卫星遥感雪盖制图方法对比与分析

利用ＬａｎｄｓａｔＴＭ、ＮＯＡＡ／ＡＶＨＲＲ和中分辨率成像光谱仪（ＭＯＤＩＳ）三个平台传感器的遥感数据,分别使用训练样本监督分类、阈值数字信号统计、雪盖指数方法制作雪盖图和提取积雪面积。结果表明：不同传感器遥感图像因时相和时空分辨率的差异,提取积雪信息的有效方法有所不同。但基于反射特性的雪盖指数计算法具有普遍的实际操作性意义,即雪盖制图精度高,分类合理,是提取积雪信息的最佳技术手段;当使用监督积雪分类时,只有取得精确的信号文件,分类结果才是可信的;而阈值数字信号统计的雪的阈值确定具有很大的经验性和随机性,但对数据不完整或只有单波段时也不失为有效和简便的途径;山影补偿处理法基本可以消除地形阴影的影响;而去云后其覆盖下的积雪恢复技术值得进一步讨论。     

卫星遥感雪盖制图方法对比与分析

利用ＬａｎｄｓａｔＴＭ、ＮＯＡＡ／ＡＶＨＲＲ和中分辨率成像光谱仪（ＭＯＤＩＳ）三个平台传感器的遥感数据,分别使用训练样本监督分类、阈值数字信号统计、雪盖指数方法制作雪盖图和提取积雪面积。结果表明：不同传感器遥感图像因时相和时空分辨率的差异,提取积雪信息的有效方法有所不同。但基于反射特性的雪盖指数计算法具有普遍的实际操作性意义,即雪盖制图精度高,分类合理,是提取积雪信息的最佳技术手段|当使用监督积雪分类时,只有取得精确的信号文件,分类结果才是可信的|而阈值数字信号统计的雪的阈值确定具有很大的经验性和随机性,但对数据不完整或只有单波段时也不失为有效和简便的途径|山影补偿处理法基本可以消除地形阴影的影响|而去云后其覆盖下的积雪恢复技术值得进一步讨论。     

东北地区MODIS亚像元积雪覆盖率反演及验证

以中巴资源卫星数据作为地面“真值”影像,根据东北地区地理环境与气候特点对Salomoson亚像元积雪覆盖率模型参数进行修正,反演东北地区MODIS像元积雪覆盖率,并用不同方案对模型的稳定性和精度进行分析。研究结果表明,经修正后的Salomoson亚像元积雪覆盖率反演模型对不同地貌--景观单元具有稳定性,其中较小的波动源于积雪物理性质差异、大气效应、积雪影像分类误差及影像配准误差。在东北平原区,NDSI值在0.52~0.65时,模型反演精度高,但反演雪盖率总体偏低,主要是由NDSI基于对波段反射率的非线性转换引起的;雪盖率高估的像元主要分布在城区外围以及农村居民点,而覆盖城区、乡、镇以及居民点之间道路的像元雪盖率误差小,其原因是人类活动频率影响像元内积雪组分与非积雪组分的光谱特性的差异程度。与MODIS雪产品进行对比分析,积雪覆盖率提供较传统雪盖制图更加丰富的信息,然而对林区冠层下积雪覆盖二者均未给出准确估计。     

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。     

Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery

Knowledge of snow cover is essential to understanding the global water and energy cycle. Thresholding the normalized difference snow index (NDSI) image is a method frequently used to map snow cover from remotely sensed data. However, the threshold is dependent on the scenario and needs to be determined accordingly. In this study, nine automatic thresholding methods were tested on the NDSI. Comparisons of the automatic thresholding methods, optimal threshold, and support vector machine (SVM) classification show that Otsu's and Nie's methods appear to be the most robust among the nine automatic thresholding methods, achieving comparable accuracies with the latter two approaches. In addition, NDSI from the digital number (DN) can be an efficient substitution for NDSI obtained from atmospherically or topographically corrected data, with similar accuracy.     

