Monitoring mangrove forests: are we taking full advantage of technology?

Mangrove forests grow in the estuaries of 124 tropical countries around the world. Because in-situ monitoring of mangroves is difficult and time-consuming, remote sensing technologies are commonly used to monitor these ecosystems. Landsat satellites have provided regular and systematic images of mangrove ecosystems for over 30 years, yet researchers often cite budget and infrastructure constraints to justify the underuse this resource. Since 2001, over 50 studies have used Landsat or ASTER imagery for mangrove monitoring, and most focus on the spatial extent of mangroves, rarely using more than five images. Even after the Landsat archive was made free for public use, few studies used more than five images, despite the clear advantages of using more images (e.g. lower signal-to-noise ratios). The main argument of this paper is that, with freely available imagery and high performance computing facilities around the world, it is up to researchers to acquire the necessary programming skills to use these resources. Programming skills allow researchers to automate repetitive and time-consuming tasks, such as image acquisition and processing, consequently reducing up to 60% of the time dedicated to these activities. These skills also help scientists to review and re-use algorithms, hence making mangrove research more agile. This paper contributes to the debate on why scientists need to learn to program, not only to challenge prevailing approaches to mangrove research, but also to expand the temporal and spatial extents that are commonly used for mangrove research

Quantum Smoluchowski equation: A systematic study

The strong friction regime at low temperatures is analyzed systematically starting from the formally exact path integral expression for the reduced dynamics. This quantum Smoluchowski regime allows for a type of semiclassical treatment in the inverse friction strength so that higher order quantum corrections to the original quantum Smoluchowski equation [PRL 87, 086802 (2001), PRL 101, 11903 (2008)] can be derived. Drift and diffusion coefficients are determined by the equilibrium distribution in position and are directly related to the corresponding action of extremal paths and fluctuations around them. It is shown that the inclusion of higher order corrections reproduces the quantum enhancement above crossover for the decay rate out of a metastable well exactly.Comment: 15 pages, 4 figure

Impacts of an extreme cyclone event on landscape-scale savanna fire, productivity and greenhouse gas emissions

North Australian tropical savanna accounts for 12% of the world\u27s total savanna land cover. Accordingly, understanding processes that govern carbon, water and energy exchange within this biome is critical to global carbon and water budgeting. Climate and disturbances drive ecosystem carbon dynamics. Savanna ecosystems of the coastal and sub-coastal of north Australia experience a unique combination of climatic extremes and are in a state of near constant disturbance from fire events (1 in 3 years), storms resulting in windthrow (1 in 5–10 years) and mega-cyclones (1 in 500–1000 years). Critically, these disturbances occur over large areas creating a spatial and temporal mosaic of carbon sources and sinks. We quantify the impact on gross primary productivity (GPP) and fire occurrence from a tropical mega-cyclone, tropical Cyclone Monica (TC Monica), which affected 10 400 km2 of savanna across north Australia, resulting in the mortality and severe structural damage to ~140 million trees. We estimate a net carbon equivalent emission of 43 Tg of CO2-e using the moderate resolution imaging spectroradiometer (MODIS) GPP (MOD17A2) to quantify spatial and temporal patterns pre- and post-TC Monica. GPP was suppressed for four years after the event, equivalent to a loss of GPP of 0.5 Tg C over this period. On-ground fuel loads were estimated to potentially release 51.2 Mt CO2-e, equivalent to ~10% of Australia\u27s accountable greenhouse gas emissions. We present a simple carbon balance to examine the relative importance of frequency versus impact for a number of key disturbance processes such as fire, termite consumption and intense but infrequent mega-cyclones. Our estimates suggested that fire and termite consumption had a larger impact on Net Biome Productivity than infrequent mega-cyclones. We demonstrate the importance of understanding how climate variability and disturbance impacts savanna dynamics in the context of the increasing interest in using savanna landscapes for enhanced carbon sinks in emission offset schemes

A novel approach to modelling mangrove phenology from satellite images: a case study from Northern Australia

