Retrieval of forest height information using spaceborne LiDAR data

Retrieval of forest height information using spaceborne LiDAR data: a comparison of GEDI and ICESat-2 missions for Crimean pine (Pinus nigra) stands

Vatandaslar, Can (Artvin Coruh University)
Narin, O.G. (TU Delft Optical and Laser Remote Sensing; Afyon Kocatepe University)
Abdikan, Saygin (Hacettepe University)

2022

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.

Canopy height model
Convolutional neural network (CNN)
Global ecosystem dynamics investigation (GEDI)
Height metrics
Ice cloud and land elevation satellite-2 (ICESat-2)
Light detection and ranging (LiDAR)

http://resolver.tudelft.nl/uuid:fe3f14eb-c6c5-4a2d-8c6d-d4737a3a8fef

https://doi.org/10.1007/s00468-022-02378-x

2023-07-01

0931-1890

Trees - Structure and Function, 37 (3), 717-731

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 Can Vatandaslar, O.G. Narin, Saygin Abdikan