Application of satellite image time series and texture information in land cover characterization and burned area detection

Land cover is critical information to various land management and scientific applications, including biogeochemical and climate modeling. In addition, fire is an essential factor in shaping of vegetation structures, as well as for the functioning of savanna ecosystems. Remote sensing has long been an important and effective means of mapping and monitoring land cover and burned area over large areas in a consistent and robust way. Owing to the free and open Landsat archive and the increasing availability of high spatial resolution imagery, seasonal features from the temporal domain and the use of texture features from the spatial domain create new opportunities for land cover characterization and burned area detection. This thesis examined the application of satellite image time series and texture information in land cover characterization and burned area detection. First, the utility of seasonal features derived from Landsat time series (LTS) in improving accuracies of land cover classification and attribute prediction in a savanna area in southern Burkina Faso was studied. Then, the temporal profiles from LTS were explored for mapping burned areas over a 16 year period, and MODIS burned area product was used for comparison. Finally, the application of texture features derived from high spatial resolution data in land cover classification and attribute predictions was investigated in a savanna area of Burkina Faso and an urban fringe area in Beijing. According to the results, firstly, seasonal features from LTS based on all available imagery during one year as input led to a significant increase in land cover classification accuracy in comparison to the dry and wet season single date imagery. The harmonic model used for time series modeling provided a robust method for extracting seasonal features, and the influence of burned pixels on seasonal features could be considered simultaneously. Secondly, the annual burned area mapping based on a harmonic model and breakpoint identification with LTS was capable of detecting small and patchy burn scars with higher accuracy than MODIS burned area product. The approach demonstrated the potential of LTS for improving burned area detection in savannas, and was robust against data gaps caused by clouds and Landsat 7 missing lines. Thirdly, predictive models of tree crown cover (CC) using RapidEye and LTS imagery achieved similar accuracy, indicating the importance of texture and seasonal features from RapidEye and LTS imagery, respectively. Predictions of aboveground carbon and tree species richness, which were strongly correlated with CC, were promising using RapidEye and LTS imagery. Finally, the optimized window size texture classification improved classification accuracy in comparison to the classifications with single window size texture features and multiple window size texture features in an urban fringe area in Beijing, indicating the importance of multiscale texture information. Keywords: Landsat time series, texture, land cover classification, burned area, savanna, tree crown cove

Sensitivity of Spectral Indices on Burned Area Detection using Landsat Time Series in Savannas of Southern Burkina Faso

Accurate and efficient burned area mapping and monitoring are fundamental for environmental applications. Studies using Landsat time series for burned area mapping are increasing and popular. However, the performance of burned area mapping with different spectral indices and Landsat time series has not been evaluated and compared. This study compares eleven spectral indices for burned area detection in the savanna area of southern Burkina Faso using Landsat data ranging from October 2000 to April 2016. The same reference data are adopted to assess the performance of different spectral indices. The results indicate that Burned Area Index (BAI) is the most accurate index in burned area detection using our method based on harmonic model fitting and breakpoint identification. Among those tested, fire-related indices are more accurate than vegetation indices, and Char Soil Index (CSI) performed worst. Furthermore, we evaluate whether combining several different spectral indices can improve the accuracy of burned area detection. According to the results, only minor improvements in accuracy can be attained in the studied environment, and the performance depended on the number of selected spectral indices

Mapping Cropland Burned Area in Northeastern China by Integrating Landsat Time Series and Multi-Harmonic Model

Accurate cropland burned area estimation is crucial for air quality modeling and cropland management. However, current global burned area products have been primarily derived from coarse spatial resolution images which cannot fulfill the spatial requirement for fire monitoring at local levels. In addition, there is an overall lack of accurate cropland straw burning identification approaches at high temporal and spatial resolution. In this study, we propose a novel algorithm to capture burned area in croplands using dense Landsat time series image stacks. Cropland burning shows a short-term seasonal variation and a long-term dynamic trend, so a multi-harmonic model is applied to characterize fire dynamics in cropland areas. By assessing a time series of the Burned Area Index (BAI), our algorithm detects all potential burned areas in croplands. A land cover mask is used on the primary burned area map to remove false detections, and the spatial information with a moving window based on a majority vote is employed to further reduce salt-and-pepper noise and improve the mapping accuracy. Compared with the accuracy of 67.3% of MODIS products and that of 68.5% of Global Annual Burned Area Map (GABAM) products, a superior overall accuracy of 92.9% was obtained by our algorithm using Landsat time series and multi-harmonic model. Our approach represents a flexible and robust way of detecting straw burning in complex agriculture landscapes. In future studies, the effectiveness of combining different spectral indices and satellite images can be further investigated.Peer reviewe

A systematic review of studies on stress during the COVID-19 pandemic by visualizing their structure through COOC, VOS viewer, and Cite Space software

