HemiPy: A Python module for automated estimation of forest biophysical variables and uncertainties from digital hemispherical photographs

Digital hemispherical photography (DHP) is widely used to derive forest biophysical variables including leaf, plant, and green area index (LAI, PAI and GAI), the fraction of intercepted photosynthetically active radiation (FIPAR), and the fraction of vegetation cover (FCOVER). However, the majority...

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Vydáno v:Methods in ecology and evolution Ročník 14; číslo 9; s. 2329 - 2340
Hlavní autoři: Brown, Luke A., Morris, Harry, Leblanc, Sylvain, Bai, Gabriele, Lanconelli, Christian, Gobron, Nadine, Meier, Courtney, Dash, Jadunandan
Médium: Journal Article
Jazyk:angličtina
Vydáno: London John Wiley & Sons, Inc 01.09.2023
Wiley
Témata:
Automation
Batch processing
Classification
digital hemispherical photography
Eye
Forests
fraction of absorbed photosynthetically active radiation
fraction of intercepted photosynthetically active radiation
fraction of vegetation cover
Graphical user interface
green area index
Image processing
leaf area index
Litter fall
Modules
Neon
Photography
Photosynthetically active radiation
plant area index
Propagation
Radiation
Root-mean-square errors
Software
Software packages
Uncertainty
Uncertainty analysis
Variables
Vegetation
Vegetation cover
ISSN:2041-210X, 2041-210X
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Popis
Shrnutí:Digital hemispherical photography (DHP) is widely used to derive forest biophysical variables including leaf, plant, and green area index (LAI, PAI and GAI), the fraction of intercepted photosynthetically active radiation (FIPAR), and the fraction of vegetation cover (FCOVER). However, the majority of software packages for processing DHP data are based on a graphical user interface, making programmatic analysis difficult. Meanwhile, few natively support analysis of RAW image formats, while none incorporate the propagation or provision of uncertainties. 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. Multiannual time‐series of PAI, FIPAR and FCOVER demonstrate HemiPy's outputs realistically represent expected temporal patterns. Comparison against litterfall data reveals reasonable accuracies are achievable, with RMSE values close to the error of ~1 unit typically attributed to optical LAI measurement approaches. HemiPy's PAI, FIPAR and FCOVER outputs demonstrate good agreement with CAN‐EYE. Consistent with previous studies, when compared to simulated hemispherical images, better agreement is observed for PAI derived using gap fraction near the hinge angle of 57.5° only, as opposed to values derived using gap fraction over a wider range of zenith angles. HemiPy should prove a useful tool for processing DHP images, and its open‐source nature means that it can be adopted, extended and further refined by the user community.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:2041-210X
2041-210X
DOI:10.1111/2041-210X.14199