AbstractTo incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM (1, 1) model generated from the snapshot sequence reduces the randomness of discrete snapshot and generates the holistic measure of object's movements. Comparisons to traditional linear models show that when information is limited this model can be used in the interpolation and near future prediction of uncertain continuously moving spatio-temporal objects

Avitabile, Valerio

Bartholomeus, Harm

Burt, Andrew

Calders, Kim

Disney, Mathias

Gonzalez De Tanago, Jose

Goodman, Rosa C.

Herold, Martin

Lau, Alvaro

Manuri, Solichin

Martius, Christopher

Raumonen, Pasi

1. Tropical forest biomass is a crucial component of global carbon emission estimations. However, calibration and validation of such estimates require accurate and effective methods to estimate in situ above-ground biomass (AGB). Present methods rely on allometric models that are highly uncertain for large tropical trees. Terrestrial laser scanning (TLS) tree modelling has demonstrated to be more accurate than these models to infer forest AGB. Nevertheless, applying TLS methods on tropical large trees is still challenging. We propose a method to estimate AGB of large tropical trees by three-dimensional (3D) tree modelling of TLS point clouds. 2. Twenty-nine plots were scanned with a TLS in three study sites (Peru, Indonesia and Guyana). We identified the largest tree per plot (mean diameter at breast height of 73.5 cm), extracted its point cloud and calculated its volume by 3D modelling its structure using quantitative structure models (QSM) and converted to AGB using species-specific wood density. We also estimated AGB using pantropical and local allometric models. To assess the accuracy of our and allometric methods, we harvest the trees and took destructive measurements. 3. AGB estimates by the TLS–QSM method showed the best agreement in comparison to destructive harvest measurements (28.37% coefficient of variation of root mean square error [CV-RMSE] and concordance correlation coefficient [CCC] of 0.95), outperforming the pantropical allometric models tested (35.6%–54.95% CV-RMSE and CCC of 0.89–0.73). TLS–QSM showed also the lowest bias (overall underestimation of 3.7%) and stability across tree size range, contrasting with the allometric models that showed a systematic bias (overall underestimation ranging 15.2%–35.7%) increasing linearly with tree size. The TLS–QSM method also provided accurate tree wood volume estimates (CV RMSE of 23.7%) with no systematic bias regardless the tree structural characteristics. 4. Our TLS–QSM method accounts for individual tree biophysical structure more effectively than allometric models, providing more accurate and less biased AGB estimates for large tropical trees, independently of their morphology. This non-destructive method can be further used for testing and calibrating new allometric models, reducing the current under-representation of large trees in and enhancing present and past estimates of forest biomass and carbon emissions from tropical forests

