基于ENVISAT-MERIS数据的过火区制图方法研究

森林或草原在发生火灾后,过火区内的植被层在近红外波段的反射率通常要比健康植被低,利用光学遥感数据的近红外波段和红光波段可以探测出植被层的反射率在大气上界的明显变化。对过火区域的提取是利用卫星数据进行测算森林或草原火灾过火面积的关键技术之一。根据实验区内近年来发生的多次重特大森林或草原火灾,在对ENVISAT\|MERIS数据中典型地物光谱特征进行分析的基础上,分别采用图像处理方法、植被指数法和面向对象的图像分析方法对过火区制图方法进行对比研究。研究结果表明,通过面向对象的图像分析方法获得的过火区域,可以较好地适用于过火区面积的估测,该方法是一项实现定量提取过火区域的行之有效的方法。     

TM验证MODIS过火面积产品精度分析——以黑龙江省重大森林火灾为例

鉴于准确估测森林的过火面积对森林火灾的损失评估和过火区植被的恢复所具有的重要作用,选取了2006年～2010年黑龙江省51个重大森林火灾记录,分别利用MODIS的MOD14A2（Terra）火产品数据和TM遥感影像数据估算过火面积,并利用Kappa指数分析过火面积在数量和空间位置上的一致性。结果表明：在单个火场尺度上,小于3.72km2的森林火灾不适于利用MOD14A2产品来估算过火面积,而年过火总面积的相对误差小于15%。MOD14A2火产品可以有效地估测年度尺度上森林的过火面积;数量Kappa指数明显大于位置Kappa指数和标准Kappa指数,位置Kappa指数较低,这可能是由于MODIS数据的空间分辨率较低、林火记录坐标位置不够准确等原因造成的,有待进一步研究。     

TM验证MODIS过火面积产品精度分析——以黑龙江省重大森林火灾为例

鉴于准确估测森林的过火面积对森林火灾的损失评估和过火区植被的恢复所具有的重要作用,选取了2006年~2010年黑龙江省51个重大森林火灾记录,分别利用MODIS的MOD14A2(Terra)火产品数据和TM遥感影像数据估算过火面积,并利用Kappa指数分析过火面积在数量和空间位置上的一致性。结果表明:在单个火场尺度上,小于3.72km2的森林火灾不适于利用MOD14A2产品来估算过火面积,而年过火总面积的相对误差小于15%。MOD14A2火产品可以有效地估测年度尺度上森林的过火面积;数量Kappa指数明显大于位置Kappa指数和标准Kappa指数,位置Kappa指数较低,这可能是由于MODIS数据的空间分辨率较低、林火记录坐标位置不够准确等原因造成的,有待进一步研究。     

基于线性光谱模型的混合像元分解方法与比较

线性光谱模型是目前解决城市中等空间分辨率遥感(如Landsat)中存在的混合像元问题的简单、有效的策略。本实验以广州区域为研究区,利用ENVI/IDL影像处理和开发平台对4种混合像元线性光谱分解方法进行了对比,即无约束条件法、带部分约束条件法、普通带全约束条件法和带全约束条件的可变端元法。结果表明,普通带全约束条件法和带全约束条件的可变端元法的分解结果比无约束条件法和带部分约束条件法的分解结果合理,均方根误差明显要小;同时,带全约束条件的可变端元法要优于普通带全约束条件法。光谱归一化处理则对不同分解方法带来不同的影响,应依据实际需要采取合适的光谱处理方式。     

用NOAA—AVHRR资料监测红花尔基森林火灾

使用自行研制的林火实时监测软件对ＮＯＡＡ—ＡＶＨＲＲ资料进行林火信息增强、过火区增强、坐标精校正等分析处理，实时监测林火及其分布，快速测算火场和过火面积，及时得出林火遥感监测分析报告。展望今后相关技术的发展。     

