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在野外考察的基础上,应用3S技术,完成了云南西北部梅里雪山地区的1:50000的植被图.对已完成的植被图通过野外收集的GPS点进行校正,GPS样点数的多少依据统计学抽样调查的样本大小计算而得,用这些校正样点数建立混淆矩阵进行植被图精度计算,最后利用计算成数方差进行检验.混淆矩阵计算得出植被图总的判对精度即整体精度OA为84.7%,利用计算成数方差检验,结果表明大部分类型为90%以上.基于3S技术完成的植被图精度取决于区域面积大小和植被分类等级,而利用遥感技术来划分的植被等级与传统的植被分类等级不完全一致.  相似文献
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对于2SUR回归模型的参数估计问题,给出了一些一般均方误差矩阵比较结果,据此提出了一类线性估计和一类基于离差阵广义非限定估计的非线性两步估计,并获得了该两步估计类的一些有限样本性质。  相似文献
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Riparian areas contain structurally diverse habitats that are challenging to monitor routinely and accurately over broad areas. As the structural variability within riparian areas is often indiscernible using moderate-scale satellite imagery, new mapping techniques are needed. We used high spatial resolution satellite imagery from the QuickBird satellite to map harvested and intact forests in coastal British Columbia, Canada. We distinguished forest structural classes used in riparian restoration planning, each with different restoration costs. To assess the accuracy of high spatial resolution imagery relative to coarser imagery, we coarsened the pixel resolution of the image, repeated the classifications, and compared results. Accuracy assessments produced individual class accuracies ranging from 70 to 90% for most classes; whilst accuracies obtained using coarser scale imagery were lower. We also examined the implications of map error on riparian restoration budgets derived from our classified maps. To do so, we modified the confusion matrix to create a cost error matrix quantifying costs associated with misclassification. High spatial resolution satellite imagery can be useful for riparian mapping; however, errors in restoration budgets attributable to misclassification error can be significant, even when using highly accurate maps. As the spatial resolution of imagery increases, it will be used more routinely in ecosystem ecology. Thus, our ability to evaluate map accuracy in practical, meaningful ways must develop further. The cost error matrix is one method that can be adapted for conservation and planning decisions in many ecosystems.  相似文献
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