Research on Remaining Useful Life Prediction Method of Rolling Bearing Based on Health Indicator Extraction and Trajectory Enhanced Particle Filter

Aiming at the difficulty of mining fault prognosis starting points and constructing prognostic models for remaining useful life(RUL)prediction of rolling bearings,a RUL prediction method is proposed based on health indicator(HI)extraction and trajectory-enhanced particle filter(TE-PF).By extracting a HI that can accurately track the trending of bearing degradation and combining it with the early fault enhancement technology,early abnormal sample nodes can be mined to provide more samples with fault information for the construction and training of subsequent prediction models.Aiming at the problem that traditional degradation rate models based on PF are vulnerable to HI mutations,a TE-PF prediction method is proposed based on comprehensive utilization of historical degradation information to timely modify prediction model parameters.Results from a rolling bearing prognostic study show that prediction starting points can be accurately detected and a reasonable prediction model can be conveniently constructed by the RUL prediction method based on HI amplitude abnormal detection and TE-PF.Furthermore,aiming at the RUL prediction problem under the condition of HI mutation,RUL prediction with probability and statistics characteristics under a confidence interval can be obtained based on the method proposed.

The Interrelation of Urban Health Indicators and COVID-19:A Study of Passo Fundo/RS

The COVID-19(Coronavirus Disease 19)pandemic has demonstrated that cities are at the center of major contemporary events.The epidemiological crisis has highlighted the importance of the urban environment,challenging public managers on managing cities.An initiative that aims to assist in this management process is the concept of Smart Cities,which uses ICT(Information and Communication Technology)as tools for transforming urban dynamics,and through urban indicators,measures information about cities.Thus,the research aimed to analyze the health indicators of Passo Fundo/RS,seeking to analyze the interrelationship of these indicators with the epidemiological data from COVID-19.In the methodology,multi-method procedures were applied,using the indicators of the Connected Smart Cities Ranking as reference,as well as a regional selection of medium-sized cities in the southern region of Brazil.The results show that the health indices of Passo Fundo are,for the most part,lower than those of the analyzed cities,with the indicator related to Population Coverage of the Family Health team as the main weakness.However,it also presents satisfactory indices as is the case of the indicator of Beds/1,000 inhabitants.Regarding the epidemiological picture of COVID-19,Passo Fundo had a high lethality rate when compared to the other analyzed cities.

Unsupervised construction of health indicator for rotating machinery via multi-criterion feature selection and attentive variational autoencoder

Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating machinery.Excluding interference from artificial labeling,the HI construction approaches in an unsupervised manner have attracted substantial attention.Nevertheless,current unsupervised methods generally struggle with two problems:(1)ignorance of both redundancy between features and global variability of features during the feature selection process;(2)inadequate utilization of information from different sampling moments.To tackle these problems,this work develops a novel unsupervised approach for HI construction that integrates multi-criterion feature selection and the Attentive Variational Autoencoder(Attentive VAE).Explicitly,a multi-criterion feature selection(Mc FS)algorithm together with an elaborately designed metric is proposed to determine a superior feature subset,considering the relevance,the redundancy,and the global variability of features simultaneously.Then,for the adequate utilization of the information from distinct sampling moments,a deep learning model named Attentive VAE is established.The Attentive VAE is solely fed with the selected features in the health state for model training and the HI is derived through the reconstruction error to reveal the degradation degree of machinery.Two case studies based on genuine experimental datasets are involved to quantitatively evaluate the superiority of the developed approach,demonstrating its superiority over other unsupervised methods for characterizing degradation processes.The effectiveness of both the Mc FS algorithm and the Attentive VAE is verified by ablation experiments,respectively.

State of health prediction for lithium-ion batteries based on ensemble Gaussian process regression

The performance of lithium-ion batteries(LIBs)gradually declines over time,making it critical to predict the battery’s state of health(SOH)in real-time.This paper presents a model that incorporates health indicators and ensemble Gaussian process regression(EGPR)to predict the SOH of LIBs.Firstly,the degradation process of an LIB is analyzed through indirect health indicators(HIs)derived from voltage and temperature during discharge.Next,the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA),and the EGPR is employed to estimate the SOH of LIBs.Finally,the proposed model is tested under various experimental scenarios and compared with other machine learning models.The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration(NASA)LIB.The root mean square error(RMSE)is maintained within 0.20%,and the mean absolute error(MAE)is below 0.16%,illustrating the proposed approach’s excellent predictive accuracy and wide applicability.

