Analysis of ecological quality in Lhasa Metropolitan Area during 1990-2017 based on remote sensing and Google Earth Engine platform

doi:10.1007/s11442-021-1846-8

Abstract

Abstract:

Based on a total of 519 images, the composite images with the lowest possible cloud cover were generated at pixel level with image synthesis method on Google Earth Engine (GEE) platform. The Remote Sensing Ecological Index (RSEI) was adopted, and calculated in an efficient way with the assistance of parallel cloud computing of the GEE platform. The RSEI was used in this paper to evaluate and monitor the eco-environmental quality of the Lhasa Metropolitan Area. Results show that: (1) The ecological quality is better in the west than in the east of Lhasa Metropolitan Area, with Lhasa as an approximate dividing point. The ecological quality improved and then deteriorated dramatically before 2000, with the mean RSEI value dropping from 0.51 to 0.46; the trend was followed by a gradual increase up until 2017, with the mean RSEI value increased from 0.46 to 0.55. (2) The RSEI is weakly and positively correlated with socioeconomic indicators. This indicates that the population growth and economic development did not negatively influence the ecological quality, but actually boosted it. (3) The GEE can serve as an efficient computing platform for the assessment and monitoring of eco-environmental quality in vast regions.

Key words: Lhasa Metropolitan Area, ecological quality, remote sensing, ecological index, Google Earth Engine

HUANG Huiping, CHEN Wei, ZHANG Yuan, QIAO Lin, DU Yunyan. Analysis of ecological quality in Lhasa Metropolitan Area during 1990-2017 based on remote sensing and Google Earth Engine platform[J].Journal of Geographical Sciences, 2021, 31(2): 265-280.

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Year	2005				2017
Year	PC1	PC2	PC3	PC4	PC1	PC2	PC3	PC4
NDVI	0.130	0.964	0.178	-0.145	0.012	-0.076	0.923	0.376
Wet	0.195	0.021	-0.762	-0.617	0.329	-0.921	-0.143	0.154
LST	-0.139	-0.205	0.584	-0.773	-0.027	0.199	-0.355	0.913
Dry	-0.962	0.165	-0.215	-0.033	-0.944	-0.327	-0.028	0.033
Eigenvalue,	0.0112	0.0050	0.0012	0.0002	0.0113	0.0050	0.0009	0.0001
Contribution rate	63.63%	28.48%	6.82%	1.07%	51.24%	32.09%	15.05%	1.62%