Revisión de protocolos del muestreo in-situpara apoyar estudios de vegetación con teledetección

  1. Vicente Burchard-Levine 1
  2. David Riaño Arribas 1
  3. Lara Vilar del Hoyo 2
  4. María del Pilar Martín Isabel 1
  5. Javier Becerra Corral 3
  1. 1 Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab)Spanish National Research Council (CSIC), Madrid, Spain
  2. 2 Department of Geology, Geography and Environment. University of Alcalá
  3. 3 Grupo Tecopy, Madrid, Spain
Revista:
Geofocus: Revista Internacional de Ciencia y Tecnología de la Información Geográfica

ISSN: 1578-5157

Ano de publicación: 2022

Número: 29

Páxinas: 59-87

Tipo: Artigo

Outras publicacións en: Geofocus: Revista Internacional de Ciencia y Tecnología de la Información Geográfica

Resumo

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.

Referencias bibliográficas

  • Andreu, A., Kustas, W., Polo, M., Carrara, A., & González-Dugo, M. (2018). Modeling Surface Energy Fluxes over a Dehesa (Oak Savanna) Ecosystem Using a Thermal Based Two Source Energy Balance Model (TSEB) II—Integration of Remote Sensing Medium and Low Spatial Resolution Satellite Images. Remote Sensing, 10(4), 558. https://doi.org/10.3390/rs10040558
  • Asner, G. P., & Martin, R. E. (2016). Spectranomics: Emerging science and conservation opportunities at the interface of biodiversity and remote sensing. Global Ecology and Conservation, 8, 212–219. https://doi.org/10.1016/j.gecco.2016.09.010
  • Burchard-Levine, V., Nieto, H., Riaño, D., Migliavacca, M., El-Madany, T. S., Guzinski, R., Carrara, A., & Martín, M. P. (2021). The effect of pixel heterogeneity for remote sensing based retrievals of evapotranspiration in a semi-arid tree-grass ecosystem. Remote Sensing of Environment, 260, 112440. https://doi.org/10.1016/j.rse.2021.112440
  • Casas, A., Riaño, D., Ustin, S. L., Dennison, P., & Salas, J. (2014). Estimation of water-related biochemical and biophysical vegetation properties using multitemporal airborne hyperspectral data and its comparison to MODIS spectral response. Remote Sensing of Environment, 148, 28–41.
  • Ceccato, P., Flasse, S., & Gregoire, J.-M. (2002). Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications. Remote Sensing of Environment, 82(2), 198–207.
  • Chuvieco, E., Aguado, I., Cocero, D., & Riaño, D. (2003). Design of an empirical index to estimate fuel moisture content from NOAA-AVHRR images in forest fire danger studies. International Journal of Remote Sensing, 24(8), 1621–1637. https://doi.org/10.1080/01431160210144660b
  • Cohen, W. B., & Justice, C. O. (1999). Validating MODIS Terrestrial Ecology Products: Linking In Situ and Satellite Measurements. Remote Sensing of Environment, 70(1), 1–3. https://doi.org/10.1016/S0034-4257(99)00053-X
  • Cornelissen, J. H. C., Lavorel, S., Garnier, E., Diaz, S., Buchmann, N., Gurvich, D. E., Reich, P. B., Ter Steege, H., Morgan, H. D., Van Der Heijden, M. G. A., Pausas, J. G., & Pooter, H. (2003). Handbook of protocols for standardised and easy measurement of plant functional traits worldwide. Australian Journal of Botany, 51(4), 335–380. https://doi.org/10.1071/BT02124
  • Fernández, N., Ferrier, S., Navarro, L. M., & Pereira, H. M. (2020). Essential biodiversity variables: Integrating in-situ observations and remote sensing through modeling. In Remote sensing of plant biodiversity (pp. 485–501). Springer, Cham.
  • Garnier, E., Shipley, B., Roumet, C., & Laurent, G. (2001). A standardized protocol for the determination of specific leaf area and leaf dry matter content. Functional Ecology, 15(5), 688–695.
  • Gielen, B., Op de Beeck, M., & Papale, D. (2017a). ICOS Ecosystem Instructions for Ancillary Vegetation Measurements in Croplands (version 20180426). ICOS Ecosystem Thematic Centre.
