Next Article in Journal
Spatiotemporal Patterns of Wildfire Likelihood and Intensity in Ardabil Province, NW Iran
Previous Article in Journal
Harmonizing Wildfire Risk Assessment in Western Balkans through the IPAFF Project
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Deep Learning-Based Surface Fuel Type Classification from Forest Stand Photographs and Sentinel-2 Time Series †

Institute of Geography and Geoecology, Karlsruhe Institute of Technology, Kaiserstr. 12, 76131 Karlsruhe, Germany
Department of Remote Sensing and Geoinformation, Institute of Geographic Sciences, Freie Universität Berlin, Malteserstraße 74-100, 12249 Berlin, Germany
Author to whom correspondence should be addressed.
Presented at the Third International Conference on Fire Behavior and Risk, Sardinia, Italy, 3–6 May 2022.
Environ. Sci. Proc. 2022, 17(1), 22;
Published: 8 August 2022
(This article belongs to the Proceedings of The Third International Conference on Fire Behavior and Risk)


Surface fuel composition and structure are important drivers of fire behaviour and information on the spatial arrangement of surface fuel types is valuable for fire risk assessment in forest ecosystems. However, field assessment of surface fuel types is very time consuming and mapping using airborne or spaceborne sensors is hindered by the tree canopy. We, therefore, suggest a novel remote and proximate sensing-based approach for mapping surface fuel types, while developing a surface fuel type classification for central European forests based on field inventories and expert opinion. Our approach combines in-forest RGB photographs with time series of multispectral satellite data in a deep learning framework: a convolutional neural network (CNN) based on the VGG-16 architecture and a bidirectional long short-term memory network (LSTM) are trained to classify seven different fire-relevant understory and six litter fuel types. Results are compared between different input data sources: horizontal forest stand photos, forest floor photos, Sentinel-2 time series and combinations thereof. The first results show that understory fuel types can be classified with a cross-validated accuracy of 0.78 and litter fuel types with an accuracy of 0.68 using multiple data sources. In addition, we investigate how majority votes using multiple photos in combination with class probabilities can be used to increase the reliability of the predictions. Our algorithm allows one to validate and improve fuel-type maps derived from remote sensing products with GNSS-located photos of the forest stands. The latter can, for example, be obtained by local forest managers or through citizen science. Our approach can, thus, help create more detailed and reliable surface fuel information for central European forests, which forest practitioners and fire fighters urgently need.

Author Contributions

Conceptualization, P.L., M.E. and F.E.F.; methodology, P.L.; formal analysis, P.L.; writing—original draft preparation, P.L.; writing—review and editing, M.E. and F.E.F.; supervision, F.E.F.; funding acquisition, F.E.F. All authors have read and agreed to the published version of the manuscript.


This research was funded by the German Federal Ministry of Food and Agriculture and Federal Ministry for the Environment, Nature Conservation and Nuclear Safety as part of the project “ErWiN” grant number 2219WK54A4.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available from the authors upon request.

Conflicts of Interest

The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Labenski, P.; Ewald, M.; Fassnacht, F.E. Deep Learning-Based Surface Fuel Type Classification from Forest Stand Photographs and Sentinel-2 Time Series. Environ. Sci. Proc. 2022, 17, 22.

AMA Style

Labenski P, Ewald M, Fassnacht FE. Deep Learning-Based Surface Fuel Type Classification from Forest Stand Photographs and Sentinel-2 Time Series. Environmental Sciences Proceedings. 2022; 17(1):22.

Chicago/Turabian Style

Labenski, Pia, Michael Ewald, and Fabian Ewald Fassnacht. 2022. "Deep Learning-Based Surface Fuel Type Classification from Forest Stand Photographs and Sentinel-2 Time Series" Environmental Sciences Proceedings 17, no. 1: 22.

Article Metrics

Back to TopTop