Abstract
Key message
Despite showing a cost-effective potential for quantifying vertical forest structure, the GEDI and ICESat-2 satellite LiDAR missions fall short of the data accuracy standards required by tree- and stand-level forest inventories.
Abstract
Tree and stand heights are key inventory variables in forestry, but measuring them manually is time-consuming for large forestlands. For that reason, researchers have traditionally used terrestrial and aerial remote sensing systems to retrieve forest height information. Recent developments in sensor technology have made it possible for spaceborne LiDAR systems to collect height data. However, there is still a knowledge gap regarding the utility and reliability of these data in varying forest structures. The present study aims to assess the accuracies of dominant stand heights retrieved by GEDI and ICESat-2 satellites. To that end, we used stand-type maps and field-measured inventory data from forest management plans as references. Additionally, we developed convolutional neural network (CNN) models to improve the data accuracy of raw LiDAR metrics. The results showed that GEDI generally underestimated dominant heights (RMSE = 3.06 m, %RMSE = 21.80%), whereas ICESat-2 overestimated them (RMSE = 4.02 m, %RMSE = 30.76%). Accuracy decreased further as the slope increased, particularly for ICESat-2 data. Nonetheless, using CNN models, we improved estimation accuracies to some extent (%RMSEs = 20.12% and 19.75% for GEDI and ICESat-2). In terms of forest structure, GEDI performed better in fully-covered stands than in sparsely-covered forests. This is attributable to the smaller height differences between canopy tops in dense forest conditions. ICESat-2, on the other hand, performed better in thin forests (DBH < 20 cm) than in large-girth and mature stands of Crimean pine. We conclude that GEDI and ICESat-2 missions, particularly in hilly landscapes, rarely achieve the standards needed in stand-level forest inventories when used alone.
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Data availability
The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.
Abbreviations
- LiDAR:
-
Light detection and ranging
- LS:
-
Laser scanning
- ALS:
-
Airborne laser scanning
- SLS:
-
Spaceborne laser scanning
- MLS:
-
Mobile laser scanning
- TLS:
-
Terrestrial laser scanning
- RMSE:
-
Root mean square error
- GLAS:
-
Geoscience laser altimeter system
- GEDI:
-
Global ecosystem dynamics investigation
- ISS:
-
International space station
- ICESat-2:
-
Ice, cloud and land elevation satellite-2
- NN:
-
Neural network
- CNN:
-
Convolutional neural network
- WGS84:
-
World geodetic system 1984
- ATL08:
-
Land and vegetation height product of ICESat-2
- GDF:
-
Turkish general directorate of forest
- GIS:
-
Geographic information systems
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Acknowledgements
Forest management plan data were purchased from the Turkish General Directorate of Forest (GDF) in official ways. We thank the GDF for the data provision.
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All authors contributed to the study’s conception and design. OGN and CV performed data processing, analysis, and mapping. OGN developed convolutional neural network models. CV wrote the first draft of the manuscript and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Vatandaslar, C., Narin, O.G. & Abdikan, S. Retrieval of forest height information using spaceborne LiDAR data: a comparison of GEDI and ICESat-2 missions for Crimean pine (Pinus nigra) stands. Trees 37, 717–731 (2023). https://doi.org/10.1007/s00468-022-02378-x
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DOI: https://doi.org/10.1007/s00468-022-02378-x