Himawari-8静止气象卫星时序法火点探测

随着新一代静止气象卫星的发射,高频次和高时效的观测特性对于火点探测具有独特优势。本文基于Himawari-8新一代静止气象卫星高频次观测特点,提出有利于火情初期火点判识的时序探测方法。与传统的极轨气象卫星遥感火情监测采用的上下文法不同,时序探测法判识火点的方法依据为探测像元亮温在观测时间上的差异。研究结果显示,在无云及无异常热源条件下,相邻时次中红外亮温差异较小,当前后时次亮温差达到3K时,可判识出火点,而上下文法的阈值均在6 K以上,时序法的火点判识阈值较上下文法明显降低,探测相应的亚像元火点面积减小一倍以上,从而提高了火情判识的灵敏度,实现火点早期发现。本文介绍了时序法火点判识方法,并以黑龙江桦川县的星地同步观测实验进行验证,研究表明,时序法较上下文法在初发火点探测灵敏度方面有明显优势,时序法和上下文法的结合可提高气象卫星对火情发展过程的监测能力。     

青海高寒草地春季火情的多源卫星遥感动态监测

多源卫星遥感数据源能为现场扑火指挥决策提供快速、准确的空间信息。根据火点在中红外波段辐射率和亮温急剧增大的特点,利用10个时次的国内外极轨气象卫星资料(包括首次利用了NPP卫星数据),针对2016年3月18—19日发生在青海省果洛藏族自治州久治县的草原火灾进行了实时动态监测,并采用高分数据提取了灾后过火面积。研究结果表明,气象卫星火点判识方法及阈值设定同样适用于青海高寒草地春季的火情监测;快速判识阈值的确定,可为日常火情实时监测业务提供快速、有效的技术支持。     

利用青海湖水面辐射校正场对FY—1C气象卫星热红外传感器进行绝对辐射定标

利用青海湖水面场FY-1C气象卫星热红外窗区通道进行绝对辐射定标,由CE312野外热红外辐射计在水面测得辐亮度经大气订正传递到卫星入瞳处,考虑到大气吸收削弱影响,同时卫星观测路径大气产生热发散,这两部分对卫星信号的贡献由探空廓线和卫星观测几何输入MODTRAN37计算出来,同时进行CE312野外辐射计与卫星通道光谱响应匹配计算,最终得到卫星入瞳处的表观亮度.这个辐亮度与卫星通道的计数值比较得到该通道绝对定标系数.结果表明利用辐射校正场辐射定标与星上定标相差5%左右,相当于3K的亮温差.     

FY-3C微波成像仪海面温度产品算法及精度检验

海洋表面温度(SST)是海洋学和气候学一个十分重要的物理因子,而卫星被动微波遥感能够穿透云层,实现全天候、大范围观测,因此利用中国FY-3C微波成像仪(MWRI)反演SST具有重要意义。FY-3C MWRI SST产品采用统计算法,首先利用MWRI降水和海冰产品剔除含降水和海冰的像元,之后选择时空间隔0.2 h和0.2°离海岸100 km以外的FY-3C MWRI观测亮温与浮标观测值进行匹配,再将全球在空间上分为4个纬度带,时间上分为12个月,并分升轨和降轨,分别建立浮标海温观测结果和MWRI亮温之间的统计关系,实现对SST的估算。将|估算海温-30年月平均海温|≥2.5 K的像元标识为51,发现这些像元基本分布在陆地边缘地区及大风速地区,剔除标识为51的像元后的精度验证结果表明:与全球浮标资料相比,FY-3C MWRI SST轨道产品升轨精度为–0.02±1.22 K,降轨精度为–0.15±1.28 K;与全球分析场日平均海温OISST相比,FY-3C MWRI SST日产品升轨精度为0.00±1.03 K,降轨精度为–0.09±1.08 K。微波辐射计的性能及其定位定标精度、上游卫星产品(降水检测和海冰检测)的精度、陆地的干扰及高风速对微波信号的影响均会造成SST估算误差,如何改进算法中风速大于12 m/s时的估算精度是下一步的工作重点。     

