Developing a method for assessing environmental sustainability based on the Google Earth Engine platform

Xia, Xianwang; Jiao, Chentai; Song, Shuang; Zhang, Ling; Feng, Xingyun; Huang, Qingxu

doi:10.1007/s11356-022-19773-z

Cited by 7 publications

(3 citation statements)

References 46 publications

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“…The spatial and temporal scales of most studies using traditional remote sensing water extraction methods have been limited by local data storage capacity and processing ability. Large data volume, intensive data processing requirements, and low calculation efficiency cause difficulties for researchers in large-scale and long-term research (Xia et al, 2022). The Google Earth Engine (GEE) platform provides an efficient solution for analysis of remote sensing big data.…”

Section: Multiple Remote Sensing Datasets Have Been Utilized In Open-mentioning

confidence: 99%

Long‐term and seasonal variation of open‐surface water bodies in the Yellow River Basin during 1990–2020

Jiao

Wang

Song

et al. 2023

Hydrological Processes

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Open-surface water bodies are essential terrestrial water sources and ecosystem service providers, and their evaluation is necessary for effective water management. In this study, 36 000 Landsat images of the Yellow River Basin (YRB) were collected, and a multiple index (Modified Normalized Difference Water Index, Normalized Difference Vegetation Index and Enhanced Vegetation Index, i.e. MNDWI, NDVI and EVI) method was used to identify open-surface water bodies in the flood and dry seasons during 1990-2020. Our results showed that during these 30 years, YRB surface waters were characterized by spatial and seasonal heterogeneities. The open-surface water coverage in the upper and middle reaches of the YRB was 8.74‰ and 4.24‰, respectively, less than the average coverage in the whole YRB of 9.45‰. Surface water area was significant larger in flood seasons than in dry seasons, while during 1990-2020, the surface water area increased by 27.5% in the flood season and by 58.9% in the dry season, reaching 7239.2 km 2 (flood season) and 6654.8 km 2 (dry season) in 2020. Meanwhile the seasonal variation has decreased. Climate factors (winter temperature and summer precipitation) positively correlated with open-surface water only in the source region of the YRB. In other regions, anthropic factors (e.g. water conservation projects) strongly affected open-surface water bodies by increasing their spatial area overall and decreasing seasonal differences in their coverage.

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Section: Multiple Remote Sensing Datasets Have Been Utilized In Open-mentioning

confidence: 99%

Long‐term and seasonal variation of open‐surface water bodies in the Yellow River Basin during 1990–2020

Jiao

Wang

Song

et al. 2023

Hydrological Processes

Self Cite

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“…All Collection 2 ST products are developed with a single-channel algorithm jointly created by the Rochester Institute of Technology (RIT) and the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). This method solves the problem of reflectivity differences between different data types [48]. The cloud cover of these remote sensing images is less than 5%, and the pixels with clouds in the images are processed by the cloud removal algorithm [49].…”

Section: Datamentioning

confidence: 99%

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

et al. 2022

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The Ulan Mulun River Basin is an essential ecological protective screen of the Mu Us Desert and a necessary energy base in Ordos City. With the acceleration of industrialization and urbanization, human activities have caused enormous challenges to the local ecological environment. To achieve the region’s economic sustainability and make local development plans more objective, it is necessary to evaluate the basin’s ecological environment quality over a period of time. First, in the Landsat historical images, we selected 5 years of data to investigate the changes in this time-period (2000–2020). Second, based on the opened remote sensing database on Google Earth Engine, we calculated the remote-sensing ecological index (RSEI) distribution map. RSEI includes greenness, temperature, humidity, and dryness. Thirdly, we assessed the ecological-environmental distribution and change characteristics in the Ulan Mulun River Basin. Finally, we analyzed the RSEI spatial auto-correlation distribution characteristics in the study area. The mean values of RSEI in 2000, 2005, 2010, 2015, and 2020 were 0.418, 0.421, 0.443, 0.456, and 0.507, respectively, which indicated that the ecological environment quality had gradually improved. The ecological environment quality from 2000 to 2005 had the biggest change, as the area with drastically changed water levels accounted for 78.98% of the total basin. It showed a downward trend in the central and western regions. It showed an upward trend in the eastern region. For 20 years, the area of deterioration decreased by 24.37%, and the slight change area increased by 45.84%. The Global Moran’s I value ranged from 0.324