多源时序国产卫星影像的森林火灾动态监测——以四川省木里县及其周边林区为例

四川省木里县及周边林区是全国林火最为高发和易发区之一,近两年连续发生了扑火人员重大伤亡的事件。利用时序国产卫星影像、无人机影像和现场勘查数据等,从监测火灾蔓延时空过程的角度,对该区林火热点进行了动态监测,并分析了重点火场火灾发展过程,结果表明:以国产GF-4卫星影像为主,辅助以2 m/8 m光学卫星星座影像,可较好地监测林火热点;研究提出林火热点判定阈值为白天亮温值T≥360 K或夜间亮温值T≥330 K;监测发现了该区3月30日至4月6日间共6处火场的25次林火事件,并重点反演了(1)号木里和(2)号西昌火灾发展的时空过程。通过将卫星监测热点与现场勘查热点、无人机影像解译热点对比,表明在火灾早期和中期卫星林火热点监测精度可达89%。建议利用时序国产多源卫星影像对该区林火进行持续监测,并结合权威部门现场勘查数据适时发布预警信息,避免造成重大生命财产损失。     

地性线提高卫星林火监测几何精度的方法

利用卫星热点监测森林火灾,在我国已有近20年的应用历史,但提高火场或火点定位精度的一直没有大的突破,经过系统级及GCP控制校正的监测图像,往往还存在2~4个像元的误差,影响了开展地面核查和处置的工作效率。针对以上问题,提出了一种基于90m DEM(数字高程模型)提取地性线,并集成立体可视地形地貌图、江河及大型湖泊矢量地图,以此为地理参照数据控制校正MODIS林火监测图像的方法。实验结果表明,与原有的几何纠正技术相比,利用地性线控制精校正的监测图像,其火场的定位误差从2~4个像元提高到1个像元内,GCP(控制点)的均方误差仅为431m,该技术方法能极大地提高卫星林火监测的几何精度。     

森林火灾的气象卫星监测方法

森林火灾是造成森林资源破坏的重大灾害之一。作者根据近年来从事森林火灾卫星监刚的实践，结合国内外有关利用气象卫星监测林火的资料，介绍了林火卫星监测的原理、方法和实用效果。     

EOS-MODIS 数据林火识别算法的验证和改进

EOS-MODIS 数据在森林火情监测中的应用研究日益受到世界各国的重视。为了获得适用于中国不同地区森林火情监测的成熟技术, 很有必要对现有MODIS 数据林火监测理论算法进行验证分析, 探讨其在中国不同地域和季节中使用时的通用性。为此, 利用中国境内9 起森林火灾事件对MODIS 数据火点识别的理论算法进行验证分析。结果显示9 起森林火灾有8 起被有效检测到, 1 起森林火情被遗漏。通过对9 起森林火点及其邻近像元的统计分析, 发现如下两个重要规则:利用火点亮温偏离统计均值3 倍标准差的关系来确定阈值, 可以避免火点的遗漏; 林火点在CH21和CH22 上的亮温值一般有CH21- CH22< 20 K, 而噪声点在两个波段上的差异却比较大。用以上规则改进的MODIS 林火热点识别算法可以检测出用来验证的全部9 起林火事件, 从而证明了改进算法的有效性和通用性。     

一种利用HJ-1B红外相机数据自动识别林火的方法

森林火灾是一种世界性的重要自然灾害,它分布广、发生频度高,破坏森林资源,干扰人民正常生活秩序,造成全球性环境污染,越来越受到各国政府的重视。环境减灾1B卫星上的红外相机(简称HJ-1BIRS),其空间分辨率提高到了150m,高温饱和点达到了500K,是国内目前卫星上可探测地表温度最高的相机。在对HJ-1BIRS数据相关波段进行抽样统计分析基础上,针对HJ-1BIRS数据各波段特性,采用自适应的劈窗检测算法识别林火;在IDL语言环境下,实现了基于HJ-1BIRS和背景信息集成的林火自动识别算法程序。同时,通过近1a的试运行,并选取发生在东北林区和南方林区的森林火灾为验证案例,对算法及其监测精度进行验证。验证结果表明:该方法的判对率达到了90%以上,遗失率都低于10%,错判率为0,该方法基本能满足我国林火监测业务的精度要求。     

GF-1 WFV影像的中小流域洪涝淹没水深监测

针对目前水深监测仍主要依赖Landsat、SPOT等国外遥感卫星的问题,提出采用国产高分一号(GF-1)卫星16m分辨率WFV影像进行洪涝淹没水深监测。通过以中小流域为研究区,采取RS和GIS结合的水深测算方法计算淹没水深:在利用RS影像提取淹没范围的基础上,运用GIS方法由水面高程和地面高程之差计算得出淹没水深的空间分布。结果表明,GF-1卫星作为新兴的国产遥感数据源,凭借空间分辨率高和回访周期短的优势,可帮助摆脱对国外卫星遥感数据的依赖;GF-1卫星WFV影像提取水体精度较高,能广泛应用于中小流域洪涝灾害监测;RS和GIS结合的洪涝淹没水深监测算法简单易行,可快速计算在淹没范围已知情况下的水深。相关结果可为洪涝灾害监测与评估提供信息依据。     