基于VIIRS数据的哈尔滨市秸秆焚烧火点监测

采用哈尔滨市2020年秋收季开始至2021年春耕季结束的VIIRS SDR 742 m分辨率下的第5、7、11波段反射率产品和13、15、16波段亮温值产品,分析火点像元亮温特征及与背景亮温的偏差来提取哈尔滨市秸秆焚烧火点信息,对VIIRS卫星影像数据提取算法进行验证,同时对火点监测算法监测到的哈尔滨市秸秆焚烧火点进行时空分布研究。结果表明,此算法监测率较高,可以为监管部门提供重点有效的监测信息,提高对重点区域的监管力度。     

基于VIIRS影像的秸秆焚烧火点监测方法

利用VIIRS SDR数据,基于火点像元亮温特征及其与背景亮温的偏差来提取秸秆焚烧火点信息。采用湖北省2015年秋收季节的VIIRS SDR 750 m分辨率下的第5、7、11波段反射率产品和13、15、16波段亮温值产品,基于VIIRS卫星影像秸秆焚烧火点监测算法和GIS平台叠加火点信息和地理基础数据生成湖北省秸秆焚烧火点分布图。结果表明,小火点检测率较高,漏检率降低。     

基于FY-2E卫星数据的福建沿海海雾遥感监测

利用我国自主研制的风云二号静止气象卫星资料,结合地面自动气象站能见度资料,通过对大量不同时相卫星资料的试验分析,找出台湾海峡海雾、云以及晴空海表等典型下垫面的可见光、热红外和中红外3个通道的光谱特征和变化规律,在此基础上运用反射率阈值实现云雾与海洋表面的自动分离,用亮温阈值实现海雾和低云与中高云的自动分离,用中红外和热红外通道的归一化差值指标实现夜间海雾的自动识别,最后建立台湾海峡海雾自动监测业务软件系统,并选择2015年和2016年地面实测资料对遥感监测结果进行精度验证。研究结果表明:风云静止卫星逐时海雾产品能有效地弥补极轨卫星在监测时次上的不足,很好地为台湾海峡海雾动态监测业务提供数据支持;遥感监测结果与地面观测结果相吻合,总体上较为理想,海雾监测平均准确率白天超过70%,但夜间判识精度低于白天;但同时风云静止卫星遥感技术在海雾和低云的有效分离方面仍然存在局限性。     

利用FY-2F数据检测快速发展对流

快速发展对流RDC(Rapid Developing Convection)是灾害性天气发生的先兆。如何利用卫星遥感技术快速捕捉到RDC信息,是天气临近预报非常关注的内容。随着中国FY-2F气象卫星快速扫描工作模式的业务化,可提供时间分辨率为6 min,空间分辨率为5 km的多通道快速扫描数据,这使得利用卫星检测RDC业务化成为可能。本文根据FY-2F多通道快速扫描数据的特点,设计了一种快速发展对流检测方法。该方法主要根据云顶快速降温率CTC(Cloud Top Cooling rate)的多通道判识,设计了带有3个测试的CTC过滤器,过滤之后得到每个RDC的局部亮温极小值点,然后以该点为中心对周围像元进行漫水填充,得到快速发展对流体。对2013年8月(94°E—129°E,11°N—26°N)的扫描数据进行试验验证,结果表明,本文方法对RDC检测的准确率POD、误报率FAR和临界成功指数CSI分别为0.89、0.15和0.77,能够较为准确地检测快速发展对流。     

FY-2E红外通道晴空沙尘区时间差分法导风研究

利用大气红外辐射传输模式,结合FY-2E静止卫星红外窗区光谱响应函数,模拟计算了标准大气环境下0-5 km各高度层气溶胶消光系数增量为50%和30%两种情况下对FY-2E红外1通道亮温增量的贡献。定量分析了春季气溶胶消光系数对亮温增量的影响以及主要影响高度。结合FY-2E红外1通道灵敏度,提出了可用于红外通道晴空沙尘气溶胶微弱示踪信号移动追踪的"时间差分法"。理论模拟计算和实例分析表明,在一定条件下,"时间差分法"追踪红外通道晴空沙尘气溶胶微弱示踪信号的移动,可以获得传统云导风所无法得到的干旱半干旱沙尘爆发区的风场信息,且反演得到的风场与美国国家环境预报中心NCEP再分析资料850 hPa风场有着很好的一致性。     

