High-resolution debris-cover mapping using UAV-derived thermal imagery: limits and opportunities

Abstract

Abstract. Debris-covered glaciers are widespread in high mountain ranges on earth. However, the dynamic evolution of debris-covered glacier surfaces is not well understood, in part due to difficulties in mapping debris-cover thickness in high spatiotemporal resolution. In this study, we present land surface temperatures (LSTs) of supraglacial debris cover and their diurnal variability measured from an unpiloted aerial vehicle (UAV) at a high (15 cm) spatial resolution. We test two common approaches to derive debris-thickness maps by (1) solving a surface energy balance model (SEBM) in conjunction with meteorological reanalysis data and (2) least squares regression of a rational curve using debris-thickness field measurements. In addition, we take advantage of the measured diurnal temperature cycle and estimate the rate of change of heat storage within the debris cover. Both approaches resulted in debris-thickness estimates with an RMSE of 6 to 8 cm between observed and modeled debris thicknesses, depending on the time of the day. Although the rational curve approach requires in situ field measurements, the approach is less sensitive to uncertainties in LST measurements compared to the SEBM approach. However, the requirement of debris-thickness measurements can be an inhibiting factor that supports the SEB approach. Because LST varies throughout the day, the success of a rational function to express the relationship between LST and debris thickness also varies predictably with the time of day. During the period when the debris cover is warming, LST is heavily influenced by the aspect of the terrain. As a result, clear-sky morning flights that do not consider the aspect effects can be problematic. Our sensitivity analysis of various parameters in the SEBM highlights the relevance of the effective thermal conductivity when LST is high. The residual and variable bias of UAV-derived LSTs during a flight requires calibration, which we achieve with bare-ice surfaces. The model performance would benefit from more accurate LST measurements, which are challenging to achieve with uncooled sensors in high mountain landscapes.

A low-cost and open-source approach for supraglacial debris thickness mapping using UAV-based infrared thermography

“…For the photogrammetric processing of UAV imagery (from glacial environments) and the generation of accurate orthophotos and DSMs, both proprietary software (e.g. Kraaijenbrink et al, 2018;Bisset et al, 2022;Gök et al, 2023) and open-source software (e.g. Groos et al, 2019Groos et al, , 2022a) are available and have been successfully applied.…”

“…However, Herreid ( 2021) and Aubry-Wake et al ( 2023) pointed out that the surface of a thick debris layer may become isothermal during the hottest hours of a clear summer day. Morning hours seem in general unfavourable for debris thickness mapping as surface temperatures rise quickly and have a distinct terrain bias at this time of day (Gök et al, 2023). Days with partial cloud cover have the disadvantage that spatial surface temperature variations caused by shadows of fast-moving clouds must be corrected for when processing and analysing the radiometric images.…”

“…Since accurate and consistent surface temperatures are the prerequisite for reliable debris thickness mapping, radiometric thermal images collected with UAVs must be corrected for the effects of inherent sensors biases, thermal drift, atmospheric impacts, reflected thermal radiation and spatially varying emissivity (e.g. Ribeiro-Gomes et al, 2017;Kraaijenbrink et al, 2018;Virtue et al, 2021;Bisset et al, 2022;Gök et al, 2023). Low-cost uncooled microbolometers that are commonly used in UAV thermography are notoriously sensitive to changing atmospheric and environmental conditions.…”

“…UAVs equipped with lightweight TIR cameras have proven to be a suitable alternative for mapping surface temperatures on debris-covered glaciers in mountainous terrain (Kraaijenbrink et al, 2018). Two recent studies have successfully demonstrated that debris thickness can be simulated from radiometric thermal UAV imagery if the images are carefully calibrated and corrected (Bisset et al, 2022;Gök et al, 2023). However, in the study by Bisset et al (2022) the number of debris thickness measurements for validation was small (n = 3), and in the study by Gök et al (2023) the methodological uncertainties (a root-mean-square error, RMSE, of 6-8 cm for a mean debris thickness of 9 cm) were relatively large, highlighting the need of further exploring and developing this mapping approach.…”

“…Compared to previous studies that mainly rely on proprietary software (e.g. Kraaijenbrink et al, 2018;Bisset et al, 2022;Gök et al, 2023), our open-source approach supports the generation of accurate radiometric orthophotos that can be further processed and corrected according to the users' needs. We use thermal imagery and in situ measurements of debris thickness and debris temperature from the Kanderfirn (also known as Kander Neve) in the Swiss Alps (Fig.…”