Forest resource mapping using 3D remote sensing: Combining national forest inventory data and digital aerial photogrammetry

Johannes Christoph Peter Rahlf successfully defended his doctoral thesis, “Forest resource mapping using 3D remote sensing: Combining national forest inventory data and digital aerial photogrammetry” on 24 March 2017.

The topic for the trial lecture was “New possibilities for updating and improving nationwide scale forest maps, given the expected future flow of remotely sensed data”. We congratulate!

Thesis abstract

National forest inventories (NFI) provide information at national and regional scales. At smaller scales, however, often too few sample plots are available for accurate estimates. The increasing availability of large area 3D remote sensing data gives the opportunity to create wall-to-wall forest maps based on reference data from NFIs. Digital aerial photogrammetry (DAP) allows the creation of detailed 3D information from overlapping digital aerial images over large areas at low costs. The objective of this thesis was to assess the use of DAP in combination with NFI data. 

In the first study, DAP was compared to other 3D remote sensing techniques, namely airborne laser scanning (ALS), satellite interferometric synthetic aperture radar (InSAR), and satellite radargrammetry based on the accuracies of timber volume models. All models had good model fits. It could be shown that at stand level predictions with ALS were slightly more accurate than predictions based on DAP, which were more accurate than predictions using satellite SAR data. The second study analyzed the use of DAP in a semi individual tree crown (semi-ITC) approach for modeling various forest parameters. At plot level, timber volume predictions of the semi-ITC approach had accuracies and systematic errors similar to the area based approach (ABA). Multivariate kNN models were slightly more accurate with the semi-ITC approach than with the ABA, but had larger systematic errors. In the third study a timber volume model was fit for a large study area and the influence of large-area factors on the accuracy of timber volume predictions was investigated. The obtained accuracy of the predictions was lower than reported for earlier studies conducted on smaller study areas. The solar incidence angle relative to the terrain had a significant influence on the model. Finally, the use of DAP for an operational forest resource map was analyzed. Various forest parameters were mapped for a large area using 3D and spectral information from DAP combined with NFI data. Forest parameter models were less accurate than reported for earlier studies on small areas, but stand volume estimates were in line with existing forest management inventories. Model-assisted estimates at regional and municipality level were more precise than estimates based on NFI sample plots alone. The update of a forest mask produced a highly accurate classification of forest and non-forest. A tree species classification showed low accuracies, which, however did not differ greatly from accuracies reported in earlier studies. 

In conclusion, the combination of DAP with NFI data allows cost-efficient mapping of forest parameters over large areas with high detail. Such maps showed to improve the estimates of the Norwegian NFI at various scales. Stand-level estimates of large mapping applications might be sufficiently accurate to be used in forest management planning or in the design of forest management inventories.


Main supervisor: Professor Erik Næsset, MINA, NMBU
Co-supervisor: Research Professor, Dr. Svein Solberg, Norwegian Institute of Bioeconomy Research, NIBIO
Co-supervisor: Research Professor, Dr. Johannes Breidenbach, Norwegian Institute of Bioeconomy Research, NIBIO
Co-supervisor: Head of Research, Dr. Rasmus Astrup, Norwegian Institute of Bioeconomy Research, NIBIO

Evaluation committee

Professor Håkan Olsson, Swedish University of Life Sciences, Sweden
Associate Professor Ludmila Monika Moskal, University of Washington, USA
Professor Dr. Terje Gobakken, MINA, NMBU


[bibtex file=SkogRoverAll.bib key=Rahlf2017]

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