Time series calculation of remote sensing ecological index based on GEE
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摘要: 生态评价对城市发展规划起到重要支撑作用。利用遥感指数进行生态评价是一种可行的方法。在云计算发达的今天,针对大数据计算过程中出现的不同传感器计算结果差异大的问题,探索了一种适用于谷歌地球引擎的遥感生态指数时序计算方法。首先,以新疆维吾尔自治区奎屯市为研究区,对1989—2019年的Landsat影像进行去云融合处理; 其次,计算了融合影像的4大分量,并在湿度分量和温度分量的计算方式上进行了优选; 最后,提出了整体最值的归一化方法,并以此方法为基础计算了各年份的遥感生态指数。通过对所得结果进行分析发现,该方法得到的第一主成分分量具有更高的贡献率,在此基础上的时序结果有更高的多项式拟合度。说明该方法能为不同传感器规定统一标准,增强不同传感器之间计算结果的可对比性,优化遥感生态指数的计算结果,保证生态评价分级结果的可解释性。Abstract: Ecological evaluation plays an important role in supporting urban development planning and using a remote sensing index to carry out ecological evaluation is a feasible method. Today, with the development of cloud computing, this paper explores a time-series calculation method of remote sensing ecological index suitable for Google Earth Engine, to address the problem that the calculation results of different sensors differ greatly in the process of big data calculation. Firstly, by taking Kuitun City, Xinjiang Uygur Autonomous Region, as the study area, this paper performs the de-clouded fusion process on Landsat images from 1989 to 2019. Secondly, this paper calculates the four major components of the fused images and makes preferences in the calculation of the humidity component and temperature component. Finally, this paper proposes the normalization method of the overall optimum and calculates the remotely sensed ecological index for each year on this basis. The analysis of the obtained results shows that the first principal component under the calculation by this method has a higher contribution rate, and the time series results on this basis have a higher polynomial fitting effect. It indicates that the method can specify uniform standards for different sensors, enhance the comparability of calculated results between different sensors, optimize the calculated results of remote sensing ecological indices, and ensure the interpretability of ecological evaluation grading results.
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