Improved satellite imaging has allowed scientists to look at species diversity and analyse biodiversity changes using the images.

Analysis of texture differences in satellite images may be an effective way to monitor changes in vegetation, soil and water patterns over time, with potential implications for measuring biodiversity as well, according to experts at the University of Florida.

On 24th October, Matteo Convertino, of the University of Florida released his findings in journal PLOS ONE. Dr Convertino and his colleagues designed statistical models to estimate two aspects of biodiversity in satellite images: the number of species in a given region, or 'species richness', and the rate at which species entered or were removed from the ecosystem, a parameter termed 'species turnover'.

They tested their models on data gathered over 28 years in a water conservation area in the Florida Everglades and compared their results to previous reports from the region. They found that their models were nearly 100 percent accurate when predicting species turnover; compared to currently used methods, which are only 85 percent accurate.

According to the authors, their automated method using satellite images could help improve the efficiency and decrease the cost of campaigns to monitor biodiversity and guide policy and conservation decisions. Falling biodiversity has caused great concern in the United States and Europe in particular, where farmland bird populations have been halved in the past 30 years and populations of many vital insect pollinators have also extremely hard hit.

However, experts have said that overall figures on declines are hiding a pattern wherein some 'generalist' species of birds are thriving, while numbers and diversity of 'specialist' species have plummeted by up to 92 per cent over the same period.

Dr Convertino said of his team's finding, "Texture-based statistical image analysis is a promising method for quantifying seasonal differences and, consequently, the degree to which vegetation, soil, and water patterns vary as a function of natural and anthropic stressors. The application of the presented model to other fields and scales of analysis of ecosystems is a promising research direction.''

The researchers’ study is available here

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