基于MODIS数据的玛纳斯河山区雪盖年内变化特征研究

基于2000年～2010年的MODIS/Terra积雪8d合成数据(MOD10A2)与DEM数据,通过计算和分析不同高程带、不同坡向和不同坡度的积雪覆盖率,研究了新疆玛纳斯河山区雪盖的年内变化特征。结果表明:①研究区平均积雪覆盖率最高为一月中旬的67.8%,最低为七月中旬的11.9%,年内变化总体上呈V字型,积雪分布与气温关系密切;②可将研究区雪盖年内分布情况归纳为1600m以下、1600m～3800m和3800m以上共三个高程带,各高程带内雪盖分布的年内变化较为相似,不同高程带则差异明显。从年内波动情况来看,低海拔地区年内波动幅度最大,随着海拔上升,波动幅度逐渐减小;③3800m以下各坡向和坡度地区积雪覆盖率均表现为一月最高,七月最低,四月和十月介于二者之间,而3800m以上地区积雪覆盖率全年最高值则出现在四月和十月;④各坡度和坡向区域雪盖的年内变化与所在高程带的总体情况基本相似,说明坡度和坡向对雪盖分布的影响是在高程影响的基础上产生的。     

基于MODIS数据的玛纳斯河山区雪盖时空分布分析

基于2000~2010年的MODIS/Terra积雪8 d合成数据(MOD10A2)与DEM数据,通过计算和分析积雪频率与积雪覆盖率,研究了新疆玛纳斯河山区雪盖的时空分布特征。结果表明：① 研究区一月份积雪覆盖丰富,积雪频率高值区主要分布在北部中低山地区、南部中海拔地区以及清水河与塔西河的河源地区;四月与十月的雪盖分布规律相似,总体上积雪频率随高程上升而上升;七月份只有少部分高山区域被积雪覆盖;② 积雪频率始终保持较高水平的区域是玛纳斯河、金沟河、清水河以及塔西河的河源高山地区,而玛纳斯河流域中上游的河谷地区则始终保持较低水平;③ 一月份,1 400 m以下地区的积雪覆盖率超过95%,随着高程上升,迅速下降至2 600 m的最低值约41%,此后逐渐上升至5 000 m以上80%左右;④ 一月、四月和十月份积雪覆盖率在大部分高程带上均表现为北坡、东北坡和西北坡最高,东坡和西坡次之,南坡、东南坡和西南坡最低的规律;七月份各高程带的雪盖分布没有明显的坡向差异。     

基于光谱指数的植被含水率遥感反演模型研究——以岷江上游毛尔盖地区为例

利用研究区植被样本实测含水率和实测光谱数据,基于植被光谱指数法,建立植被含水率与植被光谱指数之间的数学模型,同时利用该模型对研究区的遥感数据进行分析,反演植被含水率。结果证明:简单比值光谱指数与植被含水率有较好的相关性,线性模型更适合该研究区的植被含水率反演。1999年和2007年两年的植被含水率反演结果显示:9年间植被含水率提高,含水率高的面积增大。     

基于城镇指数提取城镇用地信息的方法研究

基于TM影像,结合城市用地特点和热辐射信息,提出一个新型的城镇指数(UI=B6×B5/B4),利用UI对南京及其周围地区的城镇用地信息进行了提取,并用混淆矩阵方法对提取的城市用地信息进行了精度评价。研究结果表明,城镇指数提取城镇用地信息是一种有效、可行的方法,而且具有很高的精确度。     

提高森林覆盖区积雪提取精度的方法研究——以玛纳斯河流域为例

森林覆盖区积雪的提取精度很低,由于植被冠层的遮挡,冠层下的积雪很难被提取出来。基于Landsat 8OLI数据,针对玛纳斯河流域下游有大面积森林覆盖的特点,通过传统的积雪指数法,结合NDVI数据的积雪指数法和面向对象图像特征法分别提取积雪面积。结果表明:1传统的NDSI和S3积雪指数法无法较好地提取出森林覆盖下的积雪,提取精度分别为85.23%和87.54%。这两种方法适用于空间尺度较大、植被覆盖面积较大的区域,并不适合所选研究区;2结合NDVI数据后的NDSI、S3积雪指数模型能大大提高森林覆盖下的积雪面积,提取精度分别达到91.47%和90.60%。在影像空间分辨率较高,流域尺度较小,林区覆盖较多的情况下可采用此方法提取积雪;3随着海拔的升高,地形阴影影响逐渐增大,NDVI辅助积雪指数方法提取林区覆盖下积雪面积逐渐减小。因此采用光谱、纹理和空间信息结合的面向对象图像特征方法提取积雪,能够较好地识别出受地形影响下的雪像元,精度达到89.75%,可以满足实际应用的需求。     

