Spatiotemporally continuous temperature monitoring using optical fibers (Loop1) in the internal forest areas in Alaska for the period from 2015 to 2016

• Format: Article in Journal/Newspaper
• Language: English
• Published: National Institute of Polar Research 2023
• Subjects: Distributed temperature sensing; fiber-optic cable sensor; boreal forest; surface and subsurface temperatures; Thermokarst; Alaska
• Online Access: http://id.nii.ac.jp/1434/00000049/; https://pdr.repo.nii.ac.jp/?action=repository_uri&item_id=64; https://pdr.repo.nii.ac.jp/?action=repository_action_common_download&item_id=64&item_no=1&attribute_id=22&file_no=1; https://pdr.repo.nii.ac.jp/?action=repository_action_common_download&item_id=64&item_no=1&attribute_id=22&file_no=2; https://pdr.repo.nii.ac.jp/?action=repository_action_common_download&item_id=64&item_no=1&attribute_id=22&file_no=3

A fiber-optic DTS (distributed temperature sensing) system using Raman-scattering optical time domain reflectometry was implemented at a research site in the interior of Alaska (Poker Flat Research Range) to delineate the spatiotemporal characteristics of the variations in air, surface, and ground temperatures associated with the boreal forest heterogeneity in terms of surface and canopy conditions. A fiber-optic cable sensor (multi-mode, GI50/125, dual-core; 3.4 mm) was deployed across the landscape that enables temperature measurements at high spatiotemporal resolution (0.5 m intervals at every 30 minutes) across the different land surface cover types (i.e., relict thermokarst lake, open moss, shrub, deciduous forest, sparse spruce, and dense spruce), together with four sub-sections of the cable sensor set in coil configurations (1.2 m high) and installed vertically half below and half above the ground to capture high-resolution vertical temperature profiles from the subsurface up to snowpack height at approximately 5 mm intervals. The total cable ran 2.3 km, within which about a 1.8 km section covered horizontal surface paths. Measurements were made from June 17, 2015, to September 19, 2016 (331 observation days). It provides “big data” for diurnal to seasonal variation analysis; however, it is also susceptive to interruptions resulting from occasional power shutdowns, subsequent recovery failures, and partial failures or damage in the cable sensor caused by animals.