ETM与SPOT影像融合方法比较和评价——以庐山及其周边区域为例

高空间分辨率影像与多光谱影像融合,为影像处理提供了更加丰富的资源,有利于进行影像的分析和制图。本文以庐山及其周边区域的2009年5月11日Landsat ETM+与2009年5月6日SPOT 5影像数据为例,对典型的融合方法进行了对比评价。融合方法包括加权融合法,IHS变换,改进的IHS变换,高频调制融合法(频域采用Butterworth滤波器,指数滤波器),HSV变换,Brovey变换,CN变换,PC变换,Gram-Schmidt变换11种方法。评价指标为相关系数、信息熵、标准差、平均梯度、相对偏差、标准偏差。结果表明,不同的融合结果突出的侧重点不同。加权融合法、Gram-Schmidt变换,PC变换方法突出了影像的信息量丰富度、清晰度,提高空间分辨率能力。CN波谱锐化,改进的IHS变换,BW0高频调制融合,指数高频调制融合方法光谱保真度较好。改进的IHS变换,BW0高频调制融合,CN波谱锐化,指数高频调制融合法在空间分辨率及光谱保真度、清晰度、信息丰富度方面的能力较均衡。     

2009年喀喇昆仑山叶尔羌河冰川阻塞湖及冰川跃动监测

在气候变化的影响下,喀喇昆仑山叶尔羌河冰川突发洪水近几年频繁发生.2009-05-09,利用中国环境减灾卫星(HJ-1A/1B)影像,对叶尔羌河上游冰川突发洪水的源头进行了动态监测.在克亚吉尔冰川末端发现了冰川阻塞湖,并监测到其不断扩大.2009-08初该处冰川湖突然消失,同期影像显示其消失后,河谷中残留大量冰体,与往年情况差异巨大.结合相关水文记录及历史资料分析认为,极有可能是冰川末端突然前进(跃动)使冰川坝遭到破坏,导致冰川阻塞湖泄水.由此推测克亚吉尔冰川发生了罕见跃动现象,但需要进一步研究验证.     

An historical empirical line method for the retrieval of surface reflectance factor from multi-temporal SPOT HRV,HRVIR and HRG multispectral satellite imagery

SPOT satellites have been imaging Earth's surface since SPOT 1 was launched in 1986. It is argued that absolute atmospheric correction is a prerequisite for quantitative remote sensing. Areas where land cover changes are occurring rapidly are also often areas most lacking in situ data which would allow full use of radiative transfer models for reflectance factor retrieval (RFR). Consequently, this study details the proposed historical empirical line method (HELM) for RFR from multi-temporal SPOT imagery. HELM is designed for use in landscape level studies in circumstances where no detailed overpass concurrent atmospheric or meteorological data are available, but where there is field access to the research site(s) and a goniometer or spectrometer is available. SPOT data are complicated by the ±27° off-nadir cross track viewing. Calibration to nadir only surface reflectance factor (ρ_s) is denoted as HELM-1, whilst calibration to ρ_s modelling imagery illumination and view geometries is termed HELM-2. Comparisons of field measured ρ_s with those derived from HELM corrected SPOT imagery, covering Helsinki, Finland, and Taita Hills, Kenya, indicated HELM-1 RFR absolute accuracy was ±0.02ρ_s in the visible and near infrared (VIS/NIR) bands and ±0.03ρ_s in the shortwave infrared (SWIR), whilst HELM-2 performance was ±0.03ρ_s in the VIS/NIR and ±0.04ρ_s in the SWIR. This represented band specific relative errors of 10–15%. HELM-1 and HELM-2 RFR were significantly better than at-satellite reflectance (ρ_SAT), indicating HELM was effective in reducing atmospheric effects. However, neither HELM approach reduced variability in mean ρ_s between multi-temporal images, compared to ρ_SAT. HELM-1 calibration error is dependent on surface characteristics and scene illumination and view geometry. Based on multiangular ρ_s measurements of vegetation-free ground targets, calibration error was negligible in the forward scattering direction, even at maximum off-nadir view. However, error exceeds 0.02ρ_s where off-nadir viewing was ≥20° in the backscattering direction within ±55° azimuth of the principal plane. Overall, HELM-1 results were commensurate with an identified VIS/NIR 0.02ρ_s accuracy benchmark. HELM thus increases applicability of SPOT data to quantitative remote sensing studies.     

Investigating rural poverty and marginality in Burkina Faso using remote sensing-based products

Poverty at the national and sub-national level is commonly mapped on the basis of household surveys. Typical poverty metrics like the head count index are not able to identify its underlaying factors, particularly in rural economies based on subsistence agriculture. This paper relates agro-ecological marginality identified from regional and global datasets including remote sensing products like the normalized difference vegetation index (NDVI) and rainfall to rural agricultural production and food consumption in Burkina Faso. The objective is to analyze poverty patterns and to generate a fine resolution poverty map at the national scale. We compose a new indicator from a range of welfare indicators quantified from Georeferenced household surveys, indicating a spatially varying set of welfare and poverty states of rural communities. Next, a local spatial regression is used to relate each welfare and poverty state to the agro-ecological marginality. Our results show strong spatial dependency of welfare and poverty states over agro-ecological marginality in heterogeneous regions, indicating that environmental factors affect living conditions in rural communities. The agro-ecological stress and related marginality vary locally between rural communities within each region. About 58% variance in the welfare indicator is explained by the factors of rural agricultural production and 42% is explained by the factor of food consumption. We found that the spatially explicit approach based on multi-temporal remote sensing products effectively summarizes information on poverty and facilitates further interpretation of the newly developed welfare indicator. The proposed method was validated with poverty incidence obtained from national surveys.     