  • Gielen, B., Op de Beeck, M., & Papale, D. (2017b). ICOS Ecosystem Instructions for Ancillary Vegetation Measurements in Forests (version 20180131). ICOS Ecosystem Thematic Centre.
  • Gond, V., de Pury, D. G. G., Veroustraete, F., & Ceulemans, R. (1999). Seasonal variations in leaf area index, leaf chlorophyll, and water content; scaling-up to estimate fAPAR and carbon balance in a multilayer, multispecies temperate forest. Tree Physiology, 19(10), 673–679. https://doi.org/10.1093/treephys/19.10.673
  • Held, A., Phinn, S., Soto-Berelov, M., & Jones, S. (2015). AusCover Good Practice Guidelines: A technical handbook supporting calibration and validation activities of remotely sensed data products. Version 1.2: Vol. TERN AusCover. http://www.auscover.org.au/wp-content/uploads/AusCover-Good-Practice-Guidelines_web.pdf
  • Homolová, L., Malenovský, Z., Clevers, J. G. P. W., García-Santos, G., & Schaepman, M. E. (2013). Review of optical-based remote sensing for plant trait mapping. Ecological Complexity, 15, 1–16. https://doi.org/10.1016/j.ecocom.2013.06.003
  • Houborg, R., Fisher, J. B., & Skidmore, A. K. (2015). Advances in remote sensing of vegetation function and traits. International Journal of Applied Earth Observation and Geoinformation, 43, 1–6. https://doi.org/10.1016/j.jag.2015.06.001
  • Huber, S., Kneubühler, M., Psomas, A., Itten, K., & Zimmermann, N. E. (2008). Estimating foliar biochemistry from hyperspectral data in mixed forest canopy. Forest Ecology and Management, 256(3), 491–501.
  • Hugo, W., Hobern, D., Kõljalg, U., Tuama, É. Ó., & Saarenmaa, H. (2017). Global infrastructures for biodiversity data and services. In The GEO handbook on biodiversity observation networks (pp. 259–291). Springer, Cham.
  • Jackson, T. J., Moran, M. S., & O’Neill, P. E. (2008). Introduction to Soil Moisture Experiments 2004 (SMEX04) Special Issue. Remote Sensing of Environment, 112(2), 301–303. https://doi.org/10.1016/j.rse.2007.01.021
  • Jiménez, M., & Díaz-Delgado, R. (2015). Towards a Standard Plant Species Spectral Library Protocol for Vegetation Mapping: A Case Study in the Shrubland of Doñana National Park. ISPRS International Journal of Geo-Information, 4(4), 2472–2495. https://doi.org/10.3390/ijgi404247
  • Joly, R. J. (1985). Techniques for determining seedling water status and their effectiveness in assessing stress. In M. L. Duryea (Ed.) Evaluating Seedling Quality: Principles, Procedures, and Predictive Abilities of Major Tests. Forest Research Laboratory, Oregon State University, Corvallis, OR, USA. http://agris.fao.org/agris-search/search.do?recordID=US8851002
  • Jonckheere, I., Fleck, S., Nackaerts, K., Muys, B., Coppin, P., Weiss, M., & Baret, F. (2004). Review of methods for in situ leaf area index determination: Part I. Theories, sensors and hemispherical photography. Agricultural and Forest Meteorology, 121(1–2), 19–35.
  • Jung, M., Reichstein, M., Margolis, H. A., Cescatti, A., Richardson, A. D., Arain, M. A., Arneth, A., Bernhofer, C., Bonal, D., & Chen, J. (2011). Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. Journal of Geophysical Research: Biogeosciences, 116(G3).
  • Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., … Reichstein, M. (2020). Scaling carbon fluxes from eddy covariance sites to globe: Synthesis and evaluation of the FLUXCOM approach. Biogeosciences, 17(5), 1343–1365. https://doi.org/10.5194/bg-17-1343-2020
