High-Resolution Semantic Segmentation of Woodland Fires Using Residual Attention UNet and Time Series of Sentinel-2

Southern Africa experiences a great number of wildfires, but the dependence on low-resolution products to detect and quantify fires means both that there is a time lag and that many small fire events are never identified. This is particularly relevant in miombo woodlands, where fires are frequent and predominantly small. We developed a cutting-edge deep-learning-based approach that uses freely available Sentinel-2 data for near-real-time, high-resolution fire detection in Mozambique. The importance of Sentinel-2 main bands and their derivatives was evaluated using TreeNet, and the top five variables were selected to create three training datasets. We designed a UNet architecture, including contraction and expansion paths and a bridge between them with several layers and functions. We then added attention gate units (AUNet) and residual blocks and attention gate units (RAUNet) to the UNet architecture. We trained the three models with the three datasets. The efficiency of all three models was high (intersection over union (IoU) > 0.85) and increased with more variables. This is the first time an RAUNet architecture has been used to detect fire events, and it performed better than the UNet and AUNet models-especially for detecting small fires. The RAUNet model with five variables had IoU = 0.9238 and overall accuracy = 0.985. We suggest that others test the RAUNet model with large datasets from different regions and other satellites so that it may be applied more broadly to improve the detection of wildfires.Peer reviewe

Tree biomass equations from terrestrial LiDAR : a case study in Guyana

Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates (R-2 = 0.92-0.93) than traditional pantropical models (R-2 = 0.85-0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested (R-2 = 0.89) and predicted AGB accurately across all size classes-which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees

Tropical tree and palmallometry and implications for forest carbon dynamics in southwestern Amazonia.

Tropical forests play a vital role in the global carbon cycle and international policy, such as the United Nations Collaborative Programme on Reducing Emissions from Deforestation or Degradation (REDD+), but the amount of carbon they contain and its spatial distribution remain uncertain. Allometric equations used to estimate tree mass are a key source of this uncertainty, because large-scale variation in tree allometry and fundamental differences between functional groups are not accurately represented in pantropical biomass equations. This research tests the effects of accounting for sources of variation not currently explained in treemodels (i.e., crown size and structure) and recognising important distinctions between functional groups (monocots vs. dicots) at both the level of individuals and across the landscape. Southwestern Amazonian forests are politically and ecologically important, but biomass estimates may be articularly uncertain in this region. Specifically, tree biomass estimates vary greatly among published models, but these models do not account for crown structure nor have their predictions been tested against directly measured data in the southwestern Amazon. Palms are also abundant in western Amazonia but theirmass has been widely misrepresented: using models developed for dicotyledonous trees is likely inaccurate because these two groups have very different structures. To test these ideas, 51 trees and 136 arborescent palms were harvested and weighed in Peru, including the heaviest tropical tree on record. Existing pantropical equations that included height underestimated tree biomass by 11–14%because large crowns partially compensate for lower stature. Including crown parameters in new allometric models greatly improved performance and reduced error, especially for the largest trees. Palm biomass was often underestimated by dicot models because they can be much taller at small diameters, and stem height was the most important variable in new equations. These results were confirmed on a larger scale. Based on a network of 53 forest plots, biomass carbon in trees and palms in the southwestern Amazon is 9%greater than estimated by the recommended pantropical biomass equation. Original total aboveground carbon stocks over the entire 746,653 km2 ecoregion is estimated at 11.5 Pg C. Nearly one third of forests in this region are at imminent risk of deforestation and forest degradation, which would result in emissions up to 4.4 Pg C. These results significantly advance allometricmodelling and reduce uncertainty in forest biomass estimates, especially in southwestern Amazonia, which should help to underpin effective forest management and provide better forest biomass estimates for REDD+ and other carbon-based conservation projects

‘Music is my AK-47’: performing resistance in Belfast's rebel music scene

This article examines how some Irish republicans have used ‘rebel songs’ as a means to resist the hegemonic power of the British state, and how militant republicanism is invoked musically, through sonic and physical references to gunfire. It explores how the use of rebel songs has changed, the inherent tensions within today's scene, and how republicans attempt to co‐opt other conflicts as a means to strengthen their claim as resistance fighters. The article also analyses more nuanced resistances within the rebel music scene, exploring how competing republican factions use the same music to express opposing political positions, and why some musicians ultimately leave the scene on account of the musical and political restrictions placed upon them. In so doing, the article connects with ongoing attempts to rethink, remap, and develop new approaches to resistance within anthropology, while contributing to the developing subfield of ‘ethnomusicology in times of trouble’

The Discrete Representation of Continuously Moving Indeterminate Objects

AbstractTo incorporate indeterminacy in spatio-temporal database systems, grey modeling method is used for the calculations of the discrete models of indeterminate two dimension continuously moving objects. The Grey Model GM (1, 1) model generated from the snapshot sequence reduces the randomness of discrete snapshot and generates the holistic measure of object's movements. Comparisons to traditional linear models show that when information is limited this model can be used in the interpolation and near future prediction of uncertain continuously moving spatio-temporal objects

Integrating precision medicine in the study and clinical treatment of a severely mentally ill person

Background. In recent years, there has been an explosion in the number of technical and medical diagnostic platforms being developed. This has greatly improved our ability to more accurately, and more comprehensively, explore and characterize human biological systems on the individual level. Large quantities of biomedical data are now being generated and archived in many separate research and clinical activities, but there exists a paucity of studies that integrate the areas of clinical neuropsychiatry, personal genomics and brain-machine interfaces.Methods. A single person with severe mental illness was implanted with the Medtronic Reclaim® Deep Brain Stimulation (DBS) Therapy device for Obsessive Compulsive Disorder (OCD), targeting his nucleus accumbens/anterior limb of the internal capsule. Programming of the device and psychiatric assessments occurred in an outpatient setting for over two years. His genome was sequenced and variants were detected in the Illumina Whole Genome Sequencing Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory.Results. We report here the detailed phenotypic characterization, clinical-grade whole genome sequencing (WGS), and two-year outcome of a man with severe OCD treated with DBS. Since implantation, this man has reported steady improvement, highlighted by a steady decline in his Yale-Brown Obsessive Compulsive Scale (YBOCS) score from ∼38 to a score of ∼25. A rechargeable Activa RC neurostimulator battery has been of major benefit in terms of facilitating a degree of stability and control over the stimulation. His psychiatric symptoms reliably worsen within hours of the battery becoming depleted, thus providing confirmatory evidence for the efficacy of DBS for OCD in this person. WGS revealed that he is a heterozygote for the p.Val66Met variant in BDNF, encoding a member of the nerve growth factor family, and which has been found to predispose carriers to various psychiatric illnesses. He carries the p.Glu429Ala allele in methylenetetrahydrofolate reductase (MTHFR) and the p.Asp7Asn allele in ChAT, encoding choline O-acetyltransferase, with both alleles having been shown to confer an elevated susceptibility to psychoses. We have found thousands of other variants in his genome, including pharmacogenetic and copy number variants. This information has been archived and offered to this person alongside the clinical sequencing data, so that he and others can re-analyze his genome for years to come.Conclusions. To our knowledge, this is the first study in the clinical neurosciences that integrates detailed neuropsychiatric phenotyping, deep brain stimulation for OCD and clinical-grade WGS with management of genetic results in the medical treatment of one person with severe mental illness. We offer this as an example of precision medicine in neuropsychiatry including brain-implantable devices and genomics-guided preventive health care

Socio-Economic Instability and the Scaling of Energy Use with Population Size

The size of the human population is relevant to the development of a sustainable world, yet the forces setting growth or declines in the human population are poorly understood. Generally, population growth rates depend on whether new individuals compete for the same energy (leading to Malthusian or density-dependent growth) or help to generate new energy (leading to exponential and super-exponential growth). It has been hypothesized that exponential and super-exponential growth in humans has resulted from carrying capacity, which is in part determined by energy availability, keeping pace with or exceeding the rate of population growth. We evaluated the relationship between energy use and population size for countries with long records of both and the world as a whole to assess whether energy yields are consistent with the idea of an increasing carrying capacity. We find that on average energy use has indeed kept pace with population size over long time periods. We also show, however, that the energy-population scaling exponent plummets during, and its temporal variability increases preceding, periods of social, political, technological, and environmental change. We suggest that efforts to increase the reliability of future energy yields may be essential for stabilizing both population growth and the global socio-economic system