Body Size as an Indicator of Health for Green Turtles at the TCMP （Tobago Cays Marine Park）, Eastern Caribbean

Gazetted in 1997, The TCMP （Tobago Cays Marine Park） is the only marine protected area within the territorial boundaries of St. Vincent and the Grenadines. Sea turtles are an important species in TCMP, and because their globally endangered status have become the focus of an in-water monitoring program, primarily for green turtles （Chelonia mydas）. Preliminary assessment indicated that the TCMP contains an important foraging aggregation of sub-adult green turtles. Since then two additional years of data has been collected; this data was analyzed to assess the health of the turtle population. The data also indicated that the weight was predictable according to carapace width and length and that the population is dominated by larger sub-adults.

Deep neural network based classification of rolling element bearings and health degradation through comprehensive vibration signal analysis

Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry.The likelihood of failure has the propensity of increasing under prolonged operation and varying working conditions.Hence, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating machinery.The variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault features.In this paper, the efficacy and the leverage of a pre-trained convolutional neural network(CNN) is harnessed in the implementation of a robust fault classification model.In the absence of sufficient data, this method has a high-performance rate.Initially, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a classifier.The raw vibration data are strategically segmented and transformed into two representations which are trained separately and jointly.The proposed approach is carried out on bearing vibration data and shows high-performance results.In addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator(HI) under varying operating conditions for a given fault condition.

Potentially harmful metals,and health risk evaluation in groundwater of Mardan,Pakistan:Application of geostatistical approach and geographic information system

This study investigates the values of pH,total dissolved solids(TDS),elevation,oxidative reduction potential(ORP),temperature,and depth,while the concentrations of Br,and potentially harmful metals(PHMs)such as Cr,Ni,Cd,Mn,Cu,Pb,Co,Zn,and Fe in the groundwater samples.Moreover,geographic information system(GIS),XLSTAT,and IBM SPSS Statistics 20 software were used for spatial distribution modeling,principal component analysis(PCA),cluster analysis(CA),and Quantile-Quantile(Q-Q)plotting to determine groundwater pollution sources,similarity index,and normal distribution reference line for the selected parameters.The mean values of pH,TDS,elevation,ORP,temperature,depth,and Br were 7.2,322 mg/L,364 m,188 mV,29.6℃,70 m,0.20 mg/L,and PHMs like Cr,Ni,Cd,Mn,Cu,Pb,Co,Zn,and Fe were 0.38,0.26,0.08,0.27,0.36,0.22,0.04,0.43 and 0.86 mg/L,respectively.PHMs including Cr(89%),Cd(43%),Mn(23%),Pb(79%),Co(20%),and Fe(91%)exceeded the guideline values set by the world health organization(WHO).The significant R^(2)values of PCA for selected parameters were also determined(0.62,0.67,0.78,0.73,0.60,0.87,-0.50,0.69,0.70,0.74,-0.50,0.70,0.67,0.79,0.59,and-0.55,respectively).PCA revealed three geochemical processes such as geogenic,anthropogenic,and reducing conditions.The mineral phases of Cd(OH)_(2),Fe(OH)_(3),FeOOH,Mn_(3)O_(4),Fe_(2)O_(3),MnOOH,Pb(OH)_(2),Mn(OH)_(2),MnO_(2),and Zn(OH)_(2)(-3.7,3.75,9.7,-5.8,8.9,-3.6,2.2,-4.6,-7.7,-0.9,and 0.003,respectively)showed super-saturation and under-saturation conditions.Health risk assessment(HRA)values for PHMs were also calculated and the values of hazard quotient(HQ),and hazard indices(HI)for the entire study area were increased in the following order:Cd>Ni>Cu>Pb>Mn>Zn>Cr.Relatively higher HQ and HI values of Ni,Cd,Pb,and Cu were greater than one showing unsuitability of groundwater for domestic,agriculture,and drinking purposes.The long-term ingestion of groundwater could also cause severe health concerns such as kidney,brain dysfunction,liver,stomach problems,and even cancer.

