Tutorial: Machine learning classification of Sentinel-2 satellite imagery using R

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.

Behind the Paper: Combining science and art to understand environmental change

A couple of years ago, my colleague and friend Emma Li Johansson approached me with an interesting proposition. Her idea was that the combination of remotely-sensed land cover and and land use change maps and participatory paintings of people’s perceptions of land change might provide useful information in understanding the drivers of change. She didn’t have to say any more because I was sold. It was a solid idea with an innovative interdisciplinary workflow and high potential to provide valuable outcomes.

Behind the Paper: Testing a new satellite-derived vegetation index in a new biome

The last chapter of my PhD dissertation was published earlier this year in the International Journal of Applied Earth Observation and Geoinformation. From conception to publication, this paper took about two-and-a-half years of work. It signifies the end of my PhD era, so to speak. The idea came from my supervisor, who suggested that testing the relatively new Plant Phenology Index (PPI) in semi-arid biomes would be a worthwhile cause because its only been evaluated in the boreal biome.

Downward spiral of conflict and famine in Somalia is due to the absence of good governance, not climate

Somalia is mostly dry and semi-arid with the exception of few areas of greenery in the northern mountains or the riverine agricultural fields in the south. Since the acceleration of violence in the late 1980s that propelled it into civil war, two things have been occurring in Somalia on a more or less regular basis: conflicts and famines, and both have been linked, in one way or the other, to climate change