Belonging probability inverse image approach for forest fire detection

We present a method for early forest fire detection from a satellite image using the belonging probability matrix image. We have considered each satellite image matrix line as a realization of a nonstationary random process in the thermal infra‐red (TIR) spectral band and then divided each line into very small stationary and ergodic intervals to obtain an adequate mathematical model. Furthermore, the pixels of the satellite image are considered to be statistically independent; thus, any small interval of each line behaves, naturally, as a Gaussian stationary noise. In this work, we have, therefore, selected the latter as a mathematical model for modelling these intervals of a satellite image without fire, and then, we have determined the parameters of this Gaussian realization. So, when a fire occurs in this forest zone, we can use these parameters to calculate its belonging probability to the original image without fire. This probability should be very small because the fire, in any forest, can be considered as a rare event. As a consequence, we have presented a matrix image of the probability inverse of each interval for a better fire detection observation.     

Wildland forest fire smoke: health effects and intervention evaluation,Hoopa, California, 1999

Objectives To assess the health effects of exposure to smoke fromthe fifth largest US wildfire of 1999 and to evaluate whether participation ininterventions to reduce smoke exposure prevented adverse lower respiratorytract health effects among residents of the Hoopa Valley National IndianReservation in northwestern California. Design Observational study:epidemiologists from the Centers for Disease Control and Preventionretrospectively reviewed medical records at the local medical center andconducted survey interviews of reservation residents. Setting HumboldtCounty, California. Participants Interviews were completed with 289 of385 residents, representing 26% of the households on the reservation. Of the289 participants, 92 (31.8%) had preexisting cardiopulmonary conditions.Results During the weeks of the forest fire, medical visits forrespiratory illnesses increased by 217 visits (from 417 to 634 visits, or by52%) over the previous year. Survey results indicated that although 181(62.6%) of 289 participants reported worsening lower respiratory tractsymptoms, those with preexisting cardiopulmonary conditions reported moresymptoms before, during, and after the smoke episode. An increased duration ofthe use of high-efficiency particulate air cleaners and the recollection ofpublic service announcements were associated with a reduced odds of reportingadverse health effects of the lower respiratory tract. No protective effectswere observed for duration of mask use or evacuation. ConclusionsTimely actions undertaken by the clinical staff of the local medical centerappeared beneficial to the respiratory health of the community. Futureprograms that reduce economic barriers to evacuation during smoke episodes mayalso improve intervention participation rates and decrease smoke exposures.Although promising, the effectiveness of these and other interventions need tobe confirmed in a prospective community intervention trial.Community smoke exposures resulting from wildland forest fires have beenassociated with increased emergency department and hospital admissions forchronic obstructive pulmonary disease, bronchitis, asthma, and chestpain.^1,2,3Although population expansion into wildland environments continues,interventions to prevent these smoke-related adverse health effects have notbeen validated under conditions of typicaluse.⁴In 1999, the fifth largest wildfire in the United States burned from August23 to November 3 near the Hoopa Valley National Indian Reservation in northernCalifornia. On 15 days, smoke from the fire produced ambient concentrations ofparticulate matter (PM₁₀) on the reservation that exceeded the USEnvironmental Protection Agency''s 24-hour air quality standard of 150μg/m³ of air. On October 21 and 22, particulate concentrationsexceeded the agency''s 24-hour hazardous level of 500 μg/m³.Concern over the health effects of the smoke prompted local officials andmedical officers to implement several interventions to reduce smoke exposurein the community. However, medical personnel were frustrated over the lack ofa scientific basis that could have been used to set public action levels or torecommend appropriate precautionary measures during this emergency. As aresult, on November 5, 1999, the Hoopa Valley Tribal Council requested thatthe Centers for Disease Control and Prevention (CDC) assist them inretrospectively assessing local adverse health effects and evaluating theinterventions that were implemented during the smoke episode.     

