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714 results on '"Schaepman, Michael E."'

1. Remote Sensing‐Based Forest Modeling Reveals Positive Effects of Functional Diversity on Productivity at Local Spatial Scale

Author

Schneider, Fabian D, Longo, Marcos, Paul‐Limoges, Eugénie, Scholl, Victoria M, Schmid, Bernhard, Morsdorf, Felix, Pavlick, Ryan P, Schimel, David S, Schaepman, Michael E, and Moorcroft, Paul R

Subjects
Life on Land, biodiversity, ecosystem functioning, imaging spectroscopy, lidar, terrestrial biosphere modeling, functional diversity, Geophysics
Published
2023

2. The Spectral Species Concept in Living Color

Author

Rocchini, Duccio, Santos, Maria J, Ustin, Susan L, Féret, Jean‐Baptiste, Asner, Gregory P, Beierkuhnlein, Carl, Dalponte, Michele, Feilhauer, Hannes, Foody, Giles M, Geller, Gary N, Gillespie, Thomas W, He, Kate S, Kleijn, David, Leitão, Pedro J, Malavasi, Marco, Moudrý, Vítězslav, Müllerová, Jana, Nagendra, Harini, Normand, Signe, Ricotta, Carlo, Schaepman, Michael E, Schmidtlein, Sebastian, Skidmore, Andrew K, Šímová, Petra, Torresani, Michele, Townsend, Philip A, Turner, Woody, Vihervaara, Petteri, Wegmann, Martin, and Lenoir, Jonathan

Subjects
Life Below Water, airborne sensors, biodiversity, ecoinformatics, hyperspectral images, plant optical types, remote sensing, satellite imagery, vegetation communities, Geophysics
Abstract

Biodiversity monitoring is an almost inconceivable challenge at the scale of the entire Earth. The current (and soon to be flown) generation of spaceborne and airborne optical sensors (i.e., imaging spectrometers) can collect detailed information at unprecedented spatial, temporal, and spectral resolutions. These new data streams are preceded by a revolution in modeling and analytics that can utilize the richness of these datasets to measure a wide range of plant traits, community composition, and ecosystem functions. At the heart of this framework for monitoring plant biodiversity is the idea of remotely identifying species by making use of the 'spectral species' concept. In theory, the spectral species concept can be defined as a species characterized by a unique spectral signature and thus remotely detectable within pixel units of a spectral image. In reality, depending on spatial resolution, pixels may contain several species which renders species-specific assignment of spectral information more challenging. The aim of this paper is to review the spectral species concept and relate it to underlying ecological principles, while also discussing the complexities, challenges and opportunities to apply this concept given current and future scientific advances in remote sensing.

Published
2022

4. NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

Author

Cawse-Nicholson, Kerry, Townsend, Philip A, Schimel, David, Assiri, Ali M, Blake, Pamela L, Buongiorno, Maria Fabrizia, Campbell, Petya, Carmon, Nimrod, Casey, Kimberly A, Correa-Pabón, Rosa Elvira, Dahlin, Kyla M, Dashti, Hamid, Dennison, Philip E, Dierssen, Heidi, Erickson, Adam, Fisher, Joshua B, Frouin, Robert, Gatebe, Charles K, Gholizadeh, Hamed, Gierach, Michelle, Glenn, Nancy F, Goodman, James A, Griffith, Daniel M, Guild, Liane, Hakkenberg, Christopher R, Hochberg, Eric J, Holmes, Thomas RH, Hu, Chuanmin, Hulley, Glynn, Huemmrich, Karl F, Kudela, Raphael M, Kokaly, Raymond F, Lee, Christine M, Martin, Roberta, Miller, Charles E, Moses, Wesley J, Muller-Karger, Frank E, Ortiz, Joseph D, Otis, Daniel B, Pahlevan, Nima, Painter, Thomas H, Pavlick, Ryan, Poulter, Ben, Qi, Yi, Realmuto, Vincent J, Roberts, Dar, Schaepman, Michael E, Schneider, Fabian D, Schwandner, Florian M, Serbin, Shawn P, Shiklomanov, Alexey N, Stavros, E Natasha, Thompson, David R, Torres-Perez, Juan L, Turpie, Kevin R, Tzortziou, Maria, Ustin, Susan, Yu, Qian, Yusup, Yusri, Zhang, Qingyuan, and Group, the SBG Algorithms Working

