Original scientific paper
https://doi.org/https://doi.org/10.5513/JCEA01/25.3.4342
Assessment of soil organic matter in the post-fire period using VNIR spectroscopy
Iva HRELJA
; University of Zagreb Faculty of Agriculture, Department of General Agronomy, Svetošimunska 25, 10000 Zagreb, Croatia
*
Igor BOGUNOVIĆ
; University of Zagreb Faculty of Agriculture, Department of General Agronomy, Svetošimunska 25, 10000 Zagreb, Croatia
Paulo PEREIRA
; Environment Management Centre, Mykolas Romeris University, Ateities g. 20, LT-08303 Vilnius, Lithuania
Ivana ŠESTAK
; University of Zagreb Faculty of Agriculture, Department of General Agronomy, Svetošimunska 25, 10000 Zagreb, Croatia
* Corresponding author.
Abstract
Wildfires profoundly impact ecosystems and soil organic matter (SOM), a critical factor in soil quality and carbon cycling. This research aimed to assess the impact of wildfire severity on SOM and the potential of visible-near infrared spectroscopy (VNIR) spanning the 350 - 1050 nm wavelength range for monitoring SOM in a post-fire landscape using two modelling approaches (i) Partial Least Squares Regression (PLSR) and (ii) Artificial Neural Networks (ANN). Following a comprehensive two-year investigation in Zadar County, Croatia, where a 13.5 ha mixed forest was moderately to severely affected by a wildfire, spectral reflectance analysis revealed that SOM content strongly influenced soil reflectance. High-severity samples exhibited the lowest reflectance compared to those with moderate severity and the control group. The critical region for SOM information in post-wildfire soil estimation models was between 550 and 700 nm. ANN consistently outperformed PLSR, achieving a ratio of performance to deviation (RPD) values from 1.74 to > 2.5. In contrast, PLSR achieved values between 1.62 and 2.29, demonstrating ANN's capability to provide accurate predictions of SOM content in complex post-fire SOM dynamics conditions. This research indicates that VNIR spectroscopy, particularly coupled with ANN-based models, offers a reliable and non-destructive method for assessing SOM content in post-fire environments, facilitating informed land management decisions for ecosystem recovery.
Keywords
wildfire; hyperspectral data; linear modelling; nonlinear modelling
Hrčak ID:
320922
URI
Publication date:
23.9.2024.
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