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Predicting catchment-scale methane fluxes with multi-source remote sensing

Räsänen, Aleksi ; Manninen, Terhikki ; Korkiakoski, Mika ; Lohila, Annalea ; Virtanen, Tarmo

Landscape ecology, 2021-04, Vol.36 (4), p.1177-1195 [Periódico revisado por pares]

Dordrecht: Springer Netherlands

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Título:
Predicting catchment-scale methane fluxes with multi-source remote sensing
Autor: Räsänen, Aleksi ; Manninen, Terhikki ; Korkiakoski, Mika ; Lohila, Annalea ; Virtanen, Tarmo
Assuntos: Analyzers ; Biomedical and Life Sciences ; Catchment scale ; Catchments ; Ecology ; Environmental Management ; Fluxes ; Land cover ; Land use ; Landscape ; Landscape Ecology ; Landscape/Regional and Urban Planning ; Life Sciences ; Methane ; Nature Conservation ; Peatlands ; Regression analysis ; Remote sensing ; Research Aticle ; Soil moisture ; Spatial discrimination ; Spatial resolution ; Sustainable Development ; Terrain models ; Vegetation
É parte de: Landscape ecology, 2021-04, Vol.36 (4), p.1177-1195
Descrição: Context Spatial patterns of CH 4 fluxes can be modeled with remotely sensed data representing land cover, soil moisture and topography. Spatially extensive CH 4 flux measurements conducted with portable analyzers have not been previously upscaled with remote sensing. Objectives How well can the CH 4 fluxes be predicted with plot-based vegetation measures and remote sensing? How does the predictive skill of the model change when using different combinations of predictor variables? Methods We measured CH 4 fluxes in 279 plots in a 12.4 km 2 peatland-forest-mosaic landscape in Pallas area, northern Finland in July 2019. We compared 20 different CH 4 flux maps produced with vegetation field data and remote sensing data including Sentinel-1, Sentinel-2 and digital terrain model (DTM). Results The landscape acted as a net source of CH 4 (253–502 µg m −2 h −1 ) and the proportion of source areas varied considerably between maps (12–50%). The amount of explained variance was high in CH 4 regressions (59–76%, nRMSE 8–10%). Regressions including remote sensing predictors had better performance than regressions with plot-based vegetation predictors. The most important remote sensing predictors included VH-polarized Sentinel-1 features together with topographic wetness index and other DTM features. Spatial patterns were most accurately predicted when the landscape was divided into sinks and sources with remote sensing-based classifications, and the fluxes were modeled for sinks and sources separately. Conclusions CH 4 fluxes can be predicted accurately with multi-source remote sensing in northern boreal peatland landscapes. High spatial resolution remote sensing-based maps constrain uncertainties related to CH 4 fluxes and their spatial patterns.
Editor: Dordrecht: Springer Netherlands
Idioma: Inglês

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Predicting catchment-scale methane fluxes with multi-source remote sensing

Räsänen, Aleksi ; Manninen, Terhikki ; Korkiakoski, Mika ; Lohila, Annalea ; Virtanen, Tarmo

Dordrecht: Springer Netherlands

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