秸秆焚烧火点遥感监测算法实现

为了获取华中区域的秸秆焚烧火点空间分布信息,实现对该区域秸秆焚烧的有效管控,以2014年MODIS L1B遥感数据为主要数据源,结合土地利用类型数据,以华中的农田为研究区域,基于增强型上下文火点遥感影像识别方法,充分利用定量遥感的理论知识及地理空间数据抽象库(GDAL)等技术手段,实现了华中区域秸秆焚烧火点的识别。利用中华人民共和国环境保护部发布的全国秸秆焚烧火点日报和MODIS标准火点产品(MYD14)进行空间和定量上的对比分析。研究结果表明,该算法能够有效地进行研究区域的秸秆焚烧火点遥感监测,并且可以依据研究区域的特点进行参数的实时调整,提高了秸秆焚烧火点提取的自动化和工作效率。     

基于MODIS数据的华北地区秸秆焚烧监测

秸秆焚烧给我国城乡生态环境造成巨大损害,利用遥感手段监测秸秆焚烧能够为禁烧治理工作提供有效的数据支持。“背景对比火点探测算法”(the Contextual Fire Detection Algorithm)是目前精度较高的自动探测算法,但固定的阈值参数难以适用于不同地区和不同的监测对象,因此依据实际观测情况对其中的关键参数和阈值进行了适当调整,以更好地监测中国地区的火点。基于EOS/Terra卫星的MODIS数据,利用调整阈值后的算法对我国华北地区2007年5月至8月的秸秆焚烧状况进行了遥感监测,监测精度能够满足实际业务化监测的需要。进一步结合IGBP地表分类数据,将火点像元分成秸秆焚烧、林火和草原火等3种生物焚烧类型,并分别对其亮度温度等多个参数进行了统计分析,在此基础上讨论了根据火点辐射特性判断火点类别的可行性,提出在目前,地表分类数据对于判断火点类别仍是必要的。     

基于J2EE的秸秆火点监测系统设计

秸秆焚烧火点监测在我国受到越来越多的重视,而遥感作为秸秆焚烧火点监测的有力手段也得到越来越多的应用。本文利用J2EE技术,实现了秸秆火点监测系统设计,并详细介绍了系统的设计思想,以及开发过程中的核心算法和主要技术问题。     

秸秆焚烧遥感监测进展分析与展望

秸秆焚烧是生物质燃烧的重要组成部分,不仅导致秸秆资源浪费,而且还会对环境造成严重危害。传统秸秆焚烧监测方法以人工巡查为主,监测范围受限且人力物力资源耗费大。遥感技术作为新兴的地表信息监测手段,给秸秆焚烧大范围监测带来了发展契机。介绍了遥感技术在秸秆焚烧火点监测、过火面积估算和焚烧迹地监测3个方面的基本原理、监测方法和研究进展,并分析了遥感技术在秸秆焚烧监测应用中存在的不足。在此基础上,从多源数据融合互补、监测方法优化集成、监测信息深入挖掘和时空信息决策服务等4个方面对秸秆焚烧遥感监测的未来发展进行了展望。     

基于HJ-1B卫星的作物秸秆提取及其焚烧火点判定模式

作物秸秆焚烧产生的气体和颗粒物严重污染大气环境,威胁人类健康,给交通带安全带来隐患,利用遥感技术优势监管秸秆焚烧火点具有重要的现实意义.文中基于HJ-1B卫星CCD多光谱遥感数据和IRS热红外遥感数据,以中国江苏中东部为研究区,开展作物秸秆提取及其焚烧火点判定的一体化研究.根据秸秆的光谱特征研究建立了秸秆乘积指数(SMI),结合其纹理信息可从HJ-1BCCD遥感图像上快速有效的提取出秸秆分布,继之结合修正后的火点探测算法可对HJ-1BIRS遥感数据进行火点提取.在秸秆分布和火点探测结果矢量化的基础上,通过GIS技术进行火点叠置分析,可有效地判定作物秸秆火点分布,同时结合实地调研及与MODIS火点产品比对分析验证评价了本研究方法的可行性和有效性.     

