Endre Hofstad Hansen successfully defended his doctoral thesis, “Estimation of biomass in tropical rainforest using airborne laser scanning”, on October 30, 2015.
The topic for the trial lecture was “Planning of remote sensing based forest inventory”. We congratulate!
Forest inventories based on field sample surveys, supported by auxiliary remotely sensed data, have the potential to provide transparent and confident estimates of forest carbon stocks required in climate change mitigation schemes such as the REDD+ mechanism. Three-dimensional (3D) information about the density and height of the vegetation, obtained from remotely sensed data, is particularly useful for providing accurate estimates of forest biomass. Most of the research on biomass estimation supported by 3D remotely sensed data has been carried out in boreal and sub-boreal coniferous forests with relatively low biomass quantities and open forest structure. The studies comprising the present thesis were conducted in a dense tropical forest with challenging topography.
In the present thesis two different techniques of collecting remotely sensed 3D data were used: airborne laser scanner (ALS) and spaceborne interferometric synthetic aperture radio detection and ranging (InSAR). While the main focus was on the use of ALS, the high quality digital terrain model (DTM) derived from the ALS data also facilitated the comparison of InSAR data as auxiliary information in biomass estimation.
The analyses and results presented in Paper I of modelling aboveground biomass using ALS data resulted in root mean square errors (RMSE) of about 33% of a mean value of 462 Mg·ha–1. Use of texture variables derived from a canopy surface model constructed from ALS data did not result in improved models. Analyses showed that (1) variables derived from ALS-echoes in the lower parts of the canopy and (2) canopy density variables explained more of the aboveground biomass density than variables representing the height of the canopy.
Paper II investigated the potential of using cheaper, low-pulse density ALS data. Effects of reduced pulse density on (1) the digital terrain model (DTM), and (2) explanatory variables derived from ALS data were assessed. Random variation in DTMs and ALS variables increased with reduced pulse density. A reliability ratio, quantifying replication effects in the ALS-variables, indicated that most of the common ALS variables assessed were reliable at pulse densities >0.5 pulses·m–2, and at a plot size of 0.07 ha. The plot size of 0.07 ha corresponds to the plot size used in the national forest inventory of Tanzania.
The field plot size is of importance for the precision of carbon stock estimates, and better information of the relationship between plot size and precision can be useful in designing future inventories. The effect of plot size on the precision of biomass estimates assisted by remotely sensed data was therefore assessed in Paper III. Precision estimates of forest biomass estimates developed from 30 concentric field plots with sizes of 700, 900,…, 1900 m2, were assessed in a model-based inference framework. Findings indicated that larger field plots were relatively more efficient for inventories supported by ALS and InSAR data compared to a pure field-based survey. Further, a simulation showed that a pure field-based survey would have to comprise 3.5–6.0 times as many observations for the plot sizes of 700–1900 m2 to achieve the same precision as an inventory supported by ALS data.
Professor Terje Gobakken (main supervisor) (INA, NMBU)
Professor Erik Næsset (INA, NMBU)
Dr. Ole Martin Bollandsås (INA, NMBU)
Dr. Pete Watt (Indufor Asia Pacific Ltd.)
Professor Timo Tokola (School of Forest Sciences, University of Eastern Finland)
Associate Professor Katrine Eldegard (INA, NMBU)
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