{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1361418520979074176.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1890/14-2098.1"}},{"identifier":{"@type":"URI","@value":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1890%2F14-2098.1"}},{"identifier":{"@type":"URI","@value":"https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/14-2098.1"}}],"dc:title":[{"@value":"Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:p>A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS‐Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area (<jats:italic>M</jats:italic><jats:sub>area</jats:sub>), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ<jats:sup>15</jats:sup>N) as well as pixel‐wise uncertainties in their estimates based on error propagation in the analytical methods. Both<jats:italic>M</jats:italic><jats:sub>area</jats:sub>and %N PLSR models had a<jats:italic>R</jats:italic><jats:sup>2</jats:sup>> 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted with<jats:italic>R</jats:italic><jats:sup>2</jats:sup>> 0.65 and carbon and cellulose with<jats:italic>R</jats:italic><jats:sup>2</jats:sup>> 0.45. Although<jats:italic>R</jats:italic><jats:sup>2</jats:sup>of %C and cellulose were lower than<jats:italic>M</jats:italic><jats:sub>area</jats:sub>and %N, the measured variability of these constituents (especially %C) was also lower, and their RMSE values were beneath 12% of the range in overall variability. Model performance for δ<jats:sup>15</jats:sup>N was the lowest (<jats:italic>R</jats:italic><jats:sup>2</jats:sup>= 0.48, RMSE = 0.95‰), but within 15% of the observed range. The resulting maps of chemical and morphological traits, together with their overall uncertainties, represent a first‐of‐its‐kind approach for examining the spatiotemporal patterns of forest functioning and nutrient cycling across a broad range of temperate and sub‐boreal ecosystems. These results offer an alternative to categorical maps of functional or physiognomic types by providing non‐discrete maps (i.e., on a continuum) of traits that define those functional types. A key contribution of this work is the ability to assign retrieval uncertainties by pixel, a requirement to enable assimilation of these data products into ecosystem modeling frameworks to constrain carbon and nutrient cycling projections.</jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1381418520979074177","@type":"Researcher","foaf:name":[{"@value":"Aditya Singh"}]},{"@id":"https://cir.nii.ac.jp/crid/1381418520979074179","@type":"Researcher","foaf:name":[{"@value":"Shawn P. Serbin"}]},{"@id":"https://cir.nii.ac.jp/crid/1381418520979074178","@type":"Researcher","foaf:name":[{"@value":"Brenden E. McNeil"}]},{"@id":"https://cir.nii.ac.jp/crid/1381418520979074180","@type":"Researcher","foaf:name":[{"@value":"Clayton C. Kingdon"}]},{"@id":"https://cir.nii.ac.jp/crid/1381418520979074176","@type":"Researcher","foaf:name":[{"@value":"Philip A. Townsend"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"10510761"},{"@type":"EISSN","@value":"19395582"}],"prism:publicationName":[{"@value":"Ecological Applications"}],"dc:publisher":[{"@value":"Wiley"}],"prism:publicationDate":"2015-12","prism:volume":"25","prism:number":"8","prism:startingPage":"2180","prism:endingPage":"2197"},"reviewed":"false","dc:rights":["http://onlinelibrary.wiley.com/termsAndConditions#vor"],"url":[{"@id":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1890%2F14-2098.1"},{"@id":"https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1890/14-2098.1"}],"createdAt":"2015-05-11","modifiedAt":"2024-06-08","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1050025031474460672","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Estimation of six leaf traits of East Asian forest tree species by leaf spectroscopy and partial least square regression"}]},{"@id":"https://cir.nii.ac.jp/crid/1360005514663509888","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Does the leaf economic spectrum hold within plant functional types? A Bayesian multivariate trait meta‐analysis"}]},{"@id":"https://cir.nii.ac.jp/crid/1360013168783381504","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Evaluating different methods for retrieving intraspecific leaf trait variation from hyperspectral leaf reflectance"}]},{"@id":"https://cir.nii.ac.jp/crid/1360013168785067776","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Phenology of leaf optical properties and their relationship to mesophyll development in cool-temperate deciduous broad-leaf trees."}]},{"@id":"https://cir.nii.ac.jp/crid/1360294645317321216","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1890/14-2098.1"},{"@type":"CROSSREF","@value":"10.1002/eap.2064_references_DOI_G5kJUVaJIavFdc7A41jd3FABsal"},{"@type":"CROSSREF","@value":"10.1016/j.ecolind.2021.108111_references_DOI_G5kJUVaJIavFdc7A41jd3FABsal"},{"@type":"CROSSREF","@value":"10.1016/j.agrformet.2020.108236_references_DOI_G5kJUVaJIavFdc7A41jd3FABsal"},{"@type":"CROSSREF","@value":"10.1016/j.agrformet.2021.108350_references_DOI_G5kJUVaJIavFdc7A41jd3FABsal"},{"@type":"CROSSREF","@value":"10.1016/j.rse.2019.111381_references_DOI_G5kJUVaJIavFdc7A41jd3FABsal"},{"@type":"CROSSREF","@value":"10.1101/475038_references_DOI_G5kJUVaJIavFdc7A41jd3FABsal"}]}