太阳山断裂带及周边地区多源遥感断层构造解译与分析

利用多源遥感数据及SBAS-InSAR技术,多尺度多角度开展太阳山断裂带及周边地区的断层遥感解译与对比分析工作.结果表明,研究区共发育6条主要断裂,其中太阳山断裂带构造地貌特征明显,由4条主干断裂组成,即岗市-河洑断裂、太阳山西侧断裂、肖伍铺断裂、仙峰峪断裂,主干断裂表现出线性陡坎、断层沟谷、湖泊边界、山体断错等异常地...     

多源遥感数据融合应用研究

多源遥感数据融合是遥感技术向纵深发展的必然趋势。本文对多源遥感数据融合算法的应用特点,从基于像元的融合、特征的融合以及决策级融合3个层次上进行了详细的分析,并以河北丰宁县为例,说明遥感数据融合方法在遥感信息提取中的具体应用:对所用数据进行预处理,然后对1999年Landsat TM数据进行主成分变换,前3 个主成分占总信息量的97.8%,主成分逆变换后的结果影像更清晰,层次更丰富。信息提取时选择TM全色和主成分变换后的多光谱数据融合后的影像,波段4、3、2和波段5、4、3的彩色合成方案,并对植被指数和穗帽变换后的绿度指数进行了分析,遥感影像与DEM以及与GIS空间数据的信息融合也可以提高遥感信息提取的精度。最后分析了多源遥感数据融合尚待解决的问题及努力方向。     

基于多源遥感数据的京津冀环境质量动态监测

基于PM2.5(Fine Particulate Matter)浓度、地表温度(Land Surface Temperature,LST)、归一化植被指数(Normalized Difference Vegetation Index,NDVI)、植被覆盖度(Vegetation Coverage,VC)、土地利用等数据,借助综合评价指数(Comprehensive Evaluation Index,CEI),本文对2000―2015年京津冀环境质量进行了评估.结果表明:京津冀整个地区,CEI呈逐步退化和退化的面积分别占34.88％和4.15％,主要位于东南部的天津,河北邢台、邯郸等地.京津冀城市建成区,CEI呈退化和逐步退化的比例分别达到54.80％和25.87％.46.76％和39.86％的城市扩张用地的环境质量分别呈现逐步退化和退化状态.京津冀?LST值呈现西北、东南低,中部高的分布态势,?PM2.5和?VC均呈现从东南向西北减小的趋势.相关分析表明?PM2.5、?VC和?LST的变化对城市建成区及其城市扩张用地的环境质量有显著影响,其中?PM2.5浓度变化是导致城市建成区及其扩张用地环境质量恶化的最主要因素.     

多源遥感影像数据融合方法在地学中的应用

应用遥感影像数据融合理论，研究了光学遥感、热红外遥感、微波遥感卫星在地质学中的应用，论述了不同类型的遥感数据及其他地学数据在不同层次上的同一传感器多波段数据融合、不同SAR图像数据融合、HIS变换、遥感影像与地球物理、地质和航磁等数据的融合。结果表明，数据融合技术在突出地质特征信息方面具有能突出线性构造、断裂构造、地形地貌的优势。     

基于多源数据的青藏高原雪深重建

青藏高原地形复杂,积雪时空分布异质性较强且大部分地区积雪较薄,而被动微波遥感因其空间分辨率低以及雪深反演中的不确定性,极大地限制了其反演青藏高原雪深的精度。本文尝试将多源遥感数据以及与积雪模型（SnowModel）相结合,来重建更高质量的青藏高原雪深数据。首先,利用MODIS积雪面积比例产品,根据构建的积雪衰减曲线以及经验的融合规则对低分辨率被动微波雪深进行了降尺度;然后,结合MODIS/被动微波融合雪深数据和SnowModel对研究区进行雪深数据同化实验;最后,利用地面站实测雪深数据对MODIS/被动微波融合雪深以及同化输出雪深的精度进行了分析和对比。结果表明,基于数据同化方法得到的雪深数据更接近地面观测雪深值,通过均方根误差以及相关系数的对比,同化雪深结果优于MODIS/被动微波融合雪深结果。     

