近40 a来天山台兰河流域冰川资源变化分析

台兰河流域作为阿克苏河的支流,是以冰川融水补给为主的河流,流域面积为1 324 km²。结合1：5万地形图、Landsat ETM+遥感影像及数字高程模型数据,通过综合计算机自动解译及目视解译的方法,将面向对象图像特征提取方法应用到该流域冰川信息提取中,并以影像叠加数字高程模型来提取表碛覆盖区的冰川末端边界,最后参照专家指导意见进行边界的再次修订,得到1972~2011年该流域的冰川变化数据,并分析了过去近40 a来冰川变化特征及其对气候变化的响应过程。结果表明：1972~2011年,台兰河流域冰川退缩明显,冰川总面积从435.44 km²退缩到385.38 km²,减少了50.06 km²,退缩率为11.50%,年均减少约1.25 km²,平均单条冰川面积减小0.31 km²;冰川总条数从113条减少到109条,消失冰川10条,有3条冰川分离成了9条,其余100条冰川都呈减小趋势。结合阿克苏和拜城气象站气象资料分析认为,台兰河流域冰川萎缩与该地区气温快速上升关系密切,气温上升导致的冰川消融在一定程度上抵消了降水增加对冰川的补给。     

Statistical analysis of surface urban heat island intensity variations: A case study of Babol city,Iran

The urban heat island is considered as one of the most important climate change phenomena in urban areas, which can result in remarkable negative effects on flora, concentration of pollutants, air quality, energy and water consumption, human health, ecological and economic impacts, and even on global warming. The variation analysis of the surface urban heat island intensity (SUHII) is important for understanding the effect of urbanization and urban planning. The objective of this study was to present a new strategy based on the Shannon’s entropy and Pearson chi-square statistic to investigate the spatial and temporal variations of the SUHII. In this study, Landsat TM, ETM+, OLI and TIRS images, MODIS products, meteorological data, topographic and population maps of the Babol city, Iran, from 1985 to 2017, and air temperature data recorded by ground recorder devices in 2017 were used. First, Single-Channel algorithm was used to estimate land surface temperature (LST), and the maximum likelihood classifier was employed to classify Landsat images. Then, based on LST maps, surface urban heat island ratio index was employed to calculate the SUHII. Further, several statistical methods, such as the degree-of-freedom, degree-of-sprawl and degree-of-goodness, were used to analyse the SUHII variation along different geographic directions and in various time periods. Finally, correlation between various parameters such as air temperature, SUHII, population variation and degree-of-goodness index values were investigated. The results indicated that the SUHII value increased by 24% in Babol over different time periods. The correlation coefficient yielded 0.82 between the values of the difference between the mean air temperature of the urban and suburbs and the SUHII values for the geographic directions. Furthermore, the correlation coefficient between the population variation and the degree-of-goodness index values reached 0.8. The results suggested that the SUHII variation of Babol city had a high degree-of-freedom, high degree-of-sprawl and negative degree-of-goodness.     

Evaluation of different topographic correction methods for Landsat imagery

The recent free availability of Landsat historical data provides new potentials for land-cover change studies. Multi-temporal studies require a previous radiometric and geometric homogenization of input images, to better identify true changes. Topographic normalization is one of the key steps to create consistent and radiometricly stable multi-temporal time series, since terrain shadows change throughout time. This paper aims to evaluate different methods for topographic correction of Landsat TM-ETM+ data. They were assessed for 15 ETM+ images taken under different illumination conditions, using two criteria: (a) reduction of the standard deviation (SD) for different land-covers and (b) increase in temporal stability of a time series for individual pixels. We observed that results improve when land-cover classes where processed independently when applying the more advanced correction algorithms such as the C-correction and the Minnaert correction. Best results were obtaining for the C-correction and the empiric–statistic correction. Decreases of the SD for bare soil pixels were larger than 100% for the C-correction and the empiric–statistic correction method compared to the other correction methods in the visible spectrum and larger than 50% in the IR region. In almost all tests the empiric–statistic method provided better results than the C-correction. When analyzing the multi-temporal stability, pixels under bad illumination conditions (northern orientation) improved after correction, while a deterioration was observed for pixels under good illumination conditions (southern orientation). Taken this observation into account, a simple but robust method for topographic correction of Landsat imagery is proposed.     

Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.     

遥感技术在我国海冰研究方面的进展

本文首先对遥感技术在我国海冰研究中的发展情况作了详细地论述,并介绍了几种常用于海冰研究的遥感数据;其次,从海冰厚度识别、海冰运动速度矢量计算、海冰资源量测算以及海冰灾害监测等四个方面,简单论述了遥感技术在海冰研究中的应用。最后,以渤海海冰为例,阐述了遥感技术的具体应用,同时指出了今后的发展方向。     

基于TM影响的不同季节北京城市热环境研究

利用2005～2006年4景北京Landsat TM影像,通过热红外波段反演地表温度,揭示不同季节城市热环境分布特征及其空间差异。分别利用遥感反演的地面温度和地面气象站观测的气温数据,计算地表热岛强度和空气热岛强度,并分析其季节变化,结果表明：城区相对近郊区,热岛效应在夏季显著;城区相对于乡村,四季都存在较强的热岛效应。     

遥感技术在川西甲基卡大型锂矿基地找矿填图中的应用

四川甲基卡矿田是中国乃至于世界上锂矿资源最集中的地区之一,目前运用遥感技术开展甲基卡型锂矿的研究工作尚较为薄弱,文章运用遥感技术在甲基卡特殊地貌区开展找矿填图应用研究,建立了研究区典型岩石及矿物的波谱数据库,总结了研究区黑云母片岩、十字石片岩、十字石堇青石片岩、角岩、二云母花岗岩、含锂辉石伟晶岩、无矿伟晶岩、石英脉、长石斑晶、锂辉石单晶、云母、绿柱石的波谱特征;之后分别基于中等分辨率遥感数据Landsat 8和高空间分辨率遥感数据Geoeye-1进行图像处理和信息提取,开展了地质填图应用初步研究。研究结果表明遥感技术作为一种新兴的技术手段,对甲基卡型锂矿的填图及找矿具有重要的指导意义,可以作为今后地质找矿工作的"先头兵"。     

北京城市地表温度的遥感时空分析

运用Landsat TM/ETM+和Terra ASTER数据,对北京市1990~2007年夏季的地表温度进行了反演,并对地表温度的空间分布、时间变化作出了分析。对Landsat TM/ETM+数据的温度反演采用了普适性单波段算法,ASTER数据的温度反演采用了劈窗算法。通过对地表温度数据的直方图均衡处理以及综合对比分析,总结出北京地区历年来夏季地表温度的空间分布格局及该格局随北京城市发展的变化规律,分析了研究成果的不足,提出了下一步要努力的方向。     

基于遥感技术的海岸带土地利用、覆被信息提取方法研究

本文以地处莱州湾东海岸的莱州市为研究区域,以TM影像为主要信息源,在分析各类地物分类效果的基础上,结合区域特点和地物光谱特征,综合利用两期典型季相的遥感图像,并针对每一种土地利用类型确定最佳信息提取影像,提出了基于光谱、地形等多种知识的分层信息提取方法.结果表明该方法分层目标明确,分类精度和计算效率都有明显提高,且在数...     

中国版大洋洲地表覆盖产品研制

针对全球变化研究对大洋洲地表覆盖产品的需求,该文以2000年和2010年的Landsat卫星影像为数据源,提出了对大洋洲影像按照月份分组并进行样本采集与规则训练的方法,采用GLC树分类器进行自动分类,经过分类后处理和数据集成,完成了2000年和2010年两期、30m分辨率的大洋洲地表覆盖产品研制工作。利用高分辨率影像、实地采集照片等进行室内精度评定,该大洋洲地表覆盖产品的精度达到90%以上。