Remotely sensed biomass over steep slopes: An evaluation among successional stands of the Atlantic Forest,Brazil |
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| Institution: | 1. Institute of Geography and Geoecology, Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, 76131 Karlsruhe, Germany;2. Laboratory of Geomatics and Landscape Ecology, Faculty of Forest and Nature Conservation, University of Chile, 11315 Santa Rosa, Santiago, Chile;3. Department of Environmental Sciences, School of Agronomic Sciences, University of Chile, 11315 Santiago, Chile;4. Center for Climate and Resilience Research (CR)2, University of Chile, Santiago, Chile;1. 3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Im Neuenheimer Feld 368, D-69120 Heidelberg, Germany;2. Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Telegrafenberg A 45, 14473 Potsdam, Germany;3. DLR Institute for Data Science, Mälzerstraße 3, 07745 Jena, Germany;4. Heidelberg Center for the Environment (HCE), c/o Institute of Environmental Physics, Im Neuenheimer Feld 229, 69120 Heidelberg, Germany;5. Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Postfach 12 01 61, 27515 Bremerhaven, Germany;6. Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775, USA;7. Department of Geography, Humboldt University of Berlin, Unter den Linden 6, 10099 Berlin, Germany;1. Finnish Geodetic Institute, Geodeetinrinne 2, FI-02431 Masala, Finland;2. Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 Helsinki, Finland |
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| Abstract: | Remotely sensed images have been widely used to model biomass and carbon content on large spatial scales. Nevertheless, modeling biomass using remotely sensed data from steep slopes is still poorly understood. We investigated how topographical features affect biomass estimation using remotely sensed data and how such estimates can be used in the characterization of successional stands in the Atlantic Rainforest in southeastern Brazil. We estimated forest biomass using a modeling approach that included the use of both satellite data (LANDSAT) and topographic features derived from a digital elevation model (TOPODATA). Biomass estimations exhibited low error predictions (Adj. R2 = 0.67 and RMSE = 35 Mg/ha) when combining satellite data with a secondary geomorphometric variable, the illumination factor, which is based on hill shading patterns. This improved biomass prediction helped us to determine carbon stock in different forest successional stands. Our results provide an important source of modeling information about large-scale biomass in remaining forests over steep slopes. |
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| Keywords: | Aboveground biomass Forest succession Tropical forest Steep slope Remote sensing |
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