Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national ...reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.
COVID-19 oral treatments require initiation within 5 days of symptom onset. Although antigen tests are less sensitive than RT-PCR, rapid results could facilitate entry to treatment. We collected ...anterior nasal swabs for BinaxNOW and RT-PCR testing and clinical data at a walk-up, community site in San Francisco, California between January and June 2022. SARS-CoV-2 genomic sequences were generated from positive samples and classified according to subtype and variant. Monte Carlo simulations were conducted to estimate the expected proportion of SARS-CoV-2 infected persons who would have been diagnosed within 5 days of symptom onset using RT-PCR versus BinaxNOW testing. Among 25,309 persons tested with BinaxNOW, 2,799 had concomitant RT-PCR. 1137/2799 (40.6%) were SARS-CoV-2 RT-PCR positive. We identified waves of predominant omicron BA.1, BA.2, BA.2.12, BA.4, and BA.5 among 720 sequenced samples. Among 1,137 RT-PCR positive samples, 788/1137 (69%) were detected by BinaxNOW; 94% (669/711) of those with Ct value <30 were detected by BinaxNOW. BinaxNOW detection was consistent over lineages. In analyses to evaluate entry to treatment, BinaxNOW detected 81.7% (361/442, 95% CI: 77-85%) of persons with COVID-19 within 5 days of symptom onset. In comparison, RT-PCR (24-hour turnaround) detected 84.2% (372/442, 95% CI: 80-87%) and RT-PCR (48-hour turnaround) detected 67.0% (296/442, 95% CI: 62-71%) of persons with COVID-19 within 5 days of symptom onset. BinaxNOW detected high viral load from anterior nasal swabs consistently across omicron sublineages emerging between January and June of 2022. Simulations support BinaxNOW as an entry point for COVID-19 treatment in a community field setting.
The wide spectrum of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with phenotypes impacting transmission and antibody sensitivity necessitates investigation of immune ...responses to different spike protein versions. Here, we compare neutralization of variants of concern, including B.1.617.2 (delta) and B.1.1.529 (omicron), in sera from individuals exposed to variant infection, vaccination, or both. We demonstrate that neutralizing antibody responses are strongest against variants sharing certain spike mutations with the immunizing exposure, and exposure to multiple spike variants increases breadth of variant cross-neutralization. These findings contribute to understanding relationships between exposures and antibody responses and may inform booster vaccination strategies.
L-band synthetic aperture radar (SAR) backscatter intensity is sensitive to land cover and can be used to estimate vegetation measures such as basal area (BA) and biomass. However, the estimation of ...BA, and especially change in BA, can be hampered by the influences upon backscatter of external factors such as imaging geometry, terrain topology, prevailing moisture conditions and even SAR sensor characteristics. This paper describes a method of reducing the adverse effects of such extraneous influences on vegetation and change estimates derived from single-channel SAR data. Empirical corrections for terrain slope and cross-track tendencies were applied and linear least squares difference minimization used to normalize the backscatter differences between scenes. The method was applied to state-wide coverage of L-band, fine-mode, HV polarization Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) data over New South Wales (NSW), Australia. The data were acquired with different sensors over two “observational epochs”: ALOS PALSAR in 2009 and ALOS-2 PALSAR-2 in 2016/17. The SAR datasets presented significant variations in backscatter intensity beyond those attributable to changes in vegetation cover. The corrective procedures resulted in improved uniformity of observed backscatter dependence on vegetation. Variations in backscattering coefficient between swaths were reduced by as much as 1.75 dB and 25% of the standard deviation in mean backscattering coefficients in common areas and at near- and far-range. This corresponded to a correction in BA estimate of 4.4 m2 ha−1. The method was observed to reduce ambiguities in regrowth estimates at swath boundaries and correct estimates of BA change by as much as 30% over large areas. The resulting estimates of 7-year change in BA provide spatially explicit forest structural information that is assisting in monitoring changes in woody vegetation across NSW.
•Residual terrain correction ensures the same mean backscatter for similar slopes.•Internal matching reduces the variations in backscatter due to terrain and moisture.•Cross-sensor matching places data for each observational epoch on a common footing.•Corrections make possible wide-area spatially consistent estimates of basal area.•Observed increase in basal area provides a surrogate measure of regrowth.
Abstract
Background
Sequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral genome from patient samples is an important epidemiological tool for monitoring and responding ...to the pandemic, including the emergence of new mutations in specific communities.
Methods
SARS-CoV-2 genomic sequences were generated from positive samples collected, along with epidemiological metadata, at a walk-up, rapid testing site in the Mission District of San Francisco, California during 22 November to 1 December, 2020, and 10–29 January 2021. Secondary household attack rates and mean sample viral load were estimated and compared across observed variants.
Results
A total of 12 124 tests were performed yielding 1099 positives. From these, 928 high-quality genomes were generated. Certain viral lineages bearing spike mutations, defined in part by L452R, S13I, and W152C, comprised 54.4% of the total sequences from January, compared to 15.7% in November. Household contacts exposed to the “California” or “West Coast” variants (B.1.427 and B.1.429) were at higher risk of infection compared to household contacts exposed to lineages lacking these variants (0.36 vs 0.29, risk ratio RR = 1.28; 95% confidence interval CI: 1.00–1.64). The reproductive number was estimated to be modestly higher than other lineages spreading in California during the second half of 2020. Viral loads were similar among persons infected with West Coast versus non-West Coast strains, as was the proportion of individuals with symptoms (60.9% vs 64.3%).
Conclusions
The increase in prevalence, relative household attack rates, and reproductive number are consistent with a modest transmissibility increase of the West Coast variants.
