About Tree Height Measurement: Theoretical and Practical Issues for Uncertainty Quantification and Mapping
Forest height is a fundamental parameter in forestry. Tree height is widely used to assess a site’s productivity both in forest ecology research and forest management. Thus, a precise height measure represents a necessary step for the estimation of carbon storage at the local, national, and global s...
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ftmdpi:oai:mdpi.com:/1999-4907/13/7/969/ 2023-08-20T04:10:14+02:00 About Tree Height Measurement: Theoretical and Practical Issues for Uncertainty Quantification and Mapping Samuele De Petris Filippo Sarvia Enrico Borgogno-Mondino agris 2022-06-21 application/pdf https://doi.org/10.3390/f13070969 EN eng Multidisciplinary Digital Publishing Institute Forest Inventory, Modeling and Remote Sensing https://dx.doi.org/10.3390/f13070969 https://creativecommons.org/licenses/by/4.0/ Forests; Volume 13; Issue 7; Pages: 969 tree height uncertainty hypsometer forest biomes variance propagation law Google Earth Engine Text 2022 ftmdpi https://doi.org/10.3390/f13070969 2023-08-01T05:27:08Z Forest height is a fundamental parameter in forestry. Tree height is widely used to assess a site’s productivity both in forest ecology research and forest management. Thus, a precise height measure represents a necessary step for the estimation of carbon storage at the local, national, and global scales. In this context, error in height measurement necessarily affects the accuracy of related estimates. Ordinarily, forest height is surveyed by ground sampling adopting hypsometers. The latter suffers from many errors mainly related to the correct tree apex identification (not always well visible in dense stands) and to the measurement process itself. In this work, a statistically based operative method for estimating height measurement uncertainty (σH) was proposed using the variance propagation law. Some simulations were performed involving several combinations of terrain slope, tree height, and survey distances by modelling the σH behaviour and its sensitivity to such parameters. Results proved that σH could vary between 0.5 m and up to 20 m (worst case). Sensitivity analysis shows that terrain slopes and distance poorly affect σH, while angles are the main drivers of height uncertainty. Finally, to give a practical example of such deductions, tree height uncertainty was mapped at the global scale using Google Earth Engine and summarized per forest biomes. Results proved that tropical biomes have higher uncertainty (from 1 m to 4 m) while shrublands and tundra have the lowest (under 1 m). Text Tundra MDPI Open Access Publishing Forests 13 7 969 |
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tree height uncertainty hypsometer forest biomes variance propagation law Google Earth Engine |
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tree height uncertainty hypsometer forest biomes variance propagation law Google Earth Engine Samuele De Petris Filippo Sarvia Enrico Borgogno-Mondino About Tree Height Measurement: Theoretical and Practical Issues for Uncertainty Quantification and Mapping |
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tree height uncertainty hypsometer forest biomes variance propagation law Google Earth Engine |
description |
Forest height is a fundamental parameter in forestry. Tree height is widely used to assess a site’s productivity both in forest ecology research and forest management. Thus, a precise height measure represents a necessary step for the estimation of carbon storage at the local, national, and global scales. In this context, error in height measurement necessarily affects the accuracy of related estimates. Ordinarily, forest height is surveyed by ground sampling adopting hypsometers. The latter suffers from many errors mainly related to the correct tree apex identification (not always well visible in dense stands) and to the measurement process itself. In this work, a statistically based operative method for estimating height measurement uncertainty (σH) was proposed using the variance propagation law. Some simulations were performed involving several combinations of terrain slope, tree height, and survey distances by modelling the σH behaviour and its sensitivity to such parameters. Results proved that σH could vary between 0.5 m and up to 20 m (worst case). Sensitivity analysis shows that terrain slopes and distance poorly affect σH, while angles are the main drivers of height uncertainty. Finally, to give a practical example of such deductions, tree height uncertainty was mapped at the global scale using Google Earth Engine and summarized per forest biomes. Results proved that tropical biomes have higher uncertainty (from 1 m to 4 m) while shrublands and tundra have the lowest (under 1 m). |
format |
Text |
author |
Samuele De Petris Filippo Sarvia Enrico Borgogno-Mondino |
author_facet |
Samuele De Petris Filippo Sarvia Enrico Borgogno-Mondino |
author_sort |
Samuele De Petris |
title |
About Tree Height Measurement: Theoretical and Practical Issues for Uncertainty Quantification and Mapping |
title_short |
About Tree Height Measurement: Theoretical and Practical Issues for Uncertainty Quantification and Mapping |
title_full |
About Tree Height Measurement: Theoretical and Practical Issues for Uncertainty Quantification and Mapping |
title_fullStr |
About Tree Height Measurement: Theoretical and Practical Issues for Uncertainty Quantification and Mapping |
title_full_unstemmed |
About Tree Height Measurement: Theoretical and Practical Issues for Uncertainty Quantification and Mapping |
title_sort |
about tree height measurement: theoretical and practical issues for uncertainty quantification and mapping |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/f13070969 |
op_coverage |
agris |
genre |
Tundra |
genre_facet |
Tundra |
op_source |
Forests; Volume 13; Issue 7; Pages: 969 |
op_relation |
Forest Inventory, Modeling and Remote Sensing https://dx.doi.org/10.3390/f13070969 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/f13070969 |
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Forests |
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13 |
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7 |
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969 |
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