DECAPIN-IA CYL
DETECTION OF THE DECLINE OF PINUS PINASTER IN CASTILLA Y LEÓN
Agresta S. Coop.
Regional Government of Castile and León (Co-funding partner under the RETECHFOR program)
Artificial Intelligence for Detecting Decay in Pinus pinaster
What is it and what is it used for?
It is an early-warning system based on deep learning architectures, whose resulting maps are integrated into a Spatial Data Infrastructure (SDI). Currently implemented in four pilot regions and designed with an architecture scalable to the entire region of Castile and León, it serves to automatically identify and monitor signs of decline in Pinus pinaster stands, transforming time series of satellite images into operational maps structured as dynamic weekly decline alerts, bimonthly aggregation maps, and annual summary maps. Web access at https://retechfor.agrestaweb.org/
The problem it solves
It addresses the progressive decline of Pinus pinaster pine forests affected by water stress—partly due to extreme droughts—and biotic factors, the effects of which are often exacerbated by historical pressure from resin harvesting. It addresses the lack of automated tools in a sector with low levels of digitization. It also overcomes the methodological limitations of traditional trend analysis algorithms, which are effective at identifying abrupt changes—such as logging or wildfires—but are unable to detect the subtle, slow, and gradual transitions associated with progressive decline.
“Artificial intelligenceapplied to the analysis of satellite image time series for the early detection of forest decline and to guide the adaptive management of Spanish stone pine populations”
What makes it innovative?
Identification of Anomalies
The system identifies a series of continuous anomalies in the generated map that indicate a gradual transition toward decline in a stand of resin pine. Trees weakened by this stress are particularly vulnerable to attacks by boring insects that act as disease vectors—for example, as vectors of the pine nematode.
Prioritize field inspections
By identifying the source of the decline early on, forest health services can prioritize a field inspection to collect samples and carry out isolation cuttings before the pathogen spreads.
Expected impact
By issuing early warnings of forest decline, the system reduces ecosystem losses by facilitating preventive silviculture that strengthens the resilience of forest stands in the face of climate change. On the socioeconomic front, it helps safeguard the viability of traditional forest use by optimizing management in stands with the highest productive potential. By providing a tool that helps optimize forest planning, it offers technical support that promotes job stability in the sector.
