A Multi-Stage Approach Combining Very High-Resolution Satellite Image, GIS Database and Post-Classification Modification Rules for Habitat Mapping in Hong Kong

Identification and mapping of various habitats with sufficient spatial details are essential to support environmental planning and management. Considering the complexity of diverse habitat types in a heterogeneous landscape, a context-dependent mapping framework is expected to be superior to traditional classification techniques. With the aim to produce a territory-wide habitat map in Hong Kong, a three-stage mapping procedure was developed to identify 21 habitats by combining very-high-resolution satellite images, geographic information system (GIS) layers and knowledge-based modification rules. In stage 1, several classification methods were tested to produce initial results with 11 classes from a WorldView-2/3 image mosaic using a combination of spectral, textural, topographic and geometric variables. In stage 2, modification rules were applied to refine the classification results based on contextual properties and ancillary data layers. Evaluation of the classified maps showed that the highest overall accuracy was obtained from pixel-based random forest classification (84.0%) and the implementation of modification rules led to an average 8.8% increase in the accuracy. In stage 3, the classification scheme was expanded to all 21 habitats through the adoption of additional rules. The resulting habitat map achieved >80% accuracy for most of the evaluated classes and >70% accuracy for the mixed habitats when validated using field-collected points. The proposed mapping framework was able to utilize different information sources in a systematic and controllable workflow. While transitional mixed habitats were mapped using class membership probabilities and a soft classification method, the identification of other habitats benefited from the hybrid use of remote-sensing classification and ancillary data. Adaptive implementation of classification procedures, development of appropriate rules and combination with spatial data are recommended when producing an integrated and accurate map.

Small-Area Estimation for the USDA Forest Service, National Woodland Owner Survey: Creating a Fine-Scale Land Cover and Ownership Layer to Support County-Level Population Estimates

Small area estimation is a powerful modeling technique in which ancillary data can be utilized to “borrow” additional information, effectively increasing sample sizes in small spatial, temporal, or categorical domains. Though more commonly applied to biophysical variables within the study of forest inventory analyses, small area estimation can also be implemented in the context of understanding social values, behaviors, and trends among types of forest landowners within small domains. Here, we demonstrate a method for deriving a continuous fine-scale land cover and ownership layer for the state of Delaware, United States, and an application of that ancillary layer to facilitate small-area estimation of several variables from the USDA Forest Service’s National Woodland Owner Survey. Utilizing a proprietary parcel layer alongside the National Land Cover Database, we constructed a continuous layer with 10-meter resolution depicting land cover and land ownership classes. We found that the National Woodland Owner Survey state-level estimations of total acreage and total ownerships by ownership class were generally within one standard error of the population values calculated from the raster layer, which supported the direct calculation of several population-level summary variables at the county levels. Subsequently, we compare design-based and model-based methods of predicting commercial harvesting by family forest ownerships in Delaware in which forest ownership acreage, taken from the parcel map, was utilized to inform the model-based approach. Results show general agreement between the two modes, indicating that a small area estimation approach can be utilized successfully in this context and shows promise for other variables, especially if additional variables, e.g., United States Census Bureau data, are also incorporated.

Identifying Epidermolysis Bullosa Patient Needs and Perceived Treatment Benefits: An Explorative Study Using the Patient Benefit Index

Epidermolysis bullosa (EB) is a genetic blistering skin condition for which no cure exists. Symptom alleviation and quality of life are therefore central to EB care. This study aimed to gain insight into EB patient needs and benefits from current clinical care. Two questionnaires were administered cross-sectionally to adult EB patients at the Dutch expertise centre for blistering diseases. Patient needs and benefits were analyzed using the patient benefit index survey (PBI-S). Ancillary data were compiled pertaining to self-reported EB severity, pain and pruritus, as well as current and previous treatments. In total, 104 participants were included (response rate 69.8%). Sixty-eight participants comprised the analyzed cohort (n = 36 omitted from analysis). The needs given the highest importance were to get better skin quickly (64.7%) and to be healed of all skin alterations (61.8%). A positive correlation between pain and EB severity and the importance of most needs was observed. Minimal clinically important differences within the PBI-S, relating to reported benefits from clinical care, were reported by 60.3% of the cohort. This study highlights a discrepancy between patient needs and feasible treatment outcomes. Utilizing the PBI-S in conjunction with well-established multidisciplinary care may catalyze the process of tailoring treatments to the needs of individual patients.

