| Autor: |
Fernández-Carrillo, Ángel, Franco-Nieto, Antonio, Yagüe-Ballester, María Julia, Gómez-Giménez, Marta |
| Předmět: |
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| Zdroj: |
Forests (19994907); Jul2024, Vol. 15 Issue 7, p1114, 27p |
| Abstrakt: |
Bark beetle outbreaks and forest mortality have rocketed in European forests because of warmer winters, intense droughts, and poor management. The methods developed to predict a bark beetle outbreak have three main limitations: (i) a small-spatial-scale implementation; (ii) specific field-based input datasets that are usually hard to obtain at large scales; and (iii) predictive models constrained by coarse climatic factors. Therefore, a methodological approach accounting for a comprehensive set of environmental traits that can predict a bark beetle outbreak accurately is needed. In particular, we aimed to (i) analyze the influence of environmental traits that cause bark beetle outbreaks; (ii) compare different machine learning architectures for predicting bark beetle attacks; and (iii) map the attack probability before the start of the bark beetle life cycle. Random Forest regression achieved the best-performing results. The predicted bark beetle damage reached a high robustness in the test area (F1 = 96.9, OA = 94.4) and showed low errors (CE = 2.0, OE = 4.2). Future improvements should focus on including additional variables, e.g., forest age and validation sites. Remote sensing-based methods contributed to detecting bark beetle outbreaks in large extensive forested areas in a cost-effective and robust manner. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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