Assessing the potential of synthetic and ex situ airborne laser scanning and ground plot data to train forest biomass models

Author:

Schäfer Jannika1,Winiwarter Lukas234,Weiser Hannah2,Novotný Jan5,Höfle Bernhard26,Schmidtlein Sebastian1,Henniger Hans7,Krok Grzegorz8,Stereńczak Krzysztof8,Fassnacht Fabian Ewald9

Affiliation:

1. Institute of Geography and Geoecology (IFGG), Karlsruhe Institute of Technology (KIT) , Kaiserstraße 12, 76131 Karlsruhe , Germany

2. 3DGeo Research Group, Institute of Geography, Heidelberg University , Im Neuenheimer Feld 368, 69120 Heidelberg , Germany

3. Department of Forest Resources Management, Faculty of Forestry, University of British Columbia , 2424 Main Mall, Vancouver, BC V6T 1Z4 , Canada

4. Photogrammetry Research Area, Department of Geodesy and Geoinformation , TU Wien, Wiedner Hauptstraße 8-10, 1040 Wien , Austria

5. Global Change Research Institute of the Czech Academy of Sciences , Bělidla 986/4a, 603 00 Brno , Czech Republic

6. Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University , Im Neuenheimer Feld 205, 69120 Heidelberg , Germany

7. Department of Ecosystem Analysis, Helmholtz Centre for Environmental Research (UFZ) , Permoserstraße 15, 04318 Leipzig , Germany

8. Department of Geomatics, Forest Research Institute , Sekocin Stary, 3 Braci Leśnej St., 05-090 Raszyn , Poland

9. Remote Sensing and Geoinformatics, Freie Universität Berlin , Malteserstraße 74-100, 12249 Berlin , Germany

Abstract

Abstract Airborne laser scanning data are increasingly used to predict forest biomass over large areas. Biomass information cannot be derived directly from airborne laser scanning data; therefore, field measurements of forest plots are required to build regression models. We tested whether simulated laser scanning data of virtual forest plots could be used to train biomass models and thereby reduce the amount of field measurements required. We compared the performance of models that were trained with (i) simulated data only, (ii) a combination of simulated and real data, (iii) real data collected from different study sites, and (iv) real data collected from the same study site the model was applied to. We additionally investigated whether using a subset of the simulated data instead of using all simulated data improved model performance. The best matching subset of the simulated data was sampled by selecting the simulated forest plot with the highest correlation of the return height distribution profile for each real forest plot. For comparison, a randomly selected subset was evaluated. Models were tested on four forest sites located in Poland, the Czech Republic, and Canada. Model performance was assessed by root mean squared error (RMSE), squared Pearson correlation coefficient (r$^{2}$), and mean error (ME) of observed and predicted biomass. We found that models trained solely with simulated data did not achieve the accuracy of models trained with real data (RMSE increase of 52–122 %, r$^{2}$ decrease of 4–18 %). However, model performance improved when only a subset of the simulated data was used (RMSE increase of 21–118 %, r$^{2}$ decrease of 5–14 % compared to the real data model), albeit differences in model performance when using the best matching subset compared to using a randomly selected subset were small. Using simulated data for model training always resulted in a strong underprediction of biomass. Extending sparse real training datasets with simulated data decreased RMSE and increased r$^{2}$, as long as no more than 12–346 real training samples were available, depending on the study site. For three of the four study sites, models trained with real data collected from other sites outperformed models trained with simulated data and RMSE and r$^{2}$ were similar to models trained with data from the respective sites. Our results indicate that simulated data cannot yet replace real data but they can be helpful in some sites to extend training datasets when only a limited amount of real data is available.

Funder

Deutsche Forschungsgemeinschaft

Polish State Forests National Forest Holding

National Centre for Research and Development

Publisher

Oxford University Press (OUP)

Subject

Forestry

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