基于Landsat的补连塔矿区植被时序特征分析

煤炭在我国能源结构中占有重要的地位。西北地区煤炭储量丰富,但是西北地区降水稀少、植被稀疏,生态环境脆弱。因此矿区植被时序分析对于西北矿区生态保护具有重要意义。本文基于神东矿区的补连塔煤矿为研究区,利用2000～2017年Landsat遥感影像,引入归一化植被指数(Normalized Difference Vegetation Index,NDVI),分析了补连塔煤矿在近15年以来矿区植被变化。结果表明:矿区植被出现明显增长趋势,分析原因是补连塔主要为井工矿,矿区开采对地表植被影响不明显,同时煤矿企业对区域生态建设投入大量资金。影像NDVI变异系数(Coefficient of Variation,CV)介于0. 2～0. 3之间,说明整个矿区NDVI离散性较小,整个矿区的植被覆盖比较平均。     

气溶胶散射对海洋水色遥感辐射偏振校正的影响

辐射偏振校正对具有相对较大偏振响应的海洋水色卫星遥感器(如Aqua MODIS)是十分必要的,可在一定程度上提高海洋水色信息提取的精度.目前, MODIS已经实现了业务化的辐射偏振校正,但其算法中忽略了气溶胶散射对大气顶辐射偏振分量的影响.利用海洋-大气耦合矢量辐射传输模型PCOART,分别模拟获得纯瑞利大气(无气溶胶)和气溶胶光学厚度为0.2大气时的大气顶辐射偏振分量.结果表明,除太阳耀斑区外,气溶胶散射对蓝光波段(443 nm)大气顶线偏振辐亮度的贡献很小,可以忽略不计,而对近红外波段(865 nm)大气顶线偏振辐亮度的贡献显著.此外,将PCOART数值模拟的大气顶瑞利散射辐射线偏振反射率与POLDER实际观测的大气顶线偏振反射率进行了比较,结果同样说明了气溶胶散射对蓝光波段(443 nm)大气顶线偏振反射率的贡献很小,而对近红外波段(865 nm).大气顶线偏振反射率的贡献显著.最后,在现有MODIS辐射偏振校正算法基础上,提出了考虑气溶胶散射的海洋水色卫星遥感辐射偏振校正算法,并利用POLDER实测的大气顶线偏振反射率对算法进行了检验,结果表明,无论是在443 nm波段,还是在865 nm波段,均比MODIS辐射偏振校正算法估算大气顶辐射偏振分量更接近POLDER实测结果.     

一种新的MODIS 0级数据地理定位方法

MODIS 1B数据文件中包含有象素的地理定位信息,该地理定位信息的精确度越高,后期的处理及应用工作质量越高.通过对利用角度和向量关系迭代算法,不断修正MODIS 0级数据的地理经纬度的一种新的MODIS 0级数据地理定位方法进行研究,最终得到比使用传统方法更精确的地理定位信息.     

利用MODIS数据计算北京地区的气溶胶直接辐射强迫

气溶胶通过吸收和反射太阳辐射直接影响地-气系统的辐射平衡.随着技术和反演算法不断改进,以卫星遥感气溶胶为基础,计算气溶胶直接辐射强迫成为可能.基于MODIS气溶胶和地表反照率数据集,结合MODTRAN辐射传输模式,计算北京地区2004～2009年气溶胶直接辐射强迫,并将计算结果与AERONET结果对比,证明了利用该方法...     

MODIS数据处理平台在旱情监测中的应用研究

研究基于MODIS（中分辨率成像光谱仪）数据和降水信息,结合地面实测数据进行区域的旱情监测。搭建了基于网格技术的MODIS数据处理平台,对遥感数据进行前期加工,利用水文气象信息,通过分析和选取合适的干旱因子构建遥感干旱综合监测模型。建立了干旱综合监测数据处理系统,初步实现了MODIS数据自动处理和旱情分析的过程。     

Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework

Long-term vegetation dynamics associated with climatic changes can be assessed using Advanced Very High Resolution Radiometer (AVHRR) red and near-infrared reflectance data provided that the data have been processed to remove the effects of non-target signal variability, such as atmospheric and sensor calibration effects. Here we present a new method that performs a relative calibration of reflectance data to produce consistent long-term vegetation information. It is based on a simple biological framework that assumes that the position of the vegetation cover triangle is invariant in reflectance space. This assumption is in fact an intrinsic assumption behind the commonly used Normalised Difference Vegetation Index (NDVI) and is violated when the NDVI is calculated from inadequately corrected reflectance data. In this new method, any temporal variability in the position of the cover triangle is removed by geometrically transforming the observed reflectance data such that two features of the triangle—the soil line and the dark point—are stationary in reflectance space. The fraction of Photosynthetically Active Radiation absorbed by vegetation (fPAR; 0.0-0.95) is then calculated, via the NDVI, from calibrated reflectances. This method was tested using two distinct, monthly AVHRR products for Australia: (i) the coarse-resolution, fully calibrated, partially atmospherically corrected PAL data (1981-1994); and (ii) the fine-resolution, fully calibrated, non-atmospherically corrected HRPT data (1992-2004). Results show that, in the 20-month period when the two datasets overlap (1992-1994), the Australia-wide, root mean square difference between the two datasets improved from 0.098 to 0.027 fPAR units. The calibrations have produced two approximately equivalent datasets that can be combined as a single input into time-series analyses. The application of this method is limited to areas that have a wide-enough variety of land-cover types so that the soil line and dark point are evident in the cover triangle in every image of the time-series. Another limitation is that the methodology performs only bulk, relative calibrations and does not remove the absolute effects of observation uncertainties. The simplicity of the method means that the calibration procedure can be easily incorporated into near-real-time operational remote-sensing environments. Vegetation information produced using this invariant-cover-triangle method is expected to be well suited to the analysis of long-term vegetation dynamics and change.     

黄河三角洲NDVI与Albedo时空分布规律

采用MODIS科学组L3级产品,详细分析了黄河三角洲地区NDVI和Albedo的时空分布规律,结果表明:①该产品具备相当高的精度,基本反映了研究区的植被覆盖和地表反照率的变化规律,并与土地利用变化情况吻合较好;②MODIS地表反照率对黄河三角洲地区具有一定的适用性;③黄河三角洲地区人类活动特别是农业耕作的季节性更替对NDVI的年内变化影响显著;④黄河三角洲地区的Albedo也具有明显的季节性变化,但其变化规律要比NDVI复杂得多。     

MODIS动态监测黄河宁蒙河段凌情的技术与方法

黄河宁夏河段和内蒙古河段（以下简称宁蒙河段）的凌情是我国大江大河中的一种特殊水文现象，由于凌情引发的凌汛灾害危害巨大，防凌历来是防汛部门的重要任务。在分析黄河宁蒙河道特点、MODIS卫星遥感数据特点和该河段所在区域天气状况的基础上，提出了MODIS遥感动态监测宁蒙河段凌情的技术与方法，研究和应用表明MODIS卫星遥感技术能够实现宁蒙河段凌情实时动态监测，在动态监测封开河长度、壅水漫滩等方面效果显著。     

Analysis of monotonic greening and browning trends from global NDVI time-series

Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.