HemiPy: A Python module for automated estimation of forest biophysical variables and uncertainties from digital hemispherical photographs
2023
BROWN Luke Ashley | MORRIS Harry | LEBLANC Sylvain | BAI Gabriele | LANCONELLI Christian | GOBRON Nadine | MEIER Courtney | DASH Jadunandan
1. Digital hemispherical photography (DHP) is widely used to derive forest biophysi- cal variables including leaf, plant, and green area index (LAI, PAI and GAI), the fraction of intercepted photosynthetically active radiation (FIPAR), and the frac- tion of vegetation cover (FCOVER). However, the majority of software packages for processing DHP data are based on a graphical user interface, making program- matic analysis difficult. Meanwhile, few natively support analysis of RAW image formats, while none incorporate the propagation or provision of uncertainties. 2. To address these limitations, we present HemiPy, an open-source Python module for deriving forest biophysical variables and uncertainties from DHP images in an automated manner. We assess HemiPy using simulated hemispherical images, in addition to multiannual time-series and litterfall data from several forested National Ecological Observatory Network (NEON) sites, as well as comparison against the CAN-EYE software package. [... continue]
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