An Efficient Contextual Algorithm to Detect Subsurface Fires With NOAA/AVHRR Data

This paper deals with the potential application of National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) data to detect subsurface fire (subsurface hotspots) by proposing an efficient contextual algorithm. Most of the solutions proposed to date are mainly focused on the problem of surface fires, and very few research works have been performed to develop techniques for the subsurface fire problem. Although few algorithms based on the fixed-thresholding approach have been proposed for subsurface hotspot detection, however, for each application, thresholds have to be specifically tuned to cope with unique environmental conditions. The main objective of this paper is to develop an instrument-independent adaptive method by which direct threshold or multithreshold can be avoided. The proposed contextual algorithm is very helpful to monitor subsurface hotspots with operational satellite data, such as the Jharia region of India, without making any region-specific guess in thresholding. Novelty of the proposed work lies in the fact that once the algorithmic model is developed for the particular region of interest after optimizing the model parameters, there is no need to optimize those parameters again for further satellite images. Hence, the developed model can be used for optimized automated detection and monitoring of subsurface hotspots for future images of the particular region of interest. The algorithm is adaptive in nature and uses vegetation index and different NOAA/AVHRR channel's statistics to detect hotspots in the region of interest. The performance of the algorithm is assessed in terms of sensitivity and specificity and compared with other well-known thresholding techniques such as Otsu's thresholding, entropy-based thresholding, and existing contextual algorithm proposed by Flasse and Ceccato. The proposed algorithm is found to give better hotspot detection accuracy with lesser false alarm rate.     

红外通道空间分辨率对火点监测应用的影响分析

目前基于卫星遥感的火点探测主要采用千米级分辨率的中红外波段数据,而对红外各波段火点探测灵敏度的定量研究很少,不利于充分发挥红外波段信息在火情监测中的作用。首次利用混合像元分解方法来定量分析分辨率为150 m、300 m和1 km的各红外波段在火点监测应用中的差异。结果表明,150 m分辨率中红外通道比1 km分辨率通道的火点探测灵敏度高30倍左右;300 m分辨率远红外通道可探测百平方米量级的火点;150 m分辨率短波红外通道对高强度明火区反应明显。另外还将气象卫星1 km分辨率以及环境减灾星150 m、300 m分辨率红外数据用于2009年春季黑龙江省逊克县林火和夏季安徽省秸秆焚烧火点的监测个例,从而验证了上述分析结论。结果表明,通过提高红外波段的分辨率,可以明显提升卫星遥感在微小火点探测、火场动态监测以及火势评估等方面的应用能力。     

A fuzzy logic approach to detect hotspots with NOAA/AVHRR image using multi-channel information fusion technique

This paper proposes a novel approach to detect hotspots using NOAA advanced very high resolution radiometer (AVHRR) for the Jharia, Jharkhand (India) region. Jharia coalfield in Jharkhand is the richest coal bearing area in India that contains a large number of mine fires which have been burning for several decades. In this paper, a fuzzy based methodology has been applied for the determination of hotspots to Jharia AVHRR images based on a theoretical model that establishes relationship among AVHRR channel 4, channel 5 and different vegetation indices. The algorithm consists of four stages: data preprocessing, multi-channel information fusion, hotspot detection using fuzzy logic approach and validation of result. The most commonly used existing algorithms like contextual algorithms, multi-thresholding, entropy based thresholding, and genetic algorithms have limitation that they need some mathematical model for training in order to get the required result. The employed fuzzy logic approach overcomes this requirement and in addition, it is flexible, tolerant of imprecise data and is based on natural language. The results were compared with the results obtained by ground survey and a good agreement has been obtained between observed and predicted hotspots.     

