1980-2010年西藏波密地区典型冰川变化特征及其对气候变化的响应

运用遥感(RS)与地理信息系统(GIS)技术, 结合波密县1960-2010年气象数据, 分析了西藏波密地区冰川的主要分布特征和典型大冰川1980-2010年的时空变化. 结果显示: 波密县共有冰川数量2 040条, 总面积为4 382.5 km², 其中, 分布在海拔4 000~6 000 m的高山冰川总面积达4 086 km², 占冰川总面积的93.2%; 南坡分布冰川1 504条, 面积3 180.04 km², 分别占波密冰川总量的73.73%和72.56%, 而北坡占还不到三分之一. 提取1980、 1990、 2000和2010年4期面积大于20 km²的24条大冰川面积进行对比分析, 1980-2010年间波密县大冰川面积总体呈减小趋势, 由1980年的1 592.78 km²退缩至2010年1 567.04 km², 共退缩了25.74 km²; 其中, 1980-1990年冰川变化贡献最大, 冰川面积退缩了16.62 km², 占冰川总面积退缩量的64.6%. 波密县气象站数据显示, 50 a来冰川退缩主要受温度持续上升的影响, 降水量变化对冰川变化影响不大.     

基于Landsat 8劈窗算法与混合光谱分解的城市热岛空间格局分析——以兰州市中心城区为例

在ENVI和GIS支持下,提出了基于Landsat 8遥感影像的地温反演劈窗算法,提取兰州市中心城区地表温度。利用FNEA和混合光谱分解法确定了兰州市中心城区的城市热岛中心、不透水面和植被盖度,分析了城市热岛空间分布格局以及地表温度与下垫面之间的关系。结果显示:基于Landsat 8数据地温反演的劈窗算法是可行的。兰州中心城区的高温区分布较集中,地表温度与植被呈较强的负相关,与不透水面呈不显著的正相关,与其他非光合物质呈正相关。     

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.     

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。     

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.     

Multitemporal settlement and population mapping from Landsat using Google Earth Engine

As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.     

Landsat TM及JERS－1 SAR数据在金矿探测中的应用研究

金矿遥感探测之关键在于成矿构造及铁氧化物、含羟基蚀变矿物的信息提取。ＪＥＲＳ—１ＳＡＲ在构造探测方面具有很大的优势，可通过噪斑消除及边缘增强的空间滤波来提取构造信息。ＬａｎｄｓａｔＴＭ可通过主成分变换，比值分析－主成分变换来提取蚀变矿物波谱信息、抑制植被波谱干扰。这两种遥感数据的复合处理可综合蚀变与构造信息。     

Characterization of aboveground biomass in an unmanaged boreal forest using Landsat temporal segmentation metrics

Canada is dominated by forested ecosystems which are subject to various inventory and management practices, with more northern boreal forests subject to neither. Our objectives were to measure the capacity of temporal trajectory metrics for estimating selected forest attributes in a northern Canadian boreal forest context using Landsat imagery and investigate the importance of different types of temporal trajectory metrics. Results indicated that Wetness was the best Tasseled Cap (TC) component for aboveground biomass estimation (R² = 50%, RMSE% = 56%), and the combination of simple and complex metrics from all TC components produced the highest R² (62%) and lowest RMSE% (49%). Using a similar combination of variables, other forest attributes were estimated equally reliably with lower RMSE% values. The most important temporal trajectory metrics were simple and described TC component values at each point of change in the temporal trajectory, however the most important variables overall were environmental variables.     

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.     

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。