group or spatial resolution. Our results showed that UAV and S2-VI imagery data may be viable and cost-efficient alternatives for quantifying arthropod biomass at large scales in shrub steppes. The relationship between VI and arthropod biomass is probably habitat-dependent, so future research should address this relationship and include several habitats to validate VIs as proxies of arthropod biomass |
|---|