Landsat 8数据地表温度反演算法对比

随着卫星遥感技术的发展,利用遥感反演地表温度的方法不断出现,如劈窗法、双角度法和单通道算法等。Landsat系列卫星的遥感数据是地表温度反演的重要数据之一。本文选择无锡周边区域为研究区,利用Landsat 8卫星遥感数据,对两种劈窗算法(Juan C.Jiménez-Muoz劈窗算法和Offer Rozenstein劈窗算法)和两种单窗算法(Juan C.Jiménez-Muoz单通道算法和覃志豪单窗算法)的地表温度反演精度进行了对比和敏感性分析。采用太湖16个浮标站的实测数据来验证了4种算法的反演精度。结果表明:两种劈窗算法的精度较高且较为接近,误差为0.7 K左右;覃志豪单窗算法和Juan C.Jiménez-Muoz单通道算法精度较低,误差分别为1.3 K和1.4 K左右。Juan C.Jiménez-Muoz劈窗算法对参数的敏感性最低,Juan C.Jiménez-Muoz单通道算法次之,覃志豪单窗算法和Offer Rozenstein劈窗算法敏感性相对最高。其中Juan C.Jiménez-Muoz单通道算法只适用于一定的水汽含量范围,有一定的局限性。     

Regional‐scale mapping of groundwater discharge zones using thermal satellite imagery

Mapping groundwater discharge zones at broad spatial scales remains a challenge, particularly in data sparse regions. We applied a regional scale mapping approach based on thermal remote sensing to map discharge zones in a complex watershed with a broad diversity of geological materials, land cover and topographic variation situated within the Prairie Parkland of northern Alberta, Canada. We acquired winter thermal imagery from the USGS Landsat archive to demonstrate the utility of this data source for applications that can complement both scientific and management programs. We showed that the thermally determined potential discharge areas were corroborated with hydrological (spring locations) and chemical (conservative tracers of groundwater) data. This study demonstrates how thermal remote sensing can form part of a comprehensive mapping framework to investigate groundwater resources over broad spatial scales. Copyright © 2013 John Wiley & Sons, Ltd.     

Change detection of surface mining activity and reclamation based on a machine learning approach of multi-temporal Landsat TM imagery

Being able to quantify land cover changes due to mining and reclamation at a watershed scale is of critical importance in managing and assessing their potential impacts to the Earth system. In this study, a remote sensing-based methodology is proposed for quantifying the impact of surface mining activity and reclamation from a watershed to local scale. The method is based on a Support Vector Machines (SVMs) classifier combined with multi-temporal change detection of Landsat TM imagery. The performance of the technique was evaluated at selected open mining sites located in the island of Milos in Greece. Assessment of the mining impact in the studied areas was based on the confusion matrix statistics, supported by co-orbital QuickBird-2 very high spatial resolution imagery. Overall classification accuracy of the thematic land cover maps produced was reported over 90%. Our analysis showed expansion of mining activity throughout the whole 23-year study period, while the transition of mining areas to soil and vegetation was evident in varying rates. Our results evidenced the ability of the method under investigation in deriving highly and accurate land cover change maps, able to identify the mining areas as well as those in which excavation was replaced by natural vegetation. All in all, the proposed technique showed considerable promise towards the support of a sustainable environmental development and prudent resource management.     

Land cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data

Land cover classification of finer resolution remote sensing data is always difficult to acquire high-frequency time series data which contains temporal features for improving classification accuracy. This paper proposed a method of land cover classification with finer resolution remote sensing data integrating temporal features extracted from time series coarser resolution data. The coarser resolution vegetation index data is first fused with finer resolution data to obtain time series finer resolution data. Temporal features are extracted from the fused data and added to improve classification accuracy. The result indicates that temporal features extracted from coarser resolution data have significant effect on improving classification accuracy of finer resolution data, especially for vegetation types. The overall classification accuracy is significantly improved approximately 4% from 90.4% to 94.6% and 89.0% to 93.7% for using Landsat 8 and Landsat 5 data, respectively. The user and producer accuracies for all land cover types have been improved.     

