HJ-1A/B卫星CCD影像大气订正

大气订正是遥感信息定量化研究中必不可少的一步,目前已有一些成熟的方法,但由于HJ-1A/B卫星CCD相机波段设置特点,常规的大气订正方法基本不适合于HJ-1A/BCCD影像.本文在大量分析HJ-1A/BCCD影像中不同地物的多种指数基础上,提出了改进暗目标法实现HJ-1A/BCCD影像的大气订正,该方法采用比值植被指数(RVI)、土壤调整植被指数(SAVI)和归一化水体指数(NDWI)的综合分析法确定暗像元自动提取,使之适用于环境减灾卫星CCD影像数据.为了客观地验证该方法的精度,本文选取地表平坦均一的敦煌校正场作为实验区,通过多次测量卫星过境时的地表反射率进行分析验证.     

基于Landsat TM/ETM+影像的武汉市冬季NPP估算及其时空变化分析

植被净第一生产力(NPP)作为反映植被固碳能力的重要指标,在全球CO2浓度上升的背景下,成为研究全球及区域生态系统对气候环境变化响应的热点之一。基于Landsat TM/ETM+遥感影像数据,采用改进的CASA模型,估算得到武汉市2001~2010年空间分辨率为30m的冬季NPP,并对其进行时空变化分析。研究结果表明:武汉市过去10a冬季平均NPP为8.55gC/m2·m。2001~2010年武汉市冬季NPP整体呈现波动上升的趋势,各区域具有不同的增长速率,其中以江夏区最快,而各植被类型中灌木林具有最快的增长速率和最高的平均NPP。武汉市冬季NPP均呈现从三环区域向四周增大的空间分布特征,过去10a武汉市冬季NPP最高的区域由黄陂区转移到了江夏区。     

基于HJ-1A/B卫星CCD数据的土地宏观监测试验研究

对HJ-1A/B卫星CCD数据质量进行分析,并采用人工解译和计算机自动分类两种方法进行土地利用信息提取试验研究。结果表明,HJ-1A/B卫星CCD数据能够满足制作1∶100 000比例尺数字正射影像图的平面精度要求,可用于不大于1∶100 000比例尺的土地利用宏观遥感监测。
     

HJ-1A/1B星CCD传感器数据在黄东海浒苔监测中的应用

基于浒苔光谱特性和归一化植被指数,利用HJ-1A/1B星CCD传感器数据对黄东海浒苔进行监测。监测结果表明HJ-1A/1B星CCD传感器,可以提供比MODIS更多的精细信息,如提供重点海域的浒苔分布范围、覆盖范围、变化状况等信息。     

HJ-1A/B星地表反照率生成算法与检验

提出了适合环境与灾害监测预报小卫星-A、B星(简称HJ-1A/B星)CCD相机的大气订正算法,并基于不同地表特性和大气条件下的辐射传输模拟数据,建立HJ-1A/B星的窄波段向宽波段反照率转换的模型.利用多级灰阶靶标实测数据、敦煌检验场实测数据验证了大气订正算法以及转换模型的可靠性和精度,并将HJ-1A/B星影像数据计算的反照率产品与同时相的MODIS反照率产品进行对比分析.结果表明:文章提出的HJ-1A/B星CCD相机大气订正算法可有效校正大气影响;窄波段向宽波段反照率转换模型反演的反照率精度可靠;基于研究成果生成的HJ-1A/B星地表反照率与MODIS反照率产品一致性较好,满足后续遥感数据定量化模型研究的精度需要.     

Landsat TM/OLI和HJ-1B CCD传感器数据的交互对比

针对Landsat TM/OLI和HJ-1B CCD的不同传感器交互定标问题,提出了关于不同传感器NDVI、地表反射率两个参数之间的交互对比及转换方程.Landsat系列影像具有较为丰富的历史存档数据,而HJ-1B数据可作为Landsat系列数据的衔接和补充.文章基于同日过境的Landsat TM/OLI和HJ-1B ...     

