The occurrence of forest fires has increased significantly in recent years across the planet. Events of this nature have resulted in the leveraging of new automated methodologies to identify and map ...burned areas. In this paper, we introduce a unified data-driven framework capable of mapping areas damaged by fire by integrating time series of remotely sensed multispectral images, statistical modeling, and unsupervised classification. We collect and analyze multiple remote-sensing images acquired by the Landsat-8, Sentinel-2, and Terra satellites between August–October 2020, validating our proposal with three case studies in Brazil and Bolivia whose affected regions have suffered from recurrent forest fires. Besides providing less noisy mappings, our methodology outperforms other evaluated methods in terms of average scores of 90%, 0.71, and 0.65 for overall accuracy, F1-score, and kappa coefficient, respectively. The proposed method provides spatial-adherence mappings of the burned areas whose segments match the estimates reported by the MODIS Burn Area product.
The economic and environmental impacts of wildfires have leveraged the development of new technologies to prevent and reduce the occurrence of these devastating events. Indeed, identifying and ...mapping fire-susceptible areas arise as critical tasks, not only to pave the way for rapid responses to attenuate the fire spreading, but also to support emergency evacuation plans for the families affected by fire-related tragedies. Aiming at simultaneously mapping and measuring the risk of fires in the forest areas of Brazil’s Amazon, in this paper we combine multitemporal remote sensing, derivative spectral indices, and anomaly detection into a fully unsupervised methodology. We focus our analysis on recent forest fire events that occurred in the Brazilian Amazon by exploring multitemporal images acquired by both Landsat-8 Operational Land Imager and Modis sensors. We experimentally confirm that the current methodology is capable of predicting fire outbreaks immediately at posterior instants, which attests to the operational performance and applicability of our approach to preventing and mitigating the impact of fires in Brazilian forest regions.
The Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of ...application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated datasets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50% the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method.
Propolis is a complex honeybee product with resinous aspect, containing plant exudates and beeswax. Their color, texture, and chemical composition vary, depending on the location of the hives and ...local flora. The most studied Brazilian propolis is the green (alecrim-do-campo) type, which contains mainly prenylated phenylpropanoids and caffeoylquinic acids. Other types of propolis are produced in Brazil, some with red color, others brown, grey, or black. The aim of the present work was to determine the chemical profiles of alcohol and chloroform extracts of eight samples of propolis, corresponding to six Brazilian regions. Methanol and chloroform extracts were obtained and analyzed by HPLC/DAD/ESI/MS and GC/MS. Two chemical profiles were recognized among the samples analyzed: (1) black Brazilian propolis, characterized chiefly by flavanones and glycosyl flavones, stemming from Picos (Piauí state) and Pirenópolis (Goiás state); (2) green Brazilian propolis, characterized by prenylated phenylpropanoids and caffeoylquinic acids, stemming from Cabo Verde (Bahia state), Lavras and Mira Bela (Minas Gerais state), Pariquera-Açu and Bauru (São Paulo state), and Ponta Grossa (Paraná state). The present work represents the first report of prenylated flavonoids in Brazilian propolis and schaftoside (apigenin-8-C-glucosyl-6-C-arabinose) in green propolis.
Progressively monitoring water quality is crucial, as aquatic contaminants can pose risks to human health and other organisms. Machine learning can support the development of new effective tools for ...water monitoring, including the detection of algal blooms from remotely sensed image series. Therefore, in this paper, we introduce the Algal Bloom Forecast (ABF) framework, a fully automated framework for algal bloom prediction in inland water bodies. Our approach combines machine learning, time series of remotely sensed products (i.e., Moderate-Resolution Imaging Spectroradiometer (MODIS) images), environmental data and spectral indices to build anomaly detection models that can predict the occurrence of algal bloom events in the posterior period. Our assessments focused on the application of the ABF framework equipped with the support vector machine (SVM), random forest (RF), and long short-term memory (LSTM) methods, the outcomes of which were compared through different evaluation metrics such as global accuracy, the kappa coefficient, F1-Score and R2-Score. Case studies covering the Erie (USA), Chilika (India) and Taihu (China) lakes are presented to demonstrate the effectiveness and flexibility of our learning approach. Based on comprehensive experimental tests, we found that the best algal bloom predictions were achieved by bringing together the ABF design with the RF model.
