All models of satellite-derived phenology are wrong, but some are useful: a case study from northern Australia

Satellite-derived phenology (or apparent phenology) is frequently used to illustrate changes in plant phenology (i.e. true phenology) and the effects of climate forcing. However, each study uses a different method to detect phenology. Plant phenology refers to the relationship between the life cycle of plants and weather and climate events. Phenology is often studied in the field, but recently studies have transitioned towards using satellite images to monitor phenology at the plot, country, and continental scales. The problem with this approach is that there is an ever-increasing variety of earth observation satellites collecting data with different spatial, spectral, and temporal characteristics. In this paper we ask if studies that detect phenology using different sensors over the same site produce comparable results. Mangrove forests are one example where different methods have been used to examine their apparent phenology. In general, plant phenology, including mangroves, is described using few individual plants, but continental-scale descriptions of phenological events are scarce or inexistent. Few attempts have been made to describe the phenology of mangroves using satellite imagery, and each study presents a different method. We hypothesize that apparent phenology changes with: 1) areal extent; 2) site location; 3) frequency of observation; 4) spatial resolution; 5) temporal coverage; and 6) the number of cloud contaminated observations. Intuitively, one would assume that these hypotheses hold true, yet few studies have investigated this. For example, one would expect that clouds change the observed phenology of vegetation, that the number of species captured at spatial resolution will impact the apparent phenology, or that mangroves in different places display different phenologies, but how are these changes represented in the apparent phenology? We use the Enhanced Vegetation Index (EVI) to examine the changes in the start of season and peak growing season dates, as well as the shape and amplitude of the apparent phenology in each hypothesis. We use Landsat and Sentinel 2 imagery over the mangrove forests in Darwin Harbour (Northern Territory, Australia) as a case study, and found that apparent phenology does change with the sensor, site, and cloud contamination. Importantly, the apparent phenology is comparable between Landsat and Sentinel 2 sensors, but it is not comparable to phenology derived from MODIS. This is due to differences in the spatial resolution of the sensors. Cloud contamination also significantly changes the apparent phenology of vegetation. In this paper we expose the complexity of modelling phenology with remote sensing and help guide future phenology investigations

Automating drone image processing to map coral reef substrates using Google Earth Engine

While coral reef ecosystems hold immense biological, ecological, and economic value, frequent anthropogenic and environmental disturbances have caused these ecosystems to decline globally. Current coral reef monitoring methods include in situ surveys and analyzing remotely sensed data from satellites. However, in situ methods are often expensive and inconsistent in terms of time and space. High-resolution satellite imagery can also be expensive to acquire and subject to environmental conditions that conceal target features. High-resolution imagery gathered from remotely piloted aircraft systems (RPAS or drones) is an inexpensive alternative; however, processing drone imagery for analysis is time-consuming and complex. This study presents the first semi-automatic workflow for drone image processing with Google Earth Engine (GEE) and free and open source software (FOSS). With this workflow, we processed 230 drone images of Heron Reef, Australia and classified coral, sand, and rock/dead coral substrates with the Random Forest classifier. Our classification achieved an overall accuracy of 86% and mapped live coral cover with 92% accuracy. The presented methods enable efficient processing of drone imagery of any environment and can be useful when processing drone imagery for calibrating and validating satellite imagery

Correlation of the Initial Bone Height to the Middle Facial Height and the Maxillary Sinus Volume: A Retrospective Study

This study aims to measure and establish a correlation between the initial bone height (IBH) at the dentate posterior maxillary sextants to the middle facial height (MFH) and the maxillary sinus volume (SV). Thirty CBCTs split evenly between males and females were retrospectively selected and processed to measure the SV and the IBH from the first premolar to the second molar. A clinical measurement was performed on each patient to evaluate the MFH. A significant positive correlation was found between MFH and SV. No significant correlation was found between the IBH of the first premolar and the SV or MFH on both sides. The results showed a significant negative correlation between the IBH of the other teeth examined and the SV on both sides. A significant negative correlation was found between the MFH and the IBH of the second premolar, first molar, and second molar of each side. It can be concluded that the maxillary SV increases with the MFH, and the IBH decreases when the SV and the MFH increase except for the first premolar. The clinical relevance of this study lies in the capability of the general practitioner to estimate the IBH in the posterior dentate maxilla by measuring the MFH. Patients with a risk of having a low IBH can be advised, in the case of an extracted tooth, to consider ridge preservation techniques or implant therapy as soon as possible to avoid future complex surgical interventions

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

Cdc42 promotes transendothelial migration of cancer cells through β1 integrin.

Cancer cells interact with endothelial cells during the process of metastatic spreading. Here, we use a small interfering RNA screen targeting Rho GTPases in cancer cells to identify Cdc42 as a critical regulator of cancer cell-endothelial cell interactions and transendothelial migration. We find that Cdc42 regulates β1 integrin expression at the transcriptional level via the transcription factor serum response factor (SRF). β1 integrin is the main target for Cdc42-mediating interaction of cancer cells with endothelial cells and the underlying extracellular matrix, as exogenous β1 integrin expression was sufficient to rescue the Cdc42-silencing phenotype. We show that Cdc42 was required in vivo for cancer cell spreading and protrusion extension along blood vessels and retention in the lungs. Interestingly, transient Cdc42 depletion was sufficient to decrease experimental lung metastases, which suggests that its role in endothelial attachment is important for metastasis. By identifying β1 integrin as a transcriptional target of Cdc42, our results provide new insight into Cdc42 function

Mesoscopic models for DNA stretching under force: new results and comparison to experiments

Single molecule experiments on B-DNA stretching have revealed one or two structural transitions, when increasing the external force. They are characterized by a sudden increase of DNA contour length and a decrease of the bending rigidity. It has been proposed that the first transition, at forces of 60--80 pN, is a transition from B to S-DNA, viewed as a stretched duplex DNA, while the second one, at stronger forces, is a strand peeling resulting in single stranded DNAs (ssDNA), similar to thermal denaturation. But due to experimental conditions these two transitions can overlap, for instance for poly(dA-dT). We derive analytical formula using a coupled discrete worm like chain-Ising model. Our model takes into account bending rigidity, discreteness of the chain, linear and non-linear (for ssDNA) bond stretching. In the limit of zero force, this model simplifies into a coupled model already developed by us for studying thermal DNA melting, establishing a connexion with previous fitting parameter values for denaturation profiles. We find that: (i) ssDNA is fitted, using an analytical formula, over a nanoNewton range with only three free parameters, the contour length, the bending modulus and the monomer size; (ii) a surprisingly good fit on this force range is possible only by choosing a monomer size of 0.2 nm, almost 4 times smaller than the ssDNA nucleobase length; (iii) mesoscopic models are not able to fit B to ssDNA (or S to ss) transitions; (iv) an analytical formula for fitting B to S transitions is derived in the strong force approximation and for long DNAs, which is in excellent agreement with exact transfer matrix calculations; (v) this formula fits perfectly well poly(dG-dC) and $\lambda$-DNA force-extension curves with consistent parameter values; (vi) a coherent picture, where S to ssDNA transitions are much more sensitive to base-pair sequence than the B to S one, emerges.Comment: 14 pages, 9 figure

High-resolution sea surface reconstructions off Cape Hatteras over the last 10 ka

International audienceThis study presents high-resolution foraminiferal-based sea surface temperature, sea surface salinity and upper water column stratification reconstructions off Cape Hatteras, a region sensitive to atmospheric and thermohaline circulation changes associated with the Gulf Stream. We focus on the last 10,000 years (10 ka) to study the surface hydrology changes under our current climate conditions and discuss the centennial to millennial time scale variability. We observed opposite evolutions between the conditions off Cape Hatteras and those south of Iceland, known today for the North Atlantic Oscillation pattern. We interpret the temperature and salinity changes in both regions as co-variation of activities of the subtropical and subpolar gyres. Around 8.3 ka and 5.2-3.5 ka, positive salinity anomalies are reconstructed off Cape Hatteras. We demonstrate, for the 5.2-3.5 ka period, that the salinity increase was caused by the cessation of the low salinity surface flow coming from the north. A northward displacement of the Gulf Stream, blocking the southbound low-salinity flow, concomitant to a reduced Meridional Overturning Circulation is the most likely scenario. Finally, wavelet transform analysis revealed a 1000-year period pacing the δ18O signal over the early Holocene. This 1000-year frequency band is significantly coherent with the 1000-year frequency band of Total Solar Irradiance (TSI) between 9.5 ka and 7 ka and both signals are in phase over the rest of the studied period

Aerosol indirect effects

Aerosol indirect effects continue to constitute one of the most important uncertainties for anthropogenic climate perturbations. Within the international AEROCOM initiative, the representation of aerosol-cloud-radiation interactions in ten different general circulation models (GCMs) is evaluated using three satellite datasets. The focus is on stratiform liquid water clouds since most GCMs do not include ice nucleation effects, and none of the model explicitly parameterises aerosol effects on convective clouds. We compute statistical relationships between aerosol optical depth (tau a) and various cloud and radiation quantities in a manner that is consistent between the models and the satellite data. cloud droplet number concentration (N d) compares relatively well to the satellite data at least over the ocean. The relationship between (tau a) and liquid water path is simulated much too strongly by the models. This suggests that the implementation of the second aerosol indirect effect mainly in terms of an autoconversion parameterisation has to be revisited in the GCMs. A positive relationship between total cloud fraction (fcld) and tau a as found in the satellite data is simulated by the majority of the models, albeit less strongly than that in the satellite data in most of them. In a discussion of the hypotheses proposed in the literature to explain the satellite-derived strong fcld–tau a relationship, our results indicate that none can be identified as a unique explanation. Relationships similar to the ones found in satellite data between tau a and cloud top temperature or outgoing long-wave radiation (OLR) are simulated by only a few GCMs. The GCMs that simulate a negative OLR - tau a relationship show a strong positive correlation between tau a and fcld. The short-wave total aerosol radiative forcing as simulated by the GCMs is strongly influenced by the simulated anthropogenic fraction of tau a, and parameterisation assumptions such as a lower bound on Nd. Nevertheless, the strengths of the statistical relationships are good predictors for the aerosol forcings in the models. An estimate of the total short-wave aerosol forcing inferred from the combination of these predictors for the modelled forcings with the satellite-derived statistical relationships yields a global annual mean value of −1.5±0.5Wm−2. In an alternative approach, the radiative flux perturbation due to anthropogenic aerosols can be broken down into a component over the cloud-free portion of the globe (approximately the aerosol direct effect) and a component over the cloudy portion of the globe (approximately the aerosol indirect effect). An estimate obtained by scaling these simulated clearand cloudy-sky forcings with estimates of anthropogenic tau a and satellite-retrieved Nd–tau a regression slopes, respectively, yields a global, annual-mean aerosol direct effect estimate of −0.4±0.2Wm−2 and a cloudy-sky (aerosol indirect effect) estimate of −0.7±0.5Wm−2, with a total estimate of −1.2±0.4Wm−2