A Raspberry Pi-based camera system and image processing procedure for low cost and long-term monitoring of forest canopy dynamics

Abstract

In-situ, high-frequency and long-term monitoring of forest canopy development is vital for improved understanding in many fields of ecology. Although there are well-established commercial instruments measuring light interception and digital hemispherical photography methods, the equipment cost (thousands of pounds) and lack of field robustness make these instruments not suitable for long-term unattended measurements and can only give the high-resolution spatial sampling usually required in forests when used manually, with substantial effort. Here we present a low-cost Raspberry Pi camera system that is robust and suitable for the acquisition of long-term, high-frequency forest canopy images. The low cost of each camera means multiple systems could be used to provide the required spatial sampling. We also present an open-source R script for post-processing these images to provide a semi-automated processing chain, easily adaptable for use at other forest sites. Four individual camera systems were tested at a deciduous oak forest in SE England over three growing seasons. These ground-based camera systems were used to take upward-facing images of a forest canopy every 30 min. The timing of canopy seasonal development, derived from the novel Raspberry Pi method, compared well with an established technique based on canopy light interception measurements. With a cost of approximately £80, these systems have the potential to enable unattended widespread forest canopy monitoring at a very low cost, providing high spatial and temporal resolution that has previously been unattainable.