A method to derive smoke emission rates from MODIS fire radiative energy measurements

Present methods of emissions estimation from satellite data often use fire pixel counts, even though fire strengths and smoke emission rates can differ by some orders of magnitude between pixels. Moderate Resolution Imaging Spectroradiometer (MODIS) measurements of fire radiative energy (FRE) release rates R/sub fre/ range from less than 10 to more than 1700 MW per pixel at 1-km resolution. To account for the effect of such a wide range of fire strengths/sizes on smoke emission rates, we have developed direct linear relationships between the MODIS-measured R/sub fre/ and smoke aerosol emission rates R/sub sa/ (in kilograms per second), derived by analyzing MODIS measurements of aerosol spatial distribution around the fires with National Center for Environmental Prediction/National Center for Atmospheric Research wind fields. We applied the technique to several regions around the world and derived a FRE-based smoke emission coefficient, C/sub e/ (in kilograms per megajoule), which can be simply multiplied by R/sub fre/ to calculate R/sub sa/. This new coefficient C/sub e/ is an excellent remote sensing parameter expressing the emission strength of different ecosystems and regions. Analysis of all 2002 MODIS data from Terra and Aqua satellites yielded C/sub e/ values of 0.02-0.06 kg/MJ for boreal regions, 0.04-0.08 kg/MJ for both tropical forests and savanna regions, and 0.08-0.1 kg/MJ for Western Russian regions. These results are probably overestimated by about 50% because of uncertainties in some of the data, parameters, and assumptions involved in the computations. This 50% overestimation is comparable to uncertainties in traditional emission factors. However, our satellite method shows great promise for accuracy improvement, as better knowledge is gained about the sources of the uncertainties.     

Landsat8卫星影像的多子区决策树土地覆被分类方法

乌达矿区的煤火自燃造成了严重的环境、经济和安全灾害, 对该地区的土地覆被变化研究有助于评估煤火灾害的影响程度和范围, 而Landsat8 卫星影像为煤火区的土地覆被分类探测与研究提供了可能。依据乌达地区的地形、地貌和地表辐射特征划分5个子区域, 基于通用单决策树模型, 利用光谱特征分析、高程、坡度和热红外信息对每个子区域分别构建5种不同参数的决策树模型。相比通用单决策树模型以及其他4种普通分类方法, 因减少了土地覆被的混淆度, 多子区决策树模型土地覆被分类的整体精度和Kappa系数更高, 分别达到87.63%和0.86, 尤其是建筑物和煤灰的分类精度有较为明显的提升。     

基于Landsat-8陆地卫星数据的火点检测方法研究

传统的火点检测算法通常利用高温地物在中红外波段或热红外波段的高发射率特性来提取火点, 然而受制于影像空间分辨率的限制如MODIS、AVHRR等, 使得很多小规模火情现象被漏检.研究发现短波红外数据也同样能被用于高温地物的识别和检测, 并且相较于热红外波段数据对低温和高温地物的区分度更大, 在精确识别和定位高温目标方面更加准确.文章利用空间分辨率为30米的Landsat-8 OLI传感器数据, 根据高温火点在近红外及短波红外波段的波谱特性, 利用改进的归一化燃烧指数(NBRS)结果自适应地确定阈值来提取疑似火点, 然后再利用高温火点在短波红外的峰值关系进行误检点剔除, 从而得到最终的火点产品.提出的算法能检测到所占像元面积10%左右的火点, 并能够有效地排除云层及建筑物的干扰, 在保证较低漏检率的同时还能达到90%左右的准确率, 相比于传统算法的火点提取精度有很大的提高.     

A fuzzy logic approach to fire detection in aircraft dry bays andengine compartments

In this paper, a fuzzy logic approach is applied to detect hydrocarbon fires in aircraft dry bays and engine compartments. The inputs to the fuzzy system consist of a set of statistical measures derived from the histogram and image subtraction analyses of successive image frames. Specifically, fuzzy rules based on the median, standard deviation, and normalized first-order moment statistical measures of histogram data and the mean statistical measure of image subtraction data of successive frames are used to compute the probability of a fire event. This fuzzy logic approach is also tested for false alarms such as those due to flashlights and high-power halogen lights. It is shown that image subtraction analysis can be used to accurately distinguish fires from false alarms     

Evaluating satellite and climate data-derived indices as fire risk indicators in savanna ecosystems

