All natural spaces have a direct producing function, through which essential raw materials for society are obtained, such as food or wood, but they also provide services, such as hydrological protection or quality water generation, biodiversity, landscape, the enjoyment of public use or carbon sequestration, which are gradually being perceived by society as preferential functions of ecosystems, with a high impact on the health and quality of citizens’ life.
Ecosystems’ biophysical structures and processes create the basis for their functioning and are the basis for their capacity to provide services. Although the concept of ecosystem services has a clear anthropocentric vision, the capacity to provide a service exists regardless of whether anyone wants or needs it, but ecosystem services serve as indicators of the quality of our interaction with the environment and now are being placed at the centre of environmental decision-making and policy. They give us the tools to identify which management options help us to mitigate the effects of global change, to optimise societal benefits, and to avoid potential costs and risks to ecosystems and society.
In practical terms, obtaining basic information on ecosystem services from forests other than those directly related to timber remains a challenge. For ecosystem services to be managed effectively and efficiently, they need to be quantified and mapped. Mapping based on in situ surveys is limited to local scales and is limited by its high costs. Remote sensing provides reliable data from large areas and opens the opportunity to quantify and map ecosystem services at comparatively low cost and with the possibility of rapid, frequent, and continuous observations. These data allow us to monitor and manage forests in a holistic approach and to focus on other actors that have not been easy to observe so far.
Fungi are key organisms in the functioning of ecosystems. In addition to the provisioning and cultural ecosystem services provided directly by wild mushrooms, their interaction with fauna and flora is essential in soil mineralisation and humification processes and they provide important regulatory and supporting ecosystem services. The great social interest in this non-wood forest product is reflected in the development of numerous tools for modelling and predicting wild mushroom yields, both at the scientific level and for land management and direct information to pickers, whose development is marked by the extreme complexity that characterises fungi’s life cycles, with extreme variability in their structures both intra- and between years, linked to the biotic and abiotic variables of the ecosystems in which they live.
It is no surprise that wild mushrooms come out when it rains and temperatures are pleasant, and that some species only come out in certain soils, such as the black truffle which only fructifies in calcareous soils, and that the slope and altitude also influence in what we are going to find when we go for a walk in the forest. What the forest is like also has a direct influence: the trees that compose it, their age, or their amount determine whether we will find chanterelles or boletus, for example. Measuring all these variables in situ is a highly complex challenge, but the data obtained through remote sensing can help us to meet it.
Thus, we have found that soil moisture data from European Space Agency (ESA) sensors are as accurate in predicting wild mushroom harvests in Mediterranean pine forests as rainfall, and that, using multispectral sensors, we can also use NDVI in these models, linking the net primary productivity of ecosystems to mushroom yields. The use of terrestrial LiDAR data has also shown a high potential for predicting saffron milk cap yields, a potential reinforced by its joint use with NDVI. Finally, with RADAR data from ESA missions we can build time series that allow us to characterise mushroom harvests at plot level, allowing the development of long-term predictive models. In all three approaches, the remotely sensed data significantly improve the results and predictions of the wild mushroom yields models developed so far.
We still have a lot to know about fungi and mushrooms, but what is clear is that remote sensing is a key tool to advance their study. Open access to these data, with high temporal and spatial resolutions, together with advanced modelling techniques supported by artificial intelligence will help us to model the production of wild mushroom harvests with greater precision and will enable real-time management of the direct ecosystem services they generate. But they will also improve the monitoring of soil mycelial dynamics, opening new opportunities to increase knowledge about the interaction of ecosystems biotic and abiotic variables on these organisms.
This is a small example of how remote sensing has changed the way we approach the study of ecosystems in recent years. Every day, these data are directly applied to the monitoring of forests and the quantification of their resources and services. If you would like to know more about how to apply these tools in your daily activity, the EiFAB of the UVa, where cambium research group is located, will start in September to teach the micro-credential ‘Technician in Basic Tools for Environmental Remote Sensing with Open Data’, which aims to introduce professionals to the world of remote sensing. This micro-credential is part of a training itinerary. The first one provides professionals with the basic pillars to enter the world of remote sensing: geographic information systems, environmental data analysis with R, environmental remote sensing, and Google Earth Engine. The second one, which will start in the 2024-2025 academic year, focuses on advanced applications of remote sensing, and will address specialised environmental applications based on multispectral, LiDAR and RADAR data and the use of drones. Both micro-credentials seek an eminently practical training in the world of remote sensing, focused on the use of free software, which will allow its immediate application in the work context.
You can find more information about our work on the use of remote sensing data to quantify wild mushroom yields in these papers:
Olano JM, Martínez-Rodrigo R, Altelarrea JM, Ágreda T, Fernández-Toirán M, García-Cervigón AI, Rodríguez-Puerta F, Águeda B. 2020. Primary productivity and climate control mushroom yields in Mediterranean pine forests. Agr For Met 288-289: 108015. https://doi.org/10.1016/j.agrformet.2020.108015
Martínez-Rodrigo R, Gómez C, Toraño-Caicoya A, Bohnhorst L, Uhl E, Águeda B. 2022. Stand Structural Characteristics Derived from Combined TLS and Landsat Data Support Predictions of Mushroom Yields in Mediterranean Forest, Remote Sens 14: 5025. https://doi.org/10.3390/rs14195025
Martínez-Rodrigo R, Águeda B, López-Sánchez JM, Altelarrea JM, Alejandro P, Gómez C. 2023. Towards Prediction of Forest Wild Mushroom Yields with Time Series of Sentinel-1 Interferometric Coherence Data. Agr For Met. Under review. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4435864
and on EiFAB’s remote sensing micro-credentials here: https://eifab.uva.es/microcredenciales/