基于二阶段算法的HJ-CCD数据过火区制图

利用环境减灾小卫星多光谱数据研究过火区的制图方法,并分析对比了4种植被指数对于过火区的分离性。结果表明:基于可见光和近红外的BAI(Burned Area Index)与GEMI(Global Environment Monitoring Index)指数对于过火区的分离能力较好。在此基础上,采用二阶段识别算法对实验区的过火区进行提取。首先采用严格的阈值提取燃烧较为严重的过火像元,并以此作为第二阶段过火区识别的“种子”点,该阶段以减少误判为目的;第二阶段采用区域生长提取其他过火区域,同时采取较为宽松的阈值作为生长准则,以减少漏判,最后得出过火区边界。精度验证结果表明:该方法提取的过火区误判率为5.5%,漏判率为12.7%。     

基于显著性增强的过火区域提取研究

过火区域信息对灾后评估、保护和恢复生态系统具有重要意义。目前现有的过火区域提取方法实用性较弱。基于FY-3C MERSI卫星数据,充分利用过火区域的多种特征,通过显著性增强,创建了一种新的过火区域提取方法。以美国西北部两个过火区域为研究区,将3种对过火区域敏感的植被指数(NDVI、GEMI和NDVIT)和影像的显著性特征结合起来,对研究区中的过火区域进行增强、提取。通过对研究区中的过火区域人工目视解译,对实验结果进行验证,同时与NBR阈值法提取的结果进行比较。两个研究区中显著性增强法的Kappa系数达到了0.68以上,比NBR阈值法高0.2。实验证明显著性增强法提取的过火区域整体精度较高,非火灾引起的植被变化对其影响较少,与NBR阈值法相比该方法具有一定的稳定性。     

基于随机森林的高光谱遥感图像分类

为了充分利用高光谱图像的光谱信息和空间结构信息,提出了一种新的基于随机森林的高光谱遥感图像分类方法,首先,利用主成分分析降低数据的维数,并对主成分进行独立成分分析提取其光谱特征,同时消除像元的空间相关性,再采用形态学分析提取像元的空间结构特征,然后,根据像元的谱域和空域特征分别构造随机森林,并引入空间连续性对像元点的预测结果进行约束修正,最后由投票机制决定最后的分类结果。在AVIRIS和ROSIS高光谱图像上的实验结果表明,所提方法的分类性能要优于传统的高光谱图像分类方法,且分类精度高于基于单一特征的方法。     

遥感图像大气校正方法综述

对目前常用的大气校正方法作了较详细的介绍，包括辐射传输模型法、黑暗像元法、不变目标法、直方图匹配法等8种方法。分析了各种方法的优缺点，以及它们各自的适用范围，并对各种方法在国内外应用的情况作了概括。     

大兴安岭森林火灾遥感调查方法初探

1987年5月,黑龙江省大兴安岭林区东经121°30′—125°,北纬52°30′—53°30′地域内发生了一起特大森林火灾。为搞清火灾区的范围,过火面积及林火的轻重程度,我们利用遥感技术对火区进行了初步调查。一、使用的资料及工作程序 1/20万的火灾期Landsat TM影像、     

Detection of burned areas from mega-fires using daily and historical MODIS surface reflectance

The detection and mapping of burned areas from wildland fires is one of the most important approaches for evaluating the impacts of fire events. In this study, a novel burned area detection algorithm for rapid response applications using Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m surface reflectance data was developed. Spectra from bands 5 and 6, the composite indices of the Normalized Burn Ratio, and the Normalized Difference Vegetation Index were employed as indicators to discover burned pixels. Historical statistical data were used to provide pre-fire baseline information. Differences in the current (post-fire) and historical (pre-fire) data were input into a support vector machine classifier, and the fire-affected pixels were detected and mapped by the support vector machine classification process. Compared with the existing MODIS level 3 monthly burned area product MCD45, the new algorithm is able to generate burned area maps on a daily basis when new data become available, which is more applicable to rapid response scenarios when major fire incidents occur. The algorithm was tested in three mega-fire cases that occurred in the continental USA. The experimental results were validated against the fire perimeter database generated by the Geospatial Multi-Agency Coordination Group and were compared with the MCD45 product. The validation results indicated that the algorithm was effective in detecting burned areas caused by mega-fires.     