Estimating fractional snow cover from MODIS using the normalized difference snow index

Snow-cover information is important for a wide variety of scientific studies, water supply and management applications. The NASA Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) provides improved capabilities to observe snow cover from space and has been successfully using a normalized difference snow index (NDSI), along with threshold tests, to provide global, automated binary maps of snow cover. The NDSI is a spectral band ratio that takes advantage of the spectral differences of snow in short-wave infrared and visible MODIS spectral bands to identify snow versus other features in a scene. This study has evaluated whether there is a “signal” in the NDSI that could be used to estimate the fraction of snow within a 500 m MODIS pixel and thereby enhance the use of the NDSI approach in monitoring snow cover. Using Landsat 30-m observations as “ground truth,” the percentage of snow cover was calculated for 500-m cells. Then a regression relationship between 500-m NDSI observations and fractional snow cover was developed over three different snow-covered regions and tested over other areas. The overall results indicate that the relationship between fractional snow cover and NDSI is reasonably robust when applied locally and over large areas like North America. The relationship offers advantages relative to other published fractional snow cover algorithms developed for global-scale use with MODIS. This study indicates that the fraction of snow cover within a MODIS pixel using this approach can be provided with a mean absolute error less than 0.1 over the range from 0.0 to 1.0 in fractional snow cover.     

A new water-resistant snow index for the detection and mapping of snow cover on a global scale

An up-to-date spatio-temporal change analysis of global snow cover is essential for better understanding of climate–hydrological interactions. The normalized difference snow index (NDSI) is a widely used algorithm for the detection and estimation of snow cover. However, NDSI cannot discriminate between snow cover and water bodies without use of an external water mask. A stand-alone methodology for robust detection and mapping of global snow cover is presented by avoiding external dependency on the water mask. A new spectral index called water-resistant snow index (WSI) with the capability of exhibiting significant contrast between snow cover and other cover types, including water bodies, was developed. WSI uses the normalized difference between the value and hue obtained by transforming red, green, and blue, (RGB) colour composite images comprising red, green, and near-infrared bands into a hue, saturation, and value (HSV) colour model. The superiority of WSI over NDSI is confirmed by case studies conducted in major snow regions globally. Snow cover was mapped by considering monthly variation in snow cover and availability of satellite data at the global scale. A snow cover map for the year 2013 was produced at the global scale by applying the random walker algorithm in the WSI image supported by the reference data collected from permanent snow-covered and non-snow-covered areas. The resultant snow-cover map was compared to snow cover estimated by existing maps: MODIS Land Cover Type Product (MCD12Q1 v5.1, 2012), Global Land Cover by National Mapping Organizations (GLCNMO v2.0, 2008), and European Space Agency’s GlobCover 2009. A significant variation in snow cover as estimated by different maps was noted, and was was attributed to methodological differences rather than annual variation in snow cover. The resultant map was also validated with reference data, with 89.46% overall accuracy obtained. The WSI proposed in the research is expected to be suitable for seasonal and annual change analysis of global snow cover.     

Evaluation of a useful method to identify snow‐covered areas under vegetation – comparisons among a newly proposed snow index,normalized difference snow index,and visible reflectance

Objective methods of monitoring snow‐covered areas by optical remote sensing were evaluated using synchronous observations conducted with the passage of the Landsat‐7 satellite over the plains of Niigata prefecture, one of the snowiest regions in Japan. The observations were conducted in the springs of 2002 and 2003. Snow‐covered areas were identified using three methods: (1) visible (red) reflectance, (2) Normalized Difference Snow Index (NDSI) which uses visible and shortwave‐infrared reflectances, and (3) a newly proposed snow index called S3 which uses visible, near‐infrared and shortwave‐infrared reflectances. The Snow‐Cover Ratio (SCR) was defined as the ratio of the number of pixels in snow‐covered areas to the total number of pixels in an image. The threshold value for the three indices used to identify snow‐covered areas was defined as 50% of SCR, which converged to nearly the same value regardless of the images analysed. Under clear conditions, visible (red) reflectance can identify snow‐covered areas accurately if no vegetation is present. NDSI can distinguish snow‐covered areas from mixels (mixed pixels) of snow and vegetation by referring to the Normalized Difference Vegetation Index (NDVI). S3 can distinguish snow‐covered areas from mixels of snow and vegetation without any reference data. S3 is, therefore, more useful than NDSI because it automatically distinguishes snow‐covered areas from mixels of snow and vegetation.     

