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Environmental Modeling Using Satellite Imaging and Dataset Re-processing

  • Moses Eterigho EmetereEmail author

Part of the Studies in Big Data book series (SBD, volume 54)

Table of contents

  1. Front Matter
    Pages i-x
  2. Moses Eterigho Emetere
    Pages 1-18
  3. Moses Eterigho Emetere
    Pages 19-37
  4. Moses Eterigho Emetere
    Pages 39-69
  5. Moses Eterigho Emetere
    Pages 71-140
  6. Moses Eterigho Emetere
    Pages 141-170
  7. Moses Eterigho Emetere
    Pages 171-173
  8. Back Matter
    Pages 175-227

About this book

Introduction

This book introduces methods of re-processing images to extract numerical information that can be used to quantify the observables in environmental modelling. Experiments or procedures that yield large images can be statistically or parametrically examined. Through the use of open source libraries, the book shows how ‘big data’ in the form of images or datasets can be comparatively analysed along same defined procedures or standards. 
This book helps to solve the challenges of discarding datasets that are relevant directly or indirectly to the research. The habit of screening datasets leads to the discard of over 90% of the original dataset or images generated in the experiments or procedure. If the images or datasets are generated under the same principles or conditions, then each measurement may be the narrative of unique events. The focus of this book is to enlighten researchers on how to analyse measurements with the aim of ensuring 100% utilization.

Keywords

Image-processing Model Design Big Data Cern Root Opensource Libraries environmenal research design big data analysis

Authors and affiliations

  1. 1.Department of PhysicsCovenant UniversityOtaNigeria
  2. 2.Department of Mechanical Engineering ScienceUniversity of JohannesburgJohannesburgSouth Africa

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-13405-1
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-13404-4
  • Online ISBN 978-3-030-13405-1
  • Series Print ISSN 2197-6503
  • Series Online ISSN 2197-6511
  • Buy this book on publisher's site