  • Kattge, J., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., Garnier, E., Westoby, M., Reich, P. B., Wright, I. J., Cornelissen, J. H. C., Violle, C., Harrison, S. P., Van BODEGOM, P. M., Reichstein, M., Enquist, B. J., Soudzilovskaia, N. A., Ackerly, D. D., Anand, M., … Wirth, C. (2011). TRY – a global database of plant traits. Global Change Biology, 17(9), 2905–2935. https://doi.org/10.1111/j.1365-2486.2011.02451.x
  • Kissling, W. D., Ahumada, J. A., Bowser, A., Fernandez, M., Fernández, N., García, E. A., Guralnick, R. P., Isaac, N. J. B., Kelling, S., Los, W., McRae, L., Mihoub, J.-B., Obst, M., Santamaria, M., Skidmore, A. K., Williams, K. J., Agosti, D., Amariles, D., Arvanitidis, C., … Hardisty, A. R. (2018). Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biological Reviews, 93(1), 600–625. https://doi.org/10.1111/brv.123592
  • Klimešová, J., Martínková, J., Pausas, J. G., de Moraes, M. G., Herben, T., Yu, F.-H., Puntieri, J., Vesk, P. A., de Bello, F., Janeček, Š., Altman, J., Appezzato-da-Glória, B., Bartušková, A., Crivellaro, A., Doležal, J., Ott, J. P., Paula, S., Schnablová, R., Schweingruber, F. H., & Ottaviani, G. (2019). Handbook of standardized protocols for collecting plant modularity traits. Perspectives in Plant Ecology, Evolution and Systematics, 40, 125485. https://doi.org/10.1016/j.ppees.2019.125485
  • Kustas, W. P., & Anderson, M. C. (2009). Advances in thermal infrared remote sensing for land surface modeling. Agricultural and Forest Meteorology, 149(12), 2071–2081.
  • Law, B. E., Arkebauer, T., Campbell, J. L., Chen, J., Sun, O., Schwartz, M., van Ingen, C., & Verma, S. (2008). Terrestrial carbon observations: Protocols for vegetation sampling and data submission. FAO, Rome.
  • LICOR Bioscience USA. (2011). LAI-2200 Plant Canopy Analyzer. Instruction Manual.
  • McCoy, R. M. (2005). Field methods in remote sensing. Guilford Press.
  • Melendo-Vega, J. R., Martín, M. P., Vilar del Hoyo, L., Pacheco-Labrador, J., Echavarría, P., & Martínez-Vega, J. (2017). Estimación de variables biofísicas del pastizal en un ecosistema de dehesa a partir de espectro-radiometría de campo e imágenes hiperespectrales aeroportadas. Revista de Teledetección, 48, 13.https://doi.org/10.4995/raet.2017.7481
  • Mendiguren, G., Pilar Martín, M., Nieto, H., Pacheco-Labrador, J., & Jurdao, S. (2015). Seasonal variation in grass water content estimated from proximal sensing and MODIS time series in a Mediterranean Fluxnet site. Biogeosciences, 12(18), 5523–5535.
  • Milton, E. J., Schaepman, M. E., Anderson, K., Kneubühler, M., & Fox, N. (2009). Progress in field spectroscopy. Remote Sensing of Environment, 113, S92–S109. https://doi.org/10.1016/j.rse.2007.08.001
  • Neinavaz, E., Schlerf, M., Darvishzadeh, R., Gerhards, M., & Skidmore, A. K. (2021). Thermal infrared remote sensing of vegetation: Current status and perspectives. International Journal of Applied Earth Observation and Geoinformation, 102, 102415. https://doi.org/10.1016/j.jag.2021.102415
  • Nelson, B. W., Mesquita, R., Pereira, J. L., De Souza, S. G. A., Batista, G. T., & Couto, L. B. (1999). Allometric regressions for improved estimate of secondary forest biomass in the central Amazon. Forest Ecology and Management, 117(1–3), 149–167.
  • Op de Beeck, M., Sabbatini, S., & Papale, D. (2017a). ICOS Ecosystem Instructions for Ancillary Vegetation Measurements in Grasslands (version 20180129). ICOS Ecosystem Thematic Centre.
  • Op de Beeck, M., Sabbatini, S., & Papale, D. (2017b). ICOS Ecosystem Instructions for Ancillary Vegetation Measurements in Mires (version 20180615). ICOS Ecosystem Thematic Centre.
  • Pereira, H. M., Ferrier, S., Walters, M., Geller, G. N., Jongman, R. H. G., Scholes, R. J., Bruford, M. W., Brummitt, N., Butchart, S. H. M., Cardoso, A. C., Coops, N. C., Dulloo, E., Faith, D. P., Freyhof, J., Gregory, R. D., Heip, C., Hoft, R., Hurtt, G., Jetz, W., … Wegmann, M. (2013). Essential Biodiversity Variables. Science, 339(6117), 277–278. https://doi.org/10.1126/science.1229931
