Exploring the Potential of Feature Selection Methods in the Classification of Urban Trees Using Field Spectroscopy Data

Mapping of vegetation at the species level using hyperspectral satellite data can be effective and accurate because of its high spectral and spatial resolutions that can detect detailed information of a target object. Its wide application, however, not only is restricted by its high cost and large data storage requirements, but its processing is also complicated by challenges of what is known as the Hughes effect. The Hughes effect is where classification accuracy decreases once the number of features or wavelengths passes a certain limit. This study aimed to explore the potential of feature selection methods in the classification of urban trees using field hyperspectral data. We identified the best feature selection method of key wavelengths that respond to the target urban tree species for effective and accurate classification. The study compared the effectiveness of Principal Component Analysis Discriminant Analysis (PCA-DA), Partial Least Squares Discriminant Analysis (PLS-DA) and Guided Regularized Random Forest (GRRF) in feature selection of the key wavelengths for classification of urban trees. The classification performance of Random Forest (RF) and Support Vector Machines (SVM) algorithms were also compared to determine the importance of the key wavelengths selected for the detection of the target urban trees. The feature selection methods managed to reduce the high dimensionality of the hyperspectral data. Both the PCA-DA and PLS-DA selected 10 wavelengths and the GRRF algorithm selected 13 wavelengths from the entire dataset (n = 1523). Most of the key wavelengths were from the short-wave infrared region (1300-2500 nm). SVM outperformed RF in classifying the key wavelengths selected by the feature selection methods. The SVM classifier produced overall accuracy values of 95.3%, 93.3% and 86% using the GRRF, PLS-DA and PCA-DA techniques, respectively, whereas those for the RF classifier were 88.7%, 72% and 56.8%, respectively

High prevalence of urinary schistosomiasis in two communities in South Darfur: implication for interventions

<p>Abstract</p> <p>Background</p> <p>There are few data on the prevalence of schistosomiasis in Darfur. We conducted this study in response to reports of 15 laboratory confirmed cases of schistosomiasis and visible haematuria among children from two communities in South Darfur. The aim of the study was to estimate the prevalence of schistosomiasis in the area and to decide on modalities of intervention.</p> <p>Methods</p> <p>A cross-sectional survey involving 811 children and adults from schools and health facilities was conducted in two communities of South Darfur in March 2010. Urine samples were collected and examined for ova of <it>Schistosoma haematobium </it>using a sedimentation technique. A semi-structured format was used to collect socio-demographic characteristics of the participants.</p> <p>Results</p> <p>Eight hundred eleven (811) urine samples were collected, 415 from Alsafia and 396 from Abuselala. Of the collected samples in 56.0% (95% Confidence Interval (CI); 52.6-59.4) <it>Schistosoma </it>eggs were found. The prevalence was high in both Abuselala 73.3% (95% CI; 68.9-77.6) and Alsafia 39.5% (95% CI; 34.8-44.2). More males (61.7%, 95%CI; 56.5-64.9) were infected than females (52.1%, 95%CI; 48.2-56.0). Children in the age group 10-14 has the highest (73.0%, 95%CI; 68.7-77.2) infection rate. School age children (6-15 years) are more likely to be infected than those >15 years (Adjusted Odds Ratio (AOR) = 2.70, 95% CI; 1.80-4.06). Individuals in Abuselala are more likely to be infected than those who live in Alsafia (AOR = 4.3, 95% CI; 3.2-5.9).</p> <p>Conclusion</p> <p>The findings of this study indicate that <it>S. hematobium </it>is endemic in Alsafia and Abuselala South Darfur in Sudan with a high prevalence of infection among older children. This signifies the importance of urgent intervention through Mass Drug Administration (MDA) to halt the infection cycle and tailored health messages to targeted groups. Based on the findings MDA was conducted in the villages.</p

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Exploring the Effect of Balanced and Imbalanced Multi-Class Distribution Data and Sampling Techniques on Fruit-Tree Crop Classification Using Different Machine Learning Classifiers

