Facts:
Data assimilation in forest remote sensing concerns how to utilize all sources of data, even from previous years, for estimating current forest conditions.
SLU news
Award for data assimilation of remote sensing data
Published: 26 June 2023
At a ceremony in Canada on 21 June, Nils Lindgren (in the centre) received the award for the second best paper in the Canadian Journal of Remote Sensing in 2022. On the left Gordon Staples and on the right Christopher Hopkinson, both representatives of the Canadian Remote Sensing Society's Board.
The article Data Assimilation of Growing Stock Volume Using a Sequence of Remote Sensing Data from Different Sensors was the last in Nils Lindgren's thesis, presented at SLU in 2021.
The article shows how different types of remote sensing data over time can be assimilated into a model of the forest condition. There are currently many types of remote sensing data, such as satellite images, which can be frequently obtained, but which stand-alone provide poorer estimates than when, for example, airborne laser data are used. However, with proper weighting and calibration, even remotely sensed data that provide poor estimates, can contribute to improving an existing model of forest conditions. This approach has the potential to revolutionise forest inventories in the future.
Contact
Håkan Olsson, Professor emeritus
Department of Forest Resource Management
Department of Forest Resource Management
hakan.olsson@slu.se, +46907868376, +46705535701
Göran Ståhl, Professor
Department of Forest Resource Management, Division of Forest Resource Analysis
Department of Forest Resource Management, Division of Forest Resource Analysis
goran.stahl@slu.se, +46907868459, +46706618216