Satellite Remote Sensing Data Bootcamp With Opensource Tools

Packt via Coursera

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Details

Go to class

Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

5 hours 38 minutes
Sessions

On-Demand
Level

Intermediate
Subtitles

English

Found in

Overview

Class Central Tips

Explore the dynamic world of satellite remote sensing data through a comprehensive bootcamp that equips you with essential skills using open-source tools. Beginning with the fundamentals, you'll be introduced to the core concepts of remote sensing, including various data types and the tools essential for their analysis, such as R and QGIS. As you progress, you will delve into the intricacies of optical remote sensing, learning to download, preprocess, and interpret Landsat data while mastering tools like the Semi-Automatic Classification Plugin in QGIS. The course then guides you through more advanced topics, including the many uses of optical data for various indices and transformations, using a range of tools like GRASS GIS, ESA SNAP, and R. You will explore critical processes such as texture indices, tasseled cap transformations, and dimension reduction, ensuring a thorough understanding of how to handle and manipulate data for your specific geospatial needs. Each section builds upon the last, culminating in the application of machine learning techniques to classify remote sensing satellite data. To round out your expertise, the course introduces active remote sensing with Synthetic Aperture Radar (SAR). You'll learn the practical aspects of obtaining and preprocessing ALOS PALSAR data, filtering for speckles, and deriving valuable backscatter information. By the end of this bootcamp, you'll be fully equipped to analyze and interpret both optical and SAR data, making you a valuable asset in the field of geospatial analysis. This course is ideal for geospatial professionals, environmental scientists, and data analysts looking to expand their expertise in satellite remote sensing. A basic understanding of GIS and remote sensing concepts is recommended but not required.

Syllabus

Introduction to Satellite Remote Sensing Data Analysis

In this module, we will lay the groundwork for your journey into satellite remote sensing data analysis. You'll begin by learning about the course structure, then explore the fundamentals of remote sensing, different data types, and the essential tools you will use throughout the course. By the end of this module, you'll have a solid understanding of the basics and be ready to dive deeper into the practical aspects of the field.

Introduction to Optical Remote Sensing Data

In this module, we will delve into the world of optical remote sensing data, starting with the fundamental principles that govern its collection. You'll examine the different types of optical data and how they are used, particularly focusing on Landsat data. Additionally, you'll explore the specifics of Landsat sensors and gain hands-on experience in using QGIS to download and view this data. By the end of this section, you'll be equipped with the knowledge and skills needed to work with optical remote sensing data in your analyses.

Pre-Processing Optical Data

In this module, we will focus on the crucial steps involved in pre-processing optical remote sensing data. You'll learn why pre-processing is essential, particularly for improving data accuracy. The module will guide you through performing atmospheric correction on Landsat data using R, and introduce you to the Semi-Automatic Classification Plugin in QGIS for efficient pre-processing. Additionally, you'll assess the quality of atmospherically corrected outputs and explore the practical applications of pre-processed data. By the end of this section, you'll have the skills to refine raw satellite data for meaningful analysis.

The Many Uses of Optical Data

In this module, we will explore the diverse applications of optical remote sensing data across various analytical processes. You'll begin by mastering band manipulation in QGIS, followed by the application of band math to derive critical insights. The module will introduce you to texture indices and tasseled cap transformations, offering both theoretical knowledge and practical implementation using GRASS GIS and ESA SNAP. Additionally, you'll delve into vegetation indices and learn how to reduce data dimensionality for more efficient analysis. By the end of this section, you'll be well-versed in multiple advanced techniques for leveraging optical data in your projects

Classification of Remote Sensing Satellite Data

In this module, we will delve into the classification of remote sensing satellite data, covering both unsupervised and supervised methods. You’ll begin by exploring the theory behind these approaches, followed by practical applications using ESA SNAP and QGIS. The module also introduces machine learning concepts and their integration into remote sensing classification, guiding you through creating training data and applying advanced algorithms to satellite imagery. By the end of this section, you’ll be equipped with comprehensive skills to classify and analyze remote sensing data accurately and efficiently

Introduction to Active Remote Sensing Data: Synthetic Aperture Radar

In this module, we will explore active remote sensing data, focusing on Synthetic Aperture Radar (SAR). You'll begin by understanding the reasons for using active remote sensing over passive methods, with a particular emphasis on SAR technology. The module will guide you through the process of obtaining ALOS PALSAR data and applying essential pre-processing steps. You'll also learn to filter speckles from SAR imagery to improve data quality, and finally, you'll extract back-scatter values, a critical step for interpreting SAR data. By the end of this section, you'll have a solid foundation in working with active remote-sensing data