Quality indicators for the care of older adults with disabilities in longterm care facilities based on Maslow’s hierarchy of needs

Purpose:This study aimed to develop quality indicators for the care of older adults with disabilities in long-term care facilities(LTCFs)based on Maslow’s hierarchy of needs.Methods:The draft of the quality indicators was drawn up based on a literature review and research group discussion.The quality indicators were finalized by two rounds of expert consultation(involving 15 experts)using the Delphi method.The Analytic Hierarchy Process was applied to calculate the indicators’weight.Results:The response rates of the two rounds of consultation were 100%and 93%,and the expert authority coefficients were 0.86 and 0.87.After two rounds of consultation,the expert opinion coordination coefficients of the first-,second-and third-level indicators were 0.42,0.25,and 0.96,respectively(P<0.05),and the variation coefficient was0.25.The final quality indicators for the care of older adults with disabilities in LTCFs included 7 first-level,19 second-level,and 107 third-level indicators.Conclusion:The quality indicators for the care of older adults with disabilities in LTCFs are reliable,scientific,comprehensive,and practical and specify the content of person-centered care needs.This can provide a reference for evaluating and improving care quality in LTCFs.

ICT-Enabled Universal Health Coverage Monitoring and Evaluation in Sub-Saharan Health Facilities： Study in 8 Reference Hospitals of Rwanda, Burundi, the Democratic Republic of Congo and Mali

Abstract： UHC （Universal Health Coverage） is at the center of the Sustainable Development Agenda. In this study, the authors made an evaluation of the indicators allowing quantification of the impact of health coverage schemes on patients, applied in 4 sub-Saharan Africa countries： Rwanda, Burundi, the DRC （Democratic Republic of Congo） and Mall After an analysis of potential health coverage indicators, the most relevant ones were calculated on the basis of patient administrative and health insurance data, collected via OpenClinic GA, an HIMS （health information management system） used in 8 sub-Saharan hospitals during the period 2010-2016. The results show that the PHSC （patient health services coverage） rate is highest （81.5%-92.7%） in the 2 hospitals of Rwanda and in 2 hospitals of Burundi （37.7%-77.7%）. The PHSP （patient health service payment） rate as the proportion of costs paid by the patient versus total health service costs is below the 25% threshold recommended by WHO only for the 2 hospitals in Rwanda. The POOP （patient out-of-pocket） payment is below the threshold of 180USD per patient per year for all hospitals. The HIEXs （health insurance expenditures） are funded by the university private insurance （86% of expenses covered） in 2 university teaching hospitals in DRC, by CBHI （community based health insurance） （69%） in 2 hospitals in Rwanda, by the free care policy （77%） in 2 hospitals in Burundi and by the SHI （social health insurance） （100%） in the 2 hospitals in Mali. PHSC in the 8 reference hospitals reflects the national trend towards UHC in each country. With this study, we demonstrate the possibility to assess the degree of UHC in developing countries, by a methodology based on indicators calculated via information extraction from routine data in electronic health records.

Critical Review on Improved Electrochemical Impedance Spectroscopy-cuckoo Search-Elman Neural Network Modeling Methods for Whole-life-cycle Health State Estimation of Lithium-ion Battery Energy Storage Systems

Efficient and accurate health state estimation is crucial for lithium-ion battery(LIB)performance monitoring and economic evaluation.Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems.With high adaptability and applicability advantages,battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world.Artificial neural network(ANN)-based methods are often used for state estimations of LIBs.As one of the ANN methods,the Elman neural network(ENN)model has been improved to estimate the battery state more efficiently and accurately.In this paper,an improved ENN estimation method based on electrochemical impedance spectroscopy(EIS)and cuckoo search(CS)is established as the EIS-CS-ENN model to estimate the health state of LIBs.Also,the paper conducts a critical review of various ANN models against the EIS-CS-ENN model.This demonstrates that the EIS-CS-ENN model outperforms other models.The review also proves that,under the same conditions,selecting appropriate health indicators(HIs)according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently.In the calculation process,several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods.Through the analysis of the evaluation results and the selection of HIs,conclusions and suggestions are put forward.Also,the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified.

The Sendai Framework for Disaster Risk Reduction and Its Indicators——Where Does Health Fit in?