地表温度的多源遥感数据反演算法对比分析

 多源遥感数据的综合应用是提高地表温度反演精度的有效途径.MODIS数据和Landsat TM数据在我国同一地区获取的时间相差不大,可以获取近似同步的MODIS数据和TM数据.本文将基于MODIS数据反演的大气参数应用于TM影像的地表温度反演,分别对单窗口算法和普适性单通道算法进行了实验研究,应用气象站实测的地表温度数据对反演结果进行了检验,并对比分析了不同土地覆盖条件下两种算法的精度差异.结果表明:两种算法反演精度均较高,单窗口算法反演精度为0.76K,普适性单通道算法反演精度为1.23K;在不同的土地覆盖条件下,两种算法表现出明显的差异性,水体区反演结果差异最小,均值差异仅为0.02K,植被区差异最大,均值差异为0.62K.     

多源海量统计遥感数据集成管理技术研究

统计遥感业务是国家统计局基于统计业务对空间信息的发展和应用需求,将GIS、RS和GPS空间信息技术全面应用于统计数据的获取、管理与分析中,从而提高统计效率和统计科技含量,构建新型国家统计业务体系。统计遥感业务数据具有多源、海量的特点,实现统计遥感数据的集成与管理是新型统计遥感业务成功运转的基础。针对统计遥感业务及数据应用需求,通过对各类空间数据集成与管理技术进行研究,提出在建立统计遥感空间数据库基础上,将影像以县级为单位进行拼接、然后压缩存储到统计遥感空间数据库,同时结合影像编目技术实现海量遥感影像的管理;提出将元数据管理和数据转换、数据直接访问以及数据互操作方式,实现多源统计遥感数据的集成与管理。通过多种方式,从应用层面较好地解决了海量、多源统计遥感数据的集成与管理,提升了统计遥感数据的应用范围。     

基于遥感影像信息的东昆仑活动断裂带研究

基于遥感影像（ Ｔ Ｍ 影像、黑白航片） 信息对东昆仑活动断裂带进行了研究,建立了活动构造影像空间信息识别模式,发现了新的分支活动断裂。东昆仑活动断裂带是青海省自然景观梯度带、地质灾害和气候转型带,这些特征构成其识别的宏观标志。     

基于Google Earth Engine与多源遥感数据的海南水稻分类研究

水稻是中国乃至亚洲的重要粮食作物之一,稻米产量关系到民生福祉。及时、准确的水稻分布信息是监测水稻产量、调控农业资源配给的基础。遥感(Remote Sensing)技术能够提供大范围地表的时间序列光谱变化特征,常用于大尺度范围的作物监测。然而,传统基于水稻生长关键时期光谱特征的分类、提取方法对遥感数据的时间分辨率要求较高。由于我国南方水稻产区湿热,雨季云污染降低了遥感数据的有效时间分辨率,因此上述方法在该地难以推广。融合多源遥感数据的分类方案变相缩短了卫星的重访周期,使多云气候区基于遥感影像的水稻分类成为可能。然而,集成多源数据所需更高的数据处理效率和存储需求也成为限制省级乃至更大范围水稻分类的主要因素。本研究基于谷歌地球引擎(Google Earth Engine)云平台,在线调用中分辨率的光学、微波遥感数据,创新性地采用了按月提取、按直方图大小提取特征的方式,采用随机森林分类器,绘制海南省2016年10 m分辨率水稻种植分布图。实验结果证明,该方法可以用于南方多云地区水稻分类,提取结果能够体现不同地类之间的差异,且与实际地表的地块边界、纹理符合良好。经过地表样本点的验证,总体精度为93.2%,满足实际应用需求。因此,本研究采用的自动分类流程能够准确、高效地提取海南省的水稻种植范围,可以向其他地区大范围推广。     

基于多源异构数据的粮食作物种植面积提取

利用遥感影像进行农作物面积提取已经普遍应用到农作物种植面积提取工作中,但是普遍存在着提取效率低,精度达不到生产要求。随着高分辨率传感器的相继问世,融合多平台遥感数据有利于提高分类和动态监测的精度。本文以日照市莒县粮食种植面积提取为例,融合多时相和多源异构数据,利用深度学习算法提取粮食种植区域,深入挖掘多源数据的互补效应,提出了融合多时段、多源地理信息提取农作物种植面积的方法,为实现农作物种植面积快速高效的提取奠定了基础。     

Geometrical feature analysis and disaster assessment of the Xinmo landslide based on remote sensing data

At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.     

