Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing
Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acq...
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MDPI AG
2024-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/21/3926 |
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| author | Thomas Leditznig Hermann Klug |
| author_facet | Thomas Leditznig Hermann Klug |
| author_sort | Thomas Leditznig |
| collection | DOAJ |
| description | Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area <i>Dürrenstein-Lassingtal</i> in Austria. The entry-level consumer drone (DJI Mavic Mini) and freely available Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) machine learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO<sub>2</sub> and applied to the ML-generated class Forest. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living above-ground biomass (AGB) was estimated with an accuracy within 5.9% of field measurements. The proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data. |
| format | Article |
| id | doaj-art-0e3915a89eda4d3f9d8ee71bd56645bd |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-0e3915a89eda4d3f9d8ee71bd56645bd2025-08-20T02:49:55ZengMDPI AGRemote Sensing2072-42922024-10-011621392610.3390/rs16213926Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote SensingThomas Leditznig0Hermann Klug1UNIGIS, Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, AustriaUNIGIS, Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, AustriaUnmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data, and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area <i>Dürrenstein-Lassingtal</i> in Austria. The entry-level consumer drone (DJI Mavic Mini) and freely available Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) machine learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO<sub>2</sub> and applied to the ML-generated class Forest. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living above-ground biomass (AGB) was estimated with an accuracy within 5.9% of field measurements. The proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.https://www.mdpi.com/2072-4292/16/21/3926UAVSentinel-2RGB imageryNDVIrandom forestcarbon storage capacity |
| spellingShingle | Thomas Leditznig Hermann Klug Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing Remote Sensing UAV Sentinel-2 RGB imagery NDVI random forest carbon storage capacity |
| title | Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing |
| title_full | Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing |
| title_fullStr | Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing |
| title_full_unstemmed | Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing |
| title_short | Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing |
| title_sort | estimating carbon stock in unmanaged forests using field data and remote sensing |
| topic | UAV Sentinel-2 RGB imagery NDVI random forest carbon storage capacity |
| url | https://www.mdpi.com/2072-4292/16/21/3926 |
| work_keys_str_mv | AT thomasleditznig estimatingcarbonstockinunmanagedforestsusingfielddataandremotesensing AT hermannklug estimatingcarbonstockinunmanagedforestsusingfielddataandremotesensing |