雪盖指数法提取积雪范围信息的不确定性研究-以玛纳斯上游地区为例

基于2006年9月10日空间分辨率为30 m的TM影像与DEM数据,通过雪盖指数法自动提取积雪范围与目视解译结果进行对比,以粗糙度为度量,定性、定量分析影响其不确定性的地表覆被和地形因素。结果表明:①当NDSI阈值取0.57~0.72和0.4~0.8时,结果有明显差异,取0.57~0.72时漏分像元比0.4~0.8稍多,但是误分像元大幅减少;②同处于阴影区裸地的光谱曲线与积雪的光谱曲线相似,造成阴影区的积雪与裸地不能正确区分,此外处于阴影区域的植被由于反射率较低,使其NDSI刚好在阈值范围内,被误分为积雪;③半阴坡雪盖指数法提取积雪的不确定性最小,而阳坡、半阳坡雪盖指数法提取积雪的不确定性最大;④雪盖指数法提取积雪的不确定性随着坡度的增加呈下降趋势,即坡度越大不确定性越小。     

Fractional snow cover mapping through artificial neural network analysis of MODIS surface reflectance

Accurate areal measurements of snow cover extent are important for hydrological and climate modeling. The traditional method of mapping snow cover is binary where a pixel is considered either snow-covered or snow-free. Fractional snow cover (FSC) mapping can achieve a more precise estimate of areal snow cover extent by estimating the fraction of a pixel that is snow-covered. The most common snow fraction methods applied to Moderate Resolution Imaging Spectroradiometer (MODIS) images have been spectral unmixing and an empirical Normalized Difference Snow Index (NDSI). Machine learning is an alternative for estimating FSC as artificial neural networks (ANNs) have been successfully used for estimating the subpixel abundances of other surfaces. The advantages of ANNs are that they can easily incorporate auxiliary information such as land cover type and are capable of learning nonlinear relationships between surface reflectance and snow fraction. ANNs are especially applicable to mapping snow cover extent in forested areas where spatial mixing of surface components is nonlinear. This study developed a multilayer feed-forward ANN trained through backpropagation to estimate FSC using MODIS surface reflectance, NDSI, Normalized Difference Vegetation Index (NDVI) and land cover as inputs. The ANN was trained and validated with higher spatial-resolution FSC maps derived from Landsat Enhanced Thematic Mapper Plus (ETM+) binary snow cover maps. Testing of the network was accomplished over training and independent test areas. The developed network performed adequately with RMSE of 12% over training areas and slightly less accurately over the independent test scenes with RMSE of 14%. The developed ANN also compared favorably to the standard MODIS FSC product. The study also presents a comprehensive validation of the standard MODIS snow fraction product whose performance was found to be similar to that of the ANN.     

ASTER ratio indices for supraglacial terrain mapping

Supraglacial terrain, such as that found in the Himalayas, is typically composed of snow, ice, ice‐mixed‐debris (IMD) and debris. This letter presents a methodology for systematic discrimination and mapping of these supraglacial cover types using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. The Normalized Difference Snow Index (NDSI) has been used previously for discrimination of snow/ice‐bearing zones versus debris. Two new indices, the Normalized Difference Glacier Index (NDGI) and the Normalized Difference Snow Ice Index (NDSII), are presented. The combination of all three indices allows discrimination of snow, ice and IMD in a systematic manner.     

