内陆水体叶绿素a浓度遥感监测方法研究进展

叶绿素a是反映水体富营养化的一个重要参数,通过遥感方法监测水体叶绿素a浓度是实现内陆水体富营养化动态监测的一个重要途径,但内陆平原水网区地表水体的水体光学特性复杂,遥感反演模型建立困难。文章通过整理、分析国内外前人研究内陆平原水网区地表水体叶绿素a卫星遥感监测的数据源和方法,总结了当前在内陆平原水网区地表水体中精度较高的叶绿素a卫星遥感监测方法,分析了各方法的优缺点以及研究过程中的一些挑战、难点,并提出对未来的展望。     

高分六号遥感卫星新增波段下的树种分类精度分析

高分六号卫星具有覆盖广、多种分辨率、波段多的优势,能为遥感解译提供更丰富的信息。为探究高分六号卫星新增波段在森林树种识别上的应用,本文以覆盖根河市阿龙山林业局的一期高分六号宽幅影像为数据源,基于特征优化空间算法（Feature Space Optimization,FSO）和最大似然分类法,分别利用高分六号的前4个波段和所有波段（8波段）的光谱、纹理等特征进行了森林树种分类,并逐一添加新增波段特征确定了各波段的贡献率排名。结果表明：在加入了优选出的均匀性纹理、均值纹理和角二阶矩纹理3种纹理特征后,前4波段和8波段的分类精度比只基于光谱特征时的精度分别高出13.23%和24.63%;利用8波段信息比只利用前4波段在基于光谱特征上的精度高11.88%,在基于光谱+纹理特征上则高23.24%;基于8波段光谱+纹理特征的树种分类精度最高,达到68.74%,新增4波段的贡献率排名为B6>B5>B8>B7,说明新增红边波段对于本次树种分类试验的贡献率最高,能为北方树种识别提供有效帮助。     

从增强型水体指数分析遥感水体指数的创建

本文对新近提出的增强型水体指数(EWI)进行了分析和讨论,分别用经过大气校正和未经大气校正的两种影像来对该指数作了验证,并与改进的归一化差值水体指数(MNDWI)进行比较。结果表明该指数在经过大气校正的影像中对水体的增强和提取效果不理想,许多水体影像特征不但未能得到增强,反而受到抑制而被漏提。显然,该指数在创建时忽略了大气因素的影响。另外,该指数在构建上重复选择近红外和中红外波段也是造成提取效果不理想的原因之一。因此,为了创建正确的水体指数,避免水体增强和提取结果的偶然性和不确定性,水体指数的创建必须用经过大气校正的影像进行验证,而构建指数的波段也要避免不合理的重复选择,这样才能使所创建的指数具有更广的适应性。     

Application of multivariate model based on three simulated sensors for water quality variables estimation in Shitoukoumen Reservoir,Jilin Province,China

This study applied a multivariate model based on three simulated sensors to estimating water quality variables in Shitoukoumen Reservoir, Changchun City, Jilin Province, China, including concentration of total suspended matter, concentration of chlorophyll-a and non-pigment matter absorption. Two field campaigns for spectra measurements with a total of 40 samples were carried out on June 13 and September 23, 2008. The in-situ spectra were recalculated to the spectral bands and sensitivities of the instruments applied in this paper, i.e. Landsat TM, Alos and P6, by using the average method. And the recalculated spectra were used for estimating water quality variables by the single model and multivariate model. The results show that the multivariate model is superior to the single model as the multivariate model takes the combined effects of water components into consideration and can estimate water quality variables simultaneously. According to R ² and RMSE, Alos is superior to other sensors for water quality variables estimation although the precision of non-pigment matter absorption inversion performed the second.