Recent advances in remote sensing image processing

Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, image coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing.

Full-genome sequence of the plant growth-promoting bacterium Pseudomonas protegens CHA0.

We report the complete genome sequence of the free-living bacterium Pseudomonas protegens (formerly Pseudomonas fluorescens) CHA0, a model organism used in plant-microbe interactions, biological control of phytopathogens, and bacterial genetics.

Predicting current and future spatial community patterns of plant functional traits

Community-level patterns of functional traits relate to community assembly and ecosystem functioning. By modelling the changes of different indices describing such patterns - trait means, extremes and diversity in communities - as a function of abiotic gradients, we could understand their drivers and build projections of the impact of global change on the functional components of biodiversity. We used five plant functional traits (vegetative height, specific leaf area, leaf dry matter content, leaf nitrogen content and seed mass) and non-woody vegetation plots to model several indices depicting community-level patterns of functional traits from a set of abiotic environmental variables (topographic, climatic and edaphic) over contrasting environmental conditions in a mountainous landscape. We performed a variation partitioning analysis to assess the relative importance of these variables for predicting patterns of functional traits in communities, and projected the best models under several climate change scenarios to examine future potential changes in vegetation functional properties. Not all indices of trait patterns within communities could be modelled with the same level of accuracy: the models for mean and extreme values of functional traits provided substantially better predictive accuracy than the models calibrated for diversity indices. Topographic and climatic factors were more important predictors of functional trait patterns within communities than edaphic predictors. Overall, model projections forecast an increase in mean vegetation height and in mean specific leaf area following climate warming. This trend was important at mid elevation particularly between 1000 and 2000 m asl. With this study we showed that topographic, climatic and edaphic variables can successfully model descriptors of community-level patterns of plant functional traits such as mean and extreme trait values. However, which factors determine the diversity of functional traits in plant communities remains unclear and requires more investigations.

Identity and combinations of arbuscular mycorrhizal fungal isolates influence plant resistance and insect preference

1. Accumulating evidence indicates that plant resistance against above-ground herbivores can be affected by the presence of arbuscular mycorrhizal fungi (AMF) in association with the host plant. Little is known, however, about how AMF composition can influence herbivore choice to feed on a particular plant. 2. Unravelling the preference-performance hypothesis in a multitrophic context is needed to expand our knowledge of complex multitrophic interactions in natural systems. If given mycorrhizal fungal genotypes increase attractiveness for a herbivore (reduced plant resistance), then the benefits of increased unpalatability provided by the mycorrhizal fungi (increased plant resistance) might be outweighed by the increased herbivore recruitment. 3. This was addressed by designing three experiments to test the effects of different AMF genotypes, inoculated either alone or in combination, to measure intraspecific AMF effects on plant resistance and insect herbivore preference. Using strawberry (Fragaria vesca L.) plants that were colonised by eight different combinations of Rhizophagus irregularis isolates, we measured effects on plant growth, insect growth and survival, as well as feeding preferences of a generalist herbivore caterpillar (Spodoptera littoralis Boisduval). 4. Overall, it was found that: (i) AMF influenced plant resistance in an AMF genotype-specific manner; (ii) some AMF inoculations decreased insect performance; (iii) insects preferentially chose to feed more on leaves originating from non-mycorrhizal plants; but also that (iv) in a whole plant bioassay, insects preferentially chose the biggest plant, regardless of their mycorrhizal status. 5. Therefore, AMF-mediated trade-offs between growth and resistance against herbivores have been shown. Such trade-offs, particularly driven by plant attractiveness to herbivores, buffer the positive effects of the mycorrhizal symbiosis on enhanced plant growth.