Beyond Environmental Monitoring: Are Automatic Time-Lapse Cameras Efficient Tools for Temperature Measurement in Remote Regions?

• Published in: SSRN Electronic Journal
• Main Authors: Grenier, Jérémy, Decaulne, Armelle, Bhiry, Najat
• Other Authors: Université Laval Québec (ULaval), Littoral, Environnement, Télédétection, Géomatique (LETG - Nantes ), Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Nantes Université (Nantes Univ)-Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (Nantes Univ - IGARUN), Nantes Université - pôle Humanités, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Nantes Université - pôle Humanités, Nantes Université (Nantes Univ)-Nantes Université (Nantes Univ)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (Nantes Univ - IGARUN), Nantes Université (Nantes Univ), Centre d'Etudes Nordiques (CEN), ANR-11-LABX-0010,DRIIHM / IRDHEI,Dispositif de recherche interdisciplinaire sur les Interactions Hommes-Milieux(2011)
• Format: Report
• Language: English
• Published: HAL CCSD 2023
• Subjects: temperature data; time-lapse cameras; weather station; data reliability; OHMi Nunavik; [SDE]Environmental Sciences; Umiujaq; Nunavik
• Online Access: https://hal.science/hal-04061194; https://doi.org/10.2139/ssrn.4143111

Automatic time-lapse cameras are frequently used to monitor snow and ice related slope processes on slopes in cold regions because of the many advantages they bring to researchers. In addition to providing important information about the dynamic of the studied area, most of these cameras are now equipped with thermal sensors able to register temperature data when every photograph is taken. The great instrumentation located within Tasiapik Valley, near Umiujaq, in Nunavik (northern Québec), enabled us to assess the potential of automatic time-lapse cameras for temperature measurement by comparing data retrieved on photographs with data from two nearby complete weather stations. Our results indicate that the temperature measurements taken by the time-lapse cameras from August to the onset of February are relatively accurate while the weaker performances occurred in late winter and spring (March-June). Moreover, regardless of the year, the time-lapse cameras were most accurate in the morning (09:00 AM-11:00 AM), while in the afternoon (12:00 PM-3:00 PM), they tended to overestimate temperatures. Based on our observation and data analysis, this over-estimation of air temperature seems to be caused by external factors such as the sky conditions and high values of daily mean downwelling that last from February to June. The environment near the cameras might also influence the performances at temperature measurement.