Trading more food: Implications for land use,greenhouse gas emissions,and the food system

The volume of agricultural trade increased by more than ten times throughout the past six decades and is likely to continue with high rates in the future. Thereby, it largely affects environment and climate. We analyse future trade scenarios covering the period of 2005–2045 by evaluating economic and environmental effects using the global land-use model MAgPIE (“Model of Agricultural Production and its Impact on the Environment”). This is the first trade study using spatially explicit mapping of land use patterns and greenhouse gas emissions. We focus on three scenarios: the reference scenario fixes current trade patterns, the policy scenario follows a historically derived liberalisation pathway, and the liberalisation scenario assumes a path, which ends with full trade liberalisation in 2045.Further trade liberalisation leads to lower global costs of food. Regions with comparative advantages like Latin America for cereals and oil crops and China for livestock products will export more. In contrast, regions like the Middle East, North Africa, and South Asia face the highest increases of imports. Deforestation, mainly in Latin America, leads to significant amounts of additional carbon emissions due to trade liberalisation. Non-CO₂ emissions will mostly shift to China due to comparative advantages in livestock production and rising livestock demand in the region. Overall, further trade liberalisation leads to higher economic benefits at the expense of environment and climate, if no other regulations are put in place.     

Integrating facies-based Bayesian inversion and supervised machine learning for petro-facies characterization in the Snadd Formation of the Goliat Field,south-western Barents Sea

Seismic petro-facies characterization in low net-to-gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro-facies significantly overlap. Pore fluid corrected endmember sand and shale depth trends have been used to generate stochastic forward models for different lithology and fluid combinations in order to assess the degree of separation of different petro-facies. Subsequently, a spectral decomposition and blending of selected frequency volumes reveal some seismic fluvial geomorphological features. We then jointly inverted for impedance and facies within a Bayesian framework using facies-dependent rock physics depth trends as input. The results from the inversion are then integrated into a supervised machine learning neural network for effective porosity discrimination. Probability density functions derived from stochastic forward modelling of endmember depth trends show a decreasing seismic fluid discrimination with depth. Spectral decomposition and blending of selected frequencies reveal a dominant NNE trend compared to the regional SE–NW pro-gradational trend, and a local E–W trend potentially related to fault activity at branches of the Troms-Finnmark Fault Complex. The facies-based inversion captures the main reservoir facies within the limits of the seismic bandwidth. Meanwhile the effective porosity predictions from the multilayer feed forward neural network are consistent with the inverted facies model, and can be used to qualitatively highlight the cleanest regions within the inverted facies model. A combination of facies-based inversion and neural network improves the seismic reservoir delineation of the Snadd Formation in the Goliat Field.     

Ontologies to interpret remote sensing images: why do we need them?

The development of new sensors and easier access to remote sensing data are significantly transforming both the theory and practice of remote sensing. Although data-driven approaches based on innovative algorithms and enhanced computing capacities are gaining importance to process big Earth Observation data, the development of knowledge-driven approaches is still considered by the remote sensing community to be one of the most important directions of their research. In this context, the future of remote sensing science should be supported by knowledge representation techniques such as ontologies. However, ontology-based remote sensing applications still have difficulty capturing the attention of remote sensing experts. This is mainly because of the gap between remote sensing experts’ expectations of ontologies and their real possible contribution to remote sensing. This paper provides insights to help reduce this gap. To this end, the conceptual limitations of the knowledge-driven approaches currently used in remote sensing science are clarified first. Then, the different modes of definition of geographic concepts, their duality, vagueness and ambiguity, and the sensory and semantic gaps are discussed in order to explain why ontologies can help address these limitations. In particular, this paper focuses on the capacity of ontologies to represent both symbolic and numeric knowledge, to reason based on cognitive semantics and to share knowledge on the interpretation of remote sensing images. Finally, a few recommendations are provided for remote sensing experts to comprehend the advantages of ontologies in interpreting satellite images.     

Spatial land use regression of nitrogen dioxide over a 5-year interval in Calgary,Canada

We analysed the spatial distribution of nitrogen dioxide over Calgary (Canada) in summer 2010 and winter 2011 and in summer 2015 and winter 2016, and estimated land use regressions for 2015–16 (2010–11 models were estimated previously). As nitrogen dioxide exhibited spatial clustering, we evaluated the following spatial specifications against a linear model: spatially autoregressive (lag), spatially autoregressive (error), and geographically weighted regression. The spatially autoregressive (lag) specification performed best, achieving goodness-of-fit aligned with or greater than values reported in the literature. We compared the 2015–16 spatially autoregressive models with the 2010–11 models and reparametrized them on the 2010–11 and the 2015–16 data. Finally, we identified a single set of predictors to best fit the data. Nitrogen dioxide concentration decreased over the 5 years, retaining consistent spatial and seasonal patterns, with higher concentrations over traffic corridors and industrial areas, and greater variation in summer than winter. The multi-temporal analysis suggested that spatial land use regressions were robust over the time interval, despite moderate land use change. Multi-temporal spatial land use regressions yielded consistent predictors for each season over time, which can aid estimation of air pollution at fine spatial resolution over an extended time period.     

Local and controlled prestack depth migration in complex areas

Prestack depth migrations based on wavefield extrapolation may be computationally expensive, especially in 3D. They are also very dependent on the acquisition geometry and are not flexible regarding the geometry of the imaging zone. Moreover, they do not deal with all types of wave, considering only primary reflection events through the model. Integral approaches using precalculated Green's functions, such as Kirchhoff migration and Born-based imaging, may overcome these problems. In the present paper, both finite-difference traveltimes and wavefront construction are used to obtain asymptotic Green's functions, and a generalized diffraction tomography is applied as an example of Born-based acoustic imaging. Target-orientated imaging is easy to perform, from any type of survey and subselection of shot/receiver pairs. Multifield imaging is possible using Green's functions that take into account, for instance, reflections at model boundaries. This may help to recover parts of complex structures which would be missing using a paraxial wave equation approach. Finally, a numerical evaluation of the resolution, or point-spread, function at any point of the depth-migrated section provides valuable information, either at the survey planning stage or for the interpretation.     

Evidence of a major environmental change recorded in a macrotidal bay (Marennes-Oléron Bay, France) by correlation between VHR seismic profiles and cores

New, very high-resolution (25 cm) seismic profiles have revealed the internal architecture of the infilling of a macrotidal bay, the Marennes-Oléron Bay, France. Within this geometry, a major seismic unconformity has been correlated with core data. This correlation provides evidence that the seismic unconformity corresponds to a sharp grain-size decrease. Both seismic and core data indicate that this change of grain size can be interpreted as a record of a recent (around 1,000 years b.p.) decrease in hydrodynamical energy with time and/or a larger supply of fine-grained material. This recent environmental change can be related to natural infilling of the Marennes Oléron Bay, and to tidal prism decrease, increasing human activities (e.g. land reclamation, deforestation, agricultural land use) and climate fluctuations during the late Holocene, such as the transition between the cold period of the Dark Ages (1,550–1,050 years b.p.) and the Medieval warm period (1,050–550 years b.p.).     

Lower cretaceous basalt and sediments from the Kerguelen Plateau

Geological samples from the southern Kerguelen Plateau include Lower Cretaceous basalt and lava breccia, probable Lower Cretaceous conglomerate and shelf limestone, Upper Cretaceous chert with dolomite, Upper Cretaceous-Eocene ooze, and Tertiary conglomerate. Neogene sediments are only a few hundred m thick, and include foraminiferal and diatomaceous ooze, and ice-rafted debris. In conjunction with seismic reflection profiles, the samples indicate Early Cretaceous near-shore volcanism, followed by erosion, sedimentation, and subsidence through Cretaceous; arching of the plateau at the end of Cretaceous; subsidence through Paleogene; widespread emergence in mid-Tertiary; and slow subsidence through Neogene.