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StrucNet: a global network for automated vegetation structure monitoring.
Calders, Kim; Brede, Benjamin; Newnham, Glenn; Culvenor, Darius; Armston, John; Bartholomeus, Harm; Griebel, Anne; Hayward, Jodie; Junttila, Samuli; Lau, Alvaro; Levick, Shaun; Morrone, Rosalinda; Origo, Niall; Pfeifer, Marion; Verbesselt, Jan; Herold, Martin.
Afiliação
  • Calders K; CAVElab - Computational & Applied Vegetation Ecology, Department of Environment Ghent University Coupure links 653 Ghent 9000 Belgium.
  • Brede B; School of Forest Sciences, University of Eastern Finland Joensuu 80101 Finland.
  • Newnham G; Helmholtz Center Potsdam GFZ German Research Centre for Geosciences Section 1.4 Remote Sensing and Geoinformatics Telegrafenberg Potsdam 14473 Germany.
  • Culvenor D; CSIRO Research Way Clayton Victoria 3168 Australia.
  • Armston J; Environmental Sensing Systems Bentleigh East Victoria 3165 Australia.
  • Bartholomeus H; Department of Geographical Sciences University of Maryland College Park Maryland USA.
  • Griebel A; Laboratory of Geo-Information Science and Remote Sensing Wageningen University Wageningen 6708 PB the Netherlands.
  • Hayward J; Hawkesbury Institute for the Environment, Western Sydney University Locked Bag 1797 Penrith New South Wales 2751 Australia.
  • Junttila S; CSIRO 564 Vanderlin Drive Berrimah Northern Territory 0828 Australia.
  • Lau A; School of Forest Sciences, University of Eastern Finland Joensuu 80101 Finland.
  • Levick S; Laboratory of Geo-Information Science and Remote Sensing Wageningen University Wageningen 6708 PB the Netherlands.
  • Morrone R; CSIRO 564 Vanderlin Drive Berrimah Northern Territory 0828 Australia.
  • Origo N; Climate and Earth Observation Group National Physical Laboratory Hampton Road, Teddington London UK.
  • Pfeifer M; Climate and Earth Observation Group National Physical Laboratory Hampton Road, Teddington London UK.
  • Verbesselt J; School of Natural and Environmental Sciences, Newcastle University Newcastle Upon Tyne NE1 7RU UK.
  • Herold M; Laboratory of Geo-Information Science and Remote Sensing Wageningen University Wageningen 6708 PB the Netherlands.
Remote Sens Ecol Conserv ; 9(5): 587-598, 2023 Oct.
Article em En | MEDLINE | ID: mdl-38505271
ABSTRACT
Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large-scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three-dimensional vegetation structure at local scales. Here we demonstrate the potential of high-frequency ground-based laser scanning to systematically monitor structural changes in vegetation. We present a proof-of-concept high-temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high-temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article