GF-1 WFV 影像的中小流域洪涝淹没水深监测

针对目前水深监测仍主要依赖Landsat、SPOT等国外遥感卫星的问题,提出采用国产高分一号(GF-1)卫星16 m分辨率WFV影像进行洪涝淹没水深监测。通过以中小流域为研究区,采取RS和GIS结合的水深测算方法计算淹没水深：在利用RS影像提取淹没范围的基础上,运用GIS方法由水面高程和地面高程之差计算得出淹没水深的空间分布。结果表明,GF-1卫星作为新兴的国产遥感数据源,凭借空间分辨率高和回访周期短的优势,可帮助摆脱对国外卫星遥感数据的依赖;GF-1卫星WFV 影像提取水体精度较高,能广泛应用于中小流域洪,涝灾害监测;RS和GIS结合的洪涝淹没水深监测算法简单易行,可快速计算在淹没范围已知情况下的水深。相关结果可为洪涝灾害监测与评估提供信息依据。     

资源三号卫星影像DEM提取与精度分析

为提高国产遥感卫星影像定位精度,提取高精度DEM,该文基于卫星遥感的成像模型和间接平差理论,利用有理函数模型和像面仿射变换模型,构建了卫星影像平差数学模型;利用该数学模型对资源三号卫星立体影像进行了平差处理,建立了研究区域DEM。分析结果表明:DEM在无控制点情况下,平地区域平面精度达到6.12m,高程精度达到8.39m;山地区域平面精度达到6.20m的,高程精度达到8.62m,数据精度能够满足1∶50000比例尺地形图测图标准和要求。研究结果验证了国产卫星数据的定位精度。     

环境减灾卫星影像森林火灾监测技术方法研究

森林火灾是世界性的、频繁发生的重大自然灾害。随着国内外航天科技的迅猛发展,卫星遥感技术特别是红外卫星遥感已成为森林火灾监测的一种有效手段。我国的环境一号卫星A、B星(简称HJ-1A卫星、1B卫星)于2008年9月成功发射,其中的HJ-1B卫星搭载了红外多光谱相机,在森林火灾监测方面具有得天独厚的优势,可在早期的火灾发现、中期的灾害跟踪、后期的灾害损失评估中发挥重要作用。本文主要分析了环境减灾卫星在森林火灾监测方面的优势,对环境减灾卫星森林火灾监测技术和方法进行了研究。     

基于北京一号小卫星的北京及周边五大流域地表水资源监测与分析

地表水作为水资源的重要组成部分,能从特定角度反映水资源状况。研究了基于北京一号小卫星影像的地表水体分类标准、水信息提取方法,并对2007年北京及周边五大流域地表水资源进行了动态监测,同时结合气象水文数据分析其时空分布规律、年内变化特征和变化原因。研究表明:① 利用北京一号小卫星32 m多光谱影像辅以4 m全色影像,基于面向对象分类等方法可以较准确地提取各类地表水信息;② 2007年北京及周边五大流域地表水资源变化特征与该区域2007年平均降水量、降水量的年内变化以及水库调蓄等有关。     

Forest change monitoring of a remote biosphere reserve

A forest monitoring framework using Universal Transverse Mercator (UTM) grid cells to report forest change estimates derived from time-series satellite imagery was established for the Maya Biosphere Reserve (MBR) in northern Guatemala. Five dates of Landsat Thematic Mapper imagery were acquired and digitally processed to quantify forest change for four time periods: 1986 to 1990, 1990 to 1993, 1993 to 1995, and 1995 to 1997. Time-series change estimates are reported for 215 UTM grid cells approximately 100 km2 each. For the period 1990 to 1997, after the designation of the MBR, the percentage of grid cells with detectable annual forest clearing increased from 38% (1990-93) to 41% (1993-95) and 45% (1995-97). Prior to the establishment of the MBR (1986-90), none of the grid cells exhibited greater than 4.0% annual forest clearing. However in the next three time periods, 7.0%, 8.8% and 9.3% of the grid cells had clearing rates exceeding 4.0% per year. The accuracy of detecting forest clearing was 86.5% over all time periods (Kappa 0.82). Estimates of forest change and user's and producer's accuracy are reported for each time period between 1990 and 1997. The time-series forest change and spatial arrangement of grid locations indicate hot spots where rates and trends of agricultural expansion can be monitored. The baseline survey and the establishment of the UTM grid network to localize forest change estimates provides a framework for future satellite estimates of forest and land cover conversion to be monitored through time. The UTM grid is proposed as the first level in a multi-level ecological monitoring system for the Maya Biosphere Reserve where there are few permanent landmarks in the remote forest region.     

