Hidden becomes clear : Optical remote sensing of vegetation reveals water table dynamics in northern peatlands

Iuliia Burdun*, Michel Bechtold, Mika Aurela, Gabrielle De Lannoy, Ankur R. Desai, Elyn Humphreys, Santtu Kareksela, Viacheslav Komisarenko, Maarit Liimatainen, Hannu Marttila, Kari Minkkinen, Mats B. Nilsson, Paavo Ojanen, Sini Selina Salko, Eeva Stiina Tuittila, Evelyn Uuemaa, Miina Rautiainen

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

9 Sitaatiot (Scopus)
133 Lataukset (Pure)

Abstrakti

The water table and its dynamics are one of the key variables that control peatland greenhouse gas exchange. Here, we tested the applicability of the Optical TRApezoid Model (OPTRAM) to monitor the temporal fluctuations in water table over intact, restored (previously forestry-drained), and drained (under agriculture) northern peatlands in Finland, Estonia, Sweden, Canada, and the USA. More specifically, we studied the potential and limitations of OPTRAM using water table data from 2018 through 2021, across 53 northern peatland sites, i.e., covering the largest geographical extent used in OPTRAM studies so far. For this, we calculated OPTRAM based on Sentinel-2 data with the Google Earth Engine cloud platform. First, we found that the choice of vegetation index utilised in OPTRAM does not significantly affect OPTRAM performance in peatlands. Second, we revealed that the tree cover density is a major factor controlling the sensitivity of OPTRAM to water table dynamics in peatlands. Tree cover density greater than 50% led to a clear decrease in OPTRAM performance. Finally, we demonstrated that the relationship between water table and OPTRAM often disappears when WT deepens (ranging between 0 to −100 cm, depending on the site location). We identified that the water table where OPTRAM ceases to be sensitive to variations is highly site-specific. Overall, our results support the application of OPTRAM to monitor water table dynamics in intact and restored northern peatlands with low tree cover density (below 50%) when the water table varies from shallow to moderately deep. Our study makes significant steps towards the broader implementation of optical remote sensing data for monitoring peatlands subsurface moisture conditions over the northern region.

AlkuperäiskieliEnglanti
Artikkeli113736
Sivumäärä14
JulkaisuRemote Sensing of Environment
Vuosikerta296
DOI - pysyväislinkit
TilaJulkaistu - 1 lokak. 2023
OKM-julkaisutyyppiA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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