Using UAV imagery to perform fine-scale mapping of wetland vegetation

<p>Differentiating between species of plants in aerial imagery is often challenging and, in some cases, can be impossible without significant field data collection. However, remote sensing technology is developing to the point where it is increasingly possible to eliminate the need for extensive fieldwork entirely and conduct non-disruptive monitoring of fragile environments. The increasing availability of UAV platforms with integrated high-resolution cameras and low-cost image processing software is also making remote sensing operations accessible to those outside the scientific community with an interest in environmental monitoring. This project trialled an emerging set of image analysis techniques called ‘object-based image analysis’ to create fine scale maps of a recovering wetland area, based on aerial photographs collected using a consumer-grade UAV (unmanned aerial vehicle). The effects of including additional ancillary data (such as digital surface models (DSMs) and multispectral imagery) in the classification process were also assessed to compare the ability of a standard digital camera to produce high-accuracy classifications to that of a more specialised multispectral sensor. The inclusion of this extra information was found to significantly improve classification accuracy in almost all cases, making a strong argument for the inclusion of ancillary data whenever possible, especially when considering the ease with which ancillary datasets can be produced. The high-resolution (between 2 and 4cm/pixel) imagery provided sufficient detail to observe 28 distinct land cover classes in total, with around 20 classes per image. While the number of classes in the classification scheme may have imposed limits on the overall accuracy of the classified maps, several classes were classified with a high (70% or greater) level of accuracy, including two invasive species, showing that the object-based school of image classification has potential to be a powerful tool for detecting and tracking individual vegetation types.</p>

Using UAV imagery to perform fine-scale mapping of wetland vegetation

<p>Differentiating between species of plants in aerial imagery is often challenging and, in some cases, can be impossible without significant field data collection. However, remote sensing technology is developing to the point where it is increasingly possible to eliminate the need for extensive fieldwork entirely and conduct non-disruptive monitoring of fragile environments. The increasing availability of UAV platforms with integrated high-resolution cameras and low-cost image processing software is also making remote sensing operations accessible to those outside the scientific community with an interest in environmental monitoring. This project trialled an emerging set of image analysis techniques called ‘object-based image analysis’ to create fine scale maps of a recovering wetland area, based on aerial photographs collected using a consumer-grade UAV (unmanned aerial vehicle). The effects of including additional ancillary data (such as digital surface models (DSMs) and multispectral imagery) in the classification process were also assessed to compare the ability of a standard digital camera to produce high-accuracy classifications to that of a more specialised multispectral sensor. The inclusion of this extra information was found to significantly improve classification accuracy in almost all cases, making a strong argument for the inclusion of ancillary data whenever possible, especially when considering the ease with which ancillary datasets can be produced. The high-resolution (between 2 and 4cm/pixel) imagery provided sufficient detail to observe 28 distinct land cover classes in total, with around 20 classes per image. While the number of classes in the classification scheme may have imposed limits on the overall accuracy of the classified maps, several classes were classified with a high (70% or greater) level of accuracy, including two invasive species, showing that the object-based school of image classification has potential to be a powerful tool for detecting and tracking individual vegetation types.</p>

Outcomes of revision anterior cruciate ligament reconstruction in soccer players

Aims There is limited information on outcomes of revision ACL reconstruction (rACLR) in soccer (association football) athletes, particularly on return to sport and the rate of additional knee surgery. The purpose of this study was to report return to soccer after rACLR, and to test the hypothesis that patient sex and graft choice are associated with return to play and the likelihood of future knee surgery in soccer players undergoing rACLR. Methods Soccer athletes enrolled in a prospective multicentre cohort were contacted to collect ancillary data on their participation in soccer and their return to play following rACLR. Information regarding if and when they returned to play and their current playing status was recorded. If they were not currently playing soccer, they were asked the primary reason they stopped playing. Information on any subsequent knee surgery following their index rACLR was also collected. Player demographic data and graft choice were collected from their baseline enrolment data at rACLR. Results Soccer-specific follow-up was collected on 76% (33 male, 39 female) of 95 soccer athletes. Subsequent surgery information was collected on 95% (44 male, 46 female). Overall, 63% of athletes returned to soccer a mean 9.6 months (SD 5.8) after index revision surgery but participation in soccer decreased to 19% at a mean of 6.4 years (SD 1.3) after surgery. There was no significant association of patient sex or graft choice with return to play, time of return to play, or long-term return to play. Females were more likely than males to have subsequent knee surgery following rACLR (20% (9/46) vs 5% (2/44); p = 0.050). The rate of recurrent graft tear (5.6%; 5/90) was similar between males and females. Conclusion Approximately two-thirds of soccer players return to sport after rACLR, but the rate of participation drops significantly over time. Neither patient sex nor graft choice at the time of rACLR were associated with return to play. Female soccer players face a higher risk for additional knee surgery after rACLR than male soccer players. Cite this article: Bone Jt Open 2021;2(12):1043–1048.