A rapid lateral-flow immunoassay for phytosanitary detection of Erwinia amylovora and on-site fire blight diagnosis

Fire blight is an invasive disease caused by Erwinia amylovora that threatens pome fruit production globally. Effective implementation of phytosanitary control measures depends upon rapid, reliable pathogen detection and disease diagnosis. We developed a lateral-flow immunoassay specific for E. amylovora with a detection limit of log 5.7 CFU/ml, typical of pathogen concentrations in symptomatic plant material. The simple assay had comparable sensitivity to standard culture plating, serum agglutination and nested PCR when validated for application in a phytosanitary laboratory as a confirmatory test of cultured isolates and for first-line diagnosis of phytosanitary samples that represent the full range of commercial, ornamental and forestry host species. On-site validation in ring-trials with local plant inspectors demonstrated robust and reliable detection (compared to subsequent plating and PCR analysis). The simplicity, inspector acceptance and facilitation of expedited diagnosis (from 2 days for laboratory submitted samples to 15 min with the immunoassay), offers a valuable tool for improved phytosanitary control of fire blight.     

森林火灾碳排放计量模型研究进展

森林火灾是森林生态系统重要的干扰因子,是导致植被和土壤碳储量减少的重要途径之一.森林火灾含碳气体排放对大气碳平衡及全球气候变化具有重要影响,科学有效地对其进行计量,对了解森林火灾在全球碳循环和碳平衡中的地位具有重要意义.本文从3个方面阐述森林火灾碳排放计量模型的研究进展： 森林火灾直接排放总碳和含碳气体计量方法;森林火灾碳排放计量模型的影响因子及计量参数;森林火灾碳排放计量中不确定性原因剖析.最后提出了提高碳排放计量定量化的3种路径选择： 利用高分辨率遥感数据、改进算法、提高森林火灾面积的估测精度、结合有效可燃物计量模型,提高估测可燃物载量的准确率;使用高分辨率遥感影像,并结合室内控制实验、野外试验与火烧迹地调查确定燃烧效率;通过大量室内燃烧实验和野外空中采样来确定排放因子和排放比.     

混合效应模型在林火发生预测中的适用性

林火是森林生态系统的重要影响因子,建立科学准确的林火预测预报模型对林火管理工作至关重要。本研究以不同气象因子为主要预测变量,基于Logistic回归和广义线性混合效应模型建立福建省林火发生预测模型,通过对比Logistic基础模型和广义线性混合效应模型的拟合度和预测精度,研究混合效应模型在林火预报中的适用性。结果表明: Logistic基础模型的受试者工作特征曲线下面积(AUC)值为0.664,验证准确率为60.4%。添加随机效应后,模型的拟合和检验精度均获得了提升。其中,考虑行政区划和海拔差异效应的两水平混合效应模型的表现最优,其AUC值和验证准确率分别比基础模型提升0.057和6.0%。用此混合效应模型对福建省各地区的林火发生概率进行预测的结果表明,福建省西北部和南部为林火中高发区域,西南部和东部为林火低发区域,与实际观测的火点分布一致。混合效应模型在数据拟合和林火发生预测方面均优于Logistic基础模型,可作为林火预测和管理的重要工具。     

基于广义线性混合效应模型的森林树木死亡研究

基于计数模型方法,同时考虑样地的随机效应,构建林分水平死亡模型,探究影响树木死亡的因素,以期为森林资源的监测与管理提供参考依据。以美国德州东部森林连续清查的样地数据为数据源,按4∶1的比例将其进行随机抽样,划分为训练集和验证集数据,将立地因子、林分因子和气候因子作为模型的自变量,林木死亡株数则作为模型的因变量,运用计数模型和混合效应模型方法进行模型的构建,并分析影响林木死亡株数的因子。使用赤池信息准则(AIC)、贝叶斯信息准则(BIC)和-2倍对数似然函数值(-2logL) 3种模型评价指标评估各模型间的拟合效果;采用平均绝对误差(MAE)和均方根误差(RMSE) 2种评价指标评估其预测效果,以便筛选出最佳的林分水平死亡模型。结果表明：立地因子方面,林木死亡株数与海拔(P<0.01)呈显著的负效应,与坡度(P<0.05)呈显著的正效应,说明林木死亡株数随海拔的升高而减少,随坡度的增加而增多;林分因子方面,林木死亡株数与林分年龄(P<0.001)和树木基面积(P<0.001)呈显著的正效应,与林分平方平均胸径(P<0.001)和林分密度(P<0.05)...     

Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids

Species Distribution Models (SDMs) are a powerful tool to derive habitat suitability predictions relating species occurrence data with habitat features. Two of the most frequently applied algorithms to model species-habitat relationships are Generalised Linear Models (GLM) and Random Forest (RF). The former is a parametric regression model providing functional models with direct interpretability. The latter is a machine learning non-parametric algorithm, more tolerant than other approaches in its assumptions, which has often been shown to outperform parametric algorithms. Other approaches have been developed to produce robust SDMs, like training data bootstrapping and spatial scale optimisation. Using felid presence-absence data from three study regions in Southeast Asia (mainland, Borneo and Sumatra), we tested the performances of SDMs by implementing four modelling frameworks: GLM and RF with bootstrapped and non-bootstrapped training data. With Mantel and ANOVA tests we explored how the four combinations of algorithms and bootstrapping influenced SDMs and their predictive performances. Additionally, we tested how scale-optimisation responded to species' size, taxonomic associations (species and genus), study area and algorithm. We found that choice of algorithm had strong effect in determining the differences between SDMs' spatial predictions, while bootstrapping had no effect. Additionally, algorithm followed by study area and species, were the main factors driving differences in the spatial scales identified. SDMs trained with GLM showed higher predictive performance, however, ANOVA tests revealed that algorithm had significant effect only in explaining the variance observed in sensitivity and specificity and, when interacting with bootstrapping, in Percent Correctly Classified (PCC). Bootstrapping significantly explained the variance in specificity, PCC and True Skills Statistics (TSS). Our results suggest that there are systematic differences in the scales identified and in the predictions produced by GLM vs. RF, but that neither approach was consistently better than the other. The divergent predictions and inconsistent predictive abilities suggest that analysts should not assume machine learning is inherently superior and should test multiple methods. Our results have strong implications for SDM development, revealing the inconsistencies introduced by the choice of algorithm on scale optimisation, with GLM selecting broader scales than RF.     

Adaptive rival penalized competitive learning and combined linear predictor model for financial forecast and investment

We propose a prediction model called Rival Penalized Competitive Learning (RPCL) and Combined Linear Predictor method (CLP), which involves a set of local linear predictors such that a prediction is made by the combination of some activated predictors through a gating network (Xu et al., 1994). Furthermore, we present its improved variant named Adaptive RPCL-CLP that includes an adaptive learning mechanism as well as a data pre-and-post processing scheme. We compare them with some existing models by demonstrating their performance on two real-world financial time series--a China stock price and an exchange-rate series of US Dollar (USD) versus Deutschmark (DEM). Experiments have shown that Adaptive RPCL-CLP not only outperforms the other approaches with the smallest prediction error and training costs, but also brings in considerable high profits in the trading simulation of foreign exchange market.     

大兴安岭塔河地区林火发生的优势预测模型选择

选取在经济学和社会科学领域广泛应用的零膨胀模型(zero-inflated models)和栅栏模型(Hurdle models)对大兴安岭地区林火发生进行模拟,应用赤池准则(AIC)、似然比检验(LR)和模型残差平方和(SSR)对两类共4个回归模型——零膨胀泊松模型(ZIP)、零膨胀负二项模型(ZINB)、栅栏泊松模型(PH)、栅栏负二项模型(NBH)进行拟合分析,最终选取适合此林火发生特性的预测模型.模型的AIC和SSR值表明,ZINB模型对当地林火数据的拟合度最高.运用LR检验对嵌套模型(ZINB与ZIP,NBH与PH)进行检验,结果显示: ZINB和NBH均优于各自的嵌入模型,说明负二项(NB)模型对数据结构中的过度离散现象可以很好地模拟和解释.根据研究区林火实际发生规律和两类不同模型的应用假设条件判断,零膨胀模型更适合塔河地区的林火特性.     

