Monitoring the Sea Surface Microlayer (SML) on Sentinel images.

Slicks on the sea surface are usually related to oil spills, algal blooms or organic runoff around coastlines. An extensive network of slicks extending across the English Channel is seen on Sentinel 1 and Sentinel 2 images and are identified as comprising a film of natural surfactant material within the sea surface microlayer (SML). As the SML represents the interface between ocean and atmosphere, controlling the vital exchange of gases and aerosols, identification of the slicks on images can add a new dimension to climate modelling. Current models use primary productivity often combined with wind speed, but quantifying the global extent of surface films spatially and temporally is difficult due to their patchy nature. The slicks are shown to be visible on Sentinel 2 optical images affected by sun glint, due to the wave dampening effect of the surfactants. On a Sentinel 1 SAR image of the same day, they can be identified using the VV polarised band. The paper investigates the nature and spectral properties of the slicks in relation to sun glint, and evaluates the performance of chlorophyll-a, floating algae and floating debris indices on the slick-affected areas. No index was able to distinguish slicks from non-slick areas as successfully as the original sun glint image. This image was used to devise a tentative Surfactant Index (SI) which indicates over 40 % of the study area covered by slicks. As ocean sensors have lower spatial resolution and are generally designed to avoid sun glint, Sentinel 1 SAR may offer a useful alternative for monitoring the global spatial extent of surface films, until dedicated sensors and algorithms can be developed.

The effect of financial crises on deforestation: a global and regional panel data analysis.

Managing our transition to sustainability requires a solid understanding of how conditions of financial crisis affect our natural environment. Yet, there has been little focus on the nature of the relationship between financial crises and environmental sustainability, especially in relation to forests and deforestation. This study addressed this gap by providing novel evidence on the impact of financial crises on deforestation. A panel data approach is used looking at Global Forest Watch deforestation data from > 150 countries in > 100 crises in the twenty-first century. This includes an analysis of crises effects on principle drivers of deforestation; timber and agricultural commodities-palm oil, soybean, coffee, cattle, and cocoa. At a global level, financial crises are associated with a reduction in deforestation rates (- 36 p.p) and deforestation drivers; roundwood (- 6.7 p.p.), cattle (- 2.3 p.p.) and cocoa production (- 8.3 p.p.). Regionally, deforestation rates in Asia, Africa, and Europe decreased by - 83, - 43, and 22 p.p, respectively. Drivers behind these effects may be different, from palm oil (- 1.3 p.p.) and cocoa (- 10.5 p.p.) reductions in Africa, to a combination of timber (- 9.5 p.p) and palm oil in Asia. Moreover, financial crises have a larger effect on deforestation in low-income, than upper middle- and high-income countries (- 51 vs - 39 and - 18 p.p. respectively). Using another main dataset on yearly forest cover-the ESA-Climate Change Initiative-a picture arises showing financial crises leading to small global decreases in forest cover (- 0.1 p.p.) with a small agricultural cover increase (0.1 p.p). Our findings point to financial crises as important moments for global deforestation dynamics. Yet, to consolidate benefits on decreasing deforestation, governments need to enhance their sustainable forest management during crisis periods rather than let it slip down national agendas. Finally, to achieve the SDGs related to forests, better global forest cover datasets are needed, with better forest loss/gain data, disturbance history, and understanding of mosaicked landscape dynamics within a satellite pixel.

Impacts of the 2012-2015 Californian drought on carbon, water and energy fluxes in the Californian Sierras: Results from an imaging spectrometry-constrained terrestrial biosphere model.

Accurate descriptions of current ecosystem composition are essential for improving terrestrial biosphere model predictions of how ecosystems are responding to climate variability and change. This study investigates how imaging spectrometry-derived ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional-scale carbon, water and energy fluxes. Incorporating imaging spectrometry-derived composition of five plant functional types (Grasses/Shrubs, Oaks/Western Hardwoods, Western Pines, Fir/Cedar and High-elevation Pines) into the Ecosystem Demography (ED2) terrestrial biosphere model improves predictions of net ecosystem productivity (NEP) and gross primary productivity (GPP) across four flux towers of the Southern Sierra Critical Zone Observatory (SSCZO) spanning a 2250 m elevational gradient in the western Sierra Nevada. NEP and GPP root-mean-square-errors were reduced by 23%-82% and 19%-89%, respectively, and water flux predictions improved at the mid-elevation pine (Soaproot), fir/cedar (P301) and high-elevation pine (Shorthair) flux tower sites, but not at the oak savanna (San Joaquin Experimental Range [SJER]) site. These improvements in carbon and water predictions are similar to those achieved with model initializations using ground-based inventory composition. The imaging spectrometry-constrained ED2 model was then used to predict carbon, water and energy fluxes and above-ground biomass (AGB) dynamics over a 737 km2 region to gain insight into the regional ecosystem impacts of the 2012-2015 Californian drought. The analysis indicates that the drought reduced regional NEP, GPP and transpiration by 83%, 40% and 33%, respectively, with the largest reductions occurring in the functionally diverse, high basal area mid-elevation forests. This was accompanied by a 54% decline in AGB growth in 2012, followed by a marked increase (823%) in AGB mortality in 2014, reflecting an approximately 10-fold increase in per capita tree mortality from ~55 trees km-2  year-1 in 2010-2011, to ~535 trees km-2  year-1 in 2014. These findings illustrate how imaging spectrometry estimates of ecosystem composition can constrain and improve terrestrial biosphere model predictions of regional carbon, water, and energy fluxes, and biomass dynamics.

Actions on sustainable food production and consumption for the post-2020 global biodiversity framework.

Current food production and consumption trends are inconsistent with the Convention on Biological Diversity's 2050 vision of living in harmony with nature. Here, we examine how, and under what conditions, the post-2020 biodiversity framework can support transformative change in food systems. Our analysis of actions proposed in four science-policy fora reveals that subsidy reform, valuation, food waste reduction, sustainability standards, life cycle assessments, sustainable diets, mainstreaming biodiversity, and strengthening governance can support more sustainable food production and consumption. By considering barriers and opportunities of implementing these actions in Peru and the United Kingdom, we derive potential targets and indicators for the post-2020 biodiversity framework. For targets to support transformation, genuine political commitment, accountability and compliance, and wider enabling conditions and actions by diverse agents are needed to shift food systems onto a sustainable path.

Using Lidar and Radar measurements to constrain predictions of forest ecosystem structure and function.

Insights into vegetation and aboveground biomass dynamics within terrestrial ecosystems have come almost exclusively from ground-based forest inventories that are limited in their spatial extent. Lidar and synthetic-aperture Radar are promising remote-sensing-based techniques for obtaining comprehensive measurements of forest structure at regional to global scales. In this study we investigate how Lidar-derived forest heights and Radar-derived aboveground biomass can be used to constrain the dynamics of the ED2 terrestrial biosphere model. Four-year simulations initialized with Lidar and Radar structure variables were compared against simulations initialized from forest-inventory data and output from a long-term potential-vegtation simulation. Both height and biomass initializations from Lidar and Radar measurements significantly improved the representation of forest structure within the model, eliminating the bias of too many large trees that arose in the potential-vegtation-initialized simulation. The Lidar and Radar initializations decreased the proportion of larger trees estimated by the potential vegetation by approximately 20-30%, matching the forest inventory. This resulted in improved predictions of ecosystem-scale carbon fluxes and structural dynamics compared to predictions from the potential-vegtation simulation. The Radar initialization produced biomass values that were 75% closer to the forest inventory, with Lidar initializations producing canopy height values closest to the forest inventory. Net primary production values for the Radar and Lidar initializations were around 6-8% closer to the forest inventory. Correcting the Lidar and Radar initializations for forest composition resulted in improved biomass and basal-area dynamics as well as leaf-area index. Correcting the Lidar and Radar initializations for forest composition and fine-scale structure by combining the remote-sensing measurements with ground-based inventory data further improved predictions, suggesting that further improvements of structural and carbon-flux metrics will also depend on obtaining reliable estimates of forest composition and accurate representation of the fine-scale vertical and horizontal structure of plant canopies.