Subjects
Life on Land, Hyperspectral, Remote sensing, Thermal infrared, Vegetation, Snow, Volcano, Aquatic, Physical Geography and Environmental Geoscience, Geomatic Engineering, Geological & Geomatics Engineering
Abstract

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Published
2021

5. Leaf reflectance spectra capture the evolutionary history of seed plants

Author

Meireles, José Eduardo, Cavender‐Bares, Jeannine, Townsend, Philip A, Ustin, Susan, Gamon, John A, Schweiger, Anna K, Schaepman, Michael E, Asner, Gregory P, Martin, Roberta E, Singh, Aditya, Schrodt, Franziska, Chlus, Adam, and O'Meara, Brian C

Subjects
Climate Change Impacts and Adaptation, Biological Sciences, Environmental Sciences, Evolutionary Biology, Phylogeny, Plant Leaves, Plants, Seeds, evolution, leaf spectra, phylogenetic signal, remote sensing, seed plants, Agricultural and Veterinary Sciences, Plant Biology & Botany, Plant biology, Climate change impacts and adaptation, Ecological applications
Abstract

Leaf reflectance spectra have been increasingly used to assess plant diversity. However, we do not yet understand how spectra vary across the tree of life or how the evolution of leaf traits affects the differentiation of spectra among species and lineages. Here we describe a framework that integrates spectra with phylogenies and apply it to a global dataset of over 16 000 leaf-level spectra (400-2400 nm) for 544 seed plant species. We test for phylogenetic signal in spectra, evaluate their ability to classify lineages, and characterize their evolutionary dynamics. We show that phylogenetic signal is present in leaf spectra but that the spectral regions most strongly associated with the phylogeny vary among lineages. Despite among-lineage heterogeneity, broad plant groups, orders, and families can be identified from reflectance spectra. Evolutionary models also reveal that different spectral regions evolve at different rates and under different constraint levels, mirroring the evolution of their underlying traits. Leaf spectra capture the phylogenetic history of seed plants and the evolutionary dynamics of leaf chemistry and structure. Consequently, spectra have the potential to provide breakthrough assessments of leaf evolution and plant phylogenetic diversity at global scales.

Published
2020

13. Climatic and soil factors explain the two-dimensional spectrum of global plant trait variation

Author

Joswig, Julia S., Wirth, Christian, Schuman, Meredith C., Kattge, Jens, Reu, Björn, Wright, Ian J., Sippel, Sebastian D., Rüger, Nadja, Richter, Ronny, Schaepman, Michael E., van Bodegom, Peter M., Cornelissen, J. H. C., Díaz, Sandra, Hattingh, Wesley N., Kramer, Koen, Lens, Frederic, Niinemets, Ülo, Reich, Peter B., Reichstein, Markus, Römermann, Christine, Schrodt, Franziska, Anand, Madhur, Bahn, Michael, Byun, Chaeho, Campetella, Giandiego, Cerabolini, Bruno E. L., Craine, Joseph M., Gonzalez-Melo, Andres, Gutiérrez, Alvaro G., He, Tianhua, Higuchi, Pedro, Jactel, Hervé, Kraft, Nathan J. B., Minden, Vanessa, Onipchenko, Vladimir, Peñuelas, Josep, Pillar, Valério D., Sosinski, Ênio, Soudzilovskaia, Nadejda A., Weiher, Evan, and Mahecha, Miguel D.

