Satellite remote sensing is an important tool for monitoring the status of biodiversity and associated environmental parameters, including certain elements of habitats. However, satellite data are currently underused within the biodiversity research and conservation communities. Three factors have significant impact on the utility of remote sensing data for tracking and understanding biodiversity change. They are its continuity, affordability, and access. Data continuity relates to the maintenance of long term satellite data products. Such products promote knowledge of how biodiversity has changed over time and why. Data affordability arises from the cost of the imagery. New data policies promoting free and open access to government satellite imagery are expanding the use of certain imagery but the number of free and open data sets remains too limited. Data access addresses the ability of conservation biologists and bio diversity researchers to discover, retrieve, manipulate, and extract value from satellite imagery as well as link it with other types of information. Tools are rapidly improving access. Still, more cross community interactions are necessary to strengthen ties between the biodiversity and remote sensing communities.
A meta-analysis was conducted to quantitatively assess the effects of ethylenediurea (EDU) on ozone (O 3 ) injury, growth, physiology and productivity of plants grown in ambient air conditions. Results indicated that EDU significantly reduced O 3 -caused visible injury by 76%, and increased photosynthetic rate by 8%, above-ground biomass by 7% and crop yield by 15% in comparison with non-EDU treated plants, suggesting that ozone reduces growth and yield under current ambient conditions. EDU significantly ameliorated the biomass and yield of crops and grasses, but had no significant effect on tree growth with an exception of stem diameter. EDU applied as a soil drench at a concentration of 200e400 mg/L has the highest positive effect on crops grown in the field. Long-term research on full-grown tree species is needed. In conclusion, EDU is a powerful tool for assessing effects of ambient [O 3 ] on vegetation.
Although conservation intervention has reversed the decline of some species, our success is outweighed by a much larger number of species moving towards extinction. Extinction risk modelling can identify correlates of risk and species not yet recognized to be threatened. Here, we use machine learning models to identify correlates of extinction risk in African terrestrial mammals using a set of variables belonging to four classes: species distribution state, human pressures, conservation response and species biology. We derived information on distribution state and human pressure from satellite-borne imagery. Variables in all four classes were identified as important predictors of extinction risk, and interactions were observed among variables in different classes (e.g. level of protection, human threats, species distribution ranges). Species biology had a key role in mediating the effect of external variables. The model was 90% accurate in classifying extinction risk status of species, but in a few cases the observed and modelled extinction risk mismatched. Species in this condition might suffer from an incorrect classification of extinction risk (hence require reassessment). An increased availability of satellite imagery combined with improved resolution and classification accuracy of the resulting maps will play a progressively greater role in conservation monitoring.
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