Fruit-tree crops generate food and income for local households and contribute to South Africa&rsquo;s gross domestic product. Timely and accurate phenotyping of fruit-tree crops is essential for innovating and achieving precision agriculture in the horticulture industry. Traditional methods for fruit-tree crop classification are time-consuming, costly, and often impossible to use for mapping heterogeneous horticulture systems. The application of remote sensing in smallholder agricultural landscapes is more promising. However, intercropping systems coupled with the presence of dispersed small agricultural fields that are characterized by common and uncommon crop types result in imbalanced samples, which may limit conventionally applied classification methods for phenotyping. This study assessed the influence of balanced and imbalanced multi-class distribution and data-sampling techniques on fruit-tree crop detection accuracy. Seven data samples were used as input to adaptive boosting (AdaBoost), gradient boosting (GB), random forest (RF), support vector machine (SVM), and eXtreme gradient boost (XGBoost) machine learning algorithms. A pixel-based approach was applied using Sentinel-2 (S2). The SVM algorithm produced the highest classification accuracy of 71%, compared with AdaBoost (67%), RF (65%), XGBoost (63%), and GB (62%), respectively. Individually, the majority of the crop types were classified with an F1 score of between 60% and 100%. In addition, the study assessed the effect of size and ratio of class imbalance in the training datasets on algorithms&rsquo; sensitiveness and stability. The results show that the highest classification accuracy of 71% could be achieved from an imbalanced training dataset containing only 60% of the original dataset. The results also showed that S2 data could be successfully used to map fruit-tree crops and provide valuable information for subtropical crop management and precision agriculture in heterogeneous horticultural landscapes

Machine Learning Classification of Fused Sentinel-1 and Sentinel-2 Image Data towards Mapping Fruit Plantations in Highly Heterogenous Landscapes

Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapping spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit trees mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well suited for accurate smallholder fruit plantation mapping

Mapping and Monitoring Spatio-Temporal Patterns of Rainfed Agriculture Lands of North Darfur State, Sudan, Using Earth Observation Data

Rainfed agriculture in Northern Darfur is influenced by erratic seasonal and decadal rainfall patterns and frequent droughts. Understanding the spatio-temporal variation in rainfed agriculture is crucial for promoting food security, socio-economic stability and protecting the vulnerable ecosystem. This study aimed to investigate the spatio-temporal dynamics of rainfed agriculture in North Darfur State from 1984–2019 using multitemporal Landsat observation data. Using the random forest technique, the multitemporal images were classified into common land use/land cover classes and rainfed agriculture on goz (sandy) and wadi (seasonal river) lands. Overall accuracies were assessed using a confusion matrix. Overall accuracies were assessed using a confusion matrix has ranging between 94.7% and 96.9%, while the kappa statistics were greater than 0.90. The results showed that the high spatial variability in goz land used for rainfed agriculture increased of (889,622.46 ha) over 1994–1999, while it decreased (658,568.61 ha) over 2004–2009 south of the 232.9 mm isohyet. Rainfed cultivation of wadi lands expanded significantly of (580,515.03 ha) over 2014–2019 and decreased (182,701.8 ha) over 1994–1999, especially in the 362.8–477.2 mm isohyets (beyond the climate-adapted 500 mm isohyet agronomic dry limit). These spatial trends need further investigation as they may exacerbate both regional land degradation and disputes among farmers over scarce wadi lands. This study provides essential spatial data which are lacking owing to ongoing conflicts; this can help decision-makers formulate sustainable land use monitoring systems

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population

Delayed colorectal cancer care during covid-19 pandemic (decor-19). Global perspective from an international survey

Background The widespread nature of coronavirus disease 2019 (COVID-19) has been unprecedented. We sought to analyze its global impact with a survey on colorectal cancer (CRC) care during the pandemic. Methods The impact of COVID-19 on preoperative assessment, elective surgery, and postoperative management of CRC patients was explored by a 35-item survey, which was distributed worldwide to members of surgical societies with an interest in CRC care. Respondents were divided into two comparator groups: 1) ‘delay’ group: CRC care affected by the pandemic; 2) ‘no delay’ group: unaltered CRC practice. Results A total of 1,051 respondents from 84 countries completed the survey. No substantial differences in demographics were found between the ‘delay’ (745, 70.9%) and ‘no delay’ (306, 29.1%) groups. Suspension of multidisciplinary team meetings, staff members quarantined or relocated to COVID-19 units, units fully dedicated to COVID-19 care, personal protective equipment not readily available were factors significantly associated to delays in endoscopy, radiology, surgery, histopathology and prolonged chemoradiation therapy-to-surgery intervals. In the ‘delay’ group, 48.9% of respondents reported a change in the initial surgical plan and 26.3% reported a shift from elective to urgent operations. Recovery of CRC care was associated with the status of the outbreak. Practicing in COVID-free units, no change in operative slots and staff members not relocated to COVID-19 units were statistically associated with unaltered CRC care in the ‘no delay’ group, while the geographical distribution was not. Conclusions Global changes in diagnostic and therapeutic CRC practices were evident. Changes were associated with differences in health-care delivery systems, hospital’s preparedness, resources availability, and local COVID-19 prevalence rather than geographical factors. Strategic planning is required to optimize CRC care