Published
2022
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17. Priority list of biodiversity metrics to observe from space

Author

Skidmore, Andrew K., Coops, Nicholas C., Neinavaz, Elnaz, Ali, Abebe, Schaepman, Michael E., Paganini, Marc, Kissling, W. Daniel, Vihervaara, Petteri, Darvishzadeh, Roshanak, Feilhauer, Hannes, Fernandez, Miguel, Fernández, Néstor, Gorelick, Noel, Geijzendorffer, Ilse, Heiden, Uta, Heurich, Marco, Hobern, Donald, Holzwarth, Stefanie, Muller-Karger, Frank E., Van De Kerchove, Ruben, Lausch, Angela, Leitão, Pedro J., Lock, Marcelle C., Mücher, Caspar A., O’Connor, Brian, Rocchini, Duccio, Roeoesli, Claudia, Turner, Woody, Vis, Jan Kees, Wang, Tiejun, Wegmann, Martin, and Wingate, Vladimir

Published
2021
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18. Predicting missing values in spatio-temporal satellite data

Author

Gerber, Florian, Furrer, Reinhard, Schaepman-Strub, Gabriela, de Jong, Rogier, and Schaepman, Michael E.

Subjects
Statistics - Applications
Abstract

Remotely sensed data are sparse, which means that data have missing values, for instance due to cloud cover. This is problematic for applications and signal processing algorithms that require complete data sets. To address the sparse data issue, we present a new gap-fill algorithm. The proposed method predicts each missing value separately based on data points in a spatio-temporal neighborhood around the missing data point. The computational workload can be distributed among several computers, making the method suitable for large datasets. The prediction of the missing values and the estimation of the corresponding prediction uncertainties are based on sorting procedures and quantile regression. The algorithm was applied to MODIS NDVI data from Alaska and tested with realistic cloud cover scenarios featuring up to 50% missing data. Validation against established software showed that the proposed method has a good performance in terms of the root mean squared prediction error. The procedure is implemented and available in the open-source R package gapfill. We demonstrate the software performance with a real data example and show how it can be tailored to specific data. Due to the flexible software design, users can control and redesign major parts of the procedure with little effort. This makes it an interesting tool for gap-filling satellite data and for the future development of gap-fill procedures., Comment: 35 pages

Published
2016
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24. Monitoring plant functional diversity from space.

Author

Jetz, Walter, Cavender-Bares, Jeannine, Pavlick, Ryan, Schimel, David, Davis, Frank W, Asner, Gregory P, Guralnick, Robert, Kattge, Jens, Latimer, Andrew M, Moorcroft, Paul, Schaepman, Michael E, Schildhauer, Mark P, Schneider, Fabian D, Schrodt, Franziska, Stahl, Ulrike, and Ustin, Susan L

Subjects
Plants, Spectrum Analysis, Biodiversity, Extraterrestrial Environment
Published
2016

29. Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS-3)

Author

Schaepman, Michael E, Mouroulis, Pantazis, Geier, Sven, Shaw, Lucas, Hueini, Andreas, Bernas, Michael, Smith, Christopher, Wehbe, Rami, Eastwood, Michael, Vinckier, Quentin, Liggett, Elliott, Zandbergen, Sander, Thompson, David, Sullivan, Peter, Van Gorp, Byron, and Helmlinger, Mark

Published
2022

30. Airborne Visible/Infrared Imaging Spectrometer 3 (AVIRIS-3)

Author

Helmlinger, Mark, Van Gorp, Byron, Sullivan, Peter, Thompson, David, Zandbergen, Sander, Liggett, Elliott, Vinckier, Quentin, Eastwood, Michael, Wehbe, Rami, Smith, Christopher, Bernas, Michael, Hueini, Andreas, Shaw, Lucas, Geier, Sven, Mouroulis, Pantazis, and Schaepman, Michael E

Abstract

Green, Robert O.

Published
2022

35. Diversity–functioning relationships across hierarchies of biological organization.

Author

Mayor, Sarah, Allan, Eric, Altermatt, Florian, Isbell, Forest, Schaepman, Michael E., Schmid, Bernhard, and Niklaus, Pascal A.