Classification of vegetative types in Changbai Mountain based on optical and microwave remote sensing data

Highly accurate vegetative type distribution information is of great significance for forestry resource monitoring and management. In order to improve the classification accuracy of forest types, Sentinel-1 and 2 data of Changbai Mountain protection development zone were selected, and combined with DEM to construct a multi-featured random forest type classification model incorporating fusing intensity, texture, spectral, vegetation index and topography information and using random forest Gini in...     

Study on the correlation between chlorophyll maximum and remote sensing data

Based on the in situ optical measurements in the Bohai Sea of China, which belongs to a typical case-2 water area, we studied the characteristics of DCM （deep chlorophyll maximum） such as its spatial distribution, vertical profile, etc. We found that when the depth of the chlorophyll maximum is comparatively small, even in turbid coastal water regions, there is always a good correlation between the concentrations of chlorophyll maximum and the satellite-received signals in blue-green spectral bands; the correlation is even better than that between the surface chlorophyll concentrations and the satellite-received signals. The strong correlation existing even in turbid coastal water regions indicates that an ocean color model to retrieve the concentration of DCM can be constructed for coastal waters if a comprehensive knowledge of the vertical distribution of chlorophyll concentration in the Bohai Sea of China is available.     

Integrating CART algorithm and multi-source remote sensing data to estimate sub-pixel impervious surface coverage: a case study from Beijing Municipality,China

The sub-pixel impervious surface percentage(SPIS) is the fraction of impervious surface area in one pixel,and it is an important indicator of urbanization.Using remote sensing data,the spatial distribution of SPIS values over large areas can be extracted,and these data are significant for studies of urban climate,environment and hydrology.To develop a stabilized,multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons,an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree(CART) algorithm.First,models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data.The optimal model was selected through the analysis and comparison of the assessed accuracy of these models.Subsequently,multi-temporal SPIS mapping was carried out based on the optimal model.The results are as follows:1) multi-seasonal images and nighttime light(NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality,where the intra-annual variability in vegetation is distinct.The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively.NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces.After testing,the SPIS modeling correlation coefficient(r) is approximately 0.86,the average error(AE) is approximately 12.8%,and the relative error(RE) is approximately 0.39.2) The SPIS results have been divided into areas with high-density impervious cover(70%–100%),medium-density impervious cover(40%–70%),low-density impervious cover(10%–40%) and natural cover(0%–10%).The SPIS model performed better in estimating values for high-density urban areas than other categories.3) Multi-temporal SPIS mapping(1991–2016) was conducted based on the optimized SPIS results for 2005.After testing,AE ranges from 12.7% to 15.2%,RE ranges from 0.39 to 0.46,and r ranges from 0.81 to 0.86.It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation.     

Human settlement analysis based on multi-temporal remote sensing data: A case study of Xuzhou City,China

To evaluate urban human settlement, we propose a human settlement environment development index (HSEDI) model by choosing vegetation coverage, land surface temperature, impervious surfaces, slope, wetness, and water condition as the evaluation factors. We applied the proposed model to Xuzhou City, Jiangsu Province, China. Landsat-5 Thematic Mapper (TM) images from 1998 to 2010 and digital elevation model (DEM) data with a 30-m resolution were used to calculate the values of the six evaluation factors. The HSEDI value in Xuzhou City was found to be between 2.24 and 8.10 from 1998 to 2010, and it was further divided into five levels, uninhabitable, moderately uninhabitable, generally inhabitable, moderately inhabitable, and inhabitable. The best HSEDI value was in 2007. The generally inhabitable region was about 100.98 km², covering 30.87% of the total area in 2007; the moderately inhabitable region was about 170.58 km² covering 52.15% of the total area; the inhabitable region was about 32.03 km², covering 9.79% of the total area; the percentage of the uninhabitable region was zero; and that of the moderately uninhabitable region was very small, less than 1.00%. Moreover, we analyzed the habitability in the respect of spatial patterns and change detection. Results show that the degraded regions of habitability quality are mainly located in the urban fringe and the improved regions are mainly located in the main urban and rural areas. Reason for the degraded habitability quality is the rapid progress of urbanization. However, the increase in urban green spaces and the construction of the main urban area promoted the improved habitability quality. Besides, we further analyzed socio-economic and socio-demographic data to confirm the results of the habitability analysis. The results indicate that the human settlement in Xuzhou City is in a satisfactory condition, but some efforts should be made to control the possible uninhabitable and moderately uninhabitable regions, and to improve the quality of the generally inhabitable regions.     

