基于SAILH模型的植被冠层NDVI二向性分析

NDVI是植被遥感中最为常用的一种植被指数,建立NDVI与其他冠层参数模型必须考虑其方向性问题.本文基于SAILH模型讨论了连续植被冠层NDVI的二向性特征,并分析了叶面积指数、叶倾角分布、热点参数以及太阳天顶角和相对方位角对NDVI的影响.研究表明冠层NDVI在主平面观测方向存在一个明显的负热点,前向散射方向的NDV...     

Estimation of diurnal air temperature using MSG SEVIRI data in West Africa

Spatially distributed air temperature data with high temporal resolution are desired for several modeling applications. By exploiting the thermal split window channels in combination with the red and near infrared channels of the geostationary MSG SEVIRI sensor, multiple daily air temperature estimates can be achieved using the contextual temperature-vegetation index method. Air temperature was estimated for 436 image acquisitions during the 2005 rainy season over West Africa and evaluated against in situ data from a field test site in Dahra, Northern Senegal. The methodology was adjusted using data from the test site resulting in RMSE = 2.55 K, MBE = − 0.30 K and R² = 0.63 for the estimated versus observed air temperatures. A spatial validation of the method using 12 synoptic weather stations from Senegal and Mali within the Senegal River basin resulted in overall values of RMSE = 2.96 K, MBE = − 1.11 K and R² = 0.68. The daytime temperature curve is interpolated using a sine function based on the multiple daily air temperature estimates from the SEVIRI data. These estimates (covering the 8:00-20:00 UCT time window) were in good agreement with observed values with RMSE = 2.99 K, MBE = − 0.70 K and R² = 0.64. The temperature-vegetation index method was applied as a moving window technique to produce distributed maps of air temperature with 15 min intervals and 3 km spatial resolution for application in a distributed hydrological model.     

一种遥感影像水体信息自动提取方法

本文提出了一种基于卫星图像数据的LBV变换与归一化植被指数NDVI的遥感影像水体信息自动提取的方法.水体经过LBV变换后形成的B分量图的灰度值很大,水体信息的归一化植被指数值小于0;构建水体信息自动提取模型条件是水体信息满足B分量数值大于某一阈值并且归一化植被指数值小于0;与其它方法进行实验比较该方法可以较准确的进行水体信息的自动提取,同时准确地将水域与低密度覆盖的水植混合体分开.     

Detecting trend and seasonal changes in satellite image time series

A wealth of remotely sensed image time series covering large areas is now available to the earth science community. Change detection methods are often not capable of detecting land cover changes within time series that are heavily influenced by seasonal climatic variations. Detecting change within the trend and seasonal components of time series enables the classification of different types of changes. Changes occurring in the trend component often indicate disturbances (e.g. fires, insect attacks), while changes occurring in the seasonal component indicate phenological changes (e.g. change in land cover type). A generic change detection approach is proposed for time series by detecting and characterizing Breaks For Additive Seasonal and Trend (BFAST). BFAST integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting change within time series. BFAST iteratively estimates the time and number of changes, and characterizes change by its magnitude and direction. We tested BFAST by simulating 16-day Normalized Difference Vegetation Index (NDVI) time series with varying amounts of seasonality and noise, and by adding abrupt changes at different times and magnitudes. This revealed that BFAST can robustly detect change with different magnitudes (> 0.1 NDVI) within time series with different noise levels (0.01-0.07 σ) and seasonal amplitudes (0.1-0.5 NDVI). Additionally, BFAST was applied to 16-day NDVI Moderate Resolution Imaging Spectroradiometer (MODIS) composites for a forested study area in south eastern Australia. This showed that BFAST is able to detect and characterize spatial and temporal changes in a forested landscape. BFAST is not specific to a particular data type and can be applied to time series without the need to normalize for land cover types, select a reference period, or change trajectory. The method can be integrated within monitoring frameworks and used as an alarm system to flag when and where changes occur.     

基于多时相MODIS监测冬小麦的种植面积

论文基于时相和波谱信息，利用MODIS数据监测了北京冬小麦的种植面积。首先，基于地形高度对地物光谱反射值的影响，借助DEM数据对研究区域进行划分。其次，在分析并提取北京地区主要农作物时间谱曲线特征的基础上，设计决策函数，成功提取了北京地区冬小麦的种植面积。最后，比较并分析了非遥感数据对监测精度的影响。研究结果表明，（1）时相信息可以极大的提高农作物种植面积的监测精度。（2）辅助数据的利用，使决策函数的设计更具有针对性，监测结果更可靠。（3）多源多时相遥感数据在农作物种植面积的提取中具有明显的技术优势和重要的应用潜力。     