Subjects
BIOLOGICAL variation, ECOSYSTEMS, SPECIES diversity, PLANT productivity, PLANT communities, ORGANIZATION
Abstract

Numerous biodiversity–ecosystem functioning (BEF) experiments have shown that plant community productivity typically increases with species diversity. In these studies, diversity is generally quantified using metrics of taxonomic, phylogenetic, or functional differences among community members. Research has also shown that the relationships between species diversity and functioning depends on the spatial scale considered, primarily because larger areas may contain different ecosystem types and span gradients in environmental conditions, which result in a turnover of the species set present locally. A fact that has received little attention, however, is that ecological systems are hierarchically structured, from genes to individuals to communities to entire landscapes, and that additional biological variation occurs at levels of organization above and below those typically considered in BEF research. Here, we present cases of diversity effects at different hierarchical levels of organization and compare these to the species‐diversity effects traditionally studied. We argue that when this evidence is combined across levels, a general framework emerges that allows the transfer of insights and concepts between traditionally disparate disciplines. Such a framework presents an important step towards a better understanding of the functional importance of diversity in complex, real‐world systems. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Monitoring biodiversity change through effective global coordination

Author

Navarro, Laetitia M, Fernández, Néstor, Guerra, Carlos, Guralnick, Rob, Kissling, W Daniel, Londoño, Maria Cecilia, Muller-Karger, Frank, Turak, Eren, Balvanera, Patricia, Costello, Mark J, Delavaud, Aurelie, El Serafy, GY, Ferrier, Simon, Geijzendorffer, Ilse, Geller, Gary N, Jetz, Walter, Kim, Eun-Shik, Kim, HyeJin, Martin, Corinne S, McGeoch, Melodie A, Mwampamba, Tuyeni H, Nel, Jeanne L, Nicholson, Emily, Pettorelli, Nathalie, Schaepman, Michael E, Skidmore, Andrew, Sousa Pinto, Isabel, Vergara, Sheila, Vihervaara, Petteri, Xu, Haigen, Yahara, Tetsukazu, Gill, Mike, and Pereira, Henrique M

Published
2017
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44. Author Correction: Priority list of biodiversity metrics to observe from space

Author

Skidmore, Andrew K., Coops, Nicholas C., Neinavaz, Elnaz, Ali, Abebe, Schaepman, Michael E., Paganini, Marc, Kissling, W. Daniel, Vihervaara, Petteri, Darvishzadeh, Roshanak, Feilhauer, Hannes, Fernandez, Miguel, Fernández, Néstor, Gorelick, Noel, Geijzendorffer, Ilse, Heiden, Uta, Heurich, Marco, Hobern, Donald, Holzwarth, Stefanie, Muller-Karger, Frank E., Van De Kerchove, Ruben, Lausch, Angela, Leitão, Pedro J., Lock, Marcelle C., Mücher, Caspar A., O’Connor, Brian, Rocchini, Duccio, Roeoesli, Claudia, Turner, Woody, Vis, Jan Kees, Wang, Tiejun, Wegmann, Martin, and Wingate, Vladimir

Published
2021
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50. Imputing missing data in plant traits: A guide to improve gap-filling

Author

Joswig, Julia S, Kattge, Jens, Kraemer, Guido, Mahecha, Miguel D, Rüger, Nadja, Schaepman, Michael E, Schrodt, Franziska, Schuman, Meredith Christine, University of Zurich, and Joswig, Julia S

Subjects
10120 Department of Chemistry, Global and Planetary Change, 10122 Institute of Geography, 1105 Ecology, Evolution, Behavior and Systematics, Ecology, Behavior and Systematics, UFSP13-8 Global Change and Biodiversity, Evolution, 2306 Global and Planetary Change, 910 Geography & travel, 2303 Ecology, Ecology, Evolution, Behavior and Systematics
Published
2023

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