The repeated occurrence of severe wildfires has highlighted the need for development of effective vegetation monitoring tools. We compared the performance of indices derived from satellite and climate data as a first step toward an operational tool for fire risk assessment in savanna ecosystems. Field collected fire activity data were used to evaluate the potential of the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and the meteorological Keetch-Byram drought idex (KBDI) to assess fire risk. Performance measures extracted from the binary logistic regression model fit were used to quantitatively rank indices in terms of their effectiveness as fire risk indicators. NDWI performed better when compared to NDVI and KBDI based on the results from the ranking method. The c-index, a measure of predictive ability, indicated that the NDWI can be used to predict seasonal fire activity (c=0.78). The time lag at the start of the fire season between time-series of fire activity data and the selected indices also was studied to evaluate the ability to predict the start of the fire season. The results showed that NDVI, NDWI, and KBDI can be used to predict the start of the fire season. NDWI consequently had the highest capacity to monitor fire activity and was able to detect the start of the fire season in savanna ecosystems. It is shown that the evaluation of satellite- and meteorological fire risk indices is essential before the indices are used for operational purposes to obtain more accurate maps of fire risk for the temporal and spatial allocation of fire prevention or fire management.     

Byzantine robust trust establishment for mobile ad hoc networks

In a mobile ad hoc network (MANET), the nodes act both as traffic sources and as relays that forward packets from other nodes along multi-hop routes to the destination. Such networks are suited to situations in which a wireless infrastructure is unavailable, infeasible, or prohibitively expensive. However, the lack of a secure, trusted infrastructure in such networks make secure and reliable packet delivery very challenging. A given node acting as a relay may exhibit Byzantine behavior with respect to packet forwarding, i.e., arbitrary, deviant behavior, which disrupts packet transmission in the network. For example, a Byzantine node may arbitrarily choose to drop or misroute a certain percentage of the packets that are passed to it for forwarding to the next hop. In earlier work, we proposed a trust establishment framework, called Hermes, which enables a given node to determine the “trustworthiness” of other nodes with respect to reliable packet delivery by combining first-hand trust information obtained independently of other nodes and second-hand trust information obtained via recommendations from other nodes. A deficiency of the Hermes scheme is that a node can fail to detect certain types of Byzantine behavior, such as packet misforwarding directed at a particular source node. In this paper, we propose new mechanisms to make Hermes robust to Byzantine behavior and introduce a punishment policy that discourages selfish node behavior. We present simulation results that demonstrate the effectiveness of the proposed scheme in a variety of scenarios involving Byzantine nodes that are malicious both with respect to packet forwarding and trust propagation.     

基于FY-3D/MERSI-II远红外数据的火情监测研究

风云三号D极轨气象卫星中分辨率成像光谱仪(FY-3D/MERSI-Ⅱ)具有250 m分辨率的10. 8μm和12μm波长的远红外通道,为气象卫星遥感火情应用提供更为丰富的数据源。文章研究了FY-3D/MERSI-Ⅱ的10. 8μm远红外通道监测火情的特点,10. 8μm远红外通道虽在光谱方面对高温热源探测灵敏度不如FY-3D/MERSI-Ⅱ的3. 8μm中红外通道,但由于空间分辨率较1 km分辨率的中红外通道高4倍,因而对较大的火点有明显反映,火点探测能力较1 km分辨率远红外通道有明显提高。利用混合像元线性波谱分离方法计算,对于平均温度为750 K,面积400 m^2的明火区,在1 km分辨率远红外通道像元引起的亮温增量约0. 47 K,而在250 m分辨率远红外通道像元引起的亮温增量约为7. 30 K,可与周边背景像元亮温形成较明显差异。利用1 km分辨率的中红外通道判识火点范围,利用250 m分辨率的远红外通道进一步确定明火区位置,可将火点定位精度从公里级提高到百米级。利用该方法开展了森林草原火灾应用实例分析,基于250 m分辨率远红外通道确定的火点位置与实地考察信息吻合较好,说明了提出方法的有效性。多个应用实例表明,在反映大范围火场中较强火势区域位置和草原火灾明火线分布等方面,FY-3D/MERSI-Ⅱ远红外通道较中红外通道具有明显优势,可以更加精细化和准确的反映火情的空间分布,在火情监测方面具有实际应用价值。     