基于MPI和OpenCV遥感植被指数产品的并行计算

遥感传感器和计算机技术的发展,每天都会汇集大量新的地理空间数据。地球科学许多应用要求数据实时或接近实时地处理,发展高性能计算是进行海量数据处理的必然趋势。本文以 TM 影像制备黑河流域归一化指数产品为例,基于高性能集群,实现了植被指数快速提取的并行计算方法,并采用对等并行编程模式,通过 C 语言调用 MPI（Message Passing Interface,消息传递接口）和 OpenCV（Open Source Computer Vision Library,开源计算机视觉库）函数库,实现了 NDVI（Normalized Difference Vegetation Index,归一化植被指数）的并行计算,获得了黑河流域的 NDVI。性能测试表明,并行计算可以显著提高遥感图像处理的速度。文章最后讨论了从原始影像提取植被指数产品的流程。     

The Impact of Agricultural Practices in China on Land-Atmosphere Interactions

Human-induced land use changes and the resulting alterations in vegetation features are major but poorly recognized drivers of regional climatic patterns.In order to investigate the impacts of anthropogenicallyinduced seasonal vegetation cover changes on regional climate in China,harmonic analysis is applied to 1982-2000 National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVVHRR)-derived normalized difference vegetation index (NDVI) time series (ten day interval data).For two climatic divisions of South China,it is shown that the first harmonic term is in phase with air temperature,while the second and third harmonics are in phase with agricultural cultivation.The Penman-Monteith Equation and the Complementary Relationship Areal Evapotranspiration (CRAE) model suggest that monthly mean evapotranspiration is out of phase with temperature and precipitation in regions with significant second or third harmonics.Finally,seasonal vegetation cover changes associated with agricultural cultivation are identified:for cropped areas,the temperature and precipitation time series have a single maximum value,while the monthly evapotranspiration time series has a bimodal distribution.It is hypothesized that multi-cropping causes the land surface albedo to sharply increase during harvesting,thereby altering the energy distribution ratio and contributing to observed seasonal vegetation cover changes.     

使用单窗算法研究北京城区热岛效应

随着全球变暖和城市化进程的加快,大城市城区的热岛效应日益严重。城市下垫面对地表能量交换的影响巨大,引起地表温度分布的不均一性。遥感技术的发展为地表温度的反演提供了可能。近年来人们使用劈窗算法对均一的海面温度的反演很成功,但是受空间分辨率的限制以及陆面的不均一性,陆面温度的反演一直是一个没有解决好的问题。覃志豪提出了一种TM热红外波段单窗算法,可以利用辅助气象资料快速计算出地表温度。本文以北京市城区为研究区,采用LandsetETM第6波段的单窗算法,反演了亮度温度和地表实际温度,分析了城市下垫面情况下NDVI与地表温度的相关关系,并解释了北京城区热岛在空间上的分布及其可能的原因。结果表明:北京市城区热岛效应显著;地表温度与NDVI相关性显著;城区绿地和水体在区域的温度分布中起到重要作用。     

黑河流域湿地、农田、草地生态系统碳通量变化特征及驱动因子分析

为了认识黑河流域湿地、农田、草地生态系统不同时间尺度的碳通量特征及与环境因子的关系,并为干旱区生态系统碳源/汇效应评估提供理论依据。采用涡度相关技术对黑河流域湿地、农田、草地生态系统进行长达7 a的碳通量、气象因子观测,分析了净生态系统生产力(NEP)、生态系统呼吸(Reco)、总初级生产力(GPP)在日际、季节、年际3种尺度的动态变化机制,并比较了碳通量与植被指数NDVI、EVI的季节变化异同。经分析发现：(1)黑河流域湿地与草地、农田生态系统均在日尺度上呈现明显的单峰“倒U”分布,草地于12:00到达峰值,湿地与农田于13:00到达峰值,峰值碳通量农田>湿地>草地;(2)季节尺度上,湿地与农田、草地生态系统碳通量以及NDVI、EVI均呈单峰“倒U”分布,6～9月生长季为明显碳吸收,7月份到达全年峰值,碳吸收峰值为农田>湿地>草地,NDVI、EVI峰值则为阿柔站>湿地站>大满站>大沙龙站。(3)年固碳能力为农田(648.90 gC/m²/a)>湿地(627.51 gC/m²/a)>草地(...     

鄱阳湖区植被覆盖度的遥感估算

植被覆盖度是一个十分重要的生态学参数,对于全球环境变化和监测研究具有重要意义.为了有效地从遥感资料中提取植被覆盖度,在像元二分模型两个重要参数推导的基础上,对已有模型进行了改进,建立了用归一化植被指数(NDVI)估算植被覆盖度的模型.运用该模型对鄱阳湖区2003年的植被覆盖度进行了估算,通过鄱阳湖部分地区的实地考察,对2003年的植被覆盖度估算结果进行了验证.结果表明使用这种改进模型进行植被覆盖度遥感监测是有效可行的.