VNIR spectroscopy for assessment of post-fire impacts on soil properties using linear and non-linear calibration methods

Abstract

<p>Fire is an important element of the ecosystems, nevertheless, high severity fires can have negative impacts on the ecosystems as consequence of the high temperatures reaches. High temperatures normally have detrimental impacts on soil properties. The objective of this work was to determine the relationship of spectral reflectance and soil pH, electrical conductivity (EC), carbonates (CaCO<sub>3</sub>) and total carbon (TC) content after a medium to high severity wildfire occurred in Croatia using linear and nonlinear calibration models.</p><p>Soils were sampled 2 days after a medium to high severity wildfire in Zadar County, Croatia. A total of 120 soil samples (0-5 cm) were collected from three different treatments (n= 40 per treatment): control (C), mean severity (MS), high severity (HS). Soil pH, EC, CaCO<sub>3</sub> and TC content were determined using standard laboratory methods.  Soil spectral measurements were carried out using a portable spectroradiometer (20 per treatment, 60 in total). Linear statistical model - partial least squares regression (PLSR) and non-linear - artificial neural network (ANN) were generated to estimate changes in soil pH, EC, CaCO<sub>3</sub> and TC content based on the original spectral reflectance and its first derivative in form of principal components (PC). One-way ANOVA revealed pH values were significantly different in all three treatments. EC, CaCO<sub>3</sub> and TC were significantly higher in HS plots compared with the other treatments.</p><p>Different wildfire severity indicated very collinear soil spectral response, but with certain variations of reflectance intensity. Control samples showed a higher reflectance than MS and HS samples. This is attributed to the low pH and TC content. Low reflectance of MS and HS samples could be explained by their increased pH and TC values. Soil pH was the only parameter that showed a high R<sup>2</sup> and low root mean squared error (RMSE) after Savitzky Golay smoothing and the first derivation. In PLSR model, strong to very strong correlation and low RMSE were obtained. ANN model also showed a high R<sup>2</sup> and lower RMSE for all properties except pH. Both models showed satisfactory results for prediction of the studied soil properties. ANN model predicted EC, CaCO<sub>3</sub>, and TC better, while PLSR proved to be a better model for pH prediction.</p><p><strong>Key words:</strong> soil reflectance, fire severity, principal components, partial least squares regression, neural networks</p><p>Acknowledgements</p><p>This work was supported by Croatian Science Foundation through the project "Soil erosion and degradation in Croatia" (UIP-2017-05-7834) (SEDCRO).</p>

Groundwater and Soil Pollution Caused by Forest Fires, and Its Effects on the Distribution and Transport of Radionuclides in Subsurface Environments: Review

“…산불은 산림 토양의 물리화학적 특성에 상당한 변화를 가져올 수 있으며, 열과 재의 유입은 화재로 인한 토양 의 변화를 결정하는 주요 요인이다 (Agbeshie et al, 2022;Bodí et al, 2014;Certini, 2005;Hrelja et al, 2020). 예를 들어, 토양의 수분 침투와 보유에 영향을 미치는 토양의 발수성은 화재 온도에 따라 증가되거나 감소될 수 있으 며, 토양 발수성 강화는 표면의 식생 제거와 함께 토양 의 유출수 증가 및 침식 가속화의 주요 원인이 된다 (Doerr et al, 2006).…”

“…뿐만 아니라, 산불 후 연소 토양에서 포타슘(K), 나트 륨(Na), Ca, Mg 등 주요 양이온의 증가가 다수 보고되었 는데, 토양에 퇴적된 재는 양이온이 풍부하여 일시적으 로 토양의 양이온 함량을 증가시킬 수 있다 (Badía et al, 2014;Bodí et al, 2014). 하지만, 장기적으로 증가한 양 이온은 침식, 강수 등으로 재의 유실과 침출이 발생하여 연소 전 수준으로 돌아가거나 고갈될 수 있다 (Agbeshie et al, 2022;Hrelja et al, 2020). Alauzis et al ( 2004 (Fultz et al, 2016;Iglesias et al, 1997;Litton and Santelices, 2003).…”