ORCID:
'@context': http://schema.org '@id': https://orcid.org/0000-0002-1201-5459 '@reverse': creator: - '@id': https://doi.org/10.3390/rs15194894 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3390/rs15194894 name: "Crop Water Productivity from Cloud-Based Landsat Helps Assess California\u2019\ s Water Savings" - '@id': https://doi.org/10.1109/jstars.2022.3204223 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.1109/jstars.2022.3204223 name: New Generation Hyperspectral Data From DESIS Compared to High Spatial Resolution PlanetScope Data for Crop Type Classification - '@id': https://doi.org/10.1080/17538947.2019.1651912 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.1080/17538947.2019.1651912 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 - '@id': https://doi.org/10.3390/rs10122027 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3390/rs10122027 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 - '@id': https://doi.org/10.1002/ece3.2876 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.1002/ece3.2876 name: "Correlating species and spectral diversities using hyperspectral remote\ \ sensing in early\u2010successional fields" - '@id': https://doi.org/10.1080/01431161.2016.1259682 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.1080/01431161.2016.1259682 name: Identifying invasive plant species using field spectroscopy in the VNIR region in successional systems of north-central Virginia - '@id': https://doi.org/10.1080/00091383.2016.1198186 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.1080/00091383.2016.1198186 name: Does the Document Matter? The Evolving Role of Syllabi in Higher Education - '@id': https://doi.org/10.3390/rs71215850 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3390/rs71215850 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:
'@context': https://schema.org '@type': Person affiliation: [] description: - '@type': TextObject 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 hasCredential: [] hasOccupation: - '@type': OrganizationalRole affiliatedOrganization: '@type': Organization 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' identifier: - '@type': PropertyValue propertyID: GeoKB value: https://geokb.wikibase.cloud/entity/Q44512 - '@type': PropertyValue propertyID: ORCID value: 0000-0002-1201-5459 jobTitle: Research Geographer knowsAbout: - '@type': Thing 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: '@type': OrganizationalRole 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