Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation

In this paper, we trialled different methods of data preparation for Convolutional Neural Network (CNN) training and semantic segmentation of land use land cover (LULC) features within aerial photography over the Wet Tropics and Atherton Tablelands, Queensland, Australia. This was conducted through trialling and ranking various training patch selection sampling strategies, patch and batch sizes and data augmentations and scaling. We also compared model accuracy through producing the LULC classification using a single pass of a grid of patches and averaging multiple grid passes and three rotated version of each patch. Our results showed: a stratified random sampling approach for producing training patches improved the accuracy of classes with a smaller area while having minimal effect on larger classes; a smaller number of larger patches compared to a larger number of smaller patches improves model accuracy; applying data augmentations and scaling are imperative in creating a generalised model able to accurately classify LULC features in imagery from a different date and sensor; and producing the output classification by averaging multiple grids of patches and three rotated versions of each patch produced and more accurate and aesthetic result. Combining the findings from the trials, we fully trained five models on the 2018 training image and applied the model to the 2015 test image with the output LULC classifications achieving an average kappa of 0.84 user accuracy of 0.81 and producer accuracy of 0.87. This study has demonstrated the importance of data pre-processing for developing a generalised deep-learning model for LULC classification which can be applied to a different date and sensor. Future research using CNN and earth observation data should implement the findings of this study to increase LULC model accuracy and transferability

Green space in health research : An overview of common indicators of greenness

Human environments influence human health in both positive and negative ways. Green space is considered an environmental exposure that confers benefits to human health and has attracted a high level of interest from researchers, policy makers, and increasingly clinicians. Green space has been associated with a range of health benefits, such as improvements in physical, mental, and social wellbeing. There are different sources, metrics and indicators of green space used in research, all of which measure different aspects of the environment. It is important that readers of green space research understand the terminology used in this field, and what the green space indicators used in the studies represent in the real world. This paper provides an overview of the major definitions of green space and the indicators used to assess exposure for health practitioners, public health researchers, and health policy experts who may be interested in understanding this field more clearly, either in the provision of public health-promoting services or to undertake research

A Structural Classification of Australian Vegetation Using ICESat/GLAS, ALOS PALSAR and Landsat Sensor Data

Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference map of Australian forest and woodland structure (height and cover), with this generated by integrating Landsat Thematic Mapper (TM) and Enhanced TM, Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and Ice, Cloud, and land Elevation (ICESat),and Geoscience Laser Altimeter System (GLAS) data. ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC) were used to segment and classify the Australian landscape. Then, from intersecting ICESat waveform data, vertical foliage profiles and height metrics (e.g., 95% percentile height, mean height and the height to maximum vegetation density) were extracted for each of the classes generated. Within each class, and for selected areas, the variability in ICESat profiles was found to be similar with differences between segments of the same class attributed largely to clearance or disturbance events. ICESat metrics and profiles were then assigned to all remaining segments across Australia with the same class allocation. Validation against airborne LiDAR for a range of forest structural types indicated a high degree of correspondence in estimated height measures. On this basis, a map of vegetation height was generated at a national level and was combined with estimates of cover to produce a revised structural classification based on the scheme of the Australian National Vegetation Information System (NVIS). The benefits of integrating the three datasets for segmenting and classifying the landscape and retrieving biophysical attributes was highlighted with this leading the way for future mapping using ALOS-2 PALSAR-2, Landsat/Sentinel-2, Global Ecosystem Dynamics Investigation (GEDI), and ICESat-2 LiDAR data. The ability to map across large areas provides considerable benefits for quantifying carbon dynamics and informing on biodiversity metrics

Estimating aboveground woody biomass change in Kalahari woodland: combining field, radar, and optical data sets

Maps that accurately quantify aboveground vegetation biomass (AGB) are essential for ecosystem monitoring and conservation. Throughout Namibia, four vegetation change processes are widespread, namely, deforestation, woodland degradation, the encroachment of the herbaceous and grassy layers by woody strata (woody thickening), and woodland regrowth. All of these vegetation change processes affect a range of key ecosystem services, yet their spatial and temporal dynamics and contributions to AGB change remain poorly understood. This study quantifies AGB associated with the different vegetation change processes over an 8-year period, for a region of Kalahari woodland savannah in northern Namibia. Using data from 101 forest inventory plots collected during two field campaigns (2014–2015), we model AGB as a function of the Advanced Land Observing Satellite Phased Array L-band synthetic aperture radar (PALSAR and PALSAR-2) and dry season Landsat vegetation index composites, for two periods (2007 and 2015). Differences in AGB between 2007 and 2015 were assessed and validated using independent data, and changes in AGB for the main vegetation processes are quantified for the whole study area (75,501 km2). We find that woodland degradation and woody thickening contributed a change in AGB of −14.3 and 2.5 Tg over 14% and 3.5% of the study area, respectively. Deforestation and regrowth contributed a smaller portion of AGB change, i.e. −1.9 and 0.2 Tg over 1.3% and 0.2% of the study area, respectively

Optimised U-Net for Land Use–Land Cover Classification Using Aerial Photography

Convolutional Neural Networks (CNN) consist of various hyper-parameters which need to be specifed or can be altered when defning a deep learning architecture. There are numerous studies which have tested diferent types of networks (e.g. U-Net, DeepLabv3+) or created new architectures, benchmarked against well-known test datasets. However, there is a lack of real-world mapping applications demonstrating the efects of changing network hyper-parameters on model performance for land use and land cover (LULC) semantic segmentation. In this paper, we analysed the efects on training time and classifcation accuracy by altering parameters such as the number of initial convolutional flters, kernel size, network depth, kernel initialiser and activation functions, loss and loss optimiser functions, and learning rate. We achieved this using a well-known top performing architecture, the U-Net, in conjunction with LULC training data and two multispectral aerial images from North Queensland, Australia. A 2018 image was used to train and test CNN models with diferent parameters and a 2015 image was used for assessing the optimised parameters. We found more complex models with a larger number of flters and larger kernel size produce classifcations of higher accuracy but take longer to train. Using an accuracy-time ranking formula, we found using 56 initial flters with kernel size of 5×5 provide the best compromise between training time and accuracy. When fully training a model using these parameters and testing on the 2015 image, we achieved a kappa score of 0.84. This compares to the original U-Net parameters which achieved a kappa score of 0.73

Mapping the multi-decadal mangrove dynamics of the Australian coastline

Mangroves globally provide a diverse array of ecosystem services but these are impacted upon by both natural and anthropogenic drivers of change. In Australia, mangroves are protected by law and hence the natural drivers predominate. To determine annual national level changes in mangroves between 1987 and 2016, their extent (by canopy cover type)and dynamics were quantified using dense time-series (nominally every 16 days cloud permitting)of 25 m spatial resolution Landsat sensor data available within Digital Earth Australia (DEA). The potential area that mangroves occupied over this period was established as the union of mangrove maps generated for 1996, 2007–2010 and 2015/16 through the Global Mangrove Watch (GMW). Within this area, the green vegetation fractional cover (GVpc)was retrieved from each available cloud-masked Landsat scene through linear spectral unmixing. The 10th percentile (GVpc10)was then determined for each calendar year by comparing these data in a time-series. The percentage Planimetric Canopy Cover (PCC%)for each Landsat pixel was then estimated using a relationship between GVpc10 and LiDAR-derived PCC% (20%; resolvable at the Landsat resolution)varied from a minima of 10,715 ± 36 km (95% confidence interval)in 1992 to a maxima of 11,388 km ± 38 km (95% CI)in 2010, declining to 11,142 ± 57 km (95% CI)in 2017. In 2010 (maximum extent), the forests were classified as closed canopy (38.8%), open canopy (49.0%)and woodland mangrove (12.2%). The majority of change occurred along the northern Australian coastline and was concentrated in the major gulfs and sounds. The 30 national maps of annual mangrove extent represent a reference dataset, which is publicly available through the Terrestrial Environment Research Network (TERN)landscapes portal. Future efforts are focusing on the routine production of annual mangrove maps beyond 2019 as part of Australia's efforts to monitor the coastal environment

Intravenous anakinra can achieve experimentally effective concentrations in the central nervous system within a therapeutic time window: results of a dose-ranging study

The naturally occurring antagonist of interleukin-1, IL-1RA, is highly neuroprotective experimentally, shows few adverse effects, and inhibits the systemic acute phase response to stroke. A single regime pilot study showed slow penetration into cerebrospinal fluid (CSF) at experimentally therapeutic concentrations. Twenty-five patients with subarachnoid hemorrhage (SAH) and external ventricular drains were sequentially allocated to five administration regimes, using intravenous bolus doses of 100 to 500 mg and 4 hours intravenous infusions of IL-1RA ranging from 1 to 10 mg per kg per hour. Choice of regimes and timing of plasma and CSF sampling was informed by pharmacometric analysis of pilot study data. Data were analyzed using nonlinear mixed effects modeling. Plasma and CSF concentrations of IL-1RA in all regimes were within the predicted intervals. A 500-mg bolus followed by an intravenous infusion of IL-1RA at 10 mg per kg per hour achieved experimentally therapeutic CSF concentrations of IL-1RA within 45 minutes. Experimentally, neuroprotective CSF concentrations in patients with SAH can be safely achieved within a therapeutic time window. Pharmacokinetic analysis suggests that IL-1RA transport across the blood–CSF barrier in SAH is passive. Identification of the practicality of this delivery regime allows further studies of efficacy of IL-1RA in acute cerebrovascular disease

Copy Number Variation Affecting the Photoperiod-B1 and Vernalization-A1 Genes Is Associated with Altered Flowering Time in Wheat (Triticum aestivum)

The timing of flowering during the year is an important adaptive character affecting reproductive success in plants and is critical to crop yield. Flowering time has been extensively manipulated in crops such as wheat (Triticum aestivum L.) during domestication, and this enables them to grow productively in a wide range of environments. Several major genes controlling flowering time have been identified in wheat with mutant alleles having sequence changes such as insertions, deletions or point mutations. We investigated genetic variants in commercial varieties of wheat that regulate flowering by altering photoperiod response (Ppd-B1 alleles) or vernalization requirement (Vrn-A1 alleles) and for which no candidate mutation was found within the gene sequence. Genetic and genomic approaches showed that in both cases alleles conferring altered flowering time had an increased copy number of the gene and altered gene expression. Alleles with an increased copy number of Ppd-B1 confer an early flowering day neutral phenotype and have arisen independently at least twice. Plants with an increased copy number of Vrn-A1 have an increased requirement for vernalization so that longer periods of cold are required to potentiate flowering. The results suggest that copy number variation (CNV) plays a significant role in wheat adaptation

Germline pathogenic variants in PALB2 and other cancer-predisposing genes in families with hereditary diffuse gastric cancer without CDH1 mutation: a whole-exome sequencing study.

BACKGROUND: Germline pathogenic variants in the E-cadherin gene (CDH1) are strongly associated with the development of hereditary diffuse gastric cancer. There is a paucity of data to guide risk assessment and management of families with hereditary diffuse gastric cancer that do not carry a CDH1 pathogenic variant, making it difficult to make informed decisions about surveillance and risk-reducing surgery. We aimed to identify new candidate genes associated with predisposition to hereditary diffuse gastric cancer in affected families without pathogenic CDH1 variants. METHODS: We did whole-exome sequencing on DNA extracted from the blood of 39 individuals (28 individuals diagnosed with hereditary diffuse gastric cancer and 11 unaffected first-degree relatives) in 22 families without pathogenic CDH1 variants. Genes with loss-of-function variants were prioritised using gene-interaction analysis to identify clusters of genes that could be involved in predisposition to hereditary diffuse gastric cancer. FINDINGS: Protein-affecting germline variants were identified in probands from six families with hereditary diffuse gastric cancer; variants were found in genes known to predispose to cancer and in lesser-studied DNA repair genes. A frameshift deletion in PALB2 was found in one member of a family with a history of gastric and breast cancer. Two different MSH2 variants were identified in two unrelated affected individuals, including one frameshift insertion and one previously described start-codon loss. One family had a unique combination of variants in the DNA repair genes ATR and NBN. Two variants in the DNA repair gene RECQL5 were identified in two unrelated families: one missense variant and a splice-acceptor variant. INTERPRETATION: The results of this study suggest a role for the known cancer predisposition gene PALB2 in families with hereditary diffuse gastric cancer and no detected pathogenic CDH1 variants. We also identified new candidate genes associated with disease risk in these families. FUNDING: UK Medical Research Council (Sackler programme), European Research Council under the European Union's Seventh Framework Programme (2007-13), National Institute for Health Research Cambridge Biomedical Research Centre, Experimental Cancer Medicine Centres, and Cancer Research UK