基于Terra/MODIS数据的HJ-1B/CCD1交叉定标方法研究

交叉辐射定标是国际上新近发展起来的一种无场地定标方法,它的应用弥补了场地定标成本较高、定标参数更新周期较长的不足。对于我国2008年发射的环境与灾害监测预报小卫星CCD数据而言,探索交叉辐射定标方法的适用性,对及时发现传感器辐射性能的变化,促进CCD遥感数据的定量化应用具有重要意义。本研究以辐射定标精度较高的Terra/MODIS数据为参考,分别使用光线匹配法（RM）和辐射传输模型方法（RTM）对HJ-1B/CCD1数据进行交叉辐射定标,并与相同条件下进行的场地定标结果比较。实验结果表明,使用这两种方法获取的CCD1的第2、3、4波段的定标结果与场地定标结果差异较小,只有第1波段定标结果与场地定标结果差异相对较大,这证明了交叉辐射定标方法的有效性。另外,虽然RTM方法考虑了参考传感器和待定标传感器光谱响应和观测几何的差异,但是由于RTM方法会受到所使用的6S模型本身的误差以及输入的大气参数、地表参数测量误差的影响,该方法并不总是优于RM方法。     

基于HJ-1星CCD数据的溢油遥感特性分析与信息提取

基于HJ-1星CCD多光谱数据,以2010年美国墨西哥湾深海油井溢油污染海域为研究区,对溢油迁移转化过程进行分析,认为油水混合物是该次事故的主要污染类型之一.研究HJ-1星CCD数据多种目标的影像特征与光谱响应特征,指出油水混合物能明显改变海水对入射光的后向散射性能,可以被有效地识别与判定.利用决策树分类方法能有效地提取墨西哥湾溢油污染中的油水混合物;对误判信息进行分块合并处理后,可进一步提高油水混合物的提取精度.研究表明,HJ-1星CCD多光谱数据能有效提取海洋溢油污染信息,具备了开展海洋溢油污染遥感监测的能力.     

基于HJ-1卫星CCD数据的厦门海域叶绿素a浓度反演

针对HJ-1A/B卫星CCD数据,建立适合于厦门海域的叶绿素a浓度反演模型,将为持续监测该海域的赤潮提供时间序列的叶绿素a浓度数据。基于2013年7月31日厦门海域水体实测光谱与叶绿素a浓度同步测量数据,及HJ|1B卫星CCD2光谱响应函数,对各波段遥感反射率与叶绿素a浓度的相关性进行比较,证实蓝、绿波段比值与叶绿素a浓度相关性最高。对OC3模型在内的5种模型的反演结果和实测叶绿素a浓度做相关性分析,发现各模型相关系数均达到0.7以上。利用2013年7月30日实测数据对同期厦门海域HJ-1B卫星CCD2数据叶绿素a浓度反演结果进行精度验证,结果表明本地化的10指数模型在反演叶绿素a浓度动态范围较大的区域具有更高的精度。     

基于HJ-1A高光谱数据的藏北高原草地分类方法对比

环境减灾星星座A星(HJ-1A)携带的超光谱仪填补了我国星载高光谱影像采集领域的空白,但目前国内关于该高光谱数据的应用较少.本文基于HJ-1A高光谱(HSI)数据预处理技术,以申扎县北部为研究区,采用SPCA-MLC和HSI-SAM分类方法,结合野外实测样本,将研究区分为沼泽草甸、高寒草甸、高寒草原、荒漠化草原和裸地5种类型,并结合分类精度和分类图对2种分类方法进行了对比分析,可得基于HJ-1A高光谱数据的藏北高原草地分类方法中SPCA-MLC法优于HSI-SAM法.2种方法的分类精度皆大于80％,证明了HJ-1A的HSI数据在实现藏北草地高精度分类方面的巨大潜力.     

Mapping northern land cover fractions using Landsat ETM+

The goal of fractional mapping is to obtain land cover fraction estimates within each pixel over a region. Using field, Ikonos and Landsat data at three sites in northern Canada, we evaluate a physical unmixing method against two modeling approaches to map five land cover fractions that include bare, grass, deciduous shrub, conifer, and water along an 1100 km north-south transect crossing the tree-line of northern Canada. Error analyses are presented to assess factors that affect fractional mapping results, including modeling method (linear least squares inversion (LLSI) vs. linear regression vs. regression trees), number of Landsat spectral bands (3 vs. 5), local and distant fraction estimation using locally and globally calibrated models, and spatial resolution (30 m vs. 90 m). The ultimate purpose of this study is to determine if reliable land cover fractions can be obtained for biophysical modeling over northern Canada from a three band, resampled 90 m Landsat ETM+ mosaic north of the tree-line. Of the three modeling methods tested, linear regression and regression trees with five spectral bands produced the best local fraction estimates, while LLSI produced comparable results when unmixing was sufficiently determined. However, distant fraction estimation using both locally and globally calibrated models was most accurate using the three spectral bands available in the Landsat mosaic of northern Canada at 30 m resolution, and only slightly worse at 90 m resolution. While local calibrations produced more accurate fractions than global calibrations, application of local calibration models requires stratification of areas where local endmembers and models are representative. In the absence of such information, globally calibrated linear regression and regression trees to estimate separate fractions is an acceptable alternative, producing similar root mean square error, and an average absolute bias of less than 2%.     