家庭厨房安全综合报警系统

目前厨房火灾在家庭安全事故中占有很大比例,特别是煤气、天然气泄漏导致的窒息、爆炸日益增多,极大地威胁着人民的生命和财产安全。提出一种新型报警系统,它通过传感器网络实时检测房间里是否有火焰和可燃气体,通过分析传感器传回的数据判断是否发生火灾或燃气泄漏,并作出相应的动作(关闭燃气阀门,开排气扇,喷水),最后发短信告知住户,实现了对厨房火灾的有效监控和报警。     

An efficient bandwidth allocation scheme for hierarchical cellular networks with energy harvesting: an actor-critic approach

In this paper, we propose an efficient bandwidth allocation strategy for multiclass services in hierarchical cellular networks that consist of an operation controller, several small-cell base stations (SBSs), and a number of mobile users. Each SBS is equipped with a finite-capacity battery that is regularly recharged by a solar harvester. We aims to find the optimal bandwidth allocation policy in order to enhance the network performance in terms of user satisfaction and energy efficiency under energy harvesting and bandwidth sharing constraints. Since the arrivals of harvested energy and traffic requests are unknown due to users’ mobility and stochastic request generation, it is necessary to design a learning framework for the controller in order to predict these dynamics through interaction with the environment. For this purpose, we first formulate the resource allocation problem as the framework of a Markov decision process, and then, we employ an actor-critic algorithm to find the optimal policy under which the controller can effectively allocate the limited bandwidth to the SBSs for their data transmissions. We evaluate the performance of the proposed scheme through comprehensive simulations with different settings, and show that the proposed bandwidth allocation scheme can enhance the network’s performance in the long run.     

火灾早期过程特征的红外光谱探测和诊断

提出用CO作为早期火灾探测的观察对象,利用红外光谱法进行气体探测,并在此基础上建立了基于FTIR的试验系统.通过该方法成功获得CO的浓度值.并用时间序列分析方法建立二阶自回归模型,提取出特征参数进行分析,能在较短时间内将真假火灾区分开来.     

A novel dynamic localisation system for indoor and outdoor tracking

ABSTRACT

Hybrid indoor and outdoor localisation system has generated substantial interest from researchers due to its potential use in many fields. Many research papers on hybrid localisation were focused on localisation accuracy and computation time instead of the process for seamless switching during the subject’s indoor-outdoor movement. Seamless and stable switching can improve the usability and practicality of a system in an ever-changing scenario. This paper presents a hybrid localisation system that can adapt to the changes in the environment. Two hybrid systems – Enhanced Fusion Hybrid (EFH) and Enhanced Unified Hybrid (EUH), and novel signal selection and switching algorithms suitable for dynamic environment where signal fluctuations and transient condition frequently occur, i.e. the Clean Radio Signal Label (CRSL) classification algorithm and the Adaptive Signal Thresholding (AST) algorithm are proposed. From the experimental results of EFH and EUH methods, the GPS and Wi-Fi combination produces the best results as compared to the combinations of GPS and GSM or GSM and Wi-Fi. The accuracy of estimates was improved to 2.0 m to 5.0 m. Further work shown that an accuracy of 2.0 m or less could be achieved with an increase in Wi-Fi nodes from three to four for the system.     

模糊神经网络在火灾探测中的应用

本文提出了串联模糊神经网络应用于火灾探测系统，首先将输入信号模糊化后迭送入神经网络进行处理，其模糊系统用指征定义隶属度函数，神经网络采用三层前馈ＢＰ网络结构，用ＢＰ算法进行网络参数的训练。其后由模糊逻辑判决火灾或非火灾。实验结果表明该网络能够准确探测各种标准试验火并有很强的抗干扰能力。     

Understanding the relationship between complicated crossings and frequently visited locations – a case study with boro taxis in Manhattan