基于HJ-1卫星CCD遥感数据的作物秸秆光谱识别模型与提取方法研究

作物秸秆信息的准确提取对于农业可持续发展与秸秆焚烧火点的探测有重要意义。本文在野外作物秸秆光谱采集与光谱特征分析的基础上,以江苏省扬州市区为典型研究区,依照HJ卫星CCD相机多光谱遥感波段进行光谱重采样,并结合HJ卫星CCD遥感数据,建立了作物秸秆光谱识别乘积指数(SMI),最后通过改进PSO最大类间方差算法对SMI影像进行"动态-全局"阈值分割,实现了作物秸秆信息的有效提取。     

秸秆焚烧定位系统中的DV-Hop算法的改进

焚烧秸秆会对大气产生严重污染,危害人体健康,及时准确获得焚烧秸秆的位置信息显得尤为重要。这对秸秆焚烧定位系统的定位精度要求很高。传统的DV-Hop算法定位误差很大,秸秆焚烧定位系统中采用一种基于最小均方误差准则的DV-Hop改进型算法,能够有效提高定位精度,其主要分为两个方面,一种是使用均方差的方法求解平均每跳距离,另一种是用传统方法算出平均每跳距离之后采用加权平均的方式求得加权平均每跳距离。在Matlab仿真测试中表明,在同样的参考节点环境中,改进后算法的定位精度比改进前的有大幅度提高。     

海量点云的边缘快速提取算法

提出一种海量点云边缘快速提取算法。该算法先对点云数据进行格网组织,然后排除非边缘的离散点,最后采用Alpha Shapes判断条件提取边缘。该算法牺牲少量格网数据组织时间,节约大量的Alpha Shapes条件判断时间,从而显著提高算法效率。在VC环境下实现了该算法,实验结果表明该算法不仅具有提取外边界、空洞等功能,而且效率高。     

基于建筑物轮廓的地面激光点云与影像匹配点云配准研究

为了高精度融合异源数据,进而充分表达建筑物的顶面及立面信息,提出基于建筑物轮廓特征的地面激光点云与影像匹配点云配准方法.通过边缘估计提取影像匹配点云建筑物屋顶轮廓,利用α-shape算法匹配提取地面激光点云建筑物屋檐轮廓,运用主成分分析算法、质心约束及罗德里格斯公式实现两种轮廓点云的粗配准,根据ICP算法完成精配准.实验结果表明该方法能够实现跨模态数据的优势互补,有效提高影像与点云配准的计算效率和配准精度.     

机载三维激光成像系统点云数据滤波与地面点提取算法研究

使用原始的las标准格式的雷达数据,首先根据激光的回波次数和反射强度信息进行滤波,经过初步滤波之后根据角度限制法则和窗口移动法进行地面点提取工作,使用Delphi编程语言实现了该算法,并使用不同的阈值对不同的数据进行了结果的对比。     

CropStraw Fire Remote Sensing Monitoring and Its Algorithm Implementation

in order to obtain the information and achieve the effective control of crop straw fire spatial distribution in Central China Region.The MODIS L1B remote sensing datasets during 2014 for the main data source in this article,and combined with land use data,the farmland of Central China Region was taken as study region.Based on the enhanced contextual fire remote sensing detection algorithm,and make full use of the theoretical knowledge of quantitative remote sensing and Geospatial Data Abstraction Library (GDAL)and other technical means,to achieve the crop straw fire recognition in Central China Region.Using Ministry of Environmental Protection of the People’s Republic of China release the daily newspaper of crop straw fire in China and the standard fire products (MYD14)of MODIS for the comparative analysis of the quantitative and spatial.The results indicate that the algorithmof this paper can achieve crop straw fire remote sensing monitoring of this study region effectively,and the parameters can be adjusted in real time based on the characteristic of the study region,and improve the automation and working efficiency of crop straw fire monitoring.     

基于DSP的远程火灾监控系统设计

针对传统图像型火灾监控系统在火灾监控时需要实时地将现场图像传送到调度中心,并由工作人员长时间不间断监控,存在传输的图像数据量过大、工作人员工作量大等问题,在新的火情自动识别算法的基础上,设计了一种新型的基于图像识别的火灾监控系统。系统设计了一种有效火情识别算法,并应用到DM642芯片上,可以对现场图像中的火情自动识别,发现异常再将图像通过GPRS模块远程无线传输到监控中心,由工作人员做进一步处理。结果表明,本系统对火情能有效识别,传送数据量低,并且节省了大量人力。     

