基于时相变化的晴空条件下Himawari-8 AHI火点检测     

鄢俊洁  瞿建华  冉茂农  张芳芳 《遥感学报》2020,24(5):571-577

Himawari-8静止气象卫星具有高空间分辨率、高观测频次和高时效特点,对于火点检测具有很强优势。对Himawari-8卫星的3.9μm和11.2μm两通道亮温进行了连续时相变化研究,得出两通道的亮温在时间上的变化差值稳定且规律明显。根据两通道的亮温时相特征,考虑白天可见光对3.9μm通道的影响,并结合火点产生时引起的亮温变化特征,提出了适用于晴空条件下改进的火点检测算法。在多处进行了此算法的实验,例如2018-11-27 T 16:40(UTC时)河北张家口市桥东区一化工厂附近发生的严重爆炸起火事件以及2019-02-28澳大利亚西南部发生的火灾事件,均快速有效的检测到了火点。实验表明,改进的火点检测算法能很好的进行火点检测,并能解决晨昏交界、冰雪下垫面、常规火源点、太阳耀光等火点检测的难题。  相似文献   

IntCal13 calibrated ages of the Vedde and Saksunarvatn ashes and the Younger Dryas boundaries from Kråkenes,western Norway          下载免费PDF全文

ØYSTEIN S. LOHNE  JAN MANGERUD  HILARY H. BIRKS 《第四纪科学杂志》2014,29(5):506-507

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大弹涂鱼(Boleophthalmus pectinirostris) Gal-8L基因序列及其细菌凝集活性的鉴定     

梁亚芳  史雨红  苗亮  陈炯 《海洋与湖沼》2018,49(2):413-421

半乳糖凝集素-8基因(galectin-8,Gal-8)属于凝集素家族一员。已知Gal-8在哺乳动物的先天免疫中发挥重要作用,然而在鱼类中却鲜有报道。本研究通过转录组测序获得大弹涂鱼(Boleophthalmus pectinirostris)半乳糖凝集素-8样基因(galectin-8 like,Bp Gal-8L)c DNA序列。首先确定其序列特征,其次采用实时定量PCR(real-time quantitative PCR,q PCR)的方法鉴定Bp Gal-8L组织表达分布及其与迟缓爱德华氏菌(Edwardsiella tarda)感染相关性;最后分别通过原核表达获得N端和C端CRD区重组蛋白Bp Gal-8L-N和Bp Gal-8L-C,并采用玻片法观察这两个CRD区的细菌凝集活性和糖类结合特性。多重序列比对显示,Bp Gal-8L蛋白含有两个糖识别结构域(carbohydrate recognition domain,CRD),但只有C端的CRD区存在Gal家族典型的β-半乳糖苷糖类结合基序。系统进化树表明Bp Gal-8L属于鱼类Gal-8的一种亚型。q PCR结果显示,Bp Gal-8L基因m RNA在各组织中普遍表达,并且在迟缓爱德华氏菌感染后大弹涂鱼肝和脾中表达量呈上调趋势。细菌凝集实验结果显示,只有Bp Gal-8L-C重组蛋白对检测的5种革兰氏阴性菌及2种革兰氏阳性菌有凝集作用,并与乳糖和脂多糖特异性结合。综上所述,Bp Gal-8L与大弹涂鱼抗菌免疫密切相关,且其N端和C端CRD区发挥不同作用。  相似文献   

海底底质声学原位测量电路控制系统研究     

吕斌  祁国梁  李官保  王景强  刘杰 《海洋科学》2018,42(5):32-39

介绍了一种液压驱动贯入式海底沉积声学原位测量系统的电路控制单元的研究实现过程,以及该控制单元在南海北部海底沉积声学调查中的应用。该电路控制单元以Cortex-A8处理器为核心,集成大容量FLASH存储器,与单片机接口控制板进行串口通讯,实现对声学发射采集单元和机械液压贯入单元的可视化控制和监测。基于该电路控制单元,海底底质声学原位测量系统兼具自容式和在线式两种工作模式,可自容记录或实时采集声学原位测量单元在海底的工作状态数据、海底沉积物声速和声衰减系数等声学特性数据。该声学原位测量系统的实验室联调及南海海试结果表明,使用该电路控制单元对海底底质声学测量过程的监测与控制是有效的,对精确获取海底底质的原位声学特性有重要作用,可以促进海底底质声学原位测量系统的产品化。  相似文献   