Around the world, the effects of changing plant phenology are evident in many ways: from earlier and longer growing seasons to altering the relationships between plants and their natural pollinators. Plant phenology is often monitored using satellite images and parametric methods. Parametric methods assume that ecosystems have unimodal phenologies and that the phenology model is invariant through space and time. In evergreen ecosystems such as mangrove forests, these assumptions may not hold true. Here we present a novel, data-driven approach to extract plant phenology from Landsat imagery using Generalized Additive Models (GAMs). Using GAMs, we created models for six different mangrove forests across Australia. In contrast to parametric methods, GAMs let the data define the shape of the phenological curve, hence showing the unique characteristics of each study site. We found that the Enhanced Vegetation Index (EVI) model is related to leaf production rate (from in situ data), leaf gain and net leaf production (from the published literature). We also found that EVI does not respond immediately to leaf gain in most cases, but has a two- to three-month lag. We also identified the start of season and peak growing season dates at our field site. The former occurs between September and October and the latter May and July. The GAMs allowed us to identify dual phenology events in our study sites, indicated by two instances of high EVI and two instances of low EVI values throughout the year. We contribute to a better understanding of mangrove phenology by presenting a data-driven method that allows us to link physical changes of mangrove forests with satellite imagery. In the future, we will use GAMs to (1) relate phenology to environmental variables (e.g., temperature and rainfall) and (2) predict phenological changes

Association of Coronary Collaterals and Myocardial Salvage Measured by Serial Cardiac Magnetic Resonance Imaging after Acute Myocardial Infarction

Background: Coronary collateral flow in angiography has been linked with lower mortality rates in patients with coronary artery disease. However, the relevance of the underlying mechanism is sparse. Therefore, we tested the hypothesis that in patients with acute myocardial infarction (AMI), relevant coronary collateral flow is associated with more salvaged myocardium and lower risk of developing heart failure. Methods and Results: Patients with first AMI who received a percutaneous coronary intervention within 24 h after symptom onset were classified visually by assigning a Cohen–Rentrop Score (CRS) ranging between 0 (no collaterals) and 3 (complete retrograde filling of the occluded vessel). All 36 patients included in the analysis underwent cardiac magnetic resonance examination within 3 to 5 days after myocardial infarction and after 12 weeks. Patients with relevant collateral flow (CRS 2–3) to the infarct-related artery had significantly smaller final infarct size compared to those without (7 � 4% vs. 20 � 12%, p < 0.001). In addition, both groups showed improvement in left ventricular ejection fraction early after AMI, whereas the recovery was greater in CRS 2–3 (+8 � 5% vs. +3 � 5%, p = 0.015). Conclusion: In patients with first AMI, relevant collateral flow to the infarct-related artery was associated with more salvaged myocardium at 12 weeks, translating into greater improvement of systolic left ventricular function. The protective effect of coronary collaterals and the variance of infarct location should be further investigated in larger studies

Deep Learning Based Prediction of Sun-Induced Fluorescence from Hyplant Imagery

The retrieval of sun-induced fluorescence (SIF) from hyperspectral imagery is an ill-posed problem that has been tackled in different ways. We present a novel retrieval method combining semi-supervised deep learning with an existing spectral fitting method. A validation study with in-situ SIF measurements shows high sensitivity of the deep learning method to SIF changes even though systematic shifts deteriorate its absolute prediction accuracy. A detailed analysis of diurnal SIF dynamics and SIF prediction in topographically variable terrain highlights the benefits of this deep learning approach

Fast Machine Learning Simulator of At-Sensor Radiances for Solar-Induced Fluorescence Retrieval with DESIS and Hyplant

In many remote sensing applications the measured radiance needs to be corrected for atmospheric effects to study surface properties such as reflectance, temperature or emission features. The correction often applies radiative transfer to simulate atmospheric propagation, a time-consuming step usually done offline. In principle, an efficient machine learning (ML) model can accelerate the simulation step. This is the goal pursued here in the context of solar-induced fluorescence (SIF) emitted by vegetation around the O2-A band using the spaceborne DESIS and airborne HyPlant spectrometers. We present an ML simulator of at-sensor radiances trained on synthetic spectra and describe its performance in detail. The simulator is fast and accurate, constituting a promising alternative to a full-fledged, lengthy radiative transfer code for SIF retrieval in the O2-A band with DESIS and HyPlant