In this article, we introduce the wavelet energies correlation screening (WECS), an unsupervised method to detect spatio-temporal changes on multitemporal SAR images. The procedure is based on ...wavelet approximation for the multitemporal images, wavelet energy apportionment, and ultrahigh-dimensional correlation screening for the wavelet coefficients. We show WECS's performance on simulated multitemporal image data. We also evaluate the proposed method on a time series of 85 Sentinel-1 images of a forest region at the border of Brazil and French Guiana. Comparisons with well-known change detection methods found in the literature highlight the proposal's superiority in terms of change detection accuracy. Additionally, the introduced method has simple architecture and low computational cost.
Rubinstein‐Taybi syndrome (RSTS) is a rare congenital neurodevelopmental disorder characterized by postnatal growth deficiency, skeletal abnormalities, dysmorphic features and cognitive deficit. ...Mutations in two genes, CREBBP and EP300, encoding two homologous transcriptional co‐activators, have been identified in ˜55% and ˜3–5% of affected individuals, respectively. To date, only eight EP300‐mutated RSTS patients have been described and 12 additional mutations are reported in the database LOVD. In this study, EP300 analysis was performed on 33 CREBBP‐negative RSTS patients leading to the identification of six unreported germline EP300 alterations comprising one deletion and five point mutations. All six patients showed a convincing, albeit mild, RSTS phenotype with minor skeletal anomalies, slight cognitive impairment and few major malformations. Beyond the expansion of the RSTS‐EP300‐mutated cohort, this study indicates that EP300‐related RSTS cases occur more frequently than previously thought (˜8% vs 3–5%); furthermore, the characterization of novel EP300 mutations in RSTS patients will enhance the clinical practice and genotype–phenotype correlations.
This article addresses the challenges of selecting robust classifiers with increasing noise levels in real-world scenarios. We propose the WB Score methodology, which enables the identification of ...reliable classifiers for deployment in noisy environments. The methodology addresses four significant challenges that are commonly encountered: (i) Ensuring classifiers possess robustness to noise; (ii) Overcoming the difficulty of obtaining representative data that captures real-world noise; (iii) Addressing the complexity of detecting noise, making it challenging to differentiate it from natural variations in the data; and (iv) Meeting the requirement for classifiers capable of efficiently handling noise, allowing prompt responses for decision-making. WB Score provides a comprehensive approach for classifier assessment and selection to address these challenges. We analyze five classic datasets and one customized flooding dataset in São Paulo. The results demonstrate the practical effect of using the WB Score methodology is the enhanced ability to select robust classifiers for datasets in noisy real-world scenarios. Compared with similar techniques, the improvement centers around providing a visual and intuitive output, enhancing the understanding of classifier resilience against noise, and streamlining the decision-making process.
Urban Structure Types (USTs) stand for areas with homogeneous appearance over the urban matrix. The use of spatial metrics rises as a convenient alternative to quantify the homogeneity of areas on a ...specific scale. Remote sensing imagery is largely used to assess and study the urban environment, and its classification is a way to recreate the Earth’s surface digitally, both natural and urban spaces. This study proposes a method for city-scale UST mapping using remote sensing images as the unique source of information. Such a proposal comprehends the classification of images that express spatial metrics derived from previous land use and land cover (LULC) classification. We carried two case studies to assess the proposed method under different image resolutions and urban complexity conditions. For this purpose, Landsat-8 OLI and Sentinel-2 MSI images acquired from different cities in Brazil are submitted to the proposed method. An alternative object-based image classification method is included as a comparison baseline. The proposed method shows efficiency in the UST mapping process, which is highly influenced by the neighborhood size considered over the process. Also, it is verified that the proposed method is superior at a significance level of 5%.
Considering the imminence of new SARS-CoV-2 variants and COVID-19 vaccine availability, it is essential to understand the impact of the disease on the most vulnerable groups and those at risk of ...death from the disease. To this end, the odds ratio (OR) for mortality and hospitalization was calculated for different groups of patients by applying an adjusted logistic regression model based on the following variables of interest: gender, booster vaccination, age group, and comorbidity occurrence. A massive number of data were extracted and compiled from official Brazilian government resources, which include all reported cases of hospitalizations and deaths associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Brazil during the “wave” of the Omicron variant (BA.1 substrain). Males (1.242; 95% CI 1.196–1.290) aged 60–79 (3.348; 95% CI 3.050–3.674) and 80 years or older (5.453; 95% CI 4.966–5.989), and hospitalized patients with comorbidities (1.418; 95% CI 1.355–1.483), were more likely to die. There was a reduction in the risk of death (0.907; 95% CI 0.866–0.951) among patients who had received the third dose of the anti-SARS-CoV-2 vaccine (booster). Additionally, this big data investigation has found statistical evidence that vaccination can support mitigation plans concerning the current scenario of COVID-19 in Brazil since the Omicron variant and its substrains are now prevalent across the entire country.
PDF