The Sendai Framework for Disaster Risk Reduction 2015–2030 recognizes the strong connection between health and disasters and promotes the concept of health resilience throughout.Several of the seven global targets stated in the Sendai Framework are directly related to health in terms of reducing disaster mortality,the number of affected people,disaster damage to critical infrastructure,and disruption of basic services such as health facilities.The Sendai Framework also maintains close coordination with other United Nations landmark agreements relevant to health such as the Sustainable Development Goals.However,the measurement of healthrelated indicators is challenging.Issues arise,for example,in linking deaths to disasters because of the complex interplay between exposure,risk,vulnerability,and hazards.The lack of a universal classification of disasters also means that recording of health data in disasters is not standardized.Developing the guidelines to enable data onthe indicators to be collected and reported to support the Sendai targets requires detailed thinking,time,and consultation with a diverse range of stakeholders.Strong collaboration and partnership will be vital to achieving success.

Systematic review of long-term Xingnao Kaiqiao needling efficacy in ischemic stroke treatment

OBJeCTIve：To systematically evaluate the long-term effect and safety ofXingnao Kaiqiao nee-dling method in ischemic stroke treatment. DATA ReTRIevAL： We retrieved relevant random and semi-random controlled trials that used theXingnao Kaiqiao needling method to treat ischemic stroke compared with various control treatments such as conventional drugs or other acupuncture therapies. Searched databases included China National Knowledge Infrastructure, Weipu Information Resources System, Wanfang Medical Data System, Chinese Biomedical Literature Database, Cochrane Library, and PubMed, from May 2006 to July 2014. SeLeCTION CRITeRIA： Two authors independently conducted literature screening, quality evaluation, and data extraction. The quality of articles was evaluated according to the Cochrane Reviewers’ Handbook 5.1, and the study was carried out using Cochrane system assessment methods. RevMan 5.2 was used for meta-analysis of the included studies. MAIN OUTCOMe MeASUReS： Mortality rate, recurrence rate, and disability rate were observed. ReSULTS：Nine randomized and semi-randomized controlled trials treating 931 cases of ischemic stroke were included in this review. Meta-analysis results showed that there were no sig-niifcant differences in mortality reduction （risk ratio （RR） = 0.58, 95% conifdence interval （CI）： 0.17–1.93,Z = 0.89,P = 0.37） or recurrence rate （RR = 0.55, 95%CI： 0.18–1.70,Z = 1.04,P = 0.30） of ischemic stroke patients between theXingnao Kaiqiao needling and control treatment groups. However, theXingnao Kaiqiao needling method had a tendency towards higher efifcacy in mor-tality reduction and recurrence rates. TheXingnao Kaiqiao needling method was signiifcantly better than that of the control treatment in reducing disability rate （RR = 0.51, 95%CI： 0.27–0.98, Z = 2.03,P 〈 0.05）. CONCLUSION：TheXingnao Kaiqiao needling method has a better effect than control treatment in reducing disability rate. The long-term effect ofXingnao Kaiqiao needling against ischemic stroke is better than that of control treatment. However, the limitations of this study limit the strength of the conclusions. Randomized controlled trials with a strict, reasonable design, and multi-center, large-scale samples and follow-up are necessary to draw conclusions aboutXingnao Kaiqiao needling.

Biological indicators of sub-optimal health status

OBJECTIVE:To investigate biological indicators of sub-optimal health status and provide means of objective assessment of sub-optimal health status.METHODS:We set the unified standards for diagnosing a SHS.We tested various laboratory indicators in 407 cases that we selected randomly from2807 subjects and collected 15 mL of fasting venous blood from each case.We measured serum immunoglobulin A(IgA)and immunoglobulin G(IgG)concentrations,serum beta endorphins(β-EP),cortisol(C),testosterone(T),plasma adrenocorticotropic hormone(ACTH)and serum T lymphocyte subsets CD3+and CD4+.RESULTS:Mean serum testosterone concentrations and their ratio to cortisol(C)concentrations weresignificantly higher in the healthy group than in those with sub-optimal health status(P<0.01).Mean serum CD3+concentrations were significantly higher in those with sub-optimal health status than in the healthy group(P<0.05).CONCLUSION:Decreased serum testosterone/cortisol ratio may be an objective indication of sub-optimal health status.Changes in neuroendocrine and immunological indicators may explain some of the symptoms,including malaise and poor work performance,attributable to persistent or relapsing fatigue in subjects with sub-optimal health status.

