Forest canopy structure and reflectance in humid tropical Borneo: A physically-based interpretation using spectral invariants

Hafidh A Hadi, Marion Pfeifer, Lauri Korhonen, Charlotte Wheeler, Miina Rautiainen

Research output: Contribution to journalArticlepeer-review

Abstract

South East Asia region is a triple hotspot of carbon, biodiversity, and forest degradation. The latter is leading to heterogeneous forest landscapes with predominant and varying intensity of human modification. Optical Earth observation data offers the most feasible and accessible means of mapping and monitoring the forest cover heterogeneity in this region. Further, difficulties in ground data collection in this region present the need for more robust physically-based interpretation of satellite data which extends beyond simple empirical correlations of reflectance data with forest characteristics. We present an application of forest reflectance parameterization based on spectral invariants in lowland humid tropical forests of Malaysian Borneo. Our framework combined field measurements (hemispherical photos) of canopy structure, radiative transfer modelling at leaf (PROSPECT) and canopy scale (PARAS), and assessments using satellite data (Landsat-7 Enhanced Thematic Mapper Plus, ETM +). Field data were collected representing a gradient of forest degradation within a large-scale experimental landscape. Results showed limited ability of Landsat-class satellites to uncover the full gradient of forest degradation in this biome due to similarity in canopy structure as characterized by effective canopy cover, leaf area index, and angular profile of canopy gap fraction. Our findings, however, showed strong expression of leaf optical properties at canopy level. This supports the feasibility to remotely measure canopy biochemistry in dense humid tropical rainforests at medium spatial resolution using freely available Landsat-class satellites, and forthcoming hyperspectral satellite missions.
Original languageEnglish
Pages (from-to)314-330
JournalRemote Sensing of Environment
Volume201
DOIs
Publication statusPublished - 1 Nov 2017

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