Mapping fire severity and fire extent in forest in Victoria for ecological and fuel outcomes

This study shows how high‐resolution (~15 cm) simultaneous colour and infra‐red digital aerial photography can be used to map both fire severity and, particularly, fire extent, in forest in south‐eastern Australia. The results show that this methodology is capable of detecting and mapping burnt and unburnt edges under unaffected forest canopy (i.e. still green) – that is, revealing the mosaic of burnt and unburnt areas that often result from planned landscape burning under mild weather conditions (i.e. with little of the brownish canopy scorch that results from more intense bushfires). This has important implications for both fuel management and ecology. It can answer the basic questions of fire and biodiversity managers following planned burning –’how much of the planned area burnt, and, within the burnt area, what aspects were burnt, and how hot did they burn?’ The analysis of fire extent by aspect showed that about 80% of southern and eastern aspects remained unburnt during broadscale autumn prescribed burning, with many of these moister aspects potentially providing longer unburnt refuges over multiple burn rotations. The fire severity and extent mapping products, produced using the methodology outlined in this study, have the potential to substantially increase the understanding of the ecological and fuel outcomes of landscape‐scale autumn prescribed burning.     

我国北方针叶林人为火发生的预测模型

我国北方针叶林带是重要的森林资源储藏地,也是林火发生的重灾区,其自然火和人为火所占比例相当. 气象因子、地形特征、植被条件、人为基础设施等因素对人为火发生具有显著影响,国内目前应用空间分析技术对北方针叶林带人为火影响因子的研究还存在一定不确定性. 本文基于1974—2009年间人为火的空间地理坐标,结合研究地的气象因子、基础地理信息及矢量化林相图,应用ArcGIS 10.0中的空间分析工具和SPSS 19.0的逻辑斯蒂回归模型对影响人为火发生的主要驱动因子进行分析,并建立人为火发生的概率模型. 利用HADCM2模式下研究区域未来气象数据对塔河地区2015年人为火发生情况进行计算.结果表明： 距离铁路距离（x₁）和平均相对湿度（x₂）对研究区域人为火发生具有显著影响,并得到火险概率模型P=1/［1+e^{-(3.026-0.00011x1-0.047x₂)}］. 模型校验结果显示,模型的准确度可达到80%.林火发生预测结果表明,塔河地区2015年 4—6月、8月为人为火高发期,其中,4—5月的林火发生概率最高.从火险空间分布来看,高火险主要集中在塔河西部和西南部,铁路线路主要包含在此区域.     

A goal programming network for linear programming

Tank and Hopfield have shown that networks of analog neurons can be used to solve linear programming (LP) problems. We have re-examined their approach and found that their network model frequently computes solutions that are only suboptimal or that violate the LP problem's constraints. As their approach has proven unreliable, we have developed a new network model: the goal programming network. To this end, a network model was first developed for goal programming problems, a particular type of LP problems. From the manner the network operates on such problems, it was concluded that overconstrainedness, which is possibly present in an LP formulation, should be removed, and we have provided a simple procedure to accomplish this.     

Virtual Fire: A web-based GIS platform for forest fire control

Α web-based Geographic Information Systems (GIS) platform – named Virtual Fire – for forest fire control has been developed to easily, validly and promptly share and utilize information and tools among firefighting forces. This state-of-the-art system enables fire management professionals to take advantage of GIS capabilities without needing to locally install complex software components. Fire management professionals can locate fire service vehicles and other resources online and in real-time. Fire patrol aircrafts and vehicles may use tracking devices to send their coordinates directly to the platform. Cameras can augment these data by transmitting images of high-risk areas into the graphical interface of the system. Furthermore, the system provides the geographical representation of fire ignition probability and identifies high-risk areas at different local regions daily, based on a high performance computing (HPC) pilot application that runs on Windows HPC Server. Real-time data from remote automatic weather stations and weather maps based on a weather forecasting system provide vital weather data needed for fire prevention and early warning. By using these methods and a variety of fire management information and tools, the end-users are given the ability to design an operational plan to encompass the forest fire, choosing the best ways to put the fire out within the proper recourses and time.     