基于Landsat-8 OLI影像和指数法的红树林提取对比研究

红树林是全球净初级生产力最高的生态系统之一,其在全球气候变化和海岸带地理环境演变研究中发挥着重要作用。快速且准确地获取大范围红树林空间分布,对于红树林资源的有效管理和开发利用具有重要意义。Landsat系列卫星影像已成为大范围、长周期红树林分布信息提取的重要数据源。选取华南沿海的英罗湾和珍珠港作为实验区,利用Landsat-8 OLI影像结合归一化差异红树林指数（Normalized Difference Mangrove Index,NDMI）、综合红树林识别指数（Combined Mangrove Recognition Index,CMRI）、模块化红树林识别指数（Modular Mangrove Recognition Index,MMRI）、红树林指数（Mangrove Index,MI）和红树林植被指数（Mangrove Vegetation Index,MVI)5种指数来提取红树林分布信息,并对比5种指数用于红树林提取的效果,筛选适用于Landsat-8 OLI影像的最佳红树林提取指数。提出了结合归一化差异水体指数（Normalized Difference Water ...     

基于不确定性度量信息融合的团队一致法研究

给出一种新的信息融合方法--基于不确定性度量的团队一致法.这个方法是基于一个迭代的团队不确定性度量函数,它能使团队成员达到一致;该方法用于融合从多个图象传感器得到的对同一个目标的关于目标类别不确定性信息,与其它方法相比该方法有效而简单.在多图象目标识别中有着广泛的应用.还给出了确定权值系数的一般方法.把这种方法用于一个现成的实例,得到的结果和多数结合算子的结果一致.     

利用TM图像提取土地荒漠化信息的方法与效果—以阜康地区为例

利用遥感图像处理技术和数学分析方法对土地荒漠化的信息进行了定量研究,确定了绿色指数图像是土地荒漠化等级分类的有效图像;讨论了利用监督分类法对第二、第三波段TM图像和绿色指数图像进行分类所得到的各种土地荒漠化类型的精度。肯定了利用遥感技术编制土地荒漠类型图的可能性。     

基于决策树的干旱区湿地信息自动提取——以疏勒河流域为例

以疏勒河流域为研究区,探讨了干旱区湿地的遥感影像自动提取方法。以Landsat 8卫星影像数据为主要数据源并辅以数字高程模型(DEM),利用改进的干旱区湿地指数(MAZWI)、归一化植被指数(NDVI)、地表反照率(Albedo)、灰度共生矩阵(GLCM)的非相似性分量等识别指数构建决策树模型,对研究区湿地进行提取,并将结果与最大似然分类结果进行对比。结果表明:该方法在一定程度上提高了湿地提取的精度,与最大似然分类结果相比总体精度和Kappa系数分别提高了6.52%和0.124。证明决策树法是干旱区水域湿地自动提取的一种有效手段。     

基于遥感影像提取海岸线方法的研究——以黄河三角洲地区黄河口段和刁口段海岸为例

根据黄河三角洲地区不同类型的海岸线在遥感影像上的不同特征,对其遥感影像采用二值化、高通滤波、缨帽变换及非监督分类等方法进行图像增强处理,应用此方法准确地提取了黄河三角洲地区黄河口段和刁口段海岸的海岸线。     

Enhanced algorithm for estimating snow depth from geostationary satellites

Observations in the visible and infrared spectral bands from the Imager instrument onboard Geostationary Operational Environmental Satellite (GOES) have been used to derive snow depth. The technique makes use of correlation between depth of the snow pack and satellite-derived subpixel fractional snow cover. Previous efforts to infer snow depth from satellite data with this technique were focused on grasslands and croplands, where the snow depth/snow fraction relationship is most pronounced. In this paper we improve the retrieval algorithm to extend snow depth estimates to forested areas. The enhanced algorithm accounts for the tree cover fraction and for the type of forest, deciduous or coniferous.The developed technique was used to derive maps of snow depth over mid-latitude areas of North America during winter seasons of 2003-2004 and 2004-2005. Satellite-based snow depth maps were produced daily at 4 km spatial resolution. To validate the retrievals we compared them with surface observations of snow depth and with the snow depth analysis prepared at the NOAA National Operational Hydrological Remote Sensing Center (NOHRSC). The estimated retrieval error was about 30% for snow depths below 30 cm and increased to 50% for snow depths ranging from 30 to 50 cm. Snow depth retrievals were limited to scenes with less than 80% deciduous forest cover fraction and less than 50% needle leaf forest cover.     