FY-2C积雪判识方法研究

介绍了利用FY-2C资料进行积雪判识的原理,在阈值法基础上的辅助因子函数积雪判识方法以及相应的FY-2C积雪判识结果精度验证分析等。一般较为常用的卫星遥感积雪判识方法为简单阈值法,由于其带有一定的随机性,很难客观反映下垫面条件差异对阈值选取的影响。以阈值法为基础,将所使用的主要变量以函数形式表达,以海拔高度、地理位置、季节、土地覆盖类型等作为阈值函数的变量,通过大量采样建立起多种阈值函数,从而实现随时空特点变化的阈值实时计算。该方法用于FY-2C积雪判识,较好地解决了FY-2C全圆盘范围内广大区域不同下垫面类型下的实时积雪监测。通过与NOAA-17人机交互积雪判识结果对比分析,该方法的积雪判识精度可达85%左右。     

FY-3D MERSI-II地表温度遥感反演与验证

地表温度是是决定地表辐射能量收支的重要变量,在岩石圈、水圈、生物圈和大气圈的能量平衡和水量平衡研究中起着重要作用。利用热红外遥感技术可实现区域和全球尺度地表温度的快速获取,其受到了研究者的广泛关注。目前,FY-3D是国内光谱分辨率最高的对地观测卫星,极大的提高了对地观测能力,其搭载的中分辨率光谱成像仪（MERSI-II）经过大幅升级改进,性能有了显著提升,热红外数据的空间分辨率达到了250 m。本文使用大气辐射传输模型MODTRAN 5模拟了MERSI-II传感器热红外通道星上观测数据。在此基础上,构建了通用劈窗地表温度反演模型,结合ASTER GED全球地表发射率产品以及MERSI-II自身大气水汽反演算法,发展了地表温度遥感反演方法。最后,利用2019-08内蒙古乌海沙漠地区及美国SURFRAD多个站点的实测地表温度数据对本文提出的方法进行了验证。研究结果表明,相较地表实测数据,构建的劈窗算法反演的地表温度RMSE在1.6—2.6 K,反演精度达到了预期目标,还具有较高的空间分辨率,可以用于业务化的地表温度的反演,同时也说明其辐射定标精度有了一定保证,有效满足了区域和全球尺度地表温度遥感监测应用需求。     

Snow Cover Monitoring in Qinghai-Tibetan Plateau Based on Chinese Fengyun-3/VIRR Data

Snow cover monitoring in the Qinghai-Tibetan Plateau is very important to global climate change research. Because of the geographic distribution of ground meteorological stations in Qinghai-Tibetan Plateau is too sparse, satellite remote sensing became the only choice for snow cover monitoring in Qinghai-Tibetan Plateau. In this paper, multi-channel data from Visible and Infrared Radiometer (VIRR) on Chinese polar orbiting meteorological satellites Fengyun-3(FY-3) are utilized for snow cover monitoring, in this work, the distribution of snow cover is extracted from the normalized difference snow index(NDSI), and the multi-channel threshold from the brightness temperature difference in infrared channels. Then, the monitoring results of FY-3A and FY-3B are combined to generate the daily composited snow cover product. Finally, the snow cover products from MODIS and FY-3 are both verified by snow depth of meteorological station observations, result shows that the FY-3 products and MODIS products are basically consistent, the overall accuracy of FY-3 products is higher than MODIS products by nearly 1 %. And the cloud coverage rate of FY-3 products is less than MODIS by 2.64 %. This work indicates that FY-3/VIRR data can be reliable data sources for monitoring snow cover in the Qinghai-Tibetan Plateau.     

风云三号卫星被动微波反演海洋上空云液态水含量

云液态水含量是气候和水循环研究的重要云微物理参数，也是目前气候变化研究中的最不确定因素之一。通过极轨气象卫星被动微波观测的光谱和极化特征能够实现对云液态水含量的直接测量，本文介绍了一种基于风云三号卫星微波成像仪（MWRI）观测亮温的全天候云液态水含量反演算法，利用快速辐射传输模式、云模型和大气廓线库建立MWRI模拟亮温库并训练反演系数的宽气候态物理算法可以保证算法系数在不同季节和不同地区的适应性。同时提出了一种基于观测增量（O-B）筛选晴空像元并对算法系数及比例因子进行订正的方法。利用统计直方图方法和卫星间交叉比对方法对反演产品精度进行了检验，统计直方图方法检验结果表明，FY-3C云水反演误差为0.028 mm，FY-3D为0.025 mm，与国外同类产品的精度相当；与低轨卫星微波辐射计GMI云水产品的交叉比对结果表明，两者具有较高一致性，均方根误差为0.0325 mm。FY-3C/3D CLW产品目前已经投入业务应用，上下午星组网能够一天内基本覆盖全球，实现全球云水分布监测。     

