RT info:eu-repo/semantics/article
T1 Assessment of mycological possibility using machine learning models for effective inclusion in sustainable forest management
A1 Martínez Rodrigo, Raquel
A1 Águeda Hernández, Beatriz
A1 Ágreda, Teresa
A1 Altelarrea, José Miguel
A1 Fernández Toirán, Luz Marina
A1 Rodríguez Puerta, Francisco
K1 Mushrooms
K1 Hongos
K1 Edible mushrooms
K1 Hongos comestibles
K1 Non-wood forest products
K1 Forest products
K1 Productos forestales
K1 Remote sensing
K1 Teledetección
K1 Neural networks (Computer science)
K1 Redes neuronales (Informática)
K1 Forests and forestry
K1 Bosques y silvicultura
K1 Forest management
K1 Bosques - Gestión
K1 Sustainable development
K1 Desarrollo sostenible
K1 2417.06 Micología (Setas)
K1 1203.04 Inteligencia Artificial
K1 1203.17 Informática
K1 3106 Ciencia Forestal
AB The integral role of wild fungi in ecosystems, including provisioning, regulating, cultural, and supporting services, is well recognized. However, quantifying and predicting wild mushroom yields is challenging due to spatial and temporal variability. In Mediterranean forests, climate-change-induced droughts further impact mushroom production. Fungal fruiting is influenced by factors such as climate, soil, topography, and forest structure. This study aims to quantify and predict the mycological potential of Lactarius deliciosus in sustainably managed Mediterranean pine forests using machine learning models. We utilize a long-term dataset of Lactarius deliciosus yields from 17 Pinus pinaster plots in Soria, Spain, integrating forest-derived structural data, NASA Landsat mission vegetation indices, and climatic data. The resulting multisource database facilitates the creation of a two-stage ‘mycological exploitability’ index, crucial for incorporating anticipated mycological production into sustainable forest management, in line with what is usually done for other uses such as timber or game. Various Machine Learning (ML) techniques, such as classification trees, random forest, linear and radial support vector machine, and neural networks, were employed to construct models for classification and prediction. The sample was always divided into training and validation sets (70-30%), while the differences were found in terms of Overall Accuracy (OA). Neural networks, incorporating critical variables like climatic data (precipitation in January and humidity in November), remote sensing indices (Enhanced Vegetation Index, Green Normalization Difference Vegetation Index), and structural forest variables (mean height, site index and basal area), produced the most accurate and unbiased models (OAtraining = 0.8398; OAvalidation = 0.7190). This research emphasizes the importance of considering a diverse array of ecosystem variables for quantifying wild mushroom yields and underscores the pivotal role of Artificial Intelligence (AI) tools and remotely sensed observations in modeling non-wood forest products. Integrating such models into sustainable forest management plans is crucial for recognizing the ecosystem services provided by them.
PB MDPI
SN 2071-1050
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/70415
UL https://uvadoc.uva.es/handle/10324/70415
LA eng
NO Sustainability, 2024, Vol. 16, Nº. 13, 5656
NO Producción Científica
DS UVaDOC
RD 02-ene-2025