Applied course exploring practical uses of GIS and remote sensing in environment and wildlife conservation

What you'll learn

GIS
Remote sensing
Spatial analysis for conservation
Spatial analysis for environmental and ecological applications
Google Earth Engine basics
Remote Sensing analysis in QGIS and R
Master QGIS

Requirements

An interest in both conservation and GIS. Completion of the beginner course or intermediate level understanding of GIS recommended. Basic knowledge of remote sensing concepts will be helpful but not required as will be covered in this course.

Description

Building on the foundational knowledge and skills you will have developed in the beginner's course, this course dives into more advanced applications of spatial analysis in wildlife and environment conservation. It's suitable for students who have completed the beginner's course, or who already have an intermediate level of GIS and looking to refresh or enhance their skills applied to the field of conservation. Developed by Josef Clifford, an experienced GIS & remote sensing specialist, the course curriculum and content was developed in collaboration with scientists from the Wildlife Research and Training Institute (WRTI) in Kenya and the Zoological Society of London (ZSL) to ensure the content is rigorous and relevant. Real data is used throughout, including GPS tracking data of Kenyan elephants, with hands-on activities to solve real-world problems. In addition, students will have access to a wide range of resources including an indispensable Google Earth Engine conservation code repository which can be easily adapted to conduct a wide range of remote sensing tasks. We will cover a multitude of applications ranging from harnessing Google Earth Engine to access a range of environmental raster datasets, employing raster processing and analysis tools to develop a weighted habitat suitability map for forest elephants in Gabon, exploring digital elevation models (DEMs), and analysing spatial and temporal trends in climatic and environmental data. We will introduce the essential theory of remote sensing, explore a range of open source datasets and conduct a variety of analyses such as calculating the Normalised Difference Vegetation Index (NDVI) anomaly to investigate vegetation health and charting trends to analyse the impacts of wildfires in Canada. Finally we will explore some additional workflows including habitat connectivity analysis in Linkage Mapper and home range analysis in R. The course will primarily utilise QGIS as well as Google Earth Engine, plus R and other tools. Good luck and I hope you enjoy the course!

Who this course is for

Conservationists
Ecologists
Wildlife biologists
Those with intermediate knowledge of GIS
Those with no knowledge or basic knowledge of remote sensing concepts
Professionals in GIS and remote sensing interested in exploring new applications
Homepage:

https://www.udemy.com/course/open-source-gis-remote-sensing-for-conservation-advanced/