Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory

In this study, we estimate the forest stock volume by multiplying the number of trees detected remotely by the estimated mean individual volume of the population (individual approach). A comparison was made with the conventional inventory method (area approach), which included 100 simulations of a s...

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Autores principales:	Lorena Stolle, Ana Paula Dalla Corte, Carlos Roberto Sanquetta, Alexandre Behling, Ângela Maria Klein Hentz, Rozane de Loyola Eisfeld
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	RPA (remotely piloted aircraft) CHM (canopy height model) tree detection Plant ecology QK900-989
Acceso en línea:	https://doaj.org/article/40ab1ae4cf06431ca9352690244a447a
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spelling	oai:doaj.org-article:40ab1ae4cf06431ca9352690244a447a2021-11-25T17:38:05ZPredicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory10.3390/f121115081999-4907https://doaj.org/article/40ab1ae4cf06431ca9352690244a447a2021-10-01T00:00:00Zhttps://www.mdpi.com/1999-4907/12/11/1508https://doaj.org/toc/1999-4907In this study, we estimate the forest stock volume by multiplying the number of trees detected remotely by the estimated mean individual volume of the population (individual approach). A comparison was made with the conventional inventory method (area approach), which included 100 simulations of a simple random sampling process and a Bootstrap resampling. The study area included three stands: stand 1, 16-year-old pine; stand 2, 7-year-old pine; and stand 3, 5-year-old eucalyptus. A census was carried out in each stand for the variables diameter and total height. Individual volume was estimated by a ratio estimator, and the sum of all volumes was considered as the total parametric volume. The area approach presented parametric values within the confidence interval for 91%, 94%, and 98% of the simulations for the three stands, respectively. The mean relative errors for the area approach were −3.5% for stand 1, 0.3% for stand 2, and −0.9% for stand 3. The errors in stands 1 and 3 were associated with the spatial distribution of the volume. The individual approach proved to be efficient for all stands, and their respective parametric values were within the confidence interval. The relative errors were 1% for stand 1, −0.7% for stand 2, and 1.8% for stand 3. For stand 1 and 3, this approach yielded better results than the mean values obtained by the area approach simulations (Bootstrap resampling). Future research should evaluate other remote sources of data and other forest conditions.Lorena StolleAna Paula Dalla CorteCarlos Roberto SanquettaAlexandre BehlingÂngela Maria Klein HentzRozane de Loyola EisfeldMDPI AGarticleRPA (remotely piloted aircraft)CHM (canopy height model)tree detectionPlant ecologyQK900-989ENForests, Vol 12, Iss 1508, p 1508 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	RPA (remotely piloted aircraft) CHM (canopy height model) tree detection Plant ecology QK900-989
spellingShingle	RPA (remotely piloted aircraft) CHM (canopy height model) tree detection Plant ecology QK900-989 Lorena Stolle Ana Paula Dalla Corte Carlos Roberto Sanquetta Alexandre Behling Ângela Maria Klein Hentz Rozane de Loyola Eisfeld Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory
description	In this study, we estimate the forest stock volume by multiplying the number of trees detected remotely by the estimated mean individual volume of the population (individual approach). A comparison was made with the conventional inventory method (area approach), which included 100 simulations of a simple random sampling process and a Bootstrap resampling. The study area included three stands: stand 1, 16-year-old pine; stand 2, 7-year-old pine; and stand 3, 5-year-old eucalyptus. A census was carried out in each stand for the variables diameter and total height. Individual volume was estimated by a ratio estimator, and the sum of all volumes was considered as the total parametric volume. The area approach presented parametric values within the confidence interval for 91%, 94%, and 98% of the simulations for the three stands, respectively. The mean relative errors for the area approach were −3.5% for stand 1, 0.3% for stand 2, and −0.9% for stand 3. The errors in stands 1 and 3 were associated with the spatial distribution of the volume. The individual approach proved to be efficient for all stands, and their respective parametric values were within the confidence interval. The relative errors were 1% for stand 1, −0.7% for stand 2, and 1.8% for stand 3. For stand 1 and 3, this approach yielded better results than the mean values obtained by the area approach simulations (Bootstrap resampling). Future research should evaluate other remote sources of data and other forest conditions.
format	article
author	Lorena Stolle Ana Paula Dalla Corte Carlos Roberto Sanquetta Alexandre Behling Ângela Maria Klein Hentz Rozane de Loyola Eisfeld
author_facet	Lorena Stolle Ana Paula Dalla Corte Carlos Roberto Sanquetta Alexandre Behling Ângela Maria Klein Hentz Rozane de Loyola Eisfeld
author_sort	Lorena Stolle
title	Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory
title_short	Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory
title_full	Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory
title_fullStr	Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory
title_full_unstemmed	Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory
title_sort	predicting stand volume by number of trees automatically detected in uav images: an alternative method for forest inventory
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/40ab1ae4cf06431ca9352690244a447a
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_version_	1718412160358416384

Predicting Stand Volume by Number of Trees Automatically Detected in UAV Images: An Alternative Method for Forest Inventory

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