Delineation and mapping of coal mine fire using remote sensing data – a review

Various countries around the globe face numerous hazards due to the burning of coal on the surface as well as below ground. Countries like China, India, United States of America (USA), Australia, Indonesia, and many other countries have reported the burning of coal fires, and thus it is the urgent need to control the coal fire propagation to prevent the loss of energy resources. Coal is a fossil fuel that has a tendency to catch fire for many reasons; spontaneous combustion being the most frequent reasons for its burning. Other factors leading to coal fire include forest fires close to coal seams, natural hazards, old mining techniques, and external heat sources. The review work demonstrates the application of various satellite data in fire detection and mapping. The literature reveals that remote sensing plays an important role in facilitating quick and complete delineation of coal mine fires. Many algorithms have been developed around the world for fire detection from different satellite data. A comprehensive demonstration of different algorithms along with their merits and demerits are outlined. Comparative performances of the different algorithms with their case studies are also explained. It can be inferred from the various literature that it is very difficult to select a particular sensor algorithm for generating global fire products. Suggestions are given to further explore the possibility of improvement of fire detection algorithms.     

主动数据库技术及在火灾监控系统中的应用

普通火灾监控系统均以开关型探测器为主,系统对监控对象的某一时间点的值进行监控,一旦发现超过设定的阈值,就产生报警动作。本文把主动数据库技术应用到火灾监控系统中,对系统中历史数据进行分析、处理,提取满足规则的事件并触发主动数据库中相应的动作,从而改善和提高火灾监控系统功能。     

Detection of vegetation fires and burnt areas by remote sensing in insular Southeast Asian conditions: current status of knowledge and future challenges

The humid tropical insular Southeast Asian region is one of the most biologically diverse areas in the world. It contains around 70 Gt of carbon stored in peat deposits susceptible to burning when drained and it has significantly higher population density than any other humid tropical region. This region experiences yearly fire activity of anthropogenic origin with widely varying extent and severity. At the same time, there are several geographic, climatic, and social aspects that complicate fire monitoring in the region. In this review article, we analyse the current knowledge and limitations of active fire detection and burnt area mapping in insular Southeast Asia, highlighting the special characteristics of the region that affect all types of remote-sensing-based regional-level fire monitoring. We conclude that the monitoring methods currently employed have serious limitations that directly affect the reliability of results for fire and burnt area monitoring in this region. With the materials and methods presently available, the regional and global effects of fire activity taking place in insular Southeast Asia are in danger of being underestimated. New approaches utilizing higher spatial and temporal resolution remote-sensing data are needed for more detailed quantification of fire activity and subsequently improved estimation of the effects of fires in this region.     

AVHRR数据小火点自动识别方法的研究

利用NOAA-AVHRR数据,采用多因子分析方法,通过建立小火点自动识别模型来提取小火点燃烧信息。经实验验证,该方法能较好地减少云体、裸地对火点判断的干扰,从而在一定程度上提高了对小火点的监测精度。     

Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data

The remote sensing of Earth surface changes is an active research field aimed at the development of methods and data products needed by scientists, resource managers, and policymakers. Fire is a major cause of surface change and occurs in most vegetation zones across the world. The identification and delineation of fire-affected areas, also known as burned areas or fire scars, may be considered a change detection problem. Remote sensing algorithms developed to map fire-affected areas are difficult to implement reliably over large areas because of variations in both the surface state and those imposed by the sensing system. The availability of robustly calibrated, atmospherically corrected, cloud-screened, geolocated data provided by the latest generation of moderate resolution remote sensing systems allows for major advances in satellite mapping of fire-affected area. This paper describes an algorithm developed to map fire-affected areas at a global scale using Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance time series data. The algorithm is developed from the recently published Bi-Directional Reflectance Model-Based Expectation change detection approach and maps at 500 m the location and approximate day of burning. Improvements made to the algorithm for systematic global implementation are presented and the algorithm performance is demonstrated for southern African, Australian, South American, and Boreal fire regimes. The algorithm does not use training data but rather applies a wavelength independent threshold and spectral constraints defined by the noise characteristics of the reflectance data and knowledge of the spectral behavior of burned vegetation and spectrally confusing changes that are not associated with burning. Temporal constraints are applied capitalizing on the spectral persistence of fire-affected areas. Differences between mapped fire-affected areas and cumulative MODIS active fire detections are illustrated and discussed for each fire regime. The results reveal a coherent spatio-temporal mapping of fire-affected area and indicate that the algorithm shows potential for global application.