Intercomparison of Himawari-8 AHI-FSA with MODIS and VIIRS active fire products

ABSTRACT

The AHI-FSA (Advanced Himawari Imager - Fire Surveillance Algorithm) is a recently developed algorithm designed to support wildfire surveillance and mapping using the geostationary Himawari-8 satellite. At present, the AHI-FSA algorithm has only been tested on a number of case study fires in Western Australia. Initial results demonstrate potential as a wildfire surveillance algorithm providing high frequency (every 10 minutes), multi-resolution fire-line detections. This paper intercompares AHI-FSA across the Northern Territory of Australia (1.4 million km²) over a ten-day period with the well-established fire products from LEO (Low Earth Orbiting) satellites: MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite). This paper also discusses the difficulties and solutions when comparing high temporal frequency fire products with existing low temporal resolution LEO satellite products. The results indicate that the multi-resolution approach developed for AHI-FSA is successful in mapping fire activity at 500?m. When compared to the MODIS, daily AHI-FSA omission error was only 7%. High temporal frequency data also results in AHI-FSA observing fires, at times, three hours before the MODIS overpass with much-enhanced detail on fire movement.     

Multisource Data Integration for Fire Risk Management: The Local Test of a Global Approach

In this letter, we propose an algorithm to detect the presence of forest fires using data from both geostationary and polar-orbiting satellites. The very frequent acquisitions of the Spinning Enhanced Visible and Infrared Imager radiometer placed onboard the Meteosat Second Generation-9 satellite are used as main source for the algorithm, while the MEdium Resolution Imaging Spectrometer global vegetation index and the Advanced Along-Track Scanning Radiometer measurements are used to enhance the reliability of the detection. The problem is approached in a “global” way, providing the basis for an automated system that is not dependent on the local area properties. In cooperation with the Centre de Suivi Écologique (Dakar, Senegal), the proposed algorithm was implemented in a “Multisource Fire Risk Management System” for the Senegal area, as briefly described in this letter. A field campaign of one week was carried out in order to perform a validation of the system's detections, showing a good agreement with the fire coordinates measured on the ground. Furthermore, a consistency check was performed using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Rapid Response System, showing that more than 76% of high-confidence MODIS events are detected by the algorithm.      

利用多光谱卫星遥感和深度学习方法进行青藏高原积雪判识

青藏高原积雪对全球气候变化十分重要,针对已有积雪遥感判识方法中普遍采用的可见光与红外光谱数据易受复杂地形与高海拔影响,导致青藏高原地区积雪判识精度较低的问题,提出了一种基于多光谱遥感与地理信息数据特征级融合的积雪遥感判识方法:以风云三号卫星可见光与红外多光谱遥感资料与多要素地理信息作为数据源,由地面实测雪深数据与现有积雪产品交叉筛选出样本标签,构建并训练基于层叠去噪自编码器(SDAE)的特征融合与分类网络,从而有效辨识青藏高原遥感图像中的云、积雪以及无雪地表。经地面实测雪深数据验证,该方法分类精度显著高于使用相同数据源的FY-3A/MULSS积雪产品,略高于国际主流积雪产品MOD10A1与MYD10A1,并且年均云覆盖率最低。试验结果表明该方法可有效地减少云层对积雪判识的干扰,提升分类精度。     

近实时中高空间分辨率森林火灾监测系统展望

森林火灾是最为常见的灾害之一,严重危及人类生命安全。及时准确监测森林火灾的发生及火场状况,对应对火灾及减少损失至关重要。当前,森林火灾卫星遥感监测主要以低空间分辨率的卫星遥感为主,空间分辨率过低导致无法探测规模较小火灾及掌握详细火场态势。针对这一问题,结合近些年中高空间分辨率卫星观测、共享及处理能力的发展,本文从森林火灾卫星遥感监测的基本原理、当前可用中高空间分辨率卫星数据及其特点、中高分辨率森林着火区监测算法,以及数据共享与云端存储与计算等4个技术环节,对森林火灾中高分辨率卫星遥感监测当前研究现状与存在问题进行了总结,阐述了近实时中高空间分辨率森林火灾监测系统的可行性。近实时中高空间分辨率森林火灾监测系统可对已有低空间分辨率森林火灾监测体系形成重要补充,依托其空间分辨率的优势有助于及早、准确发现小规模火情,进而为森林火灾的防治与管理提供更好支撑。     

