Urban aliens and threatened near-naturals: Land-cover affects the species richness of alien- and threatened species in an urban- rural setting (2020), Petersen et al., Scientific Reports, https://doi.org/10.1038/s41598-020-65459-2
Land-use changes (in particular, urbanisation and everything related to it) have huge effects on biodiversity patterns – some habitats can support populations of many different species, others cannot. This seems intuitive on a large scale (think a rainforest vs. a large, industrialised city) and on a small scale (a small patch of concrete vs. a patch of soil in a forest), but what about on a medium scale, more relevant to management organisations? How different species of plants, animals and fungi are distributed in space on such a meso-scale is far more relevant to everyday management, compared to say a global distribution, or the organisation of a 10 x 10 metre quadrant.
Today’s authors (myself and my current supervisors) looked at how species richness changes with land-cover on a municipality scale. We also looked at whether these patterns differ if one considers the total number of species, threatened- or alien ones, and whether animals, plants and fungi react to concrete vs. forests in the same way.
An optical image of Kliuchevskoi volcano on the left, with a radar image on the left (Image credit: Michigan Tech Volcanology, Image Cropped)
Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series (2020), Lopes et al., Methods in Ecology and Evolution, https://doi.org/10.1111/2041-210X.13359
Most ecologist has at some point run across or used a land cover map in their career. Whether it’s used for figuring out the canopy diversity of a forest, or figuring out which habitat a species is using, land cover maps are incredibly useful tools for everyone from conservationists to architects. But have you ever wondered how they are produced?
Until recently, land cover maps were created using either images from optical satellites or images from radar satellites with a coarse to medium spatial resolution (check out the Did You Know Section for more details, or the image above for an example). Combined with classification algorithms, land cover maps can be created automatically. That makes it sound simple, but the final output depends greatly on the quality and amount of images you use for the classification. Since 2014, the Copernicus Programme has made satellite imaginary freely available at high spatial and temporal spatial resolution. Due to this, optical and radar images can be combined more efficiently to produce land cover classification maps with enhanced accuracy. This is especially useful in tropical and boreal areas, as optical images often don’t show the entire landscape due to persistent cloud over.