We are currently living challenging times regarding climate, biodiversity, economy, energy and humanitarian crises. The forest and their use has a key role to solve many of these challenges and for that reason more diverse and accurate information on forests is needed. In addition to international and national policy makers, also local forest owners and normal citizens want and need to take into account different ecosystem services and risks in their decision-making.
Remote sensing (RS) provide nowadays cost-effective methods to produce forest information over large areas with different resolutions. Technical development has been fast and more and more decisions regarding, for example forest management is nowadays based on wall-to-wall RS data or RS based map products.
Still sometimes I face misunderstanding that RS can take over all traditional field inventories. It is crucial to understand that mapping of forest attributes are based on the combination of RS data and accurately measured and positioned field data “reference data”. The relationship between RS metrics and field data is modelled and used when predicting forest information over the forest landscape. Collection of accurate reference data over large areas is costly but necessary. To improve the cost-effectiveness of inventory is to utilize the field data from existing monitoring programs such as National forest Inventory (NFI) or National inventories of landscapes (NILS). These inventories provide diverse, accurately measured and repeated forest and environmental data. The national forest attribute maps of Sweden is an example where RS data from airborne laser scanning is combined with field data from the Swedish NFI.
Current nationwide forest maps in Sweden provide information on traditional forest variables such as stem volume, mean height, biomass above ground and tree species, recently also site-index, biomass underground and peatland maps are produced. Still not much attention has been put to different ecosystem services or risk for damage, even though there is available field data in monitoring programs to model even these variables. In my lecture I will give some examples based on my research how remote sensing data is combined with NFI data to predict new forest information focusing on berry yields and risk for wind and snow damage:
The Swedish NFI is collecting unique data of bilberry and cowberry yields that can be combined with airborne laser scanning and other RS data. By exploring the relationship between forest structure measured by RS and berry yields we can predict the most potential location for berry picking in forest landscape and use the knowledge and models to support decision making in forest management. For example, lased based canopy cover of circa 50 % indicated the highest bilberry yields in my study.
Similarly the damage data from NFI such as location of wind and snow damaged plots combined with RS data can be used to predict risk areas for damage and thereby taken account in risk management. In addition to structural and species specific information also the height difference to neighbouring stand and weather variables are important in risk prediction. This kind of other mapped information is often combination with RS data to support predictions of more complex problems.
There is need to improve knowledge on diverse forest variables, species, ecosystem services and risks, and their spatial distribution to support decision making in changing world. For example, based on the denser laser data, the future forest information can be produced more detailed such as tree level attributes, edge zones and retention objects. In addition, there is also a need to improve tools and methods for how new models and mapped data can be included and used in decision support systems such as Heureka.