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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Mapping vegetation cover change using geostatistical methods and bitemporal Landsat TM images
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Mapping vegetation cover change using geostatistical methods and bitemporal Landsat TM images

机译:使用地统计方法和时空Landsat TM影像绘制植被覆盖变化图

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Accurately mapping change in vegetation cover is difficult due to the need for permanent plots to collect field data of the change; errors from georeference, coregistration, and data analysis; a small coefficient of correlation between remote sensing and field data; and limitations of existing methods. In this study, four cosimulation procedures, two collocated cokriging procedures, and two regression procedures were compared. The results showed that with the same cosimulation or collocated cokriging methods, two postestimation procedures led to more accurate estimates than the corresponding two preestimation procedures. Among three postestimation procedures with the same image data, cosimulation resulted in the most accurate estimates and reliable variances, then regression modeling and collocated cokriging. Thus, cosimulation algorithms can be recommended for this purpose. Moreover, the accuracy by a joint cosimulation procedure of 1989 and 1992 vegetation cover was similar to that by a separate cosimulation procedure; however, the joint cosimulation overestimated the average change. In addition, adding more Thematic Mapper images increased the accuracy of mapping for the cosimulation procedures, and the increase was slight for the regression procedures.
机译:由于需要永久性地块来收集变化的现场数据,因此难以准确绘制植被覆盖的变化图;地理参考,整合和数据分析产生的错误;遥感与实地数据之间的相关系数很小;和现有方法的局限性。在这项研究中,比较了四个协同仿真程序,两个并置协同克里金程序和两个回归程序。结果表明,使用相同的协同仿真或并列协同克里金方法,两种后估计程序比相应的两种前估计程序导致更准确的估计。在具有相同图像数据的三个后估计过程中,联合仿真可以得出最准确的估计值和可靠的方差,然后进行回归建模和并置协同克里格。因此,可以为此目的推荐协同仿真算法。此外,1989年和1992年植被覆盖率的联合联合模拟程序的准确性与单独的联合模拟程序的准确性相似。但是,联合协同仿真高估了平均变化。此外,添加更多的Thematic Mapper图像可提高协同模拟过程的映射准确性,而回归过程的增加很小。

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