Molecular methods for rapid detection of aneuploidy

Rapid molecular biological methods for prenatal diagnosis of the most common aneuploidies, collectively known as rapid aneuploidy testing, are compared in this review. We discuss methodological problems and limitations of these various methods. All these techniques are believed to be accurate and carry a low risk of misdiagnosis, but they differ in terms of labour-intensity and amenability to automation and high throughput testing. The question how to apply them safely and economically in a clinical setting has not been answered yet. The discussed techniques are so far not used as stand-alone tests, but some of them are routinely applied as a preliminary test that shortens the waiting time for classic cytogenetic karyotyping. In the future, mainly because of economical reasons, these methods may replace cytogenetics in the category of patients who make up the majority of those currently offered prenatal karyotyping: patients with moderately increased risk and no abnormalities detected by ultrasound.     

Adaptive ridge regression for rare variant detection

It is widely believed that both common and rare variants contribute to the risks of common diseases or complex traits and the cumulative effects of multiple rare variants can explain a significant proportion of trait variances. Advances in high-throughput DNA sequencing technologies allow us to genotype rare causal variants and investigate the effects of such rare variants on complex traits. We developed an adaptive ridge regression method to analyze the collective effects of multiple variants in the same gene or the same functional unit. Our model focuses on continuous trait and incorporates covariate factors to remove potential confounding effects. The proposed method estimates and tests multiple rare variants collectively but does not depend on the assumption of same direction of each rare variant effect. Compared with the Bayesian hierarchical generalized linear model approach, the state-of-the-art method of rare variant detection, the proposed new method is easy to implement, yet it has higher statistical power. Application of the new method is demonstrated using the well-known data from the Dallas Heart Study.     

A rapid ex vivo tissue model for optimising drug detection and ionisation in MALDI imaging studies

The aim of this study was to establish an ex vivo model for a faster optimisation of sample preparation procedures, for example matrix choice, in matrix-assisted laser desorption/ionisation (MALDI) drug imaging studies. The ionisation properties of four drugs, afatinib, erlotinib, irinotecan and pirfenidone, were determined in an ex vivo tissue experiment by spotting decreasing dilution series onto liver sections. Hereby, the drug signals were distinctly detectable using different matrix compounds, which allowed the selection of the optimal matrix for each drug. The analysis of afatinib and erlotinib yielded high drug signals with α-cyano-4-hydroxycinnamic acid matrix, whereas 2,3-dihydroxybenzoic acid was identified as optimal matrix for irinotecan and pirfenidone detection. Our method was validated by a MALDI drug imaging approach of in vivo treated mouse tissue resulting in corresponding findings, indicating the spotting method as an appropriate approach to determine the matrix of choice. The present study shows the accordance between the detection of ex vivo spotted drugs and in vivo administered drugs by MALDI-TOF and MALDI-FT-ICR imaging, which has not been demonstrated so far. Our data suggest the ex vivo tissue spotting method as an easy and reliable model to optimise MALDI imaging measurements and to predict drug detection in tissue sections derived from treated mice prior to the recruitment of laboratory animals, which helps to save animals, time and costs.     

Phagomagnetic immunoassay for the rapid detection of Salmonella

This work explores the use of the phage P22 in a phagomagnetic immunoassay for the rapid detection of Salmonella. The covalent attachment of wild-type phages was performed on two different magnetic carriers: carboxyl-activated magnetic nanoparticles (300 nm) and tosyl-activated magnetic microparticles (2.8 μm). The bacteria were captured and preconcentrated by the phage-modified magnetic particles, followed by the detection using specific anti-Salmonella antibodies conjugated to horseradish peroxidase as an optical reporter. Outstanding selectivity and sensitivity was obtained with this approach, achieving detection limits of 19 CFU mL^?1 in 2.5 h without any pre-enrichment, in milk samples. Moreover, if the samples were pre-enriched for 6 h, the method was able to detect as low as 1.4 CFU in 25 mL of milk. Therefore, the proposed strategy based on the combined use of phagomagnetic separation with immunological labeling is promising as a rapid and simple method for food safety.