Preprint Article Version 1 This version is not peer-reviewed

The Spatiotemporal Big Data analysis of Several MODIS Satellite Sensors for Assessing A Decadal Vegetation Cover Dynamics

Version 1 : Received: 2 June 2018 / Approved: 4 June 2018 / Online: 4 June 2018 (12:09:22 CEST)

How to cite: Badreldin, N. The Spatiotemporal Big Data analysis of Several MODIS Satellite Sensors for Assessing A Decadal Vegetation Cover Dynamics. Preprints 2018, 2018060040 (doi: 10.20944/preprints201806.0040.v1). Badreldin, N. The Spatiotemporal Big Data analysis of Several MODIS Satellite Sensors for Assessing A Decadal Vegetation Cover Dynamics. Preprints 2018, 2018060040 (doi: 10.20944/preprints201806.0040.v1).

Abstract

Assessing long period of the spatiotemporal vegetation cover dynamics has received substantial attention among scholars and decision-makers in the past few years. Optical remote sensing data such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat provided worldwide invaluable historical records on land cover status at multi-spatial and temporal scales. Big data in agricultural and environmental fields is a considerable challenge which requires proper tools and skills. Understanding the big data lifecycle from acquisition to visualization is essential to building usable and effective workflow. In this research, four major challenges were tackled, developing a framework to-how-to deal effectively with raster big data of ~ 600 gigabytes (GB). Several MODIS satellite images from three different sensors for 10 years at different spatial resolution were preprocessed and analyzed using the R programming language. The Nile delta was chosen as a case study to monitor and assess the vegetation cover dynamics between spectral indices, sensors, and spatial resolutions. The pixel-based statistical analysis supports this exploratory investigation and opened the door to building deep learning algorithms for finer change detection analysis to assess more accurately the spatiotemporal dynamics of land degradation development, natural hazards, urban sprawl, and climate change.

Subject Areas

vegetation dynamics; environmental modeling; big data; drylands

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