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

• Published in: Trees (Berlin, West) West), 2023-06, Vol.37 (3), p.717-731
• Main Authors: Vatandaslar, Can, Narin, Omer Gokberk, Abdikan, Saygin
• Format: Article
• Language: English
• Subjects: Accuracy; Agriculture; Artificial neural networks; Biomedical and Life Sciences; Forest management; Forestry; Forests; Information retrieval; Inventories; Lidar; Life Sciences; Missions; Neural networks; Original Article; Pine; Pine trees; Pinus nigra; Plant Anatomy/Development; Plant Pathology; Plant Physiology; Plant Sciences; Remote sensing; Remote sensing systems; Remote sensors; Satellites
• ISSN: 0931-1890; 1432-2285
• DOI: 10.1007/s00468-022-02378-x

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. 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