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LEVERAGING MULTIMODAL DATA FOR GEOSPATIOTEMPORAL ANALYTICS

Akanbi, Olatunde David
http://orcid.org/0000-0001-7719-2619
http://rave.ohiolink.edu/etdc/view?acc_num=case1699536369560882

Abstract Details

2024, Master of Sciences, Case Western Reserve University, Materials Science and Engineering.
Advanced analytics of diverse geospatial data streams can provide invaluable insights into complex agricultural and environmental systems. This work pioneers an integrated spatiotemporal analysis approach synthesizing satellite imagery, digital soil mapping, hydrological measurements, elevation, and historical crop data. The overarching objective of this work is to quantify relationships between crop growth dynamics, soil properties, nutrient distribution, water quality, and land use patterns. The methodology employs a case study focused on major agricultural regions to show the potential of big data analytic techniques. Custom applications and tools built on distributed computing infrastructure enable the assimilating and processing of massive heterogeneous datasets. The results unveil intricate connections between vegetation indices, soil nutrients, crop types, and nutrient transport, offering strategic perspectives to enhance productivity while minimizing environmental impacts. The multi-faceted understanding achieved fills critical knowledge gaps regarding interactions within agroecosystems. While moderate-resolution regional data provided informative baseline insights, higher spatiotemporal resolution and expanded geographic scope would further strengthen the analysis. Overall, this work underscores the immense potential of data science, geospatiotemporal analytics, and systems thinking to address pressing crop, land, nutrient, water, and soil challenges. The integrated approach provides a powerful paradigm for leveraging emerging data streams toward creating a digital agriculture ecosystem.
Roger H. French (Advisor)
Jeffrey M Yarus (Advisor)
Pawan K. Tripathi (Committee Member)
Yinghui Wu (Committee Member)
Erika I. Barcelos (Committee Member)
Alp Sehirilioglu (Committee Member)
109 p.
Agriculture; Computer Science; Engineering; Environmental Geology; Environmental Studies; Food Science; Geotechnology; Materials Science
Spatiotemporal analysis; multimodal; geospatial; NDVI; data integration

Recommended Citations

Citations

  • Akanbi, O. D. (2024). LEVERAGING MULTIMODAL DATA FOR GEOSPATIOTEMPORAL ANALYTICS [Master's thesis, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1699536369560882

    APA Style (7th edition)

  • Akanbi, Olatunde. LEVERAGING MULTIMODAL DATA FOR GEOSPATIOTEMPORAL ANALYTICS. 2024. Case Western Reserve University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1699536369560882.

    MLA Style (8th edition)

  • Akanbi, Olatunde. "LEVERAGING MULTIMODAL DATA FOR GEOSPATIOTEMPORAL ANALYTICS." Master's thesis, Case Western Reserve University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=case1699536369560882

    Chicago Manual of Style (17th edition)

case1699536369560882
129
© 2023, some rights reserved.Creative Commons License LEVERAGING MULTIMODAL DATA FOR GEOSPATIOTEMPORAL ANALYTICS by Olatunde David Akanbi is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.