Biodiversity is a complex term but it essentially encompasses life in all its variety, ranging from individual genes to entire ecosystems. The legendary biologist Edward O. Wilson defines biodiversity as “the totality of all inherited variation in the life forms of Earth”. The loss of biodiversity due to human activities and its negative effect on ecosystems are well documented.
New milestone: My paper is now the most read and cited
This is an exciting milestone for me. As of December 13th, my open access study on the Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data that was published in 2020 is the most read (22,885) and most cited (101) paper in the journal GIScience & Remote Sensing.
Tutorial: Machine learning classification of Sentinel-2 satellite imagery using R [Updated]
Note: This tutorial was updated on April 20th, 2020 based on reader feedback.
In this short post, I would like to help you conduct your own machine learning classification of Sentinel-2 data using the open source package R. The process is pretty straightforward if you have experience in remote sensing and image classification. Even if you don’t have extensive experience, basic knowledge of remote sensing terminology is sufficient.
Behind the Paper: Comparing machine learning algorithms using Sentinel-2 data
The field of machine learning is moving fast, and it seems that new fancy algorithms coming out every week. Sometimes, it is confusing to figure out which algorithms are best suited for which purpose. This is particularly the case when it comes to land-use and land-cover classification using multidimensional satellite imagery because most of the new algorithms are tested with either binary or uni-dimensional data.