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

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.

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“…This result indicates that air pollution plays a key role in urban environmental assessment and demonstrates the effectiveness of the API. The average value of the index for remote sensing monitoring of the ecological environment can indicate the average ecological environment quality of a region [46]. The average AQRSEI obtained in this study is 0.6793, indicating that the ecological quality in this area is at a relatively high level.…”

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“…This result indicates that air pollution plays a key role in urban environmental assessment and demonstrates the effectiveness of the API. The average value of the index for remote sensing monitoring of the ecological environment can indicate the average ecological environment quality of a region [ 55 ]. The average AQRSEI obtained in this study is 0.6793, indicating that the ecological quality in this area is at a relatively high level.…”

Monitoring Annual Land Use/Land Cover Change in the Tucson Metropolitan Area with Google Earth Engine (1986–2020)

“…Achieving a multitemporal analysis over several decades requires overcoming numerous technical challenges, such as rectifying the differences between multiple sensors across different time periods, or addressing heterogeneous data quality and availability due to various factors, notably cloud cover [25,26]. Recent cloud computing and big data approaches [27,28] have brought new solutions to such problems, allowing researchers to assemble and process very large datasets composed of collections of remote sensing images and other ancillary data [29,30], thus potentially dramatically improving the performances of image classification and LULC analyses [31,32]. Rather than hand-picking a set of images with 5-to 10-year intervals, these new techniques now allow time-effective processing, development and analysis of vast, virtually seamless and high-resolution annual land cover map products [17,33] that can be updated each year.…”