The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images

Operational use of remote sensing as a tool for post‐fire, Mediterranean forest management has been limited by problems of classification accuracy arising from confusion of burned and non‐burned areas. Frequently, this occurs as a result of slope illumination and shadowing effects caused by the complex topography encountered in many forested areas. Cloud shadows can also be a problem. The aim of this work was to investigate how image classification results could be improved by removing the illumination effects of topography from satellite images. This was achieved by applying supervised classification to both uncorrected and topographically corrected LANDSAT TM data for a site on the Greek island of Thasos. The classification methodology included atmospheric and geometric correction, field‐based training, seperability/contingency analysis and maximum likelihood processing. The classification scheme was determined on the basis of consultation with the Greek Forest Service. Overlay of the resulting class maps enabled comparison of the total burned area and its spatial extent using the two different approaches to processing. The results of each approach were compared with the forest perimeter map generated by the Forest Service using traditional survey methods. Accuracy assessment and error analysis clearly indicated that the removal of the topographic effect from the satellite image before its classification resulted in more accurate mapping of the burned area. It is concluded that operational use of satellite remote sensing for forest fire management depends on accurate, robust, widely available and proven techniques. Topographic correction should now be regarded as an essential element of any classification methodology which will be used for operational, post‐fire management of forests in complex Mediterranean landscapes.     

一种增强的基于上下文火点遥感影像识别方法

传统的火点遥感影像识别方法大多采用阈值法,但阈值的选择受区域、季节以及云天状况等多种因素的限制,因而在实际监测中往往效果不佳。针对这些问题,提出了一种增强的基于上下文信息的火点遥感影像识别方法,考虑了火点与其相邻像元之间的内在联系,在火点背景像元的确定及真实火点的判据选择等方面做了改进,在此基础上确定一组火点判据。该方法基本不受区域、时间等因素的限制,对面积较小的火点识别较为敏感,在实验中取得了较好的效果。     

A simple method for Are growth mapping using AVHRR channel 3 data

AHVRR Channel 3 data have been used widely for forest fire detection and mapping. However, little attention has been paid to the use of these data for daily fire growth monitoring. A simple method for fire growth mapping using channel 3 data is presented. An 18000 hectare forest fire affecting the Mediterranean coast of Spain is used as a case study. Discrimination of burned area was performed on every image after multitemporal registration. A thermal threshold was established to mask out fire pixels in both diurnal and nocturnal images. GIS overlay techniques were used lo obtain a synthesis map of the daily evolution of the fire. This product can generate valuable input for fire behaviour programmes to improve our understanding of the factors affecting fire spread and fire severity.     

The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product

The results of the first consecutive 12 months of the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) global burned area product are presented. Total annual and monthly area burned statistics and missing data statistics are reported at global and continental scale and with respect to different land cover classes. Globally the total area burned labeled by the MODIS burned area product is 3.66 × 10⁶ km² for July 2001 to June 2002 while the MODIS active fire product detected for the same period a total of 2.78 × 10⁶ km², i.e., 24% less than the area labeled by the burned area product. A spatio-temporal correlation analysis of the two MODIS fire products stratified globally for pre-fire leaf area index (LAI) and percent tree cover ranges indicate that for low percent tree cover and LAI, the MODIS burned area product defines a greater proportion of the landscape as burned than the active fire product; and with increasing tree cover (> 60%) and LAI (> 5) the MODIS active fire product defines a relatively greater proportion. This pattern is generally observed in product comparisons stratified with respect to land cover. Globally, the burned area product reports a smaller amount of area burned than the active fire product in croplands and evergreen forest and deciduous needleleaf forest classes, comparable areas for mixed and deciduous broadleaf forest classes, and a greater amount of area burned for the non-forest classes. The reasons for these product differences are discussed in terms of environmental spatio-temporal fire characteristics and remote sensing factors, and highlight the planning needs for MODIS burned area product validation.     

