The conservation of species within Important Bird and Biodiversity Areas (IBAs) is critical, particularly in regions like Tunisia where wetlands serve as vital habitats for a wide array of waterbird species, including several that are globally threatened. In our recently published study on the conservation of wintering waterbirds within IBAs in Tunisia, we leveraged cutting-edge satellite remote sensing techniques to unravel the intricate dynamics between landscape structure and waterbird communities. This research is particularly novel as it integrates remote sensing data to assess the influence of wetland characteristics and surrounding landscape composition on bird habitats.

Back in 2020, I published a paper in the journal GIScience & Remote Sensing entitled "Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data". The paper has so far garnered 472 citations according to Google Scholar and 41,536 downloads, highlighting its relevance and utility in the scientific community. Since this paper is steadily marching towards the 500-citation mark, I decided to revisit it, highlight its importance, and explore why it has been so popular.

I was recently invited to talk about how remote sensing observations and data from Swedish national monitoring programs can be integrated with AI for biodiversity conservation and nature management. This talk is part of the a lecture series on functional landscapes in Sweden hosted by LU Land - a collaboration platform on land use for a sustainable future, hosted at the Center for Environmental and Climate Science, Lund University.

Most students of remote sensing know that the birth of satellite-derived Normalized Difference Vegetation Index (NDVI) dates back to the 1970s. In 1972, the Earth Resources Technology Satellite-1, ERTS-1 (later renamed Landsat 1) was the first Earth observation satellite designed to monitor and study the planet's landmasses. It carried a Multi-Spectral Scanner (MSS) that provided data in several spectral bands, which were crucial for developing vegetation indices.

Should scientists be concerned if their research output is inaccessible to non-experts? Put another way, should scientists make an effort to communicate their science to the wider public? I posed these questions to a group of graduate students and postdocs and the feedback was mixed. On the one hand, there were legitimate concerns about the time and skill needed to “translate” complex science into an accessible language. On the other hand, there was strong support for science communication as part of a scientist’s moral and professional duty.

At 7am on October 16 2012, I was on board SAS Flight 910 from Newark that landed at Copenhagen Airport. I was ready to begin a PhD at Lund University. I didn’t know much about Sweden except the usual stereotypes, for example it’s a rich country with happy people, lots of nature, and a generous welfare state.