  • Perez-Harguindeguy, N., Diaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P., Bret-Harte, M. S., Cornwell, W. K., Craine, J. M., & Gurvich, D. E. (2013). New handbook for standardised measurement of plant functional traits worldwide. Aust. Bot. 61, 167–234.
  • Pfitzner, K., Bartolo, R., Carr, G., Esparon, A., & Bollhöfer, A. (2011). Standards for reflectance spectral measurement of temporal vegetation plots. Supervising Scientist, Department of Sustainability, Environment, Water, Population and Communities.
  • Pollet, J., & Brown, A. (2007). Fuel moisture sampling guide. Utah State Office, Bureau of Land Management, Salt Lake City, UT. Rasaiah, B., Jones, Simon., Bellman, C., & Malthus, T. (2014). Critical Metadata for Spectroscopy Field Campaigns. Remote Sensing, 6(5), 3662–3680. https://doi.org/10.3390/rs6053662
  • Rüegg, J., Gries, C., Bond-Lamberty, B., Bowen, G. J., Felzer, B. S., McIntyre, N. E., Soranno, P. A., Vanderbilt, K. L., & Weathers, K. C. (2014). Completing the data life cycle: Using information management in macrosystems ecology research. Frontiers in Ecology and the Environment, 12(1), 24–30. https://doi.org/10.1890/120375
  • Schneider, F. D., Leiterer, R., Morsdorf, F., Gastellu-Etchegorry, J.-P., Lauret, N., Pfeifer, N., & Schaepman, M. E. (2014). Simulating imaging spectrometer data: 3D forest modeling based on LiDAR and in situ data. Remote Sensing of Environment, 152, 235–50.
  • Schweiger, A. K. (2020). Spectral field campaigns: Planning and data collection. In Remote sensing of plant biodiversity (pp. 385–423). Springer, Cham.
  • Skidmore, A. K., Coops, N. C., Neinavaz, E., Ali, A., Schaepman, M. E., Paganini, M., Kissling, W. D., Vihervaara, P., Darvishzadeh, R., Feilhauer, H., Fernandez, M., Fernández, N., Gorelick, N., Geijzendorffer, I., Heiden, U., Heurich, M., Hobern, D., Holzwarth, S., Muller-Karger, F. E., … Wingate, V. (2021). Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution, 5(7), 896–906. https://doi.org/10.1038/s41559-021-01451-x
  • USGS/NPS Vegetation Mapping Program. (1994). Field Methods for Vegetation Mapping. United States Department of Interior - United States Geological Survery and National Park Service.
  • Ustin, S. L., & Middleton, E. M. (2021). Current and near-term advances in Earth observation for ecological applications. Ecological Processes, 10(1), 1. https://doi.org/10.1186/s13717-020-00255-4
  • Van Cleemput, E., Helsen, K., Feilhauer, H., Honnay, O., & Somers, B. (2021). Spectrally defined plant functional types adequately capture multidimensional trait variation in herbaceous communities. Ecological Indicators, 120, 106970. https://doi.org/10.1016/j.ecolind.2020.106970
  • Verrelst, J., Rivera, J. P., & Moreno, J. (2015). ARTMO’s global sensitivity analysis (GSA) toolbox to quantify driving variables of leaf and canopy radiative transfer models. EARSeL EProceedings, Speical, 2(2015), 1–11.
  • Yebra, M., Scortechini, G., Badi, A., Beget, M. E., Boer, M. M., Bradstock, R., Chuvieco, E., Danson, F. M., Dennison, P., Resco de Dios, V., Di Bella, C. M., Forsyth, G., Frost, P., Garcia, M., Hamdi, A., He, B., Jolly, M., Kraaij, T., Martín, M. P., … Ustin, S. (2019). Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications. Scientific Data, 6(1), 155. https://doi.org/10.1038/s41597-019-0164-9
  • Zarco-Tejada, P. J., Miller, J. R., Morales, A., Berjón, A., & Agüera, J. (2004). Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops. Remote Sensing of Environment, 90(4), 463–476.