Retrieval of forest height information using spaceborne LiDAR data: a comparison of GEDI and ICESat‑2 missions for crimean pine (Pinus nigra) stands
| dc.authorid | Can Vatandaşlar / 0000-0001-5552-5670 | en_US |
| dc.contributor.author | Vatandaşlar, Can | |
| dc.contributor.author | Narin, Ömer Gökberk | |
| dc.contributor.author | Abdikan, Saygın | |
| dc.date.accessioned | 2023-01-12T07:16:20Z | |
| dc.date.available | 2023-01-12T07:16:20Z | |
| dc.date.issued | 2022 | |
| dc.department | AÇÜ, Orman Fakültesi, Orman Mühendisliği Bölümü | en_US |
| dc.description.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 feld-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 diferences between canopy tops in dense forest conditions. ICESat-2, on the other hand, performed better in thin forests (DBH<20 cm) than in largegirth 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. | |
| dc.identifier.citation | Vatandaslar, C., Narin, O. G., & Abdikan, S. (2022). Retrieval of forest height information using spaceborne LiDAR data: a comparison of GEDI and ICESat-2 missions for Crimean pine (Pinus nigra) stands. Trees. | en_US |
| dc.identifier.doi | 10.1007/s00468-022-02378-x | |
| dc.identifier.issn | 0931-1890 | |
| dc.identifier.issn | 1432-2285 | |
| dc.identifier.uri | https://doi.org/10.1007/s00468-022-02378-x | |
| dc.identifier.uri | https://hdl.handle.net/11494/4503 | |
| dc.identifier.wosquality | Q2 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.institutionauthor | Vatandaşlar, Can | |
| dc.language.iso | en | en_US |
| dc.publisher | Springer | en_US |
| dc.relation.ispartof | Trees-structure And Function | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Light detection and ranging (LiDAR) | en_US |
| dc.subject | Global ecosystem dynamics investigation (GEDI) | en_US |
| dc.subject | Ice cloud and land elevation satellite-2 (ICESat-2) | en_US |
| dc.subject | Height metrics | en_US |
| dc.subject | Canopy height model | en_US |
| dc.subject | Convolutional neural network (CNN) | en_US |
| dc.title | Retrieval of forest height information using spaceborne LiDAR data: a comparison of GEDI and ICESat‑2 missions for crimean pine (Pinus nigra) stands | en_US |
| dc.type | Article |
