机载WIDAS数据的Landsat卫星反照率初步验证

随着精细化监测的需求,中高空间分辨率的地表反照率产品逐渐成为气候模型的主要输入。目前,中高空间分辨率反照率产品的验证主要基于地表站点的通量塔观测数据,区域机载飞行数据的验证依然相对较少。因此,本文基于区域机载数据验证Landsat反照率产品。针对内蒙古自治区根河森林试验区所获取的机载红外广角双模式成像仪(WIDAS)多角度反射率数据,应用BRDF原型反演算法估算其反照率,分析了应用机载数据验证中高空间分辨率反照率产品的潜力。2016年内蒙古根河森林试验区机载WIDAS飞行多角度观测的可用多角度范围为25°,以前的研究表明BRDF原型反照率反演算法表现出对小观测角度的反照率反演结果的鲁棒性。因此,机载WIDAS反照率在一定程度可用于星载反照率的验证。首先,基于核驱动模型和各向异性平整指数(AFX)提取了试验区4种MODIS二向性反射分布函数(BRDF)原型;然后,将其作为先验知识应用到根河森林WIDAS机载数据的反照率反演中;最后,用WIDAS反照率和单个地面通量塔观测的反照率对Landsat卫星数据的反照率进行初步验证。验证结果表明Landsat反照率与WIDAS反照率结果较为一致,但略有低估,总体均方根误差(RMSE)约为0.02,偏差为0.0057。在多角度观测范围较小时,BRDF原型的反照率反演算法可为星载地表反照率的验证提供了一种有效的验证手段。     

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.     

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.     

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.     

黄土丘陵沟壑区典型村域土地利用变化及对区域乡村转型发展的启示

深入开展乡村地域土地利用变化和聚落发展规律的研究对于探究乡村地域系统优化、推进城乡融合与乡村可持续发展具有重要意义。论文以延安市碾庄流域为例,基于ENVI和GIS等软件技术,结合Landsat卫星影像、马尔科夫链模型和实地调查,从土地利用转移与交换变化、建筑用地空间转移、社会响应等角度探讨了黄土丘陵沟壑区典型村域土地利用变化特征以及对区域乡村发展转型的影响。研究表明：① 在一系列生态工程措施作用下,碾庄流域生态覆被发生明显改观,主要表现为大量的坡耕地及稀疏生态用地转化为林地,其中,林地面积占比由32.34%(2009年)增加至50.88%(2018年);② 在社会经济发展和延安新城“边缘效应”的影响下,碾庄建设用地呈现向流域的东南和西南方向拓展的趋势,10 a间建设用地面积增加了近75%;③ 在人类活动的主导作用下,黄土丘陵沟壑区的乡村正在朝着生态化和集约化的方向转型发展。研究结果有望为深入理解黄土丘陵沟壑区乡村地域系统结构和优化提供新参考。     

利用Sentinel-2A多光谱成像仪与Landsat8陆地成像仪影像进行普陀山岛植被分类效果比较

卫星遥感技术可用于海岛资源调查。Sentinel-2A与Landsat 8两颗卫星都可免费提供空间分辨率较高的多光谱遥感影像，在海岛调查中的应用潜力较大。本文以浙江舟山普陀山岛为例开展了针对这两种影像在海岛植被分类中的应用效果的研究，分别利用Sentinel-2A多光谱成像仪（MSI）和Landsat 8陆地成像仪（OLI）影像基于最大似然法分类获得了该岛阔叶林、针阔混交林、针叶林、灌丛、草丛等植被及其他地物的分布情况，并进行了精度检验，结果表明MSI的总体分类精度略高于OLI。     

Satellite-based monitoring and statistics for raft and cage aquaculture in China’s offshore waters

Based on Landsat 8 remote sensing images, a combination of an unsupervised classification algorithm and artificial review was used to extract areas for Chinese offshore raft and cage aquaculture in 2018. The results of the extraction showed that China’s 2018 coastal zone raft aquaculture area comprised 194,110 ha, of which the province having the largest raft aquaculture area was Jiangsu (28.77 %), followed by Fujian (20.42 %) and Shandong (13.11 %). The cage aquaculture area covered 57,847,799 square meters, of which the provinces with the largest cage aquaculture area were Fujian (64.81 %), Guangdong (17.45 %), and Liaoning (5.63 %). In addition, by combining high-resolution remote sensing image visual interpretation and field investigation, the classification of 1200 sample points in four regions was determined, and the accuracy of the aquaculture area extraction was found to be 87.35 %. The extraction results can be used not only to evaluate China’s aquaculture production but also offer significant reference value for scientific planning related to sea use, ecological environmental protection, and marine disaster prevention and mitigation.     

多时相Landsat影像的平原河网区不透水面信息提取方法与应用

不透水面是衡量城市生态环境的重要指标，针对平原河网区不透水面与水体、裸土等地物较难区分的问题，以苏北里下河平原快速城镇化区为研究对象，利用多时相Landsat影像为数据源，基于像元光谱特征及其季节性变化分析，构建了基于时序NDWI、NDVI和NDBI的决策树模型，并利用GIS空间分析技术对不透水面的时空变化特征进行了分析。结果表明：多时相光谱信息可有效改善水体、裸土和稀疏植被等像元与不透水面的混分，2005年和2016年的不透水面分类总体精度和Kappa系数均在0.85以上，合理反映了不透水面以向东和向南扩张为主的趋势特征，年均扩张速度约为6.7 km²。研究成果为该区城镇化下的生态环境效应研究提供数据基础，同时可为其他平原河网区不透水面信息提供借鉴和参考。     

综合多特征的Landsat 8时序遥感图像棉花分类方法

传统的多时相遥感图像分类大多拘泥于单一特征,本文基于多时相的Landsat 8遥感数据,开展了综合多特征的特征提取与特征选择方法研究。综合了NDVI时间序列、最佳时相反射率光谱特征以及纹理特征作为初始分类特征,并采用基于属性重要度的粗糙集特征选择算法对其进行特征约简。分类结果表明:(1)利用初始分类特征,分类的总体精度达到92.81%,棉花提取精度达87.4%,与仅利用NDVI时间序列相比,精度分别提高5.53%和5.05%;(2)利用粗糙集选择后的特征分类,分类总体精度可达93.66%,棉花分类精度达92.73%,与初始分类特征提取结果相比,棉花分类精度提高5.33%。基于属性重要度的粗糙集特征选择不仅提高了分类精度,同时有效降低了分类器的计算复杂度。     

知识引导的稀疏时间序列遥感数据拟合

在多云多雨的地区,光学遥感存在着获取无云数据困难的难题,这会导致时间序列应用中可用数据匮乏。因此,本文面向稀疏时间序列遥感数据,根据噪声造成遥感影像上归一化差分植被指数(NDVI)被低估的事实,提出了一种知识引导的拟合方法。首先,在遥感影像预处理的基础上,利用先验知识和时序差分法对噪声进行识别和剔除;然后,采用高斯二阶模型对原始数据进行拟合;最后,根据拟合残差更新权重,进行迭代拟合,重复上述过程直至获得稳定的结果。本文以Landsat 8 OLI作为数据源,对浙江省杭州地区的森林数据进行拟合,结果表明:在稀疏时间序列数据的情况下,本文方法与MODIS数据拟合结果的相关系数达到0.92,关键时点(如NDVI峰值点等)的时间误差在5 d;相比当前主流方法的0.88与8 d具有更高的精度。