Time series calculation of remote sensing ecological index based on GEE

doi:10.6046/zrzyyg.2021150

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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.

Keywords remote sensing ecological index time series Google Earth Engine Kuitun City ecological evaluation

ZTFLH:

TP79

Corresponding Authors: MING Dongping E-mail: 2004200020@cugb.edu.cn;mingdp@cugb.edu.cn

Issue Date: 20 June 2022

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	Hongjian LUO
	Dongping MING
	Lu XU

Cite this article:

Hongjian LUO,Dongping MING,Lu XU. Time series calculation of remote sensing ecological index based on GEE[J]. Remote Sensing for Natural Resources, 2022, 34(2): 271-277.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021150 OR https://www.gtzyyg.com/EN/Y2022/V34/I2/271

Fig.1 Flow chart of cloud reduction splicing method

Fig.2 Comparison of cloud removal effect

Fig.3 RSEI production flow chart

Fig.4 Comparison of wetness results

Fig.5 Schematic diagram of two normalization methods

Tab.1 Comparison of humidity component results in some years

Tab.2 Test results of three specific radiation algorithms based on two LST models

Tab.3 Comparison of partial contribution rate(%)

Fig.6 Time series of the mean value of RSEI

Tab.4 Comparison of trend line R² (%)

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