Revisión de protocolos del muestreo in-situpara apoyar estudios de vegetación con teledetección
- Vicente Burchard-Levine 1
- David Riaño Arribas 1
- Lara Vilar del Hoyo 2
- María del Pilar Martín Isabel 1
- Javier Becerra Corral 3
- 1 Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab)Spanish National Research Council (CSIC), Madrid, Spain
- 2 Department of Geology, Geography and Environment. University of Alcalá
- 3 Grupo Tecopy, Madrid, Spain
ISSN: 1578-5157
Año de publicación: 2022
Número: 29
Páginas: 59-87
Tipo: Artículo
Otras publicaciones en: Geofocus: Revista Internacional de Ciencia y Tecnología de la Información Geográfica
Resumen
In spite of the recognized relevance of in-situdata to properly calibrate and/orvalidate remote sensing derived products, issues related to field data acquisitions are generally overlooked and poorly addressed. There are only a few specific references available in the literature that propose or describe field protocols for in-situplant trait observations related to remote sensing studies. As such, this article aims to review the most relevant protocols available in the literature, including those developed through international initiatives, which discuss in-situsampling considerations of plant traits for the remote sensing of vegetation and ecosystems. A survey was designed to gain an understanding of the main field acquisition protocols and practices currently being applied in various European institutions participating in the Marie-Skłodowska Curie Innovative Training Network (ITN) named ‘Training on Remote Sensing for Ecosystem ModElling’ (TRuStEE).We also discuss general considerations for experimental designs of fieldsampling, including spatial/scaling issues from the field to pixel level, seasonal and phenological characterizations, data management and ecosystem specificities. The overall aim is to provide an integrated assessment of the general issues and good practices that need to be considered to design an adequate field campaign protocol to support the remote sensing of vegetation.
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