Scleractinian coral communities of Hormuz Island in the PersianGulf

The abundance and health of scleractinian coral communities of Hormuz Island were investigated.For this purpose,we employed 20 m line intercept transects—12 in the intertidal zone and 15 subtidally to evaluate coral cover and community composition.The estimated dead coral coverage was 6.21%±0.81%,while live coral coverage was 16.93%±1.81%,considered as very poor.Totally,12 genera were recorded,of which Porites with 11.9%±1.4%live cover was the dominant,while Goniopora had the least cover(0.07%±0.08%).Based on Mann-Whitney U-test,live coral coverage,dead coral coverage,algal coverage,cover of other benthic organisms and abiotic components showed significant univariate differences between zones(p<0.05).The Spearman correlation test between the abundance of biotic and abiotic components indicated significant negative correlation of live coral and sand with zoantharian and significant positive correlation of algae and other benthic organisms with rubble.The reef health indices used for the corals indicated that,in general,the environmental conditions were not suitable,which could be attributed to both natural and anthropogenic factors,the most important of which was zoantharian’overgrowth on the scleractinian corals in this region.

Challenges using electronic nursing routine data for outcome analyses:A mixed methods study

Objectives To explore the challenges of secondary use of routinely collected data for analyzing nursing-sensitive outcomes in Austrian acute care hospitals.Method A convergent parallel mixed methods design was performed.We conducted a quantitative representative survey with nursing managers from 32 Austrian general acute care hospitals and 11 qualitative semi-structured interviews with nursing quality management experts.Both results were first analyzed independently and afterward merged in the discussion.Results On average,76%of nursing documentation is already electronically supported in the surveyed Austrian hospitals.However,existing nursing data is seldom used for secondary purposes such as nursing-sensitive outcome analyses.This is due to four major reasons:First,hospitals often do not have a data strategy for the secondary use of routine data.Second,hospitals partly lack the use of standardized and uniform nursing terminologies,especially for nursing evaluation.Third,routine nursing data is often not documented correctly and completely.Fourth,data on nursing-sensitive outcomes is usually collected in specific documentation forms not integrated into routine documentation.Conclusion The awareness of the possibilities for secondary use of nursing data for nursing-sensitive outcome analyses in Austrian hospitals is still in its infancy.Therefore,nursing staff and nursing management must be trained to understand how to collect and process nursing data for nursing-sensitive outcome analyses.Further studies would be interesting in order to determine the factors that influence the decision-making processes for the secondary use of nursing data for outcome analyses.

Health assessment of plantations based on LiDAR canopy spatial structure parameters

The Yellow River Delta(YRD)has China's largest artificial Robinia pseudoacacia forest,which was planted in the late 1970s and suffered extensive dieback in the 1990s.The health grade of the R.pseudoacacia forest(named canopy vigor grade,CVG)could be achieved by using high-resolution images and canopy vigor indicators(CVIs).However,a previous study showed that there was no significant correlation between CVG and the field-estimated aboveground biomass(AGB)of R.pseudoacacia forest.Therefore,this study aims to construct forest health indicators(FHIs)based on canopy spatial structure parameters extracted from LiDAR.The FHIs included Weibull_α(the scale parameter of the Weibull density function that reflects the shape of the tree canopy),VCI(vertical complexity index),sdCC(the standard deviation of canopy cover),H99(the 99th percentile height)and cvLAD(the coefficient of variation of leaf area density),and could significantly distinguish three forest health grades(FHG)(p<0.05).The FHG was positively correlated with forest AGB(rs=0.51,p=0.004),and the similarity value with CVG was 63.33%.The results of this study confirmed that the FHIs can reflect both canopy vigor and tree productivity,and distinguish forest health status without prior classification information.