ABSTRACT

Urban mobility has complex patterns and principles. Data of moving entities on the underlying transportation infrastructure can help understanding those complex patterns and principles. Therefore, we need static infrastructural information and knowledge on spatio-temporal movement patterns of public transport services and of various vehicle fleets. We focus on inspecting data partitions of individual taxi movement acquisitions in New York City (NYC), together with OpenStreetMap (OSM) data extracts, for gaining more knowledge about the complex daily mobility patterns in NYC. We select trip information of tracked boro taxi drivers, who are restricted to pick up customers at the airports and the southern part of Manhattan. By computing with taxi customer drop-off positions, we define drop-off clusters as the customer destination hotspots of selected Saturdays in June 2015. These hotspots are then related to the OSM road network, in particular to its derivatives: complicated crossings. By comparing with a previous assumption of detecting ‘fast leaving’ behaviour within the restricted zone, we receive characteristic matching results: only few destination hotspots appear at complicated crossings. Nearly all the matching intersections have nearby situated pedestrian zones and many are associated with previous construction measures. Finally, we reason on the usefulness of the proposed method.     

用于烟尘监测的偏振激光雷达系统及实验研究

激光雷达具有探测距离远,分辨率高,对气溶胶浓度变化敏感等优势,偏振激光雷达还能够对粒子形态做区分,根据消光系数及退偏比值识别云、雾、烟尘等。利用偏振激光雷达进行扫描观测,可以实现火灾烟尘的快速识别。通过对不同波长的激光雷达探测距离进行仿真,结果表明,波长为1 064 nm的激光雷达探测距离为532 nm波长的1.3～1.4倍。通过优化扫描策略及算法,可剔除固定障碍物及临时移动障碍物的影响。为避开安装点位周边高度相近的障碍物,通常会给激光雷达设置一定仰角,对存在探测仰角时产生的水平距离偏差及垂直高度测量偏差进行计算,当激光雷达探测仰角为2°时,6 km处测量高度偏差为209.397 m。使用高斯烟羽模型对烟尘浓度分布进行仿真,当大气稳定度为B,平均风速为1 m/s时,200 m高度处烟尘浓度分布高值点距地面火点的径向距离≥1 km,为火点准确定位提供了修正依据。分别在辽宁省盘锦市盘山县绕阳湖景区,广东省东莞市观音山森林公园进行外场实验,偏振激光雷达在开阔地带及多障碍物山体地带下,均能够快速识别烟尘。     

Measurements of 11 GHz attenuation using the intelsat V beacon in Indonesia

A campaign of measurements in Indonesia has been carried out according to a co-operation agreement concluded between the Indonesian and French Administrations with the aim of estimating rain attenuation on slant paths. The experiment was planned using two earth-stations, at Cibinong and Padang, both receiving a K_u-band beacon from an INTELSAT V satellite, coupled with rain-rate measurements at a number of sites. Rain-rate statistics are given for several locations in the Indonesian Archipelago. Cumulative distributions of attenuation and simultaneous rain rate, obtained at the earth-stations, are presented. The analysis of the results has led to the proposal of an adaptation of the CCIR attenuation prediction method for low latitudes.     

Using spatial context in satellite data to infer regional scaleevapotranspiration

An estimate of evapotranspiration is developed by relating variations of satellite-derived surface temperature to a vegetation index computed from satellite visible and near-infrared data. The method requires independent estimates of evapotranspiration for a completely vegetated area and for a nonvegetated area, although such areas need not appear in the satellite data. A regional estimate of evapotranspiration is derived despite the lack of precise estimates for individual satellite measurements. The method requires spatial variability in the satellite data: it does not apply in uniform areas. In addition, a property is identified which permits discrimination of cirrus clouds from areas of varying soil moisture     

Observations of variations in ERS-1 SAR image intensity associatedwith forest fires in Alaska

Investigations have shown that fires in boreal forests result in characteristic responses which are recorded on ERS-1 SAR imagery. Using one of the many fire signatures observed on ERS-1 SAR imagery, the analysis of the data revealed there is >10 dB in variation in image intensity within the burned areas on the SAR images. Possible explanations for this variation include increases in soil moisture, increased surface roughness exposed to the incoming microwave radiation, and damage to the vegetation canopy by the fire