Comparison between a multi-scattering and multi-layer snow scattering model and its parameterized snow backscattering model

Snow Water Equivalent (SWE) is a crucial parameter in the study of climatology and hydrology. Active microwave remote sensing is one of the most promising techniques for estimating the distribution of SWE at high spatial resolutions in large areas. Development of reliable and accurate inversion techniques to recover SWE is one of the most important tasks in current microwave researches. However, a number of snow pack properties, including snow density, particle size, crystal shape, stratification, ground surface roughness and soil moisture, affect the microwave scattering signals and need to be properly modeled and exploited. In this paper, we developed a multi-layer, multi-scattering model for dry snow based on recent theoretical advances in snow and surface modeling. In the proposed multi-layer model, Matrix Doubling method is used to account for scattering from each snow layer; and Advanced Integral Equation Model (AIEM) is incorporated into the model to describe surface scattering. Comparisons were made between the model predictions and field observations from NASA Cold Land Processes Field Experiment (CLPX) during Third Intensive Observation Period (IOP3) and SARALPS-2007 field experiment supported by ESA. The results indicated that model predictions were in good agreement with field observations. With the confirmed confidence, the analyses on multiple scattering, scatterer shape, and snow stratification effects were further made based on the model simulations. Furthermore, a parameterized snow backscattering model with a simple form and high computational efficiency was developed using a database generated by the multiple-scattering model. For a wide range of snow and soil properties, this parameterized model agrees well with the multiple-scattering model, with the root mean square error 0.20 dB, 0.24 dB and 0.43 dB for VV, HH and VH polarizations, respectively. This simplified model can be useful for the development of SWE retrieval algorithm and for fast simulations of radar signals over snow cover in land data assimilation systems.     

Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM/I data

The retrieval of snow water equivalent (SWE) and snow depth is performed by inverting Special Sensor Microwave Imager (SSM/I) brightness temperatures at 19 and 37 GHz using artificial neural network ANN-based techniques. The SSM/I used data, which consist of Pathfinder Daily EASE-Grid brightness temperatures, were supplied by the National Snow and Ice Data Centre (NSIDC). They were gathered during the period of time included between the beginning of 1996 and the end of 1999 all over Finland. A ground snow data set based on observations of the Finnish Environment Institute (SYKE) and the Finnish Meteorological Institute (FMI) was used to estimate the performances of the technique. The ANN results were confronted with those obtained using the spectral polarization difference (SPD) algorithm, the HUT model-based iterative inversion and the Chang algorithm, by comparing the RMSE, the R², and the regression coefficients. In general, it was observed that the results obtained through ANN-based technique are better than, or comparable to, those obtained through other approaches, when trained with simulated data. Performances were very good when the ANN were trained with experimental data.     

新疆玛纳斯河流域卫星雪盖信息提取的影响因素分析

卫星雪盖信息的准确提取受到很多因素的影响,本文选用11幅玛纳斯河流域Landsat ETM＋影像,应用归一化差值积雪指数NDSI区分积雪与其他地物,分析传感器增益、大气因素、地形效应对雪盖信息提取造成影响的原因,并定量计算各因素影响程度的大小。研究结果表明,成像过程传感器高低增益对卫星雪盖信息提取的影响非常大,大气因素的影响相对较小,NDSI对地形不具有适应性,尤其在阴影区进行积雪判读不适用。     

利用实测资料评估被动微波遥感雪深算法

利用SSM/I微波亮温数据,结合地面站点实测资料,比较Chang算法和Che算法在前苏联、中国及蒙古境内6种不同积雪类型的反演精度,结果表明:被广泛应用于全球雪深反演的Chang算法低估了前苏联境内雪深7.6cm,相对误差为-24.3%,而分别高估中国及蒙古境内雪深9.2cm与11.4cm,相对误差分别为108.8%和180.9%,区域反演效果很差;针对中国境内积雪的Che算法严重低估前苏联境内雪深,整体低估21.3cm,相对误差为-68.6%,RMSE为31.4cm;在中国及蒙古境内反演效果有所改善。6个积雪类型中,植被较单一,地形较平坦的苔原型积雪和草原型积雪雪深的反演效果较好。随着纬度和积雪深度的增加被动微波雪深反演有由高估变为低估的趋势。Che算法反演的雪深大体以40°N为界,以北表现为低估,以南表现为高估,另一方面,整体上该算法在雪深低于6.7cm时表现为低高估,高于6.7cm表现为低估;因此,全球算法应用到局部地区需要进行修正,不同下垫面性质以和气候条件下形成的积雪的被动微波反演应区别对待。     

