Comparison of Advanced Terrestrial and Aerial Remote Sensing Methods for Above-Ground Carbon Stock Estimation—A Comparative Case Study for a Hungarian Temperate Forest
The increasing pace of climate-driven changes in forest ecosystems calls for reliable remote sensing techniques for quantifying above-ground carbon storage. In this article, we compare the methodology and results of traditional field surveys, mobile laser scanning, optical drone imaging and photogra...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/13/2173 |
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| Summary: | The increasing pace of climate-driven changes in forest ecosystems calls for reliable remote sensing techniques for quantifying above-ground carbon storage. In this article, we compare the methodology and results of traditional field surveys, mobile laser scanning, optical drone imaging and photogrammetry, and both drone-based and light aircraft-based aerial laser scanning to determine forest stand parameters, which are suitable to estimate carbon stock. Measurements were conducted at four designated sampling points established during a large-scale project in deciduous and coniferous tree stands of the Dudles Forest, Hungary. The results of the surveys were first compared spatially and quantitatively, followed by a summary of the advantages and disadvantages of each method. The mobile laser scanner proved to be the most accurate, while optical surveying—enhanced with a new diameter measurement methodology based on detecting stem positions from the photogrammetric point cloud and measuring the diameter directly on the orthorectified images—also delivered promising results. Aerial laser scanning was the least accurate but provided coverage over large areas. Based on the results, we recommend adapting our carbon stock estimation methodology primarily to mobile laser scanning surveys combined with aerial laser scanned data. |
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| ISSN: | 2072-4292 |