北京地区Landsat 8 OLI高空间分辨率气溶胶光学厚度反演   总被引：3，自引：0，他引：3  

田信鹏  孙林  刘强  李秀红 《遥感学报》2018,22(1):51-63

卫星气溶胶光学厚度(AOD)反演中,传统暗目标方法在反射率较低的水体、浓密植被覆盖区域取得了较好效果,在反射率较高且结构复杂的高反射地表上空目前多采用深蓝算法,但存在空间分辨率较低,对细节分布描述性较差等问题。为解决这一问题,本文首先以5年(2008年—2012年)长时间序列MODIS地表反射率产品为基础,采用最小值合成法建立500 m分辨率逐月地表反射率产品数据集,然后利用地物波谱库中典型地物波谱数据,分析建立MODIS与Landsat 8 OLI传感器蓝光波段反射率转换模型,最后北京地区AERONET地基观测数据确定了气溶胶光学物理参数,并反演获取了北京地区上空500 m分辨率的AOD分布。为验证反演算法的精度,分别将反演结果同AERONET及MODIS/Terra气溶胶产品(MOD04)进行交叉对比,同时利用相关系数R,均方根误差RMSE,平均绝对误差MAE以及MODIS AOD产品预期误差EE共4个指标进行衡量。结果表明:算法反演获取的AOD与AERONET观测值具有较高的一致性,各指标分别为R=0.963,RMSE=0.156,MAE=0.097,EE=85.3%,稍优于MOD04产品(R=0.962,RMSE=0.158,MAE=0.101,EE=75.8%),并且有效的对比点数也高于MOD04。通过与地基观测相比,卫星遥感获取的高分辨率城市地区AOD精度可作为定量评估城市空气质量的有效依据。  相似文献   

可见光波段灰度熵和热红外亮温差的沙尘遥感判识   总被引：1，自引：0，他引：1  

李彬  卢士庆  孙小龙  高健  孟翔晨  赵悦晨  吕迪波 《遥感学报》2018,22(4):647-657

沙尘作为对流层气溶胶的主要成分,对气候系统有许多影响;同时,作为环境污染物,对人类健康危害也很大。沙尘天气一般在春季爆发,对中国北方大部分区域的生产和生活有较大影响。以往针对沙尘遥感监测人们开展了许多研究,取得了一定的效果。但对于一些云和沙尘混合的复杂状况,传统方法识别效果较差,几乎不能有效识别出沙尘。采用葵花8号(Himawari-8)卫星数据,提出一种针对性的识别方法。引入了0.46μm和0.51μm反射率差值RDI,统计发现该指数在一定范围内可以表现出沙尘连续性特征,并有效地将中高云和大部分地表与沙尘区分开来。碎积云的RDI值分布与沙尘的较为相似,为此进一步引入了灰度熵方法来滤除。例举了3次沙尘过程的判识结果,并结合地面观测数据进行了验证。其中对2017年5月4日沙尘的地面验证表明,位于云沙混合区的27个站中有22个站的地面观测与判识相一致。对于一些复杂条件下的沙尘,该方法是对分裂窗亮温差的有效补充。  相似文献   

Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery     

A. Stefanidou  E. Dragozi  D. Stavrakoudis  I. Z. Gitas 《国际地球制图》2018,33(10):1064-1083

Efficient forest fire management requires precise and up-to-date knowledge regarding the composition and spatial distribution of forest fuels at various spatial and temporal scales. Fuel-type maps are essential for effective fire prevention strategies planning, as well as the alleviation of the environmental impacts of potential wildfire events. The aim of this study was to assess and compare the potential of Disaster Monitoring Constellation and Landsat-8 OLI satellite images (Operational Land Imager), combined with Object-Based Image Analysis (GEOBIA), in operational mapping of the Mediterranean fuel types at a regional scale. The results showcase that although the images of both sensors can be used with GEOBIA analysis for the generation of accurate fuel-type maps, only the OLI images can be considered as applicable for regional mapping of the Mediterranean fuel types on an operational basis.  相似文献   

Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso     

Gerald Forkuor  Kangbeni Dimobe  Idriss Serme  Jerome Ebagnerin Tondoh 《地理信息系统科学与遥感》2018,55(3):331-354

The availability of freely available moderate-to-high spatial resolution (10–30 m) satellite imagery received a major boost with the recent launch of the Sentinel-2 sensor by the European Space Agency. Together with Landsat, these sensors provide the scientific community with a wide range of spatial, spectral, and temporal properties. This study compared and explored the synergistic use of Landsat-8 and Sentinel-2 data in mapping land use and land cover (LULC) in rural Burkina Faso. Specifically, contribution of the red-edge bands of Sentinel-2 in improving LULC mapping was examined. Three machine-learning algorithms – random forest, stochastic gradient boosting, and support vector machines – were employed to classify different data configurations. Classification of all Sentinel-2 bands as well as Sentinel-2 bands common to Landsat-8 produced an overall accuracy, that is 5% and 4% better than Landsat-8. The combination of Landsat-8 and Sentinel-2 red-edge bands resulted in a 4% accuracy improvement over that of Landsat-8. It was found that classification of the Sentinel-2 red-edge bands alone produced better and comparable results to Landsat-8 and the other Sentinel-2 bands, respectively. Results of this study demonstrate the added value of the Sentinel-2 red-edge bands and encourage multi-sensoral approaches to LULC mapping in West Africa.  相似文献   

Spatiotemporal Variations of Microwave Land Surface Emissivity (MLSE) over China Derived from Four-Year Recalibrated Fengyun 3B MWRI Data     

Rui LI  Jiheng HU  Shengli WU  Peng ZHANG  Husi LETU  Yu WANG  Xuewen WANG  Yuyun FU  Renjun ZHOU  Ling SUN 《大气科学进展》2022,39(9):1536-1560

Microwave Land Surface Emissivity (MLSE) over China under both clear and cloudy sky conditions was retrieved using measurements of recalibrated microwave brightness temperatures (Tbs) from Fengyun-3B Microwave Radiation Imager (FY-3B MWRI), combined with cloud properties derived from Himawari-8 Advanced Himawari Imager (AHI) observations. The contributions from cloud particles and atmospheric gases to the upwelling Tbs at the top of atmosphere were calculated and removed in radiative transfer. The MLSEs at horizontal polarizations at 10.65, 18.7, and 36.5 GHz during 7 July 2015 to 30 June 2019 over China showed high values in the southeast vegetated area and low values in the northwest barren, or sparsely vegetated, area. The maximum values were found in the belt area of the Qinling-Taihang Mountains and the eastern edge of the Qinghai-Tibet Plateau, which is highly consistent with MLSEs derived from AMSR-E. It demonstrates that the measurements of FY-3B MWRI Tbs, including its calibration and validation, are reliable, and the retrieval algorithm developed in this study works well. Seasonal variations of MLSE in China are mainly driven by the combined effects of vegetation, rainfall, and snow cover. In tropical and southern forest regions, the seasonal variation of MLSE is small due to the enhancement from vegetation and the suppression from rainfall. In the boreal area, snow causes a significant decrease of MLSE at 36.5 GHz in winter. Meanwhile, the MLSE at lower frequencies experiences less suppression. In the desert region in Xinjiang, increases of MLSEs at all frequencies are observed with increasing snow cover.  相似文献   

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image     

Chao LIU  Shu YANG  Di DI  Yuanjian YANG  Chen ZHOU  Xiuqing HU  Byung-Ju SOHN 《大气科学进展》2022,39(12):1994-2007

Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.  相似文献