BackgroundThe COVID-19 epidemic generated different forms of stress. From this period, there has been a remarkable increase in the quantity of studies on stress conducted by scholars. However, few used bibliometric analyses to focus on overall trends in the field.PurposeThis study sought to understand the current status and trends in stress development during COVID-19, as well as the main research drives and themes in this field.Methods2719 publications from the Web of Science(WOS) core repository on stress during COVID-19 were analyzed by utilizing Co-Occurrence (COOC), VOS viewer, and Cite Space bibliometric software. The overall features of research on stress during COVID-19 were concluded by analyzing the quantity of publications, keywords, countries, and institutions.ResultsThe results indicated that the United States had the largest number of publications and collaborated closely with other countries with each other. University of Toronto was the most prolific institution worldwide. Visualization and analysis demonstrated that the influence of stress during COVID-19 on the work, life, mental and spiritual dimensions is a hot research topic. Among other things, the frequency of each keyword in research on stress during COVID-19 increased from 2021 to 2022, and the researchers expanded their scope and study population; the range of subjects included children, nurses, and college students, as well as studies focusing on different types of stress, and emphasizing the handling of stress.ConclusionOur findings reveal that the heat of stress research during COVID-19 has declined, and the main research forces come from the United States and China. Additionally, subsequent research should concern more on coping methods with stress, while using more quantitative and qualitative studies in the future

Clinical features of patients with MOG-IgG associated disorders and analysis of the relationship between fibrinogen-to-albumin ratio and the severity at disease onset

ObjectiveThe study aimed to investigate the differences in clinical features between pediatric and adult patients with first-episode MOG-IgG associated disorders (MOGAD) and evaluate the relationship between the fibrinogen-to-albumin ratio (FAR) and the severity of neurological deficits at disease onset.MethodsWe retrospectively collected and analyzed biochemical test results, imaging characteristics, clinical manifestations, expanded disability status scale (EDSS) score, and FAR. The Spearman correlation analysis and logistic regression models were used to examine the association between FAR and severity. Receiver operating characteristic (ROC) curve analysis was to analyze the predictive ability of FAR for the severity of neurological deficits.ResultsFever (50.0%), headache (36.1%), and blurred vision (27.8%) were the most common clinical manifestations in the pediatric group (<18 years old). However, in the adult group (≥18 years old), the most common symptoms were blurred vision (45.7%), paralysis (37.0%), and paresthesia (32.6%). Fever was more common in the pediatric group, while paresthesia was more common in the adult patients, with all differences statistically significant (P < 0.05). The most frequent clinical phenotype in the pediatric group was acute disseminated encephalomyelitis (ADEM; 41.7%), whereas optic neuritis (ON; 32.6%) and transverse myelitis (TM; 26.1%) were more common in the adult group. The differences in clinical phenotype between the two groups were statistically significant (P < 0.05). In both pediatric and adult patients, cortical/subcortical and brainstem lesions were the most common lesions on cranial magnetic resonance imaging (MRI), whereas, for spinal MRI, cervical and thoracic spinal cord lesions were the most commonly observed. According to binary logistic regression analysis, FAR was an independent risk factor for the severity of neurological deficits (odds ratio = 1.717; 95% confidence interval = 1.191–2.477; P = 0.004). FAR (r = 0.359, P = 0.001) was positively correlated with the initial EDSS score. The area under the ROC curve was 0.749.ConclusionThe current study found age-dependent phenotypes in MOGAD patients as ADEM was more commonly observed in patients < 18 years old, while ON and TM were more frequently found in patients ≥18 years old. A high FAR level was an independent indicator for more severe neurological deficits at disease onset in patients with a first episode of MOGAD

The Airlines’ Recent Experience Under the Railway Labor Act

Silky-feather has been selected and fixed in some breeds due to its unique appearance. This phenotype is caused by a single recessive gene (hookless, h). Here we map the silky-feather locus to chromosome 3 by linkage analysis and subsequently fine-map it to an 18.9 kb interval using the identical by descent (IBD) method. Further analysis reveals that a C to G transversion located upstream of the prenyl (decaprenyl) diphosphate synthase, subunit 2 (PDSS2) gene is causing silky-feather. All silky-feather birds are homozygous for the G allele. The silky-feather mutation significantly decreases the expression of PDSS2 during feather development in vivo. Consistent with the regulatory effect, the C to G transversion is shown to remarkably reduce PDSS2 promoter activity in vitro. We report a new example of feather structure variation associated with a spontaneous mutation and provide new insight into the PDSS2 function

Burned area detection based on Landsat time series in savannas of southern Burkina Faso

West African savannas are subject to regular fires, which have impacts on vegetation structure, biodiversity and carbon balance. An efficient and accurate mapping of burned area associated with seasonal fires can greatly benefit decision making in land management. Since coarse resolution burned area products cannot meet the accuracy needed for fire management and climate modelling at local scales, the medium resolution Landsat data is a promising alternative for local scale studies. In this study, we developed an algorithm for continuous monitoring of annual burned areas using Landsat time series. The algorithm is based on burned pixel detection using harmonic model fitting with Landsat time series and breakpoint identification in the time series data. This approach was tested in a savanna area in southern Burkina Faso using 281 images acquired between October 2000 and April 2016. An overall accuracy of 79.2% was obtained with balanced omission and commission errors. This represents a significant improvement in comparison with MODIS burned area product (67.6%), which had more omission errors than commission errors, indicating underestimation of the total burned area. By observing the spatial distribution of burned areas, we found that the Landsat based method misclassified cropland and cloud shadows as burned areas due to the similar spectral response, and MODIS burned area product omitted small and fragmented burned areas. The proposed algorithm is flexible and robust against decreased data availability caused by clouds and Landsat 7 missing lines, therefore having a high potential for being applied in other landscapes in future studies.Peer reviewe

Remote sensing approach for spatial planning of land management interventions in West African savannas

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