Item talk:Q44512

ORCID:

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 '@id': https://orcid.org/0000-0002-1201-5459
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     name: "Crop Water Productivity from Cloud-Based Landsat Helps Assess California\u2019\
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     name: New Generation Hyperspectral Data From DESIS Compared to High Spatial
       Resolution PlanetScope Data for Crop Type Classification
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     name: A meta-analysis of global crop water productivity of three leading world
       crops (wheat, corn, and rice) in the irrigated areas over three decades
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     name: Accuracies Achieved in Classifying Five Leading World Crop Types and their
       Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands
       on Google Earth Engine
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     name: "Correlating species and spectral diversities using hyperspectral remote\
       \ sensing in early\u2010successional fields"
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     name: Identifying invasive plant species using field spectroscopy in the VNIR
       region in successional systems of north-central Virginia
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     name: Does the Document Matter? The Evolving Role of Syllabi in Higher Education
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     name: Distinguishing Early Successional Plant Communities Using Ground-Level
       Hyperspectral Data
 '@type': Person
 familyName: Aneece
 givenName: Itiya
 mainEntityOfPage: https://orcid.org/0000-0002-1201-5459

USGS Staff Profile:

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 '@type': Person
 affiliation: []
 description:
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   abstract: Research Geographer with the Western Geographic Science Center
   additionalType: short description
 - '@type': TextObject
   abstract: Itiya Aneece is currently a Research Geographer at the U.S. Geological
     Survey (USGS) in Flagstaff, AZ, USA.
   additionalType: staff profile page introductory statement
 - '@type': TextObject
   abstract: Dr. Itiya Aneece is a Research Geographer at the U.S. Geological Survey
     Western Geographic Science Center using remote sensing to study globally dominant
     agricultural crops. She earned a PhD in Environmental Sciences from the University
     of Virginia, where she conducted her dissertation research on studying the impacts
     of invasive plant species in abandoned agricultural fields using ground-level
     hyperspectral remote sensing. As a Mendenhall Postdoc, she used Hyperion images
     to study globally major agricultural crops using Google Earth Engine. Her research
     interests include big data analysis, machine learning, cloud computing, and
     agricultural study.
   additionalType: personal statement
 email: ianeece@usgs.gov
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     name: Western Geographic Science Center
     url: https://www.usgs.gov/centers/western-geographic-science-center
   roleName: Research Geographer
   startDate: '2024-05-12T16:12:35.902849'
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   value: 0000-0002-1201-5459
 jobTitle: Research Geographer
 knowsAbout:
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   additionalType: self-claimed expertise
   name: Remote Sensing
 - '@type': Thing
   additionalType: self-claimed expertise
   name: Crops
 - '@type': Thing
   additionalType: self-claimed expertise
   name: Invasive Plant Species
 - '@type': Thing
   additionalType: self-claimed expertise
   name: Big Data Analysis
 - '@type': Thing
   additionalType: self-claimed expertise
   name: Machine Learning
 - '@type': Thing
   additionalType: self-claimed expertise
   name: Hyperspectral remote sensing
 - '@type': Thing
   additionalType: self-claimed expertise
   name: Machine learning and cloud computing
 - '@type': Thing
   additionalType: self-claimed expertise
   name: Crop water productivity
 memberOf:
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   member:
     '@type': Organization
     name: U.S. Geological Survey
   name: staff member
   startDate: '2024-05-12T16:12:35.900189'
 name: Itiya P Aneece
 url: https://www.usgs.gov/staff-profiles/itiya-p-aneece