Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems

This paper evaluated the capacity of SPOT VEGETATION time-series to monitor herbaceous fuel moisture content (FMC) in order to improve fire risk assessment in the savanna ecosystem of Kruger National Park in South Africa. In situ herbaceous FMC data were used to assess the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Vegetation Dryness Index (VDI), Improved VDI (IVDI), and Accumulated Relative NDVI Decrement (ARND) during the dry season. The effect of increasing amounts of dead vegetation on the monitoring capacity of derived indices was studied by sampling mixed live and dead FMC. The IVDI was proposed as an improvement of the VDI to monitor herbaceous FMC during the dry season. The IVDI is derived by replacing NDVI with the integrated Relative Vegetation Index (iRVI), as an approximation of yearly herbaceous biomass, when analyzing the 2-dimensional space with NDWI. It was shown that the iRVI offered more information than the NDVI in combination with NDWI to monitor FMC. The VDI and IVDI exhibited a significant relation to FMC with R² of 0.25 and 0.73, respectively. The NDWI, however, correlated best with FMC (R² = 0.75), while the correlation of ARND and FMC was weaker (R² = 0.60) than that found for NDVI, NDWI, and IVDI. The use of in situ herbaceous FMC consequently indicated that NDWI is appropriate as spatio-temporal information source of herbaceous FMC variation which can be used to optimize fire risk and behavior assessment for fire management in savanna ecosystems.     

基于联邦滤波的室内多传感器融合导航定位方法研究

针对室内多传感器融合导航定位问题,提出一种基于联邦滤波的INS/3D LASER/单目视觉融合的室内导航定位方法.该方法是通过联邦滤波将INS分别同3D LASER与单目视觉构成子滤波器,主滤波器根据最小方差准则,利用最优系数加权法融合各子滤波器的最优估计,从而获得全局最优融合结果.实验证明,该方法能有效提高室内导航定位的精度和鲁棒性,比传统的视觉里程计法或激光雷达里程表法更准确.     

时序NDVI数据集重建方法评价与实例研究

时序NDVI数据集已经成功地应用于全球与区域环境变化、植被动态变化、土地覆盖变化和植物生物物理量参数反演等方面的研究。受到大气条件和传感器自身因素的制约,虽然经过严格的预处理,时序NDVI数据集仍包含很多噪声,影响其进一步的应用。首先介绍了近几年来普遍使用的6种时序NDVI数据集的重建方法：改进的最佳指数斜率提取法、均值迭代滤波法、Savitzky-Golay滤波法、傅立叶变换法、非对称高斯函数拟合法和时间序列谐波分析法 |然后采用这几种方法对张掖地区2007年和2008年10 d最大值合成的SPOT/VEGETATION的时序NDVI数据进行了重建,对重建结果进行了比较和评价 |最后对人为的噪声序列进行重建,对重建结果的优缺点进行评价。     

CBERS-02与SPOT5融合数据及其在海岸带土地利用调查中应用能力综合评价

以珠海市横琴岛为试验区,对CBERS-02星多光谱数据和SPOT5全色数据进行融合处理,融合方法包括HIS变换法、Brovey法、主成分融合法、小波融合法、SFIM法、Gram\|Schmidt法、PANSHARP法,并使用定量指标对融合结果进行分析,通过计算各波段均值、信息熵、标准差、相关系数等指标对各融合结果进行分析评价,探讨适合两种数据的融合方法;对CBERS-02和SPOT5数据的PANSHARP法融合数据进行支持向量机分类,总体分类精度为82.55%,Kappa系数为0.7358;试验结果表明,通过融合CBERS\|02多光谱数据与SPOT5全色数据,既可以增强空间纹理信息,又可以弥补SPOT5多光谱数据缺少蓝光波段的缺陷,具有丰富的光谱信息,可以在海岸带土地利用调查广泛应用。     

特征权重优化高分辨率遥感影像模糊分类研究

在针对SPOT5等高分辨率遥感影像的面向对象模糊分类过程中,一般对影像对象的特征赋予相同的权重。为了体现不同特征对分类作用的差异,本文在分类时根据特征的重要与否,对参与分类的特征赋予不同的权重,提高重要的、区分度好的特征的权重,降低次要特征的权重。以北京市昌平区的SPOT5影像为例,利用多特征模糊分类和经过权重优化的多特征模糊分类进行分类对比实验。实验结果表明,经过特征权重优化的分类与权重相同的分类结果相比,分类总精度由原来的86.3%提高到了92.6%,Kappa系数由原来的0.8096提高到了0.8947。结果表明,经过权重优化的多特征模糊分类有助于提高模糊分类法的分类精度和适用性。     

