Building an Open Source Sensing System for Continuous Forest Canopy Monitoring

By Sam Vellekoop

Abstract
Building an Open Source Sensing System for Continuous Forest Canopy Monitoring Monitoring forest canopy characteristics is important for keeping track of forest ecosystem health and is invaluable in forest management. Leaf area index (LAI) is one of the most used indicators for the biophysical plant canopy characteristics in a wide range of research domains and is an important indicator for canopy water interception, radiation extinction, evapotranspiration and biomass production. Large-scale monitoring of LAI is generally done using air- or spaceborne sensors. However, these sensors require extensive calibration and validation using in situ LAI estimates. Conventional methods for in situ LAI measurement are generally either labour-intensive or require expensive equipment, making them less suited for monitoring LAI for a larger area for an extended period. This is were automated sensors could provide an opportunity. The goal of this research was to design, build and test an automated sensing system for continuous canopy monitoring using open source hardware and software. Using a bare minimum of materials, includng a Raspberry Pi and camera with fisheye lens, the PiLAI was developed, a low-cost, automatic sensing system for LAI. The PiLAI was tested against terrestrial laser scanning (TLS) and conventional digital hemispherical photography (DHP) in two forests in the Netherlands during autumn and spring. With a Pearson correlation of up to 0.647 between the PiLAI and TLS and up to 0.851 between PiLAI and DHP, the PiLAI shows potential as automated alternative for conventional methods.