Advanced Environmental Monitoring with Remote Sensing Time Series Data and R

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Advanced Environmental Monitoring with Remote Sensing Time Series Data and R

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  • 製本 Hardcover:ハードカバー版/ページ数 102 p.
  • 言語 ENG
  • 商品コード 9780367205270
  • DDC分類 621.3678

Full Description

This book provides a step-by-step guide on how to use various publicly available remotely sensed time series data sources for environmental monitoring and assessment. Readers will learn how to extract valuable information on global changes from a 20-year collection of ready-to-use remotely sensed data through the free open statistical software R and its geographic data analysis and modeling tools. The case studies are from the Mediterranean region—a designated hot spot regarding climate change effects. Each chapter is dedicated to specific remote sensing products chosen for their spatial resolution. The methods used are adapted from large-scale to smaller-scale problems for different land cover areas.

Features




Includes real-world applications of environmental remotely sensed data



Analyzes the advantages and restrictions of each data source



Focuses on a wide spectrum of applications, such as hydrology, vegetation changes, land surface temperature, fire detection, and impacts



Includes R computer codes with explanatory comments and all applications use only freely available remotely sensed data



Presents a step-by-step processing through open source GIS and statistical analysis software

Advanced Environmental Monitoring with Remote Sensing Time Series Data and R describes and provides details on recent advances concerning publicly available remotely sensed time series data in environmental monitoring and assessment. This book is a must-have practical guide for environmental researchers, professionals, and students.

Contents

Introduction 1. Environmental Applications of Medium to High Resolution Remotely Sensed Data 2. Environmental Applications of Medium Resolution Remote Sensing Imaging 3. Environmental Applications of Low Resolution Remotely Sensed Data 4. Processing Remotely Sensed Data Conclusions

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