基于稀少控制点的SPOT5卫星立体条带影像的生产应用研究

随着相关技术的发展,高分辨率卫星遥感影像在测绘产品生产中的应用越来越广泛和深入。本文主要通过西部无图区测图的应用实践,讨论利用有理多项式方法、基于稀少控制点纠正的SPOT5HRS立体条带影像的测图生产情况,并对DLG、DEM等产品进行精度检测;同时与传统的航摄方法进行对比,分析其生产效率等情况。     

单侧控制条件下SPOT5 HRG影像的定向试验

高分辨率卫星影像已经成为获取地理信息的重要数据源,而其定向是一项关键技术。本文利用单侧不同数量及分布的控制点对SPOT5 HRG影像进行了定向试验,探讨了在单侧控制条件下SPOT5 HRG影像的定向精度情况,为地面控制点获取较困难地区的影像定向提供了一条参考途径。     

基于SPOT NDVI的祁连山草地植被覆盖时变化趋势分析

基于RS、GIS技术和SPOT VGT-NDVI数据,运用累积平均法、均值法、趋势线分析法和影像差异法,分析了祁连山草地植被覆盖在时问和空间上的变化特征,并用Hurst指数动态预测祁连山草地植被覆盖未来变化趋势.结果表明:①1999-2007年祁连山草地植被NDVI呈缓慢增加趋势.典型草原和平原草地植被年均NDVIv和生长期NDVIg增加速率高于高寒草甸草地和沙漠草地.祁连山草地植被NDVI增加和减少的面积分别为69776 km2和15928 km2,植被NDVI增加的区域分布在冷龙岭、拉脊山、大通山、达坂山、青海南山、走廊南山、托来山、托来南山等地区,减少的区域分布在乌鞘岭、大通河、石羊河、黑河、北大河、疏勒河等河流河谷以及青海湖周边地区.②祁连山草地植被NDVI年内月和旬变化曲线均呈单峰型.③冷龙岭、宗务隆山、青海南山、镜铁山、拉脊山、乌鞘岭、大通河、黑河、北大河、疏勒河等河流河谷以及青海湖周边等地区未来草地植被覆盖有改善的趋势:走廊南山、托来山、托来南山、大通山以及湟水、石羊河等河流河谷地区未来草地植被覆盖有退化的趋势.沙漠草地、高寒草甸草地未来有改善的趋势;而典型草原和平原草地未来则有退化的趋势.     

Temporal and spatial response of vegetation NDVI to temperature and precipitation in eastern China

Temporal and spatial response characteristics of vegetation NDVI to the variation of temperature and precipitation in the whole year, spring, summer and autumn was analyzed from April 1998 to March 2008 based on the SPOT VGT–NDVI data and daily temperature and precipitation data from 205 meteorological stations in eastern China. The results indicate that as a whole, the response of vegetation NDVI to the variation of temperature is more pronounced than that of precipitation in eastern China. Vegetation NDVI maximally responds to the variation of temperature with a lag of about 10 days, and it maximally responds to the variation of precipitation with a lag of about 30 days. The response of vegetation NDVI to temperature and precipitation is most pronounced in autumn, and has the longest lag in summer. Spatially, the maximum response of vegetation NDVI to the variation of temperature is more pronounced in the northern and middle parts than in the southern part of eastern China. The maximum response of vegetation NDVI to the variation of precipitation is more pronounced in the northern part than in the middle and southern parts of eastern China. The response of vegetation NDVI to the variation of temperature has longer lag in the northern and southern parts than in the middle part of eastern China. The response of vegetation NDVI to the variation of precipitation has the longest lag in the southern part, and the shortest lag in the northern part of eastern China. The response of vegetation NDVI to the variation of temperature and precipitation in eastern China is mainly consistent with other results, but the lag time of vegetation NDVI to the variation of temperature and precipitation has some differences with those results of the monsoon region of eastern China.     

北巴颜喀拉山1∶5万区域地质调查中的遥感解译应用

遥感技术是地质填图的一种重要手段,充分运用遥感技术能够有效提高填图效率和质量。本研究以北巴颜喀拉山地区SPOT5、ETM系列数据为基础影像,从岩性、构造解译入手进行最大程度的信息挖掘。从4个方面详细阐述岩性判识依据,建立北巴颜喀拉山可视化解译标志系统,并对地层岩性进行基于影像的重新厘定,细分了各地层岩段或岩性组合。通过构造详细解译,识别了多种、不同级别的构造形迹,建立研究区新的构造格架,并对构造块体进行详细划分。本研究翔实、细致的工作,为北巴地区地层岩性对比、构造重新认识提供新视角,为该区区域地质调查提供新资料。     

Predictive vegetation mapping approach based on spectral data, DEM and generalized additive models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.