Monitoring snow cover variability in an agropastoral area in the Trans Himalayan region of Nepal using MODIS data with improved cloud removal methodology

Monitoring the extent and pattern of snow cover in the dry, high altitude, Trans Himalayan region (THR) is significant to understand the local and regional impact of ongoing climate change and variability. The freely available Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover images, with 500 m spatial and daily temporal resolution, can provide a basis for regional snow cover mapping, monitoring and hydrological modelling. However, high cloud obscuration remains the main limitation. In this study, we propose a five successive step approach — combining data from the Terra and Aqua satellites; adjacent temporal deduction; spatial filtering based on orthogonal neighbouring pixels; spatial filtering based on a zonal snowline approach; and temporal filtering based on zonal snow cycle — to remove cloud obscuration from MODIS daily snow products. This study also examines the spatial and temporal variability of snow cover in the THR of Nepal in the last decade. Since no ground stations measuring snow data are available in the region, the performance of the proposed methodology is evaluated by comparing the original MODIS snow cover data with least cloud cover against cloud-generated MODIS snow cover data, filled by clouds of another densely cloud-covered product. The analysis indicates that the proposed five-step method is efficient in cloud reduction (with average accuracy of > 91%). The results show very high interannual and intra-seasonal variability of average snow cover, maximum snow extent and snow cover duration over the last decade. The peak snow period has been delayed by about 6.7 days per year and the main agropastoral production areas of the region were found to experience a significant decline in snow cover duration during the last decade.     

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。     

Assessing the potential of VEGETATION sensor data for mapping snow and ice cover: A Normalized Difference Snow and Ice Index

The VEGETATION (VGT) sensor in SPOT 4 has four spectral bands that are equivalent to Landsat Thematic Mapper (TM) bands (blue, red, near-infrared and mid-infrared spectral bands) and provides daily images of the global land surface at a 1-km spatial resolution. We propose a new index for identifying and mapping of snow/ice cover, namely the Normalized Difference Snow/Ice Index (NDSII), which uses reflectance values of red and mid-infrared spectral bands of Landsat TM and VGT. For Landsat TM data, NDSII is calculated as NDSII_TM=(TM3-TM5)/(TM3+TM5); for VGT data, NDSII is calculated as NDSII_VGT=(B2-MIR)/(B2+MIR). As a case study we used a Landsat TM image that covers the eastern part of the Qilian mountain range in the Qinghai-Xizang (Tibetan) plateau of China. NDSIITM gave similar estimates of the area and spatial distribution of snow/ice cover to the Normalized Difference Snow Index (NDSI=(TM2-TM5)/(TM2+TM5)) which has been proposed by Hall et al. The results indicated that the VGT sensor might have the potential for operational monitoring and mapping of snow/ice cover from regional to global scales, when using NDSII_VGT.     

View angle effects on MODIS snow mapping in forests

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas.     

基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并在中国山东省进行了分类试验,得出以下结论:①不同比例下的训练样本与验证样本影响着总体分类精度;②从MODIS数据中得到的植被指数EVI、白天地表温度Tday、水体指数NDWI、纹理特征局部平稳Homogeneity等可以作为分类特征配合参与到多波段地表反射率Ref1-7遥感影像中,能明显提高分类精度,而土壤亮度指数NDSI则没有贡献;③提取的分类特征对总体分类精度贡献大小为:EVI贡献最大,提高近6个百分点,其次是Homogeneity、NDWI,均提高近4个百分点,而最少的Tday也贡献了近3个百分点;④各分类特征对不同地物类别具有不同的分离度,在提高某些类别的分离性时,有可能降低了其它类别的分离性。试验结果表明:在没有其它非遥感信息的前提下,仅利用MODIS遥感自身信息对宏观土地覆盖分类就可达到较高精度。     

