Monitoring biomass in two heterogeneous mountain pasture communities by image based 3D point cloud derived predictors

Primary productivity is a robust indicator of ecosystem functioning because of its close relationships with the stability of the ecological systems. In ecological research, the above ground biomass (AGB) is the most commonly used proxy of primary productivity. However, despite their ecological relev...

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Autores principales:	Nicodemo G. Passalacqua, Simona Aiello, Liliana Bernardo, Domenico Gargano
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Biomass Coefficient of density Non-destructive biomass measurements Pasture community Photogrammetry Structure from Motion Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/4b4350aff0d445dbb393ee509c1cc3ec
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id	oai:doaj.org-article:4b4350aff0d445dbb393ee509c1cc3ec
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spelling	oai:doaj.org-article:4b4350aff0d445dbb393ee509c1cc3ec2021-12-01T04:35:54ZMonitoring biomass in two heterogeneous mountain pasture communities by image based 3D point cloud derived predictors1470-160X10.1016/j.ecolind.2020.107126https://doaj.org/article/4b4350aff0d445dbb393ee509c1cc3ec2021-02-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X20310657https://doaj.org/toc/1470-160XPrimary productivity is a robust indicator of ecosystem functioning because of its close relationships with the stability of the ecological systems. In ecological research, the above ground biomass (AGB) is the most commonly used proxy of primary productivity. However, despite their ecological relevance, the estimates of primary productivity are not addressed by current protocols for monitoring the conservation status of the habitats of Community interest. In this paper, we analyse the accuracy of AGB measurements obtained by image-derived 3D reconstructions of two contrasting mountain grasslands listed as habitats of Community interest in the Annex I of the Habitats Directive. More specifically, we compared the accuracy of the AGB estimates provided by four models, based on four different predictors (height, volume, volume adjusted, and cover volume), in order to evaluate their robustness against within- and between-community heterogeneity. Our study revealed that AGB measures computed from 3D vegetation reconstructions can be an effective way to evaluate primary productivity in herbaceous communities with complex structure and composition patterns. In particular, the vegetation height showed to have the highest correlation with direct AGB measurements. However, the vegetation volume, once adjusted by the coefficient of density, resulted to be the most effective proxy due to the lowest error level. Therefore, such a parameter could be routinely used as a non-destructive indicator for monitoring habitats of particular conservation concern. As a major limitation for this approach, we detected some loss of predictivity power at very low productivity rates.Nicodemo G. PassalacquaSimona AielloLiliana BernardoDomenico GarganoElsevierarticleBiomassCoefficient of densityNon-destructive biomass measurementsPasture communityPhotogrammetryStructure from MotionEcologyQH540-549.5ENEcological Indicators, Vol 121, Iss , Pp 107126- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biomass Coefficient of density Non-destructive biomass measurements Pasture community Photogrammetry Structure from Motion Ecology QH540-549.5
spellingShingle	Biomass Coefficient of density Non-destructive biomass measurements Pasture community Photogrammetry Structure from Motion Ecology QH540-549.5 Nicodemo G. Passalacqua Simona Aiello Liliana Bernardo Domenico Gargano Monitoring biomass in two heterogeneous mountain pasture communities by image based 3D point cloud derived predictors
description	Primary productivity is a robust indicator of ecosystem functioning because of its close relationships with the stability of the ecological systems. In ecological research, the above ground biomass (AGB) is the most commonly used proxy of primary productivity. However, despite their ecological relevance, the estimates of primary productivity are not addressed by current protocols for monitoring the conservation status of the habitats of Community interest. In this paper, we analyse the accuracy of AGB measurements obtained by image-derived 3D reconstructions of two contrasting mountain grasslands listed as habitats of Community interest in the Annex I of the Habitats Directive. More specifically, we compared the accuracy of the AGB estimates provided by four models, based on four different predictors (height, volume, volume adjusted, and cover volume), in order to evaluate their robustness against within- and between-community heterogeneity. Our study revealed that AGB measures computed from 3D vegetation reconstructions can be an effective way to evaluate primary productivity in herbaceous communities with complex structure and composition patterns. In particular, the vegetation height showed to have the highest correlation with direct AGB measurements. However, the vegetation volume, once adjusted by the coefficient of density, resulted to be the most effective proxy due to the lowest error level. Therefore, such a parameter could be routinely used as a non-destructive indicator for monitoring habitats of particular conservation concern. As a major limitation for this approach, we detected some loss of predictivity power at very low productivity rates.
format	article
author	Nicodemo G. Passalacqua Simona Aiello Liliana Bernardo Domenico Gargano
author_facet	Nicodemo G. Passalacqua Simona Aiello Liliana Bernardo Domenico Gargano
author_sort	Nicodemo G. Passalacqua
title	Monitoring biomass in two heterogeneous mountain pasture communities by image based 3D point cloud derived predictors
title_short	Monitoring biomass in two heterogeneous mountain pasture communities by image based 3D point cloud derived predictors
title_full	Monitoring biomass in two heterogeneous mountain pasture communities by image based 3D point cloud derived predictors
title_fullStr	Monitoring biomass in two heterogeneous mountain pasture communities by image based 3D point cloud derived predictors
title_full_unstemmed	Monitoring biomass in two heterogeneous mountain pasture communities by image based 3D point cloud derived predictors
title_sort	monitoring biomass in two heterogeneous mountain pasture communities by image based 3d point cloud derived predictors
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/4b4350aff0d445dbb393ee509c1cc3ec
work_keys_str_mv	AT nicodemogpassalacqua monitoringbiomassintwoheterogeneousmountainpasturecommunitiesbyimagebased3dpointcloudderivedpredictors AT simonaaiello monitoringbiomassintwoheterogeneousmountainpasturecommunitiesbyimagebased3dpointcloudderivedpredictors AT lilianabernardo monitoringbiomassintwoheterogeneousmountainpasturecommunitiesbyimagebased3dpointcloudderivedpredictors AT domenicogargano monitoringbiomassintwoheterogeneousmountainpasturecommunitiesbyimagebased3dpointcloudderivedpredictors
_version_	1718405840700964864

Monitoring biomass in two heterogeneous mountain pasture communities by image based 3D point cloud derived predictors

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