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Comparing Johnson’s SB and Weibull Functions to Model the Diameter Distribution of Forest Plantations through ALS Data
Open AccessFeature PaperArticle

The Role of Improved Ground Positioning and Forest Structural Complexity When Performing Forest Inventory Using Airborne Laser Scanning

1
Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017 Lisboa, Portugal
2
3edata, Centro de iniciativas empresariais, Fundación CEL. O Palomar s/n, 27004 Lugo, Spain
3
Servicio de Inventario Forestal, Ministerio de Agricultura, Alimentación y Medio Ambiente, 28071 Madrid, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 413; https://doi.org/10.3390/rs12030413
Received: 6 December 2019 / Revised: 25 January 2020 / Accepted: 27 January 2020 / Published: 28 January 2020
(This article belongs to the Special Issue 3D Point Clouds in Forest Remote Sensing)
The level of spatial co-registration between airborne laser scanning (ALS) and ground data can determine the goodness of the statistical inference used in forest inventories. The importance of positioning methods in the field can increase, depending on the structural complexity of forests. An area-based approach was followed to conduct forest inventory over seven National Forest Inventory (NFI) forest strata in Spain. The benefit of improving the co-registration goodness was assessed through model transferability using low- and high-accuracy positioning methods. Through the inoptimality losses approach, we evaluated the value of good co-registered data, while assessing the influence of forest structural complexity. When using good co-registered data in the 4th NFI, the mean tree height (HTmean), stand basal area (G) and growing stock volume (V) models were 2.6%, 10.6% and 14.7% (in terms of root mean squared error, RMSE %), lower than when using the coordinates from the 3rd NFI. Transferring models built under poor co-registration conditions using more precise data improved the models, on average, 0.3%, 6.0% and 8.8%, while the worsening effect of using low-accuracy data with models built in optimal conditions reached 4.0%, 16.1% and 16.2%. The value of enhanced data co-registration varied between forests. The usability of current NFI data under modern forest inventory approaches can be restricted when combining with ALS data. As this research showed, investing in improving co-registration goodness over a set of samples in NFI projects enhanced model performance, depending on the type of forest and on the assessed forest attributes. View Full-Text
Keywords: LiDAR; forest modeling; 3D point clouds; model transferability; forest inventory LiDAR; forest modeling; 3D point clouds; model transferability; forest inventory
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MDPI and ACS Style

Pascual, A.; Guerra-Hernández, J.; Cosenza, D.N.; Sandoval, V. The Role of Improved Ground Positioning and Forest Structural Complexity When Performing Forest Inventory Using Airborne Laser Scanning. Remote Sens. 2020, 12, 413.

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