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Datacube-Driven Spatiotemporal Analy...

Wardle, Julee,

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Datacube-Driven Spatiotemporal Analyses of Vegetation Trends in Djibouti Using Digital Earth Africa Cloud-Based Computational Platform /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Datacube-Driven Spatiotemporal Analyses of Vegetation Trends in Djibouti Using Digital Earth Africa Cloud-Based Computational Platform // Julee Wardle.
作者:	Wardle, Julee,
面頁冊數:	1 electronic resource (155 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
標題:	Remote sensing. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31938656
ISBN:	9798283141176

Datacube-Driven Spatiotemporal Analyses of Vegetation Trends in Djibouti Using Digital Earth Africa Cloud-Based Computational Platform /
Wardle, Julee,

Datacube-Driven Spatiotemporal Analyses of Vegetation Trends in Djibouti Using Digital Earth Africa Cloud-Based Computational Platform /Julee Wardle. - 1 electronic resource (155 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

This dissertation investigates three decades of vegetation dynamics in Djibouti through an integrated spatiotemporal analysis using the cloud-based Digital Earth Africa (DE Africa) Open Data Cube (ODC) platform. Situated in the Horn of Africa, Djibouti represents a uniquely arid and agriculturally constrained environment where food security remains an ongoing challenge. Leveraging multispectral satellite imagery from the United States Landsat and European Space Agency Copernicus programs (1990-2020), this research applies a series of remote sensing methodologies-Normalized Difference Vegetation Index (NDVI), Fractional Cover (FC), and machine learning algorithms-within a unified data cube framework to elucidate patterns of environmental change and agricultural vulnerability. The study is structured around three independent yet thematically interconnected investigations. The first paper explores cropland transitions in the context of national food security, employing NDVI differencing and change detection via Welch's t-test. The second paper examines long-term vegetation trends using Landsat-derived FC metrics, supported by robust spatiotemporal statistical methods, including the Mann-Kendall test and Sen's Slope estimator. The third paper synthesizes NDVI-based change detection over decadal intervals, offering a comparative analysis of baseline and post-baseline vegetation health trends. Collectively, these analyses are conducted entirely within the cloud-based ODC environment, demonstrating the platform's utility for scalable, in-browser computation of big Earth observation datasets. This dissertation contributes a novel application of cloud-native geospatial processing to the study of arid-region vegetation monitoring, emphasizing methodological scalability, temporal consistency, and spatial granularity. By integrating traditional remote sensing approaches with contemporary computational frameworks, this work not only advances the field's technical capabilities but also delivers actionable insights into the environmental dimensions of food security in Djibouti. The findings underscore the transformative potential of data cube technologies in supporting sustainable land use management and adaptive policy planning in data-scarce, climate-vulnerable regions.

English

ISBN: 9798283141176Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

Fractional Cover

Datacube-Driven Spatiotemporal Analyses of Vegetation Trends in Djibouti Using Digital Earth Africa Cloud-Based Computational Platform /
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520 $a This dissertation investigates three decades of vegetation dynamics in Djibouti through an integrated spatiotemporal analysis using the cloud-based Digital Earth Africa (DE Africa) Open Data Cube (ODC) platform. Situated in the Horn of Africa, Djibouti represents a uniquely arid and agriculturally constrained environment where food security remains an ongoing challenge. Leveraging multispectral satellite imagery from the United States Landsat and European Space Agency Copernicus programs (1990-2020), this research applies a series of remote sensing methodologies-Normalized Difference Vegetation Index (NDVI), Fractional Cover (FC), and machine learning algorithms-within a unified data cube framework to elucidate patterns of environmental change and agricultural vulnerability. The study is structured around three independent yet thematically interconnected investigations. The first paper explores cropland transitions in the context of national food security, employing NDVI differencing and change detection via Welch's t-test. The second paper examines long-term vegetation trends using Landsat-derived FC metrics, supported by robust spatiotemporal statistical methods, including the Mann-Kendall test and Sen's Slope estimator. The third paper synthesizes NDVI-based change detection over decadal intervals, offering a comparative analysis of baseline and post-baseline vegetation health trends. Collectively, these analyses are conducted entirely within the cloud-based ODC environment, demonstrating the platform's utility for scalable, in-browser computation of big Earth observation datasets. This dissertation contributes a novel application of cloud-native geospatial processing to the study of arid-region vegetation monitoring, emphasizing methodological scalability, temporal consistency, and spatial granularity. By integrating traditional remote sensing approaches with contemporary computational frameworks, this work not only advances the field's technical capabilities but also delivers actionable insights into the environmental dimensions of food security in Djibouti. The findings underscore the transformative potential of data cube technologies in supporting sustainable land use management and adaptive policy planning in data-scarce, climate-vulnerable regions.
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