Remote estimation of terrestrial evapotranspiration by Landsat 5 TM and the SEBAL model in cold and high‐altitude regions: a case study of the upper reach of the Shule River Basin,China

Evapotranspiration (ET) is an important expenditure in water and energy balances, especially on cold and high‐altitude land surfaces. Daily ET of the upper reach of the Shule River Basin was estimated using Landsat 5 TM data and the Surface Energy Balance Algorithm for Land (SEBAL) model. Based on observations made at the Suli station, the algorithms of land surface temperature and soil heat flux in SEBAL were modified. Land surface temperature was retrieved and compared with ground truth via three methods: the radiative transfer equation method, the mono‐window algorithm, and the single‐channel method. We selected the best of these methods, mono‐window algorithm, for estimating ET. The average error of daily ET estimated by the modified SEBAL model and measured by the eddy covariance system was 16.4%, with a root‐mean‐square error of 0.52 mm d^?1. The estimated ET means were 3.09, 2.48, and 1.48 mm d^?1 on June 9 (DOY 160), June 25 (DOY 176), and July 27 (DOY 208) of the year 2010, respectively. The average estimated ET on the glacier surface of all days was more than 3 mm d^?1, a measurement that is difficult to capture in‐situ and has rarely been reported. This study will improve the understanding of water balance in cold, high‐altitude regions. Copyright © 2016 John Wiley & Sons, Ltd.     

单窗算法的大气参数估计方法

根据地表热辐射传导方程,提出了一个简单可行且精度较高的专门用于从TM6数据中演算地表温度的方法——单窗算法.这一算法把大气和地表状态对地表热传导的影响直接包括在演算公式中.该方法需要两个大气参数进行地表温度的演算,即大气平均作用温度和大气透射率.本文论述这两个大气参数的估计方法:根据大气水分含量或地表附近空气湿度来估计大气透射率;通过分析标准大气剖面资料,尤其是大气水分和气温随高程的变化规律,根据地表温度推算大气平均作用温度.     

利用天宫一号和Landsat7对地观测数据的森林变化检测

由于自然演替和一些干扰因素的影响,森林覆盖处在不断的变化中.结合云南省西双版纳地区的天宫一号高光谱数据以及Landsat影像,研究了热带森林覆盖制图与变化检测的自动化识别方法.首先分析了每景影像中红光波段的光谱属性,依据直方图提取出纯净森林像元,然后计算影像中各像元与纯净森林像元之间的光谱相似性,从而得到森林指数并以此为依据提取出每景影像对应的森林覆盖图,将多期的森林覆盖专题图进行叠加分析即可得到森林变化专题图.结果表明:(1)使用天宫一号高光谱影像可以进行森林覆盖自动化提取,生成的森林覆盖图合理地反映了森林分布状况;(2)与多期遥感影像结合进行森林变化信息提取,提取结果很好地体现了森林减少和森林恢复情况,对新恢复的未郁闭森林也可以进行有效检测.     

Predicting grain protein content of winter wheat using remote sensing data based on nitrogen status and water stress

Advanced site-specific knowledge of grain protein content of winter wheat from remote sensing data would provide opportunities to manage grain harvest differently, and to maximize output by adjusting input in fields. In this study, remote sensing data were utilized to predict grain protein content. Firstly, the leaf nitrogen content at winter wheat anthesis stage was proved to be significantly correlated with grain protein content (R² = 0.36), and spectral indices significantly correlated to leaf nitrogen content at anthesis stage were potential indicators for grain protein content. The vegetation index, VI_green, derived from the canopy spectral reflectance at green and red bands, was significantly correlated to the leaf nitrogen content at anthesis stage, and also highly significantly correlated to the final grain protein content (R² = 0.46). Secondly, the external conditions, such as irrigation, fertilization and temperature, had important influence on grain quality. Water stress at grain filling stage can increase grain protein content, and leaf water content is closely related to irrigation levels, therefore, the spectral indices correlated to leaf water content can be potential indicators for grain protein content. The spectral reflectance of TM channel 5 derived from canopy spectra or image data at grain filling stage was all significantly correlated to grain protein content (R² = 0.31 and 0.37, respectively). Finally, not only this study proved the feasibility of using remote sensing data to predict grain protein content, but it also provided a tentative prediction of the grain protein content in Beijing area using the reflectance image of TM channel 5.     

基于à trous小波变换的Landsat 7 ETM＋图像融合研究

 以胶州湾及周边海岸带为研究区，采用Landsat 7 ETM+数据，提出一种基于à trous小波变换的全色图像和多光谱图像融合改进算法。对全色图像和多光谱图像进行适当层数的小波分解，多光谱图像的低频部分采用全色图像和其低频分量的比来调制； 最高分解层外的其余分解层采用多光谱图像和全色图像在该层分解系数的加权和，加权系数由局部区域能量比来确定； 最高分解层则采用绝对值最大准则。实验表明，该方法得到的图像可提高空间分辨率，对多光谱图像的光谱信息扭曲也较小，为提高海岸带地物分类和信息提取精度奠定了基础。     

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.