A remote sensing method for resolving depth and subpixel composition of aquatic benthos

The problem of subpixel heterogeneity in cover types has been addressed in terrestrial environments by the application of linear spectral unmixing techniques. However, in aquatic systems the interceding depth of water causes the apparent reflectance of the substrate to diverge from a linear model, and if depth is unknown these methods cannot be applied. A new technique is presented in which the conventional spectral unmixing method has been modified to calculate depth at each pixel in addition to the proportions of substrate type. The technique requires knowledge of the reflectance spectra of m pure substrata in n (n > m) spectral bands at depth 0 and the water diffuse attenuation coefficients for the site in the same bands. Depth, z, can be entirely unknown. The method is comparable to "classical" spectral unmixing and proceeds by performing a Gaussian elimination for endmember quantities and then solving the remaining nonlinear function of z for f(z) = 0 by successive approximation. Computer-based models are used to test the technique with realistic water diffuse attenuation coefficients and random spectra and actual spectra of coral reef substrata. The robustness of the technique is assessed against three forms of introduced error: measurement errors on the spectra to be unmixed, differences between the true endmember spectra and those used in the analysis, and measurement error on the water diffuse attenuation coefficients. The results of these tests imply the technique is sufficiently robust for use on real data. Furthermore, spectral unmixing of aquatic systems appears to be relatively insensitive to inaccuracies in depth estimation and offers great utility for benthic mapping

Improving the optimization solution for a semi-analytical shallow water inversion model in the presence of spectrally correlated noise

In coastal regions, shallow water semi-analytical inversion algorithms may be used to derive geophysical parameters such as inherent optical properties (IOPs), water column depth, and bottom albedo coefficients by inverting sensor-derived sub-surface remote sensing reflectance, rrs. The uncertainties of these derived geophysical parameters due to instrumental and environmental noise can be estimated numerically via the addition of spectral noise to the sensor-derived rrs before inversion. Repeating this process multiple times allows the calculation of the standard error and average for each derived parameter. Apart from spectral non-uniqueness, the optimization algorithm employed in the inversion must converge onto a single minimum to obtain a true representation of the uncertainty for a given set of noise-perturbed rrs. Failure to do so inflates the uncertainty and affects the average retrieved value (accuracy). We show that the standard approach of seeding the optimization with an arbitrary, fixed initial guess, can lead to the convergence to multiple minima, each having substantially different centroids in multi-parameter solution space. We present the Update-Repeat Levenberg-Marquardt (UR-LM) and Latin Hypercube Sampling (LHS) routines that dynamically search the solution space for an optimal initial guess, that when applied to the optimization allows convergence to the best local minimum. We apply the UR-LM and LHS methods on HICO-derived and simulated rrs and demonstrate the improved computational efficiency, precision, and accuracy afforded from these methods compared with the standard approach. Conceptually, these methods are applicable to remote sensing based, shallow water or oceanic semi-analytical inversion algorithms requiring nonlinear least squares optimization

Simulations to benchmark time-varying connectivity methods for fMRI

Published: May 29, 2018There is a current interest in quantifying time-varying connectivity (TVC) based on neuroimaging data such as fMRI. Many methods have been proposed, and are being applied, revealing new insight into the brain’s dynamics. However, given that the ground truth for TVC in the brain is unknown, many concerns remain regarding the accuracy of proposed estimates. Since there exist many TVC methods it is difficult to assess differences in time-varying connectivity between studies. In this paper, we present tvc_benchmarker, which is a Python package containing four simulations to test TVC methods. Here, we evaluate five different methods that together represent a wide spectrum of current approaches to estimating TVC (sliding window, tapered sliding window, multiplication of temporal derivatives, spatial distance and jackknife correlation). These simulations were designed to test each method’s ability to track changes in covariance over time, which is a key property in TVC analysis. We found that all tested methods correlated positively with each other, but there were large differences in the strength of the correlations between methods. To facilitate comparisons with future TVC methods, we propose that the described simulations can act as benchmark tests for evaluation of methods. Using tvc_benchmarker researchers can easily add, compare and submit their own TVC methods to evaluate its performance.WHT acknowledges support from the Knut och Alice Wallenbergs Stiftelse (SE) (grant no. 2016.0473, http://kaw.wallenberg.org). PR acknowledges support from the Swedish Research Council (Vetenskapsrådet) (grants no. 2016-03352 and 773 013-61X-08276-26-4) (http://vr.se) and the Swedish e-Science Research Center (http://e- science.se/). CGR acknowledges financial support from the Spanish Ministry of Economy and Competitiveness, through the ªSevero Ochoaº Programme for Centres/Units of Excellence in R&Dº (SEV-2015-490, http://csic.es/)

A method to analyze the potential of optical remote sensing for benthic habitat mapping

Quantifying the number and type of benthic classes that are able to be spectrally identified in shallow water remote sensing is important in understanding its potential for habitat mapping. Factors that impact the effectiveness of shallow water habitat mapping include water column turbidity, depth, sensor and environmental noise, spectral resolution of the sensor and spectral variability of the benthic classes. In this paper, we present a simple hierarchical clustering method coupled with a shallow water forward model to generate water-column specific spectral libraries. This technique requires no prior decision on the number of classes to output: the resultant classes are optically separable above the spectral noise introduced by the sensor, image based radiometric corrections, the benthos’ natural spectral variability and the attenuating properties of a variable water column at depth. The modeling reveals the effect reducing the spectral resolution has on the number and type of classes that are optically distinct. We illustrate the potential of this clustering algorithm in an analysis of the conditions, including clustering accuracy, sensor spectral resolution and water column optical properties and depth that enabled the spectral distinction of the seagrass Amphibolis antartica from benthic algae

Degradation and forgone removals increase the carbon impact of intact forest loss by 626%

Intact tropical forests, free from substantial anthropogenic influence, store and sequester large amounts of atmospheric carbon but are currently neglected in international climate policy. We show that between 2000 and 2013, direct clearance of intact tropical forest areas accounted for 3.2% of gross carbon emissions from all deforestation across the pantropics. However, full carbon accounting requires the consideration of forgone carbon sequestration, selective logging, edge effects, and defaunation. When these factors were considered, the net carbon impact resulting from intact tropical forest loss between 2000 and 2013 increased by a factor of 6 (626%), from 0.34 (0.37 to 0.21) to 2.12 (2.85 to 1.00) petagrams of carbon (equivalent to approximately 2 years of global land use change emissions). The climate mitigation value of conserving the 549 million ha of tropical forest that remains intact is therefore significant but will soon dwindle if their rate of loss continues to accelerate

Associations Between Koala Faecal Pellets and Trees at Dorrigo

Surveys are an important component of the legislative basis of protection and management of the koalas and their habitat in New South Wales. The search for faecal pellets provides a substantial source of information about the koala. At Dorrigo on the north coast of New South Wales, the forests are quite variable and have a composition that reflects a long and varied history of timber harvesting. Koalas appear to be widespread in the area, but at low population levels. The distribution and abundance of pellets are associated with tree density, size and species, and types of forest. Specifically, koala faecal pellets are associated with trees in less dense forests of 75-100 stems per hectare. Trees with pellet presence were not statistically associated with tree size, although trees with many pellets tended to be larger trees and medium-sized trees (60-90cm DBHOB) were the most preferred and important size to koalas. Tallowwood was the most preferred and important tree species to koalas

Nitrogen deposition and temperature structure fungal communities associated with alpine moss-sedge heath in the UK

Funding Information: We are very grateful to Ruth Mitchell, Dave Riach, Julia Fisher and Hannah Urpeth for their help with fieldwork. Helaina Black is thanked for helpful discussion during the design of the project. Numerous conservation agency staff and landowners gave permission to carry out work on their land, without which this study would not have been possible. The study was financially supported by the Scottish Government Rural and Environment Science and Analytical Services Division (RESAS).Peer reviewe

Assessing the genetic diversity of rice originating from Bangladesh, Assam and West Bengal

Acknowledgements This work was funded by BBSRC research project BB/J00336/1. FS and a part of the proportion of the cost of the Illumina genotyping was funded by a Beachell-Borlag International Fellowship. The authors would like to acknowledge the help of Dr MK Sarmah in collecting seed samples of the landraces and improved cultivars from Assam used in this study and Dr. Ma. Elizabeth B. Naredo and Ms. Sheila Mae Q. Mercado for handling of IRGC accessions and preparation of DNAs for genotyping. All rice seeds used here were obtained with MTA agreements and seed and dry leaves imported into the UK under import licence IMP⁄SOIL⁄18⁄2009 issued by Science and Advice for Scottish Agriculture.Peer reviewedPublisher PD

Dataset of Escherichia coli O157 : H7 genes enriched in adherence to spinach root tissue

A high-throughput positive-selection approach was taken to generate a dataset of Shigatoxigenic Escherichia coli (STEC) O157:H7 genes enriched in adherence to plant tissue. The approach generates a differential dataset based on BAC clones enriched in the output, after adherence, compared to the inoculum used as the input. A BAC clone library derived from STEC isolate 'Sakai' was used since this isolate is associated with a very large-scale outbreak of human disease from consumption of contaminated fresh produce; white radish sprouts. Spinach was used for the screen since it is associated with STEC outbreaks, and the roots provide a suitable site for bacterial colonisation. Four successive of rounds of Sakai BAC clone selection and amplification were applied for spinach root adherence, in parallel to a non-plant control. Genomic DNA was obtained from a total of 7.17 x 108 cfu/ml of bacteria from the plant treatment and 1.13 x 109 cfu/ml of bacteria from the no-plant control. Relative gene abundance of the output compared to the input pools was obtained using an established E. coli DNA microarray chip for STEC. The dataset enables screening for genes enriched under the treatment condition and informs on genes that may play a role in plant-microbe interactions