RT info:eu-repo/semantics/article
T1 Exploring the relationship between time series of sentinel-1 interferometric coherence data and wild edible mushroom yields in Mediterranean forests
A1 Martínez Rodrigo, Raquel
A1 Agueda Hernández, Beatriz
A1 López Sánchez, Juan M.
A1 Altelarrea, José Miguel
A1 Alejandro, Pablo
A1 Gómez Almaraz, Cristina
K1 SAR time series data
K1 Non-wood forest products
K1 Mediterranean forest
K1 31 Ciencias Agrarias
AB Edible wild mushrooms constitute a valuable marketable non-wood forest product with high relevance worldwide. There isgrowing interest in developing tools for estimation of mushroom yields and to evaluate the effects that global change mayhave on them. Remote sensing is a powerful technology for characterization of forest structure and condition, both essentialfactors in triggering mushroom production, together with meteo-climatic factors. In this work, we explore options to applysynthetic aperture radar (SAR) data from C-band Sentinel-1 to characterize, at the plot level, wild mushroom productiveforests in the Mediterranean region, which provide saprotroph and ectomycorrhizal mushrooms. Seventeen permanentplots with mushroom yield data collected weekly during the productive season are characterized with dense time series ofSentinel-1 backscatter intensity (VV and VH polarizations) and 6-day interval interferometric VV coherence during the2018–2021 period. Weekly-regularized series of SAR data are decomposed with a LOESS approach into trend, seasonality,and remainder. Trends are explored with the Theil-Sen test, and periodicity is characterized by the Discrete Fast Fouriertransform. Seasonal patterns of SAR time-series are described and related to mycorrhizal and saprotroph guilds separately.Our results indicate that time series of interferometric coherence show cyclic patterns which might be related with annualmushroom yields and may constitute an indicator of triggering factors in mushroom production, whereas backscatter intensityis strongly correlated with precipitation, making noisy signals without a clear interpretable pattern. Exploring the potentialof remotely sensed data for prediction and quantification of mushroom yields contributes to improve our understanding offungal biological cycles and opens new ways to develop tools that improve its sustainable, efficient, and effective management.
PB Springer
SN 2509-8810
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/75216
UL https://uvadoc.uva.es/handle/10324/75216
LA eng
NO Journal of Geovisualization and Spatial Analysis, 2024, vol. 8, n.2
NO Producción Científica
DS UVaDOC
RD 06-abr-2025