Health-related behaviors in children of ethnic minorities and Han nationality in China

Objective:The purpose of this study was to understand the health-related behaviors in children of ethnic minorities and Han nationality so as to provide a basis for formulating a health promotion plan,reasonably allocating health resources,and improving health conditions of the entire population of children.Methods:The selection and processing of study subjects,as well as health-related behaviors,were based on the 2009 Chinese Health and Nutrition Survey(CHNS)data.A total of 867 children were involved in this study,including 762 Han children and 105 minority children.Comparative analysis was conducted on the reported ratio of health-related behaviors,including society and family variation,as well as dietary habit variation,and health condition scores.Results:A comparison on health-related behaviors between ethnic minority and Han children indicated the following:with respect to society and family variation,statistical significance(P<0.05)existed between the two groups in health-related behaviors influenced by parents who did or did not stay at home,level of education,and medical insurance status;and with respect to dietary habit variation,statistical significance(P<0.05)existed in the differences of dietary habits between the two groups.Moreover,differences in the weight-forage Z score(WAZ),weight-forheight Z score(WHZ),and body mass index-for-age Z score(BAZ)between the two groups were statistically significant(P<0.05).Conclusion:Health-related behavioral norms and health conditions of ethnic minority children should be further improved.Increased health awareness of families with children and health care system development should be stressed to elevate the health level of the entire population of children in China.

Trip and mode choice decisions to out-patient health care facilities:A longitudinal analysis

This paper analyzes the impact of health indicators on an individual's trip and mode choices to out-patient care facilities.The study's focus is an out-patient trip to a health care facility,and the source of data is the China Health and Retirement Longitudinal Study(CHARLS)for 2011.2013 and 2015.Based on a random utility framework,the study finds that making a rip to ureat an ilness or for a check-up increases the likelihood of an out-patient trip by 52 and 31 probability points,respectively.Out-patient visits for which in-surance is not relevant,When the individual pays most of the out:of-pocket costs and when the facility is a public facility are also important factors.Diagnosed and other per-sonal health factors have some but much more modest effects on one's trip choice.The analysis also identifies differential modal impacts of health indicators.A series of robustness tests generally confirm the results and identify areas for further research.Including a no-trip option,the biannual sunvey and infrequent out-patient trip-making mitigate endogeneity concerns.The analysis has broad health policy and transportation implications for an ageing population whose share is increasing.

The Global Leprosy Assessment Index(GLAI):A new approach for measuring the severity of disease in Brazil

Background:In Brazil,the Ministry of Health(MH)monitors leprosy using 15 indicators,with the aim of imple-menting and evaluating evidence-based public policies.However,an excessive number of variables can compli-cate the definition of objectives and verification of epidemiological goals.Methods:In this paper,we develop the Global Leprosy Assessment Index(GLAI),a composite measure that integrates two key dimensions for the control the disease:epidemiological and operational.Using a confirmatory factor analysis model to examine 2020 state-level data,we have standardized GLAI to a range of 0 to 1.Results:Higher values within this range indicate a greater severity of the disease.The mean value of the GLAI was 0.67,with a standard deviation of 0.22.Roraima has the highest value,followed by Paraíba with 0.88 while Tocantins records the lowest value of the indicator,followed by Mato Grosso with 0.14.The epidemiological and operational indicators have a positive but statistically insignificant correlation(r=0.25;p-value=0.20).Conclusions:The development of evidence-based public policies depends on the availability of valid and reliable indicators.The GLAI presented in this paper is easily reproducible and can be used to monitor the disease with disaggregated information.Furthermore,the GLAI has the potential to serve as a more robust parameter for evaluating the impact of actions designed to eradicate leprosy in Brazil.

Unmanned aerial vehicles(UAV)for assessment of qualitative classification of Norway spruce in temperate forest stands

The study investigates the potential of UAV-based remote sensing technique for monitoring of Norway spruce health condition in the affected forest areas.The objectives are:(1)to test the applicability of UAV visible an near-infrared(VNIR)and geometrical data based on Z values of point dense cloud(PDC)raster to separate forest species and dead trees in the study area;(2)to explore the relationship between UAV VNIR data and individual spruce health indicators from field sampling;and(3)to explore the possibility of the qualitative classification of spruce health indicators.Analysis based on NDVI and PDC raster was successfully applied for separation of spruce and silver fir,and for identification of dead tree category.Separation between common beech and fir was distinguished by the object-oriented image analysis.NDVI was able to identify the presence of key indicators of spruce health,such as mechanical damage on stems and stem resin exudation linked to honey fungus infestation,while stem damage by peeling was identified at the significance margin.The results contributed to improving separation of coniferous(spruce and fir)tree species based on VNIR and PDC raster UAV data,and newly demonstrated the potential of NDVI for qualitative classification of spruce trees.The proposed methodology can be applicable for monitoring of spruce health condition in the local forest sites.