基于ETM+的深圳市绿地信息提取方法研究

针对深圳市生态绿地信息系统的规划需求,利用Landsat ETM+影像通过对3种图像增强方法(原始波段组合、差值运算、NDVI与其它波段组合)进行比较,集中探讨了深圳市绿地信息提取的最佳方法。结果表明,NDVI与其它波段组合法对于深圳市绿地信息的提取,可以得到比较理想的绿地分布信息,达到了城市生态绿地监测的要求。     

Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece

Mediterranean rangelands are unique marginal ecosystems, which are characterized by a highly heterogeneous structure and are often interwoven with other ecosystems. Traditionally, rangelands provided resources for livestock grazing in transhumantic rotation schemes. In recent times, there has been a trend towards semi-intensive grazing systems, which is partly connected to the European system of agricultural and infrastructural subsidies, and which effectuates both intensification and extensification. This study employed trend analysis of a remote sensing data time series for a retrospective assessment of rangeland processes, and interpreted these in the light of land-use practices and previous management interventions.We have selected a test area in Northern Greece that is representative of typical land-use transitions of the European Mediterranean. A time series of Landsat TM and ETM+ data covering the years 1984-2000 with one image per year was acquired, and for all images a geometric correction including digital elevation information and full radiative transfer modelling were carried out to attain surface reflectance data. For further analyses, proportional vegetation cover was selected as the target indicator, which was derived using Spectral Mixture Analysis. The resulting data set was used in a linear trend analysis to characterize spatio-temporal patterns of vegetation cover development. These could be interpreted based on knowledge of the local grazing regime and factors driving it, as well as using auxiliary spatial data sets. Results showed that temporal trends in the test area reflect the underlying pattern of potential livestock distribution at the per-pixel level, with a spatially differentiated pattern of both positive and negative trends in close proximity. On the other hand, no direct relation could be established between the development of vegetation cover and animal stocking rates at the community level. This suggests that this aggregation level is too coarse given the combination of highly heterogeneous landscapes with semi-intensive to intensive land tenure systems.     

三峡库区植被生物量遥感估算方法研究

利用遥感手段估算三峡库区植被的生物量,综合野外观测数据,分析了Landsat TM数据的光谱信息与植被生物量的关系,通过相关性分析表明Landsat TM多光谱数据能较好地反映该地区的植被生物量水平。然后,利用Landsat TM数据分别建立了针对三峡库区的阔叶林、针叶林、针阔混交林、灌木林和草本植被5种主要植被类型的生物量遥感估算模型,并利用所得模型计算了该区域2002年植被的地上生物量总量为1.05×10⁸ Mg。
     

HJ-1A/1B CCD数据湖泊水体污染遥感监测

针对新型国产卫星数据源HJ-1A/1B多光谱数据特点,进行内陆湖泊水污染定性及半定量化的遥感监测。根据水体污染所产生的光谱特性变化进行水体污染的识别与分析。通过实验表明,HJ-1A/1B多光谱数据能够有效地进行湖泊水污染变化监测与应用分析。     

Assessment of fire severity and species diversity in the southern Appalachians using Landsat TM and ETM+ imagery

Relatively little is known about the disturbance ecology of large wildfires in the southern Appalachians. The occurrence of a 4000-ha wildfire in the Linville Gorge Wilderness area in western North Carolina has provided a rare opportunity to study a large fire with a range of severities. The objectives of this study were to 1) assess the potential for using multi-temporal Landsat imagery to map fire severity in the southern Appalachians, 2) examine the influences of topography and forest community type on the spatial pattern of fire severity; and 3) examine the relationship between predicted fire severity and changes in species richness. A non-linear regression equation predicted a field-based composite burn index (CBI) as a function of change in the Normalized Burn Ratio (dNBR) with an R² of 0.71. Fire severity was highest on drier landforms located on upper hillslopes, ridges, and on southwest aspects, and was higher in pine communities than in other forest types. Predicted CBI was positively correlated with changes in species richness and with the post-fire cover of pine seedlings (Pinus virginiana, P. rigida, and P. pungens), suggesting that burn severity maps can be used to predict community-level fire effects across large landscapes. Despite the relatively large size of this fire for the southern Appalachians, severity was strongly linked to topographic variability and pre-fire vegetation, and spatial variation in fire severity was correlated with changes in species richness. Thus, the Linville Gorge fire appears to have generally reinforced the ecological constraints imposed by underlying environmental gradients.