The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning Metrics
Remote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly re...
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2024-12-01
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| author | Marek Lisańczuk Krzysztof Mitelsztedt Krzysztof Stereńczak |
| author_facet | Marek Lisańczuk Krzysztof Mitelsztedt Krzysztof Stereńczak |
| author_sort | Marek Lisańczuk |
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| description | Remote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly referred to as the area-based approach (ABA). There are many sources of variation that contribute to the overall performance of this method. One of them, which is related to the core aspect of this method, is the spatial co-registration error between ground measurements and RS data. This error arises mainly from the imperfection of the methods for positioning the sample plots under the forest canopy. In this study, we investigated how this positioning accuracy affects the area-based growing stock volume (GSV) estimation under different forest conditions and sample plot radii. In order to analyse this relationship, an artificial co-registration error was induced in a series of simulations and various scenarios. The results showed that there were minimal differences in ABA inventory performance for displacements below 4 m for all stratification groups except for deciduous sites, where sub-metre plot positioning accuracy was justified, as site- and terrain-related factors had some influence on GSV estimation error (<i>r</i> up to 0.4). On the other hand, denser canopy and spatially homogeneous stands mitigated the negative aspects of weaker GNSS positioning capabilities under broadleaved forest types. In the case of RMSE, the results for plots smaller than 400 m<sup>2</sup> were visibly inferior. The BIAS behaviour was less strict in this regard. Knowledge of the actual positioning accuracy as well as the co-registration threshold required for a particular stand type could help manage and optimise fieldwork, as well as better distinguish sources of statistical uncertainty. |
| format | Article |
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| institution | Kabale University |
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| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-6a70f268f9454dabb1fe254b34785d1b2024-12-27T14:50:57ZengMDPI AGRemote Sensing2072-42922024-12-011624470910.3390/rs16244709The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning MetricsMarek Lisańczuk0Krzysztof Mitelsztedt1Krzysztof Stereńczak2Department of Geomatics, Forest Research Institute, Sękocin Stary, 3 Braci Leśnej Street, 05-090 Raszyn, PolandDepartment of Geomatics, Forest Research Institute, Sękocin Stary, 3 Braci Leśnej Street, 05-090 Raszyn, PolandDepartment of Geomatics, Forest Research Institute, Sękocin Stary, 3 Braci Leśnej Street, 05-090 Raszyn, PolandRemote sensing (RS)-based forest inventories are becoming increasingly common in forest management. However, practical applications often require subsequent optimisation steps. One of the most popular RS-based forest inventory methods is the two-phase inventory with regression estimator, commonly referred to as the area-based approach (ABA). There are many sources of variation that contribute to the overall performance of this method. One of them, which is related to the core aspect of this method, is the spatial co-registration error between ground measurements and RS data. This error arises mainly from the imperfection of the methods for positioning the sample plots under the forest canopy. In this study, we investigated how this positioning accuracy affects the area-based growing stock volume (GSV) estimation under different forest conditions and sample plot radii. In order to analyse this relationship, an artificial co-registration error was induced in a series of simulations and various scenarios. The results showed that there were minimal differences in ABA inventory performance for displacements below 4 m for all stratification groups except for deciduous sites, where sub-metre plot positioning accuracy was justified, as site- and terrain-related factors had some influence on GSV estimation error (<i>r</i> up to 0.4). On the other hand, denser canopy and spatially homogeneous stands mitigated the negative aspects of weaker GNSS positioning capabilities under broadleaved forest types. In the case of RMSE, the results for plots smaller than 400 m<sup>2</sup> were visibly inferior. The BIAS behaviour was less strict in this regard. Knowledge of the actual positioning accuracy as well as the co-registration threshold required for a particular stand type could help manage and optimise fieldwork, as well as better distinguish sources of statistical uncertainty.https://www.mdpi.com/2072-4292/16/24/4709ALSABAGSVco-registrationforest inventoryGNSS |
| spellingShingle | Marek Lisańczuk Krzysztof Mitelsztedt Krzysztof Stereńczak The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning Metrics Remote Sensing ALS ABA GSV co-registration forest inventory GNSS |
| title | The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning Metrics |
| title_full | The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning Metrics |
| title_fullStr | The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning Metrics |
| title_full_unstemmed | The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning Metrics |
| title_short | The Influence of the Spatial Co-Registration Error on the Estimation of Growing Stock Volume Based on Airborne Laser Scanning Metrics |
| title_sort | influence of the spatial co registration error on the estimation of growing stock volume based on airborne laser scanning metrics |
| topic | ALS ABA GSV co-registration forest inventory GNSS |
| url | https://www.mdpi.com/2072-4292/16/24/4709 |
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