A satellite data driven approach to monitoring and reporting fire disturbance and recovery across boreal and temperate forests

The regular and consistent measurements provided by Earth observation satellites can support the monitoring and reporting of forest indicators. Although substantial scientific literature espouses the capabilities of satellites in this area, the techniques are under-utilised in national reporting, where there is a preference for aggregating ad hoc data. In this paper, we posit that satellite information, while perhaps of low accuracy at single time steps or across small areas, can produce trends and patterns which are, in fact, more meaningful at regional and national scales. This is primarily due to data consistency over time and space. To investigate this, we use MODIS and Landsat data to explore trends associated with fire disturbance and recovery across boreal and temperate forests worldwide. Our results found that 181 million ha (9 %) of the study area (2 billion ha of forests) was burned between 2001 and 2018, as detected by MODIS satellites. World Wildlife Fund biomes were used for a detailed analysis across several countries. A significant increasing trend in area burned was observed in Mediterranean forests in Chile (8.9 % yr⁻¹), while a significant decreasing trend was found in temperate mixed forests in China (-2.2 % yr⁻¹). To explore trends and patterns in fire severity and forest recovery, we used Google Earth Engine to efficiently sample thousands of Landsat images from 1991 onwards. Fire severity, as measured by the change in the normalized burn ratio (NBR), was found to be generally stable over time; however, a slight increasing trend was observed in the Russian taiga. Our analysis of spectral recovery following wildfire indicated that it was largely dependent on location, with some biomes (particularly in the USA) showing signs that spectral recovery rates have shortened over time. This study demonstrates how satellite data and cloud-computing can be harnessed to establish baselines and reveal trends and patterns, and improve monitoring and reporting of forest indicators at national and global scales.     

Variation in tree mortality and regeneration affect forest carbon recovery following fuel treatments and wildfire in the Lake Tahoe Basin, California, USA

Background

Forest fuel treatments have been proposed as tools to stabilize carbon stocks in fire-prone forests in the Western U.S.A. Although fuel treatments such as thinning and burning are known to immediately reduce forest carbon stocks, there are suggestions that these losses may be paid back over the long-term if treatments sufficiently reduce future wildfire severity, or prevent deforestation. Although fire severity and post-fire tree regeneration have been indicated as important influences on long-term carbon dynamics, it remains unclear how natural variability in these processes might affect the ability of fuel treatments to protect forest carbon resources. We surveyed a wildfire where fuel treatments were put in place before fire and estimated the short-term impact of treatment and wildfire on aboveground carbon stocks at our study site. We then used a common vegetation growth simulator in conjunction with sensitivity analysis techniques to assess how predicted timescales of carbon recovery after fire are sensitive to variation in rates of fire-related tree mortality, and post-fire tree regeneration.

Results

We found that fuel reduction treatments were successful at ameliorating fire severity at our study site by removing an estimated 36% of aboveground biomass. Treated and untreated stands stored similar amounts of carbon three years after wildfire, but differences in fire severity were such that untreated stands maintained only 7% of aboveground carbon as live trees, versus 51% in treated stands. Over the long-term, our simulations suggest that treated stands in our study area will recover baseline carbon storage 10?C35?years more quickly than untreated stands. Our sensitivity analysis found that rates of fire-related tree mortality strongly influence estimates of post-fire carbon recovery. Rates of regeneration were less influential on recovery timing, except when fire severity was high.

Conclusions

Our ability to predict the response of forest carbon resources to anthropogenic and natural disturbances requires models that incorporate uncertainty in processes important to long-term forest carbon dynamics. To the extent that fuel treatments are able to ameliorate tree mortality rates or prevent deforestation resulting from wildfire, our results suggest that treatments may be a viable strategy to stabilize existing forest carbon stocks.