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(...     

基于多源数据的干旱区盐渍地信息提取

土壤盐渍化严重威胁着干旱区绿洲的稳定与可持续发展,因此借助遥感手段快速提取盐渍地信息并及时掌握其空间分布有着重要的现实意义。以塔里木盆地北缘盐渍地普遍发育区域库车绿洲为例,探讨了干旱区以盐生植被红柳为主要覆盖的盐渍地信息的提取方法。综合利用TM卫星图像数据以及Radarsat雷达数据,分析了研究区主要地物的光谱特征及其波段间的相互运算,从而分析盐渍地与其它地物之间的可分性。着重分析了雷达波段作为一个波段加入ETM的6波段中一起参与主成分变换后对盐渍地信息的提取。研究表明：K-L-5（第五主成分）是提取重度盐渍地信息的最佳波段,TM1是区分红柳覆盖区（轻、中度）盐渍地信息的最佳波段,提取盐渍地信息时混分的水体信息可以通过MNDWI（改进归一化差异水体指数）设定一定的阈值予以剔除,混分的植被信息可以通过NDVI设定一定的阈值予以剔除。根据以上分析,建立决策树模型进行盐渍地信息的提取。结果表明,该方法的总体提取效果较好,是干旱区监测盐渍地变化的有效手段。同时也说明由于雷达波段的参与增加了盐渍地与其它地物之间的可分性,为雷达影像在提取盐渍地信息方面提供了一条有效途径。     

Spectral mixture analysis for subpixel vegetation fractions in the urban environment: How to incorporate endmember variability?

In the urban environment both quality of life and surface biophysical processes are closely related to the presence of vegetation. Spectral mixture analysis (SMA) has been frequently used to derive subpixel vegetation information from remotely sensed imagery in urban areas, where the underlying landscapes are assumed to be composed of a few fundamental components, called endmembers. A critical step in SMA is to identify the endmembers and their corresponding spectral signatures. A common practice in SMA assumes a constant spectral signature for each endmember. In fact, the spectral signatures of endmembers may vary from pixel to pixel due to changes in biophysical (e.g. leaves, stems and bark) and biochemical (e.g. chlorophyll content) composition. This study developed a Bayesian Spectral Mixture Analysis (BSMA) model to understand the impact of endmember variability on the derivation of subpixel vegetation fractions in an urban environment. BSMA incorporates endmember spectral variability in the unmixing process based on Bayes Theorem. In traditional SMA, each endmember is represented by a constant signature, while BSMA uses the endmember signature probability distribution in the analysis. BSMA has the advantage of maximally capturing the spectral variability of an image with the least number of endmembers. In this study, the BSMA model is first applied to simulated images, and then to Ikonos and Landsat ETM+ images. BSMA leads to an improved estimate of subpixel vegetation fractions, and provides uncertainty information for the estimates. The study also found that the traditional SMA using the statistical means of the signature distributions as endmember signatures produces subpixel endmember fractions with almost the same and sometimes even better accuracy than those from BSMA except without uncertainty information for the estimates. However, using the modes of signature distributions as endmembers may result in serious bias in subpixel endmember fractions derived from traditional SMA.     

Detection of foliage conditions and disturbance from multi-angular high spectral resolution remote sensing

Disturbance of forest ecosystems, an important component of the terrestrial carbon cycle, has become a focus of research over recent years, as global warming is about to increase the frequency and severity of natural disturbance events. Remote sensing offers unique opportunities for detection of forest disturbance at multiple scales; however, spatially and temporally continuous mapping of non-stand replacing disturbance remains challenging. First, most high spatial resolution satellite sensors have relatively broad spectral ranges with bandwidths unsuitable for detection of subtle, stress induced, features in canopy reflectance. Second, directional and background reflectance effects, induced by the interactions between the sun-sensor geometry and the observed canopy surface, make up-scaling of empirically derived relationships between changes in spectral reflectance and vegetation conditions difficult. Using an automated tower based spectroradiometer, we analyse the interactions between canopy level reflectance and different stages of disturbance occurring in a mountain pine beetle infested lodgepole pine stand in northern interior British Columbia, Canada, during the 2007 growing season. Directional reflectance effects were modelled using a bidirectional reflectance distribution function (BRDF) acquired from high frequency multi-angular spectral observations. Key wavebands for observing changes in directionally corrected canopy spectra were identified using discriminant analysis and highly significant correlations between canopy reflectance and field measured disturbance levels were found for several broad and narrow waveband vegetation indices (for instance, r²_NDVI = 0.90; r²_CHL3 = 0.85; p < 0.05). Results indicate that multi-angular observations are useful for extraction of disturbance related changes in canopy reflectance, in particular the temporally and spectrally dense data detected changes in chlorophyll content well. This study will help guide and inform future efforts to map forest health conditions at landscape and over increasingly coarse scales.     

A novel ocean color index to detect floating algae in the global oceans

Various types of floating algae have been reported in open oceans and coastal waters, yet accurate and timely detection of these relatively small surface features using traditional satellite data and algorithms has been difficult or even impossible due to lack of spatial resolution, coverage, revisit frequency, or due to inherent algorithm limitations. Here, a simple ocean color index, namely the Floating Algae Index (FAI), is developed and used to detect floating algae in open ocean environments using the medium-resolution (250- and 500-m) data from operational MODIS (Moderate Resolution Imaging Spectroradiometer) instruments. FAI is defined as the difference between reflectance at 859 nm (vegetation “red edge”) and a linear baseline between the red band (645 nm) and short-wave infrared band (1240 or 1640 nm). Through data comparison and model simulations, FAI has shown advantages over the traditional NDVI (Normalized Difference Vegetation Index) or EVI (Enhanced Vegetation Index) because FAI is less sensitive to changes in environmental and observing conditions (aerosol type and thickness, solar/viewing geometry, and sun glint) and can “see” through thin clouds. The baseline subtraction method provides a simple yet effective means for atmospheric correction, through which floating algae can be easily recognized and delineated in various ocean waters, including the North Atlantic Ocean, Gulf of Mexico, Yellow Sea, and East China Sea. Because similar spectral bands are available on many existing and planned satellite sensors such as Landsat TM/ETM+ and VIIRS (Visible Infrared Imager/Radiometer Suite), the FAI concept is extendable to establish a long-term record of these ecologically important ocean plants.     

基于NDVI的人工植被碳储量估算

人工植被是吸收CO²维护生态系统健康的重要生物成分,干旱区人工碳汇林在CO²减排方面具有重要的作用。应用2009年8月TM数据,提取克拉玛依人工减排林生态景观格局信息,并应用NDVI指数估算植被碳密度。通过测定乔木层及草本层生物量,估算出人工植被乔木层及草本层碳密度。结果表明,克拉玛依人工减排林乔木层的平均碳密度值为37.04 mg/hm²,1 m×1 m样方内草本层平均碳密度为59.65 g/m²,地上植被碳密度约为37.64 mg/hm²,植被层碳储量为250 915.5 mg;随着植被的生长发育及生物量累积效应的发挥,人工植被的碳汇功能还将进一步增大。