It is well established that nighttime radiance, measured from satellites, correlates with economic prosperity across the globe. In developing countries, areas with low levels of detected radiance generally indicate limited development – with unlit areas typically being disregarded. Here we combine satellite nighttime lights and the world settlement footprint for the year 2015 to show that 19% of the total settlement footprint of the planet had no detectable artificial radiance associated with it. The majority of unlit settlement footprints are found in Africa (39%), rising to 65% if we consider only rural settlement areas, along with numerous countries in the Middle East and Asia. Significant areas of unlit settlements are also located in some developed countries. For 49 countries spread across Africa, Asia and the Americas we are able to predict and map the wealth class obtained from ~2,400,000 geo-located households based upon the percent of unlit settlements, with an overall accuracy of 87%.

Biomass structure is an important feature of terrestrial vegetation. The parameters of forest biomass structure are important for forest monitoring, biomass modelling and the optimal utilization and management of forests. In this paper, we used the most comprehensive database of sample plots available to build a set of multi-dimensional regression models that describe the proportion of different live biomass fractions (i.e., the stem, branches, foliage, roots) of forest stands as a function of average stand age, density (relative stocking) and site quality for forests of the major tree species of northern Eurasia. Bootstrapping was used to determine the accuracy of the estimates and also provides the associated uncertainties in these estimates. The species-specific mean percentage errors were then calculated between the sample plot data and the model estimates, resulting in overall relative errors in the regression model of −0.6%, −1.0% and 11.6% for biomass conversion and expansion factor (BCEF), biomass expansion factor (BEF), and root-to-shoot ratio respectively. The equations were then applied to data obtained from the Russian State Forest Register (SFR) and a map of forest cover to produce spatially distributed estimators of biomass conversion and expansion factors and root-to-shoot ratios for Russian forests. The equations and the resulting maps can be used to convert growing stock volume to the components of both above-ground and below-ground live biomass. The new live biomass conversion factors can be used in different applications, in particular to substitute those that are currently used by Russia in national reporting to the UNFCCC (United Nations Framework Convention on Climate Change) and the FAO FRA (Food and Agriculture Organization’s Forest Resource Assessment), among others.

Data fusion represents a powerful way of integrating individual sources of information to produce a better output than could be achieved by any of the individual sources on their own. This paper focuses on the data fusion of different land cover products derived from remote sensing. In the past, many different methods have been applied, without regard to their relative merit. In this study, we compared some of the most commonly-used methods to develop a hybrid forest cover map by combining available land cover/forest products and crowdsourced data on forest cover obtained through the Geo-Wiki project. The methods include: nearest neighbour, naive Bayes, logistic regression and geographically-weighted logistic regression (GWR), as well as classification and regression trees (CART). We ran the comparison experiments using two data types: presence/absence of forest in a grid cell; percentage of forest cover in a grid cell. In general, there was little difference between the methods. However, GWR was found to perform better than the other tested methods in areas with high disagreement between the inputs.

Studies on planktonic cyanobacteria have shown variability in cyanotoxin production, in response to changes in growth phase and environmental factors. Few studies have investigated cyanotoxin regulation in benthic mat-forming species, despite increasing reports on poisoning events caused by ingestion of these organisms. In this study, a method was developed to investigate changes in cyanotoxin quota in liquid cultures of benthic mat-forming cyanobacteria. Iron and copper are important in cellular processes and are well known to affect growth and selected metabolite production in cyanobacteria and algae. The effect of iron (40–4000 μg L⁻¹) and copper (2.5–250 μg L⁻¹) on growth and anatoxin-a quota in Phormidium autumnale was investigated in batch culture. These concentrations were chosen to span those found in freshwater, as well as those previously reported to be toxic to cyanobacteria. Anatoxin-a concentrations varied throughout the growth curve, with a maximum quota of between 0.49 and 0.55 pg cell⁻¹ measured within the first two weeks of growth. Growth rates were significantly affected by copper and iron concentrations (P < 0.0001); however, no statistically significant difference between anatoxin-a quota maxima was observed. When the iron concentrations were 800 and 4000 μg L⁻¹, the P. autumnale cultures did not firmly attach to the substratum. At 250 μg L⁻¹ copper or either 40 or 4000 μg L⁻¹ iron, growth was suppressed.