基于MODIS遥感数据的宏观土地覆盖特征分类方法与精度分析研究

针对宏观土地覆盖遥感分类的现状,充分利用MODIS相对于AVHRR数据具有的多光谱和分辨率优势,提出了利用MODIS数据进行分类特征选择与提取并结合多时相特征进行宏观土地覆盖分类的分类方法,并在中国山东省进行了分类试验,得出以下结论:①不同比例下的训练样本与验证样本影响着总体分类精度;②从MODIS数据中得到的植被指数EVI、白天地表温度Tday、水体指数NDWI、纹理特征局部平稳Homogeneity等可以作为分类特征配合参与到多波段地表反射率Ref1-7遥感影像中,能明显提高分类精度,而土壤亮度指数NDSI则没有贡献;③提取的分类特征对总体分类精度贡献大小为:EVI贡献最大,提高近6个百分点,其次是Homogeneity、NDWI,均提高近4个百分点,而最少的Tday也贡献了近3个百分点;④各分类特征对不同地物类别具有不同的分离度,在提高某些类别的分离性时,有可能降低了其它类别的分离性。试验结果表明:在没有其它非遥感信息的前提下,仅利用MODIS遥感自身信息对宏观土地覆盖分类就可达到较高精度。     

遥感技术在现代冰川变化研究中的应用

传统的现代冰川变化研究主要以实地观测和经验公式来获得冰川的面积变化、体积变化和冰川表面运动速度,从20世纪80年代以来随着航空遥感光学图像、数字高程模型、雷达等新技术数据的不断出现和发展,借助遥感手段研究冰川的性质和特征、监测冰川的动态变化成为冰川学研究发展的重要趋势,也有效解决了现代冰川研究中高山区资料受限等问题。冰川面积变化的计算机自动解译方法主要有阈值法和雪盖指数阈值法、监督分类非监督分类法、比值阈值法等。阈值法和雪盖指数阈值法操作简单,但是在阈值选取方面不好把握;非监督分类法操作简单但限制因素较多;如果训练区选择准确,监督分类法分类结果比较精确;波段比值阈值法操作相对简单,精度准确,是目前运用最多的计算机自动分类方法。数字高程模型和雷达数据在冰川体积变化研究中的应用,较好地提高了传统方法的精度和适用性; 而且雷达数据具有不受大气传播和气候影响的特点,很好地弥补了遥感光学影像数据极易受云雪等影响的不足。高精度GPS和雷达数据的不断发展和应用在冰川表面运动速度的研究中起着不可替代的作用。      

基于MODIS数据的雪情分析研究

利用基于MODIS数据的NDSI和基础地理信息数据对西藏地区进行雪情分析，结果表明这是可行的。MODIS数据在空间分辨率和波谱分辨率上优于AVHRR，利用MODIS数据进行雪情分析和其他监测会更有利。本研究说明利用MODIS数据可以进行大面积雪情分析、雪灾监测。     

A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images

A hybrid method that incorporates the advantages of supervised and unsupervised approaches as well as hard and soft classifications was proposed for mapping the land use/cover of the Atlanta metropolitan area using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. The unsupervised ISODATA clustering method was initially used to segment the image into a large number of clusters of pixels. With reference to ground data based on 1?:?40?000 colour infrared aerial photographs in the form of Digital Orthophoto Quarter Quadrangle (DOQQ), homogeneous clusters were labelled. Clusters that could not be labelled because of mixed pixels were clipped out and subjected to a supervised fuzzy classification. A final land use/cover map was obtained by a union overlay of the two partial land use/cover maps. This map was evaluated by comparing with maps produced using unsupervised ISODATA clustering, supervised fuzzy and supervised maximum likelihood classification methods. It was found that the hybrid approach was slightly better than the unsupervised ISODATA clustering in land use/cover classification accuracy, most probably because of the supervised fuzzy classification, which effectively dealt with the mixed pixel problem in the low-density urban use category of land use/cover. It was suggested that this hybrid approach can be economically implemented in a standard image processing software package to produce land use/cover maps with higher accuracy from satellite images of moderate spatial resolution in a complex urban environment, where both discrete and continuous land cover elements occur side by side.