Remote Sensing of Floral Resources for Pollinators – New Horizons From Satellites to Drones

Abstract

Insect pollinators are affected by the spatio-temporal distribution of floral resources, which are dynamic across time and space, and also influenced heavily by anthropogenic activities. There is a need for spatial data describing the time-varying spatial distribution of flowers, which can be used within behavioral and ecological studies. However, this information is challenging to obtain. Traditional field techniques for mapping flowers are often laborious and limited to relatively small areas, making it difficult to assess how floral resources are perceived by pollinators to guide their behaviors. Conversely, remote sensing of plant traits is a relatively mature technique now, and such technologies have delivered valuable data for identifying and measuring non-floral dynamics in plant systems, particularly leaves, stems and woody biomass in a wide range of ecosystems from local to global scales. However, monitoring the spatial and temporal dynamics of plant floral resources has been notably scarce in remote sensing studies. Recently, lightweight drone technology has been adopted by the ecological community, offering a capability for flexible deployment in the field, and delivery of centimetric resolution data, providing a clear opportunity for capturing fine-grained information on floral resources at key times of the flowering season. In this review, we answer three key questions of relevance to pollination science – can remote sensing deliver information on (a) how isolated are floral resources? (b) What resources are available within a flower patch? And (c) how do floral patches change over time? We explain how such information has potential to deepen ecological understanding of the distribution of floral resources that feed pollinators and the parameters that determine their navigational and foraging choices based on the sensory information they extract at different spatial scales. We provide examples of how such data can be used to generate new insights into pollinator behaviors in distinct landscape types and their resilience to environmental change.

Detecting Floral Resource Availability Using Small Unmanned Aircraft Systems

“…Likewise, Gallmann et al [14] utilized high-resolution drone imagery and deep learning to assess floral counts compared to manual count methods. A recent call for more spatial data describing the temporal-spatial distribution of flowers was issued by Gonzalea et al [4], who emphasized the potential utility of sUAS in fine-scale spatiotemporal floristic surveys.…”

“…While traditional ocular methods can be easily and rapidly employed by an individual or field crew, the unique perception of each individual technician collecting field data can result in variable and even biased results. Remote sensing techniques, however, present an objective, efficient, landscape-scale solution with high-accuracy cover estimations [4].…”

Demography with drones: detecting growth and survival of shrubs with unoccupied aerial systems

“…Subsampling large areas with field plots is one solution, but the lack of spatially extensive measurements presents an impediment to efforts to spatially‐target subsequent restoration treatments to areas that are underperforming. An additional benefit of remotely sensed imagery is the capacity for flexible deployment and image acquisition, in response to disturbance and phenological events (Bogdan et al 2021; Gonzales et al 2022). For arid or semi‐arid systems where plants are only visible for brief phenological windows, field surveys may underrepresent counts of crucial functional groups (Endress et al 2022).…”

A novel approach for surveying flowers as a proxy for bee pollinators using drone images

“…Optical UAV data could represent a potential answer to the lack of baseline ecological data for plant-pollinator interactions, as pointed out by the International Pollinator Initiative (Williams, 2003). UAV based methods could represent promising tools for monitoring habitat quality for bee pollinator communities because they are now affordable (Gonzales et al, 2022) and allow different operators (e.g., researchers, farmers, ecologists) to obtain high spatial resolution data from different sensors that can be carried simultaneously, covering large areas in a limited time. In time, they can be used with an "on-demand" approach that allows to capture specific stages of vegetation phenology (flowering time) in particular in areas characterized by high cloud cover (Müllerová et al, 2017;De Sa et al, 2018).…”