RT info:eu-repo/semantics/doctoralThesis
T1 Predicción de cosechas de setas silvestres en bosques mediterráneos utilizando sensores remotos activos y pasivos
A1 Martínez Rodrigo, Raquel
A2 Universidad de Valladolid. Escuela de Doctorado
K1 Setas
K1 Mushrooms
K1 Setas
K1 Remote sensing
K1 Teledetección
K1 31 Ciencias Agrarias
AB Forests provide valuable resources for society. Their sustainable management maximises the production of the products they provide, at a rate that maintains their biodiversity, productivity, and regenerative capacity. Forest resources include non-wood forest products (NWFP), including wild mushrooms. In addition to the direct provisioning and cultural ecosystem services they provide, mushrooms also offer supporting and regulatory services. Currently, the harvesting of edible wild mushrooms has increased dramatically, but their yields are directly affected by global change processes. Sustainable management of this NWFP can be supported by new tools offered by information technologies, including remote sensing, which provides a multitude of in situ data, at low cost and with high spatial and temporal resolution.The starting hypothesis that remotely sensed data can be used to predict wild mushroom yields in Mediterranean forests is the innovative approach of this PhD Thesis. The main objective is to predict and to estimate mushroom production with data obtained from active and passive remote sensors. For this purpose, three methodologies have been developed combining meteo-climatic data with remotely sensed data: multispectral optical imagery, terrestrial LiDAR (TLS) data and SAR data to estimate mushroom yields.The first chapter focuses on testing whether remote sensing data can predict wild mushroom yields from NDVI, soil moisture and multispectral optical images. The combination of remotely sensed data and meteo-climatic data predicts wild mushrooms yields better than remote sensing data alone. This chapter has been published in the article "Primary productivity and climate control mushroom yields in Mediterranean pine forests" in the JCI journal Agricultural and Forest Meteorology (2020).The second chapter, "Stand Structural Characteristics Derived from Combined TLS and Landsat Data Support Predictions of Mushroom Yields in Mediterranean Forest", published in the JCI journal Remote Sensing (2022), hypothesises that the combined use of different types of remotely sensed data has great potential for wild mushrooms yield estimation. The interaction between Landsat and TLS data has the potential to predict wild mushrooms yields in Mediterranean forests.The third chapter of this PhD Thesis focuses on the time series analysis of SAR data. There are very good correlations between interferometric coherence and mycorrhizal wild mushrooms yields. This chapter is currently under review for publication in a JCI journal: "Towards prediction of forest wild mushrooms yields with time series of Sentinel-1 interferometric coherence data".All this research provides a new perspective for the study of wild mushrooms using remote sensing, improving the temporal and spatial resolution of mushroom yield predictions, and contributing to unveiling the factors that trigger the fruiting of wild mushrooms.
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/62579
UL https://uvadoc.uva.es/handle/10324/62579
LA spa
NO Escuela de Doctorado
DS UVaDOC
RD 17-jun-2024