Ordway, Elsa M., Asner, Gregory P., Burslem, David F. R. P. , Lewis, Simon L., Nilus, Reuben, Martin, Roberta E., O'Brien, Michael J., Phillips, Oliver L., Qie, Lan, Vaughn, Nicholas R. and Moorcroft, Paul R. (2022) Mapping tropical forest functional variation at satellite remote sensing resolutions depends on key traits. Communications Earth & Environment, 3 (247). ISSN 2662-4435
Full content URL: https://doi.org/10.1038/s43247-022-00564-w
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s43247-022-00564-w.pdf - Whole Document Available under License Creative Commons Attribution 4.0 International. 4MB |
Item Type: | Article |
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Item Status: | Live Archive |
Abstract
Although tropical forests differ substantially in form and function, they are often represented as a single biome in global change models, hindering understanding of how different tropical forests will respond to environmental change. The response of the tropical forest biome to environmental change is strongly influenced by forest type. Forest types differ based on functional traits and forest structure, which are readily derived from high resolution airborne remotely sensed data. Whether the spatial resolution of emerging satellite-derived hyperspectral data is sufficient to identify different tropical forest types is unclear. Here, we resample airborne remotely sensed forest data at spatial resolutions relevant to satellite remote sensing (30 m) across two sites in Malaysian Borneo. Using principal component and cluster analysis, we derive and map seven forest types. We find ecologically relevant variations in forest type that correspond to substantial differences in carbon stock, growth, and mortality rate. We find leaf mass per area and canopy phosphorus are critical traits for distinguishing forest type. Our findings highlight the importance of these parameters for accurately mapping tropical forest types using space borne observations.
Keywords: | remote sensing, LiDAR, tropical rainforest, drought, carbon cycle, plant traits |
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Subjects: | C Biological Sciences > C180 Ecology F Physical Sciences > F832 Remote Sensing C Biological Sciences > C181 Biodiversity |
Divisions: | College of Science > School of Life and Environmental Sciences > Department of Life Sciences |
ID Code: | 52856 |
Deposited On: | 19 Dec 2022 09:43 |
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