Forest fire analysis with remote sensing data

Forest fires cause major damage to the environment, human health and property, and endanger life. Fires can be monitored and analysed over large areas in a timely and cost-effective manner by using satellite sensor imagery in combination with spatial analysis as provided by Geographical Information Systems (GIS). In this study, the forest area damage caused by a large fire which occurred in the Marmaris, province of Mugla in July 1996 was analysed using satellite sensor images. Digital image processing methods, such as spectral profile analysis, vegetation indices and multispectral classification, were applied to the satellite sensor images acquired before and after the forest fire. Besides the conventional maximum likelihood classification algorithm, a multilayer feed-forward neural network architecture was also used for comparison and evaluation of its effectiveness. A GIS database was constructed from the raster (satellite sensor data), vector (the forest type and topographical maps) and ancillary data (meteorological data). The GIS is being used to develop an information and decision support system to monitor and predict forest fire activity, and to enhance fire management efficiency. This study highlights the deficiencies in the current approach to fire management and emphasizes the need for an improved method along the lines outlined.     

Assessment of tropical forest degradation by selective logging and fire using Landsat imagery

Many studies have assessed the process of forest degradation in the Brazilian Amazon using remote sensing approaches to estimate the extent and impact by selective logging and forest fires on tropical rain forest. However, only a few have estimated the combined impacts of those anthropogenic activities. We conducted a detailed analysis of selective logging and forest fire impacts on natural forests in the southern Brazilian Amazon state of Mato Grosso, one of the key logging centers in the country. To achieve this goal a 13-year series of annual Landsat images (1992-2004) was used to test different remote sensing techniques for measuring the extent of selective logging and forest fires, and to estimate their impact and interaction with other land use types occurring in the study region. Forest canopy regeneration following these disturbances was also assessed. Field measurements and visual observations were conducted to validate remote sensing techniques. Our results indicated that the Modified Soil Adjusted Vegetation Index aerosol free (MSAVI_af) is a reliable estimator of fractional coverage under both clear sky and under smoky conditions in this study region. During the period of analysis, selective logging was responsible for disturbing the largest proportion (31%) of natural forest in the study area, immediately followed by deforestation (29%). Altogether, forest disturbances by selective logging and forest fires affected approximately 40% of the study site area. Once disturbed by selective logging activities, forests became more susceptible to fire in the study site. However, our results showed that fires may also occur in undisturbed forests. This indicates that there are further factors that may increase forest fire susceptibility in the study area. Those factors need to be better understood. Although selective logging affected the largest amount of natural forest in the study period, 35% and 28% of the observed losses of forest canopy cover were due to forest fire and selective logging combined and to forest fire only, respectively. Moreover, forest areas degraded by selective logging and forest fire is an addition to outright deforestation estimates and has yet to be accounted for by land use and land cover change assessments in tropical regions. Assuming that this observed trend of land use and land cover conversion continues, we predict that there will be no undisturbed forests remaining by 2011 in this study site. Finally, we estimated that 70% of the total forest area disturbed by logging and fire had sufficiently recovered to become undetectable using satellite data in 2004.     

基于亮温—植被指数—气溶胶光学厚度的MODIS火点监测算法研究

MODIS火灾产品的火点检测算法主要以4和11μm通道亮温数据来识别火点,在应用于不同地区和不同季节时有一定局限性。在分析MODIS现有火点检测算法的基础上,对算法相关阈值及参数进行适当调整,同时考虑火灾前后NDVI的变化,以及林火燃烧过程中伴生烟羽使火点下风方气溶胶光学厚度明显增加的特点,构建了基于亮温—植被指数—气溶胶光学厚度的火点识别算法,并应用多次火灾个例对本算法进行验证。结果表明:算法提高了对高温热点和低温焖烧火点的识别能力,有效降低了高温热点的误报率和低温火点的漏报率,使火点检测算法在不同环境的适应性有所增强。     

遥感与模糊评判在森林火灾后生态监测评价中的应用

以遥感资料为信息源，针对森林生态的复杂性与模糊性，采用模糊数学中“多层次模糊综合评判”的方法，对森林火灾后生态景观进行评价及动态监测，以期探索森林火灾后生态监测评价的新途径，为森林生态系统的恢复和重建提供科学依据和决策支持。     

基于森林火灾平台的红外图像识别技术研究

森林火灾探测一直是森林资源保护工作中的老大难问题。早期的火灾探测技术多是基于火灾的烟雾和温度特征的,但其判别标准过于单一,误报率较高。随着计算机技术的发展和红外探测器的发明,出现了基于红外图像处理的森林火灾防护技术,该技术主要对红外探测器获得的森林现场的红外图像进行一定的处理后识别图像中是否有火灾出现。本文的主要工作是进行红外图像增强算法的研究,通过对各种算法的描述和仿真实验结果分析,提出一种相对较适合森林背景红外图像的处理算法一将基于频域处理的提升小波变换与直方图修正结合起来的红外图像增强算法。     

基于高性能集群计算平台的林火监测算法

为能在森林火灾发生后快速获取火情信息和共享异地资源,提出一种基于高性能集群计算平台的林火监测算法。介绍基于多处理器的高性能计算平台,以及卫星图像火点识别算法的并行化实现方法,对影响运行效率的各种因素进行对比分析。实验结果表明,在已选择合理并行任务数的情况下,该算法可有效提高执行效率。