基于MODIS数据的亚马逊热带雨林火灾时空变化规律研究

频繁发生的森林大火对亚马逊热带雨林造成了大面积破坏,获取不同年份的火灾影响范围以及植被破坏情况,有助于了解该地区火灾时空演变规律以及火灾与植被的相互作用关系,进而探究火灾发展机理,为防灾减灾提供科学依据。为此,利用2015~2019年MODIS植被指数产品与地表温度产品,构建MODIS全球扰动指数模型（MGDI）,结合火点数据 （以下统称MOD14A1）、植被连续场数据（Vegetation Continuous Field,VCF）提取1 000 m分辨率下的燃烧范围和燃烧强度,并分析研究区域5年内的火灾分布时空规律。实验结果表明：①火灾主要分布在巴西中部以及巴西与玻利维亚的交界处,占燃烧区总面积的67%左右;②燃烧范围以及燃烧强度的综合信息显示火灾整体呈现出“升—降—升”的趋势;③火灾多发生于灌木草地（50%以上）以及阔叶林（30%）,且火灾多发在旱季;在全球变暖大背景下,火灾发生频率呈上升趋势;（4）人类活动范围扩张、不合理农业开垦、森林砍伐导致研究区内草地退化严重,农业用地以及建筑用地逐年上升,在一定程度上为火灾的发生、传导提供了良好的条件。     

A performance evaluation of a burned area object-based classification model when applied to topographically and non-topographically corrected TM imagery

Operational use of remote sensing as a tool for post-fire Mediterranean forest management has been limited by problems of classification accuracy arising from confusion between burned and non-burned land, especially within shaded areas. Object-oriented image analysis has been developed to overcome the limitations and weaknesses of traditional image processing methods for feature extraction from high spatial resolution images. The aim of this work was to evaluate the performance of an object-based classification model developed for burned area mapping, when applied to topographically and non-topographically corrected Landsat Thematic Mapper (TM) imagery for a site on the Greek island of Thasos. The image was atmospherically and geometrically corrected before object-based classification. The results were compared with the forest perimeter map generated by the Forest Service. The accuracy assessment using an error matrix indicated that the removal of topographic effects from the image before applying the object-based classification model resulted in only slightly more accurate mapping of the burned area (1.16% increase in accuracy). It was concluded that topographic correction is not essential prior to object-based classification of a burned Mediterranean landscape using TM data.     

Monitoring fire-affected areas using Thematic Mapper data

In this paper three methods for updating inventories of burned areas have been presented and examined. They include Multitemporal Principal Component Analysis (MPCA), Change Vector Analysis (CVA) and Multitemporal NDVI Classification (MNC). First, 11 Landsat-5 Thematic Mapper (TM) images of a forest area were radiometrically corrected to derive a multitemporal series of intercomparable images for each spring from 1984 to 1994. Then, in order to check the feasibility of the three approaches, they were used for mapping fire burns that occurred during 1992. The various procedures yielded different maps of burned areas; the MNC method seemed to be more reliable than the others, because it merges spectral data corresponding not only to 1992 (pre-fire) and 1993 (post-fire) but also to 1994 (the second year after the fires), which is key in the vegetation regeneration. Finally, this methodology was automated to yield an inventory of burned areas for each year during the period of study.     

Satellite-based mapping of Canadian boreal forest fires: Evaluation and comparison of algorithms

This paper evaluates annual fire maps that were produced from NOAA-14/AVHRR imagery using an algorithm described in a companion paper (Li et al., International Journal of Remote Sensing, 21, 3057-3069, 2000 (this issue)). Burned area masks covering the Canadian boreal forest were created by compositing the daily maps of fire hot spots over the summer and by examining Normalized Difference Vegetation Index (NDVI) changes after burning. Both masks were compared with fire polygons derived by Canadian fire agencies through aerial surveillance. It was found that the majority of fire events were captured by the satellite-based techniques, but burnt area was generally underestimated. The burn boundary formed by the fire pixels detected by satellite were in good agreement with the polygons boundaries within which, however, there were some fires missed by the satellite. The presence of clouds and low sampling frequency of satellite observation are the two major causes for the underestimation. While this problem is alleviated by taking advantage of NDVI changes, a simple combination of a hot spot technique with a NDVI method is not an ideal solution due to the introduction of new sources of uncertainty. In addition, the performance of the algorithm used in the International Geosphere-Biosphere Programme (IGBP) Data and Information System (IGBPDIS) for global fire detection was evaluated by comparing its results with ours and with the fire agency reports. It was found that the IGBP-DIS algorithm is capable of detecting the majority of fires over the boreal forest, but also includes many false fires over old burned scars created by fires taking place in previous years. A step-by-step comparison between the two algorithms revealed the causes of the problem and recommendations are made to rectify them.