The study on fishing ground of neon flying squid,Ommastrephes bartrami,and ocean environment based on remote sensing data in the Northwest Pacific Ocean

The relationships between the neon flying squid, Ommastrephes bartrami, and the relative ocean environmental factors are analyzed. The environmental factors collected are sea surface temperature (SST), chlorophyll concentration (Chl-a) and sea surface height (SSH) from NASA, as well as the yields of neon flying squid in the North Pacific Ocean. The results show that the favorable temperature for neon flying squid living is 10°C–22°C and the favorite temperature is between 15°C–17°C. The Chl-a concentration is 0.1–0.6 mg/m³. When Chl-a concentration changes to 0.12–0.14 mg/m3, the probability of forming fishing ground becomes very high. In most fishing grounds, the SSH is higher than the mean SSH. The generalized additive model (GAM) was applied to analyze the correlations between neon flying squid and ocean environmental factors. Every year, squids migrate northward from June to August and return southward during October–November, and the characteristics of the both migrations are very different. When squids migrate to the north, most relationships between the yields and SST are positive. The relationships are negative when squids move to southward. The relationships between the yields and Chl-a concentrations are negative from June to October, and insignificant in November. There is no obvious correlation between the catches of squid and longitude, but good with latitude. Supported by the National High Technology Research and Development Program of China (863 Program, No. 2003AA607030); National Key Technology Research and Development Program (No. 2006BAD09A05)     

The study on fishing ground of neon flying squid, Ommastrephes bartrami, and ocean environment based on remote sensing data in the Northwest Pacific Ocean

The relationships between the neon flying squid, Ommastrephes bartrami, and the relative ocean environmental factors are analyzed. The environmental factors collected are sea surface temperature (SST), chlorophyll concentration (Chl-a) and sea surface height (SSH) from NASA, as well as the yields of neon flying squid in the North Pacific Ocean. The results show that the favorable temperature for neon flying squid living is 10℃-22℃ and the favorite temperature is between 15℃-17℃. The Chl-a concentration is 0.1-0.6 mg/m3. When Chl-a concentration changes to 0.12-0.14 mg/m3, the probability of forming fishing ground becomes very high. In most fishing grounds,the SSH is higher than the mean SSH. The generalized additive model (GAM) was applied to analyze the correlations between neon flying squid and ocean environmental factors. Every year, squids migrate northward from June to August and return southward during October-November, and the characteristics of the both migrations are very different. When squids migrate to the north, most relationships between the yields and SST are positive. The relationships are negative when squids move to southward. The relationships between the yields and Chl-a concentrations are negative from June to October, and insignificant in November. There is no obvious correlation between the catches of squid and longitude, but good with latitude.     

MONITORING THE DIFFUSION OF SUSPENDED SEDIMENTS AND STABILITY OF TIDAL RADIAL SAND RIDGES AREA USING MULTI-SATELLITE REMOTE SENSING DATA

Nine Landsat TM tapes and images and MSS images, 10 NOAA tapes and images and 1 SAR image from 1973 to 1997 were used to analyse the diffusion of suspended sediments and the change of tidal radial sand ridges in the northern part of the Changjiang River delta, the South Yellow Sea. The results showed that the diffusion of suspended sediments was controlled by the tide, net current, and submarine topography in this area. The distribution of suspended sediments had close relationship with thesubmarine topography. The old Huanghe River delta and the Changjiang River comprise the main sediment supply for the formation of radial sand ridges, whose evolution can be divided into three stages since the Huangbe River changed its course and flowed northward into the Bohai Sea.     

基于遥感资料的陕北黄土高原植被覆盖率时空特征分析

选取黄土高原的典型区域--陕北作为监测区域,以MODIS和TM资料作为主要数据源,从可业务化的角度采用植被指数法进行了植被覆盖率的遥感反演,对4种生态气候区植被覆盖率季相变化特征进行了分析,并对陕北生态环境治理前后植被覆盖率的变化进行了对比,结果表明:生态治理后陕北黄土高原的植被覆盖率绝大多数地区都是正增长,各生态气候区植被覆盖状况都有了不同程度的改善,生态治理效果最明显的区域在陕北南部.