Snowpack and runoff generation using AMSR-E passive microwave observations in the Upper Helmand Watershed, Afghanistan

Passive microwave estimates of snow water equivalent (SWE) were examined to determine their usefulness for evaluating water resources in the remote Upper Helmand Watershed, central Afghanistan. SWE estimates from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Special Sensor Microwave/Imager (SSM/I) passive microwave data were analyzed for six winter seasons, 2004-2009. A second, independent estimate of SWE was calculated for these same time periods using a hydrologic model of the watershed with a temperature index snow model driven using the Tropical Rainfall Measuring Mission (TRMM) gridded estimates of precipitation. The results demonstrate that passive microwave SWE values from SSM/I and AMSR-E are comparable. The AMSR-E sensor had improved performance in the early winter and late spring, which suggests that AMSR-E is better at detecting shallow snowpacks than SSM/I. The timing and magnitude of SWE values from the snow model and the passive microwave observations were sometimes similar with a correlation of 0.53 and accuracy between 55 and 62%. However, the modeled SWE was much lower than the AMSR-E SWE during two winter seasons in which TRMM data estimated lower than normal precipitation. Modeled runoff and reservoir storage predictions improved significantly when peak AMSR-E SWE values were used to update the snow model state during these periods. Rapid decreases in passive microwave SWE during precipitation events were also well aligned with flood flows that increased base flows by 170 and 940%. This finding supports previous northern latitude studies which indicate that the passive microwave signal's lack of scattering can be used to detect snow melt. The current study's extension to rain on snow events suggests an opportunity for added value for flood forecasting.     

被动微波遥感在青藏高原积雪业务监测中的初步应用

积雪范围、积雪深度和雪水当量等参数的遥感监测与反演对气候模式的建立以及积雪灾害的评估具有重要意义。被动微波遥感在这些参数的反演方面具有明显优势,但目前尚未应用到青藏高原地区的积雪遥感业务监测上来。2001年10月至2002年4月,利用SSM/I数据对青藏高原地区的积雪范围和积雪深度进行了实时监测,为西藏、青海遥感应用部门提供逐日的雪深分布图。对这次监测的总效果进行了分析和评价,并对发生在青海省内一次较大的降雪过程进行了遥感分析,结果表明:SSM/I反演的积雪范围变化趋势与MODIS结果总体上较为一致;SSM/I的雪深监测结果为当地遥感部门对大于10 cm的雪深做出正确判断提供了重要信息,是对雪灾定位的重要信息源。     

积雪遥感动态研究的现状及展望

简要讨论了积雪遥感研究的现状,主要包括常用传感器的物理参数及其可行性和局限性,云和雪的区分技术,雪盖面积和积雪深度的提取,雪水当量换算以及积雪遥感在融雪径流模拟、雪灾监测与评价、积雪对气候变化的影响研究等方面的应用。并对积雪遥感研究的发展趋势做了简要的分析与展望。     

Merging complementary remote sensing datasets in the context of snow water equivalent reconstruction

Time series of snow covered area (SCA) estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper (ETM+) were merged with a spatially explicit snowmelt model to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3419 km²). A linear optimization scheme was used to derive SCA estimates that preserve the statistical moments of the higher spatial resolution (i.e. 30 m) ETM+ data and resolve the superior temporal signal (i.e. ∼ daily) of the MODIS data. It was found that merging the two SCA products led to an 8% decrease and an 18% increase in the basinwide SWE in 2001 and 2002, respectively, compared to the SWE estimated from ETM+ only. Relative to SWE simulations using only ETM+ data, the hybrid SCA estimates reduced the mean absolute SWE error by 17 and 84% in 2001 and 2002, respectively; errors were determined using intensive snow survey data and two separate methods of scaling snow survey field measurements of SWE to the 1-km model pixel resolution. SWE bias for both years was reduced by 49% and skewness was reduced from − 0.78 to 0.49. These results indicate that the hybrid SWE was closer to being an unbiased estimate of the measured SWE and errors were distributed more normally. The accuracy of the SCA estimates is likely dependent on the vegetation fraction.     

遥感技术在牧区雪灾监测研究中的应用

主要介绍了遥感技术在牧区雪灾信息提取及雪灾实时监测研究中的应用,并结合1995年那曲雪灾实例进行了雪灾信息提取和分析,结果与实际上报灾情数据吻合。