Uav-Borne Thermal Imaging for Forest Health Monitoring: Detection of Disease-Induced Canopy Temperature Increase

Abstract

Commission VI, WG VI/4KEY WORDS: Stress detection, unmanned aerial vehicle, unmanned aerial system, UAV, UAS, camera calibration. ABSTRACT:Climate change has a major influence on forest health and growth, by indirectly affecting the distribution and abundance of forest pathogens, as well as the severity of tree diseases. Temperature rise and changes in precipitation may also allow the ranges of some species to expand, resulting in the introduction of non-native invasive species, which pose a significant risk to forests worldwide. The detection and robust monitoring of affected forest stands is therefore crucial for allowing management interventions to reduce the spread of infections. This paper investigates the use of a low-cost fixed-wing UAV-borne thermal system for monitoring disease-induced canopy temperature rise. Initially, camera calibration was performed revealing a significant overestimation (by over 1 K) of the temperature readings and a non-uniformity (exceeding 1 K) across the imagery. These effects have been minimised with a two-point calibration technique ensuring the offsets of mean image temperature readings from blackbody temperature did not exceed ± 0.23 K, whilst 95.4% of all the image pixels fell within ± 0.14 K (average) of mean temperature reading.The derived calibration parameters were applied to a test data set of UAV-borne imagery acquired over a Scots pine stand, representing a range of Red Band Needle Blight infection levels. At canopy level, the comparison of tree crown temperature recorded by a UAV-borne infrared camera suggests a small temperature increase related to disease progression (R = 0.527, p = 0.001); indicating that UAV-borne cameras might be able to detect sub-degree temperature differences induced by disease onset.

Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective

“…Agricultural applications have been pursued more actively than forestry applications (Mcfadyen et al 2014). There is, however, a precursor study in Europe, dealing with red band needle blight (Mycosphaerella pini Rostrup ex Munk; Mycosphaerellaceae) infection in Scots pine (Pinus sylvestris Linnaeus) (Smigaj et al 2015). This disease on pine trees also occurs in Canada (https://tidcf.nrcan.gc.ca/en/diseases/ factsheet/1000003).…”

Thermal Remote Sensing With Uav-Based Workflows

“…In special cases may a later registration of generated orthophoto to an external registered dataset (e.g. LiDAR CHM) help to properly georeference thermal orthophotos (Smigaj et al, 2015), but often are datasets not acquired within an appropriate time period, too outdated or finding corresponding features in forest canopies of blurred images is impossible. Contrast enhancement processing on the sensor level is also very important for tie point matching and future improved hardand firmware may lead to even more feasible applications.…”

“…Canopy foliage temperature is mainly dominated by the canopy geometry (shape, density, leaf size, etc. ), radiation, air-temperature and transpiration (Martynenko et al, 2016;Reinert et al, 2012;Smigaj et al, 2015). Leaf temperature measurement using thermal infrared (TIR) sensing is primarily used to study plant water relations, and specifically stomatal conductance, because a major determinant of leaf temperature is the rate of evaporation or transpiration from the leaf (Jones et al, 2009).…”

Use of Miniature Thermal Cameras for Detection of Physiological Stress in Conifers

“…Conifers represent particularly challenging targets for remote sensing analysis, mainly due to their structural features, for example, narrow needle leaves. With the exception of [28,31,32], thermography of conifer trees still remains almost unreported in the literature; therefore, the investigation of stress responses in the temperature domain is largely lacking. In Kim et al [31], the daily and sub-daily mean canopy temperatures extracted from uncorrected imagery were shown to be related to climatic and soil variables, whilst Smigaj et al [32] found a correlation between disease progression and canopy temperature increase.…”