排序方式： 共有4条查询结果，搜索用时 15 毫秒
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利用Hyperion影像及盐碱地分布端元提取了鲁西北典型盐碱地光谱数据并对其特性进行了探析,以证实光谱信息对于该区盐碱地研究的可用性。利用生成的原始及包络线去除的光谱曲线,分别从土壤电导率、土壤盐分含量及一系列诊断性指标入手,借助诊断性吸收波段对本区盐碱地特性进行了分析。结果表明：该区盐碱地兼具其他地点盐碱地光谱吸收共性及自身特性,证实了基于遥感光谱信息的盐碱地特性分析是可行的,可用于区域盐碱地遥感监测。 相似文献
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植被覆盖度作为反映湿地植物生长状况的重要生态学参数,在评估和检测湿地生态环境方面起着关键的作用.以华北内陆典型的淡水湿地——北京市野鸭湖湿地自然保护区为研究对象,中等分辨率的Landsat TM影像为数据源,基于线性光谱混合模型(LSMM)对研究区的植被覆盖度进行了估算.针对湿地植被类型丰富、土地利用类型多样化的特点,利用归一化植被指数(NDVI)在反映植物生长状况、覆盖程度以及区分地表覆盖类型方面的优势,通过对原始Landsat TM影像增加NDVI数据维对影像进行维度扩展,克服了传统研究中通常从Landsat TM影像上提取3-4种端元的局限,经最小噪声分离变换(MNF变换)、纯像元指数(PPI)计算以及人机交互端元选取等一系列运算,构建以陆生植物、水生植物、高反射率地物、低反射率地物、裸露土壤为组分的五端元模型来反映研究区的地物组成;同时,以原始Landsat TM影像为基础,构建植物、高反射率地物、低反射率地物、裸露土壤为组分的四端元模型.针对两种端元模型,采用全约束下的LSMM算法进行混合像元分解以获取研究区的植被覆盖度,其次辅以研究区的纯水体信息对其进行优化.精度检验采用相同时期的高分辨率WorldView-2多光谱影像来进行.研究表明:虽然四端元模型与五端元模型对植被覆盖度的估算结果在空间上具有基本一致的分布趋势,但是前者的估算结果在数值上要普遍低于后者,在研究区的水体及其附近,四端元模型难以体现水生植物的植被覆盖信息;另外,五端元模型的估算结果与检验数据的相关系数R达到0.9023,均方根误差(RMSE)为0.0939,明显优于四端元模型的R=0.8671和RMSE=0.1711.这反映了通过对影像进行维度扩展的方法来改进端元提取的数量是可行的,而由此构建的五端元模型可以更充分的反映研究区地物之间的光谱差异,从而获得更好的估算精度. 相似文献
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Spectral unmixing techniques can be used to quantify crop canopy cover within each pixel of an image and have the potential
for mapping the variation in crop yield. This study applied linear spectral unmixing to airborne hyperspectral imagery to
estimate the variation in grain sorghum yield. Airborne hyperspectral imagery and yield monitor data recorded from two sorghum
fields were used for this study. Both unconstrained and constrained linear spectral unmixing models were applied to the hyperspectral
imagery with sorghum plants and bare soil as two endmembers. A pair of plant and soil spectra derived from each image and
another pair of ground-measured plant and soil spectra were used as endmember spectra to generate unconstrained and constrained
soil and plant cover fractions. Yield was positively related to the plant fraction and negatively related to the soil fraction.
The effects of variation in endmember spectra on estimates of cover fractions and their correlations with yield were also
examined. The unconstrained plant fraction had essentially the same correlations (r) with yield among all pairs of endmember spectra examined, whereas the unconstrained soil fraction and constrained plant
and soil fractions had r-values that were sensitive to the spectra used. For comparison, all 5151 possible narrow-band normalized difference vegetation
indices (NDVIs) were calculated from the 102-band images and related to yield. Results showed that the best plant and soil
fractions provided better correlations than 96.3 and 99.9% of all the NDVIs for fields 1 and 2, respectively. Since the unconstrained
plant fraction could represent yield variation better than most narrow-band NDVIs, it can be used as a relative yield map
especially when yield data are not available. These results indicate that spectral unmixing applied to hyperspectral imagery
can be a useful tool for mapping the variation in crop yield. 相似文献
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