An agent-based signal processing in-node environment for real-time human activity monitoring based on wireless body sensor networks

Nowadays wireless body sensor networks (WBSNs) have great potential to enable a broad variety of assisted living applications such as human biophysical/biochemical control and activity monitoring for health care, e-fitness, emergency detection, emotional recognition for social networking, security, and highly interactive games. It is therefore important to define design methodologies and programming frameworks which enable rapid prototyping of WBSN applications. Several effective application development frameworks have been already proposed for WBSNs designed for TinyOS-based sensor platforms, e.g. CodeBlue, SPINE, and Titan. In this paper we present an application of MAPS, an agent framework for wireless sensor networks based on the Java-programmable Sun SPOT sensor platform, for the development of a real-time WBSN-based system for human activity monitoring. The agent-oriented programming abstractions provided by MAPS allow effective and rapid prototyping of the sensor-side software. In particular, the architecture of the developed system is a typical star-based WBSN composed of a coordinator node and two sensor nodes located respectively on the waist and the thigh of the monitored assisted living. The coordinator relies on a JADE-based enhancement of the SPINE coordinator and allows configuring sensors, receiving their data, and recognizing pre-defined human activities. On the other hand, each sensor node runs a MAPS-based agent that performs sensing of the 3-axial accelerometer sensor, computation of significant features on the acquired data, feature aggregation and transmission to the coordinator. The experimentation phase of the prototype, which allows evaluating the obtainable monitoring performances and activity recognition accuracy, is described. Moreover, a comparison of the monitoring system based on MAPS, AFME and SPINE in terms of programming effectiveness and system performances is discussed.     

利用SPOT图象阴影提取城市建筑物高度及其分布信息

在分析SPOT卫星图象阴影与建筑物实际高度关系的基础上,阐述了依据图象建筑物来估算城市建筑物高度的原理和方法,进而探讨了以数据融合为手段的,从SPOT全色图象中准确界定阴影范围的方法,并以此为基础,研究出了一种基于图象阴影特征的城市建筑物高分级及其分布信息自生成技术,在以北京市为例的试验中,建筑物高度分级结果的抽样验证准确率达80％以上,显示出卫星遥感在城市应用方面的巨大潜力。     

SPOT 5在森林资源二类调查应用中数据优化研究

为了实现SPOT 5影像应用于森林资源二类调查过程中影像数据最优的目视效果,首先利用标准差、均值、信息熵、OIF指数等指标对影像融合及目视解译最佳波段进行选取;其次,针对性地引入了亮度信息、空间细节和纹理结构、信息量、光谱特性保持程度等融合效果评价参数,并以高空间分辨率信息继承程度作为重要参数以反映融合影像空间分辨率特征,把最佳波段作为统计对象对常用的9种像素级融合方法进行了融合效果分析。比较得出PCA融合为9种方法中综合最优,影像亮度、空间分辨率信息较高,视觉效果最佳;乘积融合和Brovey融合虽然空间分辨率信息继承程度等指标也较优,但是光谱扭曲太大。小波融合等方法总体光谱特征普遍较好,但是噪声较多,空间分辨率信息不佳,不适用于目视解译。定量评价之后的定性评价分析进一步强调了高空间分辨率信息继承程度做为评价指标的重要性。     

A land cover classification product over France at 1 km resolution using SPOT4/VEGETATION data

The present study focuses on the development of a new land cover classification product over France at 1 km resolution. It is based on data sets from the Earth observing system SPOT4/VEGETATION. The satellite measurements are aimed at supporting regional efforts to set up global mosaics on new land cover products. They have been acquired in the frame of the Global Land Cover 2000 project. The instrument design relies on advanced technology, which leads to an improved radiometric and geometric resolution data. Such characteristics allow taking full benefit of the daily repetitiveness of the VEGETATION wide field-of-view sensor without the drawback of a variable pixel size on the image edge. Several physical processing steps are successively operated to the images on a per-pixel basis to remove detector blindness, to filter cloud contamination, and finally to correct both atmospheric and surface anisotropy effects. A new thematic map using the K-means clustering method has been built. First, the results of the satellite-based land cover classification has been successfully compared with the Coordination of Information on the Environment (CORINE) database which serves as a reference to appraise the reliability of the study. Then, it has been inter-compared with land cover products derived from MODIS and AVHRR sensors. For this, an aggregative scheme particularly focused on major land units (forest, grassland, cropland) adopted in order to yield a whole mapping at the same geographic projection and space resolution. The discrepancies between maps enhance the quality of the proposed product, thanks to the use of advanced data processing and a more appropriate method.