Estimation and Uncertainty Assessment of Surface Microclimate Indicators at Local Scale Using Airborne Infrared Thermography and Multispectral Imagery

Abstract

A precise estimation and the characterization of the spatial variability of microclimate conditions (MCCs) are essential for risk assessment and site-specific management of vector-borne diseases and crop pests. The objective of this study was to estimate at local scale, and assess the uncertainties of Surface Microclimate Indicators (SMIs) derived from airborne infrared thermography and multispectral imaging. SMIs including Surface Temperature (ST) were estimated in southern Quebec, Canada. The formulation of their uncertainties was based on in-situ observations and the law of propagation of uncertainty. SMIs showed strong local variability and intra-plot variability of MCCs in the study area. The ST values ranged from 290 K to 331 K. They varied more than 17 K on vegetable crop fields. The correlation between ST and in-situ observations was very high (r = 0.99, p = 0.010). The uncertainty and the bias of ST compared to in-situ observations were 0.73 K and ±1.42 K respectively. This study demonstrated that very high spatial resolution multispectral imaging and infrared thermography present a good potential for the characterization of the MCCs that govern the abundance and the behavior of disease vectors and crop pests in a given area.

Supporting microclimate modeling with 3D UAS data acquisition

“…Using thermal UAS sensors, daily dynamics of small scale thermal variations can be easily measured by repeated flights during a given day. UAS is appropriate to estimate surface microclimate indicators by low cost acquisition and processing compared to aerial data capturing (Kotchi et al, 2016). UAS not only has the advantage of local scale data acquisition but also the repetition cycle is not limiting factor in temporal density of observations.…”