4.6 Article Proceedings Paper

Satellite-based peatland mapping: Potential of the MODIS sensor

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GLOBAL AND PLANETARY CHANGE
卷 56, 期 3-4, 页码 248-257

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ELSEVIER
DOI: 10.1016/j.gloplacha.2006.07.019

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peatlands; mapping; satellite methods; regression analysis; carbon cycle; Russian Federation

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Peatlands play a major role in the global carbon cycle but are largely overlooked in current large-scale vegetation mapping efforts. In this study, we investigated the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) to capture the extent and distribution of peatlands in the St. Petersburg region of Russia by analyzing the relationships between peatland cover fractions derived from reference maps and similar to 1-km resolution MODIS Nadir BRDF-Adjusted Reflectance (NBAR) data from year 2002. First, we characterized and mapped 50 peatlands from forest inventory and peat deposit inventory data. The peatlands represent three major nutritional types (oligotrophic, mesotrophic, eutrophic) and different sizes (0.6-7800 ha). In addition, parts of 6 peatlands were mined for peat and these were mapped separately. The reference maps provided information on peatland cover for 1105 MODIS pixels. We performed regression analysis on 50% of the pixels and reserved the remainder for model validation. Canonical correlation analysis on the MODIS reflectance bands and the peatland cover fractions produced a multi-spectral peatland cover index (PCI), which served as the predictor in a reduced major axis (RMA) regression model. The results suggest a high potential for mapping peatlands with MODIS. The RMA regression models explained much of the variance in the PCI (r(2)=0.74 for mined and r(2)=0.81 for unmined peatlands). Model validation showed high correlation between observed versus predicted peatland cover (mined: r=0.87; unmined: r=0.92). We used the models to derive peatland cover estimates for the St. Petersburg region and compared the results to current MODIS land cover maps. (C) 2006 Elsevier B.V. All rights reserved.

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