Leaf area index and aboveground biomass estimation of an alpine peatland with a UAV multi-sensor approach

ABSTRACT

Aboveground biomass (AGB) can serve as an indicator when estimating various biogeochemical processes in peatlands, an ecosystem which provides countless ecosystem services and plays a key role in climate regulation. While remote sensing has been extensively employed to assess AGB across vast areas in forested peatlands its application to small and treeless peatlands, which are typical of the alpine regions, has been limited. Due to the characteristics of peatlands, innovative approaches capable of capturing their fine-scale, highly heterogeneous and short-stature vegetation cover are needed. Likewise, other key requirements include an ability to overcome site accessibility barriers, cost-effective acquisition of datasets and minimizing damage of these protected habitats. Hence, the utilization of Unmanned Aerial Vehicles (UAVs) offers a viable means for mapping AGB in alpine peatlands. In this study, the AGB of the Val di Ciampo alpine peatland (Veneto Region, Italy) was estimated by combining datasets derived from in situ vegetation samples as well as UAV-based LiDAR, hyperspectral and RGB sensors. A limited number of vegetation samples were used to reduce the impact of the study on the ecosystem. The results indicate that a linear regression can model the relationship between AGB and Leaf Area Index (LAI) with a significant explanatory ability (R² = 0.72; p < 0.001). Several indices derived from digital terrain model (DTM) morphologies, hyperspectral data, and orthophotos were tested using a multiple regression approach to determine their potential to enhance the model’s performance. Among these only the Double Difference (DD) index, derived from hyperspectral data, was found to slightly improve the model’s explanatory ability (R² = 0.76). Overall, the findings of this study suggest that UAV LiDAR data provides the most reliable solution for estimating AGB in alpine peatlands, while the inclusion of hyperspectral data provides only a minor improvement in accuracy.

KEYWORDS:

• Peatlands
• aboveground biomass
• UAV
• LiDAR
• hyperspectral images
• vegetation indices

Acknowledgments

This work was carried out as part of the project “CHANGED - CHAracteriziNG pEatlands from Drones”, funded by the University of Padua (Italy) by means of the STARS Consolidator Grants. We thank the anonymous reviewers and the guest editor for having provided precious comments and suggestions that noticeably improved the quality of the paper.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Antonio Persichetti and Cristiano Miele work for a private company that runs UAV surveys with the sensors used in this paper.

Data availability statement

The data that support the findings of this study are openly available in the Zenodo repository at http://doi.org/10.5281/zenodo.7912969. They include:

• “Danta_dem_10cm_px.tif:” orthomosaic-derived DEM;

• “Danta_rgb_2.2cm_px.tif:” orthophoto;

• “GPS points:” list of GPS samples points;

• “Main data:” field vegetation data and indices used for the regression models;

• “Raw PointCloud:” Lidar original dataset;

• “Pre-processed PointCloud:” Lidar dataset after pre-processing (see paper’s Methods).

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/15481603.2023.2270791

Additional information

Funding

The work was supported by the Università degli Studi di Padova.