NARCIS 

Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR

1. Tropical forest biomass is a crucial component of global carbon emission estimations. However, calibration and validation of such estimates require accurate and effective methods to estimate in situ above-ground biomass (AGB). Present methods rely on allometric models that are highly uncertain for large tropical trees. Terrestrial laser scanning (TLS) tree modelling has demonstrated to be more accurate than these models to infer forest AGB. Nevertheless, applying TLS methods on tropical large trees is still challenging. We propose a method to estimate AGB of large tropical trees by three-dimensional (3D) tree modelling of TLS point clouds. 2. Twenty-nine plots were scanned with a TLS in three study sites (Peru, Indonesia and Guyana). We identified the largest tree per plot (mean diameter at breast height of 73.5cm), extracted its point cloud and calculated its volume by 3D modelling its structure using quantitative structure models (QSM) and converted to AGB using species-specific wood density. We also estimated AGB using pantropical and local allometric models. To assess the accuracy of our and allometric methods, we harvest the trees and took destructive measurements. 3. AGB estimates by the TLS-QSM method showed the best agreement in comparison to destructive harvest measurements (28.37% coefficient of variation of root mean square error [CV-RMSE] and concordance correlation coefficient [CCC] of 0.95), outperforming the pantropical allometric models tested (35.6%-54.95% CV-RMSE and CCC of 0.89-0.73). TLS-QSM showed also the lowest bias (overall underestimation of 3.7%) and stability across tree size range, contrasting with the allometric models that showed a systematic bias (overall underestimation ranging 15.2%-35.7%) increasing linearly with tree size. The TLS-QSM method also provided accurate tree wood volume estimates (CV RMSE of 23.7%) with no systematic bias regardless the tree structural characteristics. 4. Our TLS-QSM method accounts for individual tree biophysical structure more effectively than allometric models, providing more accurate and less biased AGB estimates for large tropical trees, independently of their morphology. This non-destructive method can be further used for testing and calibrating new allometric models, reducing the current under-representation of large trees in and enhancing present and past estimates of forest biomass and carbon emissions from tropical forests.1. Tropical forest biomass is a crucial component of global carbon emission estimations. However, calibration and validation of such estimates require accurate and effective methods to estimate in situ above-ground biomass (AGB). Present methods rely on allometric models that are highly uncertain for large tropical trees. Terrestrial laser scanning (TLS) tree modelling has demonstrated to be more accurate than these models to infer forest AGB. Nevertheless, applying TLS methods on tropical large trees is still challenging. We propose a method to estimate AGB of large tropical trees by three-dimensional (3D) tree modelling of TLS point clouds. 2. Twenty-nine plots were scanned with a TLS in three study sites (Peru, Indonesia and Guyana). We identified the largest tree per plot (mean diameter at breast height of 73.5cm), extracted its point cloud and calculated its volume by 3D modelling its structure using quantitative structure models (QSM) and converted to AGB using species-specific wood density. We also estimated AGB using pantropical and local allometric models. To assess the accuracy of our and allometric methods, we harvest the trees and took destructive measurements. 3. AGB estimates by the TLS-QSM method showed the best agreement in comparison to destructive harvest measurements (28.37% coefficient of variation of root mean square error [CV-RMSE] and concordance correlation coefficient [CCC] of 0.95), outperforming the pantropical allometric models tested (35.6%-54.95% CV-RMSE and CCC of 0.89-0.73). TLS-QSM showed also the lowest bias (overall underestimation of 3.7%) and stability across tree size range, contrasting with the allometric models that showed a systematic bias (overall underestimation ranging 15.2%-35.7%) increasing linearly with tree size. The TLS-QSM method also provided accurate tree wood volume estimates (CV RMSE of 23.7%) with no systematic bias regardless the tree structural characteristics. 4. Our TLS-QSM method accounts for individual tree biophysical structure more effectively than allometric models, providing more accurate and less biased AGB estimates for large tropical trees, independently of their morphology. This non-destructive method can be further used for testing and calibrating new allometric models, reducing the current under-representation of large trees in and enhancing present and past estimates of forest biomass and carbon emissions from tropical forests.A

Gonzalez de Tanago, Jose

Goodman, Rosa C

Calders, KimLA208020027123960000-0002-4562-25389286A620-A6F4-11E7-8878-09B7AD28A064

Archivsystem Ask23

1. Tropical forest biomass is a crucial component of global carbon emission estimations. However, calibration and validation of such estimates require accurate and effective methods to estimate in situ above-ground biomass (AGB). Present methods rely on allometric models that are highly uncertain for large tropical trees. Terrestrial laser scanning (TLS) tree modelling has demonstrated to be more accurate than these models to infer forest AGB. Nevertheless, applying TLS methods on tropical large trees is still challenging. We propose a method to estimate AGB of large tropical trees by three-dimensional (3D) tree modelling of TLS point clouds. 
2. Twenty-nine plots were scanned with a TLS in three study sites (Peru, Indonesia and Guyana). We identified the largest tree per plot (mean diameter at breast height of 73.5cm), extracted its point cloud and calculated its volume by 3D modelling its structure using quantitative structure models (QSM) and converted to AGB using species-specific wood density. We also estimated AGB using pantropical and local allometric models. To assess the accuracy of our and allometric methods, we harvest the trees and took destructive measurements. 
3. AGB estimates by the TLS-QSM method showed the best agreement in comparison to destructive harvest measurements (28.37% coefficient of variation of root mean square error [CV-RMSE] and concordance correlation coefficient [CCC] of 0.95), outperforming the pantropical allometric models tested (35.6%-54.95% CV-RMSE and CCC of 0.89-0.73). TLS-QSM showed also the lowest bias (overall underestimation of 3.7%) and stability across tree size range, contrasting with the allometric models that showed a systematic bias (overall underestimation ranging 15.2%-35.7%) increasing linearly with tree size. The TLS-QSM method also provided accurate tree wood volume estimates (CV RMSE of 23.7%) with no systematic bias regardless the tree structural characteristics. 
4. Our TLS-QSM method accounts for individual tree biophysical structure more effectively than allometric models, providing more accurate and less biased AGB estimates for large tropical trees, independently of their morphology. This non-destructive method can be further used for testing and calibrating new allometric models, reducing the current under-representation of large trees in and enhancing present and past estimates of forest biomass and carbon emissions from tropical forests

Ghent University Academic Bibliography

Tropical forest biomass is a crucial component of global carbon emission estimations. However, calibration and validation of such estimates require accurate and effective methods to estimate in situ above‐ground biomass (AGB). Present methods rely on allometric models that are highly uncertain for large tropical trees. Terrestrial laser scanning (TLS) tree modelling has demonstrated to be more accurate than these models to infer forest AGB. Nevertheless, applying TLS methods on tropical large trees is still challenging. We propose a method to estimate AGB of large tropical trees by three‐dimensional (3D) tree modelling of TLS point clouds. Twenty‐nine plots were scanned with a TLS in three study sites (Peru, Indonesia and Guyana). We identified the largest tree per plot (mean diameter at breast height of 73.5 cm), extracted its point cloud and calculated its volume by 3D modelling its structure using quantitative structure models (QSM) and converted to AGB using species‐specific wood density. We also estimated AGB using pantropical and local allometric models. To assess the accuracy of our and allometric methods, we harvest the trees and took destructive measurements. AGB estimates by the TLS–QSM method showed the best agreement in comparison to destructive harvest measurements (28.37% coefficient of variation of root mean square error [CV‐RMSE] and concordance correlation coefficient [CCC] of 0.95), outperforming the pantropical allometric models tested (35.6%–54.95% CV‐RMSE and CCC of 0.89–0.73). TLS–QSM showed also the lowest bias (overall underestimation of 3.7%) and stability across tree size range, contrasting with the allometric models that showed a systematic bias (overall underestimation ranging 15.2%–35.7%) increasing linearly with tree size. The TLS–QSM method also provided accurate tree wood volume estimates (CV RMSE of 23.7%) with no systematic bias regardless the tree structural characteristics. Our TLS–QSM method accounts for individual tree biophysical structure more effectively than allometric models, providing more accurate and less biased AGB estimates for large tropical trees, independently of their morphology. This non‐destructive method can be further used for testing and calibrating new allometric models, reducing the current under‐representation of large trees in and enhancing present and past estimates of forest biomass and carbon emissions from tropical forests.Tropical forest biomass is a crucial component of global carbon emission estimations. However, calibration and validation of such estimates require accurate and effective methods to estimate in situ above‐ground biomass (AGB). Present methods rely on allometric models that are highly uncertain for large tropical trees. Terrestrial laser scanning (TLS) tree modelling has demonstrated to be more accurate than these models to infer forest AGB. Nevertheless, applying TLS methods on tropical large trees is still challenging. We propose a method to estimate AGB of large tropical trees by three‐dimensional (3D) tree modelling of TLS point clouds.Twenty‐nine plots were scanned with a TLS in three study sites (Peru, Indonesia and Guyana). We identified the largest tree per plot (mean diameter at breast height of 73.5 cm), extracted its point cloud and calculated its volume by 3D modelling its structure using quantitative structure models (QSM) and converted to AGB using species‐specific wood density. We also estimated AGB using pantropical and local allometric models. To assess the accuracy of our and allometric methods, we harvest the trees and took destructive measurements.AGB estimates by the TLS–QSM method showed the best agreement in comparison to destructive harvest measurements (28.37% coefficient of variation of root mean square error [CV‐RMSE] and concordance correlation coefficient [CCC] of 0.95), outperforming the pantropical allometric models tested (35.6%–54.95% CV‐RMSE and CCC of 0.89–0.73). TLS–QSM showed also the lowest bias (overall underestimation of 3.7%) and stability across tree size range, contrasting with the allometric models that showed a systematic bias (overall underestimation ranging 15.2%–35.7%) increasing linearly with tree size. The TLS–QSM method also provided accurate tree wood volume estimates (CV RMSE of 23.7%) with no systematic bias regardless the tree structural characteristics.Our TLS–QSM method accounts for individual tree biophysical structure more effectively than allometric models, providing more accurate and less biased AGB estimates for large tropical trees, independently of their morphology. This non‐destructive method can be further used for testing and calibrating new allometric models, reducing the current under‐representation of large trees in and enhancing present and past estimates of forest biomass and carbon emissions from tropical forests

González de Tanago, J.

Lau, A.

Bartholomeus, H.M.

Raumonen, P.

Martius, C.

Goodman, R.

Disney, M.

Manuri, S.

Burt, A.

Calders, K.

CGSpace (CGIAR)

Estimation of above-ground biomass of large tropical trees with Terrestrial LiDAR

BAO, Lei

QIN, Xiao-lin

Elsevier - Publisher Connector 

The Discrete Representation of Continuously Moving Indeterminate Objects 

Tropical forest biomass is a crucial component of global carbon emission estimations. However, calibration and validation of such estimates require accurate and effective methods to estimate in situ above-ground biomass (AGB). Present methods rely on allometric models that are highly uncertain for large tropical trees. Terrestrial laser scanning (TLS) tree modelling has demonstrated to be more accurate than these models to infer forest AGB. Nevertheless, applying TLS methods on tropical large trees is still challenging. We propose a method to estimate AGB of large tropical trees by three-dimensional (3D) tree modelling of TLS point clouds.

Twenty-nine plots were scanned with a TLS in three study sites (Peru, Indonesia and Guyana). We identified the largest tree per plot (mean diameter at breast height of 73.5 cm), extracted its point cloud and calculated its volume by 3D modelling its structure using quantitative structure models (QSM) and converted to AGB using species-specific wood density. We also estimated AGB using pantropical and local allometric models. To assess the accuracy of our and allometric methods, we harvest the trees and took destructive measurements.

AGB estimates by the TLS–QSM method showed the best agreement in comparison to destructive harvest measurements (28.37% coefficient of variation of root mean square error [CV-RMSE] and concordance correlation coefficient [CCC] of 0.95), outperforming the pantropical allometric models tested (35.6%–54.95% CV-RMSE and CCC of 0.89–0.73). TLS–QSM showed also the lowest bias (overall underestimation of 3.7%) and stability across tree size range, contrasting with the allometric models that showed a systematic bias (overall underestimation ranging 15.2%–35.7%) increasing linearly with tree size. The TLS–QSM method also provided accurate tree wood volume estimates (CV RMSE of 23.7%) with no systematic bias regardless the tree structural characteristics.

Our TLS–QSM method accounts for individual tree biophysical structure more effectively than allometric models, providing more accurate and less biased AGB estimates for large tropical trees, independently of their morphology. This non-destructive method can be further used for testing and calibrating new allometric models, reducing the current under-representation of large trees in and enhancing present and past estimates of forest biomass and carbon emissions from tropical forests

The Discrete Representation of Continuously Moving Indeterminate Objects

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NARCIS

Archivsystem Ask23

Ghent University Academic Bibliography

CGSpace (CGIAR)

Elsevier - Publisher Connector

UCL Discovery

Crossref

Elsevier - Publisher Connector

MUCC (Crossref)