Remotely Estimating Beneficial Arthropod Populations: Implications of a Low-Cost Small Unmanned Aerial System

Abstract

Studies show that agricultural land requires investment in the habitat management of non-cropped areas to support healthy beneficial arthropods and the ecosystem services they provide. In a previous small plot study, we manually counted blooms over the season, and found that plots providing greater numbers of flowers supported significantly higher pollinator populations over that of spontaneous weed plots. Here, we examined the potential of deploying an inexpensive small unmanned aerial vehicle (UAV) as a tool to remotely estimate floral resources and corresponding pollinator populations. Data were collected from previously established native wildflower plots in 19 locations on the University of Georgia experimental farms in South Georgia, USA. A UAV equipped with a lightweight digital camera was deployed to capture images of the flowers during the months of June and September 2017. Supervised image classification using a geographic information system (GIS) was carried out on the acquired images, and classified images were used to evaluate the floral area. The floral area obtained from the images positively correlated with the floral counts gathered from the quadrat samples. Furthermore, the floral area derived from imagery significantly predicted pollinator populations, with a positive correlation indicating that plots with greater area of blooming flowers contained higher numbers of pollinators.

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

“…Drones may be useful for capturing floral data relevant to pollination research as they can be operated over spatial areas that roughly match the distances covered by many foraging pollinators (Osborne et al, 2008;Danner et al, 2016), with maximum operating distances of up to 10 km, depending on the aircraft model, and limited by legislative restrictions (Duffy et al, 2018). Their proximity to the Earth's • Developed by Sun et al, 2021 surface means that the grain of such observations has capacity to capture flower-scale information (e.g., Wan et al, 2018;Xavier et al, 2018;Chen et al, 2019;López-Granados et al, 2019). Furthermore, drones with high-quality inbuilt cameras are becoming more affordable (£500-£2000) for the everyday field ecologist, and there is the possibility to further modify these with customized sensors.…”

“…Fine-grain data acquired by drones could also be important to studying the efficacy of alternative agricultural practices, such as those seeking to enhance crop yields by providing a mix of diverse floral resources to attract pollinators. These studies could be carried out using relatively simple-touse and cheap consumer-grade technologies as evidenced by Xavier et al (2018), who used a drone (Solo-3DR) and GoPro Hero 4 RGB camera (12-megapixel photos) to capture spatial data on floral resources. The study assessed the suitability of wildflower strips in attracting pollinators in two distinct types of environments: one in which the wildflower strips were adjacent to woodland, and another in which the wildflower strips were adjacent to agricultural crops.…”

Drones and Geographical Information Technologies in Agroecology and Organic Farming Contributions to Technological Sovereignty

“…Only recently, some efforts and successful attempts to bring UAS technologies within an agroecological framework to manage agricultural lands and agroforest ecosystems were accomplished (Xavier et al, 2018;Padua et al, 2017;Libràn-Embid et al, 2020). The role of integrating biodiversity conservation with habitat management for agricultural-landscape diversification is widely documented.…”

Emerging vistas of Remote Sensing Tools in Pollination Studies

“…Unmanned aerial vehicles (UAV) borne with sensors along with image processing tools aid in identification and classification of floral resources to predict the occurrence and abundance of pollinators (Lino et al, 2011). Xavier et al 2018 reported that an UAV equipped with digital camera to capture the images of the floral area and the captures images were subjected supervised image classification to study the correlation between the floral counts and pollinator abundance. High spatial and temporal data especially Photosynthetic active radiation (PAR) and Leaf Area Index (LAR), greening and browning of vegetation will directly help to assess the preference of bees for foraging in an agricultural landscape (Nightingale et al, 2008).…”