Mapping drivers of tropical forest loss with satellite image time series and machine learning

The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drivers vary in space and time, producing accurate spatially explicit maps with regular temporal updates is essential. Drivers can be recognized from satellite imagery but the scale of tropical deforestation makes it unfeasible to do so manually. Machine learning opens up possibilities for automating and scaling up this process. In this study, we developed and trained a deep learning model to classify the drivers of any forest loss—including deforestation—from satellite image time series. Our model architecture allows understanding of how the input time series is used to make a prediction, showing the model learns different patterns for recognizing each driver and highlighting the need for temporal data. We used our model to classify over 588 ′ 000 sites to produce a map detailing the drivers behind tropical forest loss. The results confirm that the majority of it is driven by agriculture, but also show significant regional differences. Such data is a crucial source of information to enable targeting specific drivers locally and can be updated in the future using free satellite data

The distribution of pressure in a model silo containing cement

Summary This paper, which is an extract from a wider field of investigation of the pressure distribution in a model silo containing different materials, at rest and during flow, describes tests with cement at rest. The results are compared with some of the better-known theories on silos. The main conclusions are that the pressure distribution on the silo base with cement at rest is not uniform hut decreases from the centre outwards, that Janssen's fundamental assumptions when applied to cement are wrong, and that laky's theory over-estimates the load carried by the silo walls whereas, for silos of proportions commonly met in practice, Airy's theory gives quite satisfactory results. </jats:p

Modeling and model-based control of temperature in an SThM probe

We present a multi-scale model of a probe for scanning thermal microscopy. The probe is built by microfabrication techniques. In active mode, it is supplied by a source of harmonic and/or continuous current and the tip temperature is measured after a lock-in amplifier. The model distinguishes two time scales and two space scales. Simulation results show the potential of the model in terms of accuracy and computation speed and they are compared to experimental results. Finally, a temperature control law constructed from this model is stated