Western Honduras Copán Population–Based Cancer Registry: Initial Estimates and a Model for Rural Central America

PURPOSE Population-based cancer registries (PBCRs) are critical for national cancer control planning, yet few low- and middle-income countries (LMICs) have quality PBCRs. The Central America Four region represents the principal LMIC region in the Western hemisphere. We describe the establishment of a PBCR in rural Western Honduras with first estimates for the 2013-2017 period. METHODS The Western Honduras PBCR was established through a collaboration of academic institutions and the Honduras Ministry of Health for collection of incident cancer data from public and private health services. Data were recorded using the Research Electronic Data Capture (REDCap) web-based platform with data monitoring and quality checks. Crude and age-standardized rates (ASRs) were calculated at the regional level, following WHO methodology. RESULTS The web-based platform for data collection, available ancillary data services (eg, endoscopy), and technical support from international centers (United States and Colombia) were instrumental for quality control. Crude cancer incidence rates were 112.2, 69.8, and 154.6 per 100,000 habitants overall, males, and females, respectively (excluding nonmelanoma skin cancer). The adjusted ASRs were 84.2, 49.6, and 118.9 per 100,000 overall habitants, males, and females, respectively. The most common sites among men were stomach (ASR 26.0, 52.4%), colorectal (ASR 5.11, 10.15%), and prostate (ASR 2.7, 5.4%). The most common sites in women were cervix (ASR 34.2, 36.7%), breast (ASR 11.2, 12.3%), and stomach (ASR 10.8, 11.7%). CONCLUSION The Copán-PBCR represents a successful model to develop cancer monitoring in rural LMICs. Innovations included the use of the REDCap platform and leverage of Health Ministry resources. This provides the first PBCR data for Honduras and the Central America Four and confirms that infection-driven cancers, such as gastric and cervical, should be priority targets for cancer control initiatives.

PM2.5 Modeling and Historical Reconstruction over the Continental USA Utilizing GOES-16 AOD

In this study, we present a nationwide machine learning model for hourly PM2.5 estimation for the continental United States (US) using high temporal resolution Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) data, meteorological variables from the European Center for Medium Range Weather Forecasting (ECMWF) and ancillary data collected between May 2017 and December 2020. A model sensitivity analysis was conducted on predictor variables to determine the optimal model. It turns out that GOES16 AOD, variables from ECMWF, and ancillary data are effective variables in PM2.5 estimation and historical reconstruction, which achieves an average mean absolute error (MAE) of 3.0 μg/m3, and a root mean square error (RMSE) of 5.8 μg/m3. This study also found that the model performance as well as the site measured PM2.5 concentrations demonstrate strong spatial and temporal patterns. Specifically, in the temporal scale, the model performed best between 8:00 p.m. and 11:00 p.m. (UTC TIME) and had the highest coefficient of determination (R2) in Autumn and the lowest MAE and RMSE in Spring. In the spatial scale, the analysis results based on ancillary data show that the R2 scores correlate positively with the mean measured PM2.5 concentration at monitoring sites. Mean measured PM2.5 concentrations are positively correlated with population density and negatively correlated with elevation. Water, forests, and wetlands are associated with low PM2.5 concentrations, whereas developed, cultivated crops, shrubs, and grass are associated with high PM2.5 concentrations. In addition, the reconstructed PM2.5 surfaces serve as an important data source for pollution event tracking and PM2.5 analysis. For this purpose, from May 2017 to December 2020, hourly PM2.5 estimates were made for 10 km by 10 km and the PM2.5 estimates from August through November 2020 during the period of California Santa Clara Unite (SCU) Lightning Complex fires are presented. Based on the quantitative and visualization results, this study reveals that a number of large wildfires in California had a profound impact on the value and spatial-temporal distributions of PM2.5 concentrations.