Summary: We observed a growing prevalence and modestly elevated attack rate for “West Coast” severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants in a community testing setting in San Francisco during January 2021, suggesting its modestly higher transmissibility.
Accurate and reliable mapping of fire extent and severity is critical for assessing the impact of fire on vegetation and informing post-fire recovery trajectories. Classification approaches that ...combine pixel-wise and neighbourhood statistics including image texture derived from high-resolution satellite data may improve on current methods of fire severity mapping. Texture is an innate property of all land cover surfaces that is known to vary between fire severity classes, becoming increasingly more homogenous as fire severity increases. In this study, we compared candidate backscatter and reflectance indices derived from Sentinel 1 and Sentinel 2, respectively, together with grey-level-co-occurrence-matrix (GLCM)-derived texture indices using a random forest supervised classification framework. Cross-validation (for which the target fire was excluded in training) and target-trained (for which the target fire was included in training) models were compared to evaluate performance between the models with and without texture indices. The results indicated that the addition of texture indices increased the classification accuracies of severity for both sensor types, with the greatest improvements in the high severity class (23.3%) for the Sentinel 1 and the moderate severity class (17.4%) for the Sentinel 2 target-trained models. The target-trained models consistently outperformed the cross-validation models, especially with regard to Sentinel 1, emphasising the importance of local training data in capturing post-fire variation in different forest types and severity classes. The Sentinel 2 models more accurately estimated fire extent and were improved with the addition of texture indices (3.2%). Optical sensor data yielded better results than C-band synthetic aperture radar (SAR) data with respect to distinguishing fire severity and extent. Successful detection using C-band data was linked to significant structural change in the canopy (i.e., partial-complete canopy consumption) and is more successful over sparse, low-biomass forest. Future research will investigate the sensitivity of longer-wavelength (L-band) SAR regarding fire severity estimation and the potential for an integrated fire-mapping system that incorporates both active and passive remote sensing to detect and monitor changes in vegetation cover and structure.
Abstract
While SARS-CoV-2 vaccines prevent severe disease effectively, postvaccination “breakthrough” COVID-19 infections and transmission among vaccinated individuals remain ongoing concerns. We ...present an in-depth characterization of transmission and immunity among vaccinated individuals in a household, revealing complex dynamics and unappreciated comorbidities, including autoimmunity to type 1 interferon in the presumptive index case.
This paper addresses the shortfall in L-band SAR data availability for the purpose of extracting spatially explicit information on forest cover, and the capacity to fill the gap using shorter ...wavelength C-band data. Specifically, comparisons are made of forest/non-forest (F/NF) extent derived from independent classification of ALOS PALSAR and RADARSAT-2 data acquired over Tasmania. Focussing on a temperate forest landscape, it was demonstrated that C-band SAR data can be used interchangeably with L-band data to produce a comparable estimate of forest/non-forest cover. Only partial interoperability is achieved however, given the limitations of dual polarisation C-band SAR in discerning forests of different growth stage and biomass.
Ambiguities in F/NF status were more prevalent in the C-band classification, despite inclusion of topographic (surface elevation and slope) and textural features in the training dataset, with the key observations as follows: (i) Reduced dynamic range and greater overlap amongst F/NF classes; (ii) Similarities in volume scattering from harvested/regrowth eucalyptus areas and background native forest; (iii) Confusion between young pine plantation and harvested/regrowth areas due to comparable roughness; (iv) Less variation in backscatter and poorer separation of intact and managed eucalyptus forest; and (v) Reduced capacity for discrimination of forest types. In almost every case, the use of L-band data was preferable. The one exception was the limited separation of young pine plantation and harvested/regrowth areas in both C- and L-band data. A similar level of performance was achieved in the discrimination of mature pine plantation, and between young eucalyptus plantation and harvested/regrowth.
The findings were restricted to single-date classification of C- and L- band data. The potential to extend a time-series of L-band observations over forest using dense time-series (i.e., intra-annual) C-band observations acquired in dual or quad polarisation mode, warrants further investigation. Where a positive trade-off exists between the benefits and costs of integrating these data, multi-frequency (e.g., C- and L-band) and multi-sensor approaches (e.g., SAR and optical) are a viable way forward for operational forest monitoring and carbon accounting.
•Partial interoperability is achieved given the limitations of dual-pol C-band SAR•C- and L-band SAR data are interchangeable when total forest cover is required•Harvested/regrowth and young pine plantation are confused due to comparable roughness•Similar C-band volume scatter observed from intact and managed eucalyptus forest•C-band shows reduced capacity for discrimination of forest types
Recent technological advances in the field of radar remote sensing have allowed the deployment of an increasing number of new satellite sensors. These provide an important source of Earth observation ...data, which add to the currently existing optical data sets. In parallel, the development of robust methods for global forest monitoring and mapping is becoming increasingly important. As a consequence, there is significant interest in the development of global monitoring systems that are able to take advantage of the potential synergies and complementary nature of optical and radar data. This paper proposes an approach for the combined processing of Landsat and ALOS-PALSAR data for the purpose of forest mapping and monitoring. This is achieved by incorporating the PALSAR data into an existing operational Landsat-based processing system. Using a directed discriminant technique, a probability map of forest presence/absence is first generated from the PALSAR imagery. This SAR classification data is then combined with a time series of similar Landsat-based maps within a Bayesian multitemporal processing framework, leading to the production of a time series of joint radar-optical maps of forest extents. This approach is applied and evaluated over a pilot study area in northeastern Tasmania, Australia. Experimental outcomes of the proposed joint processing framework are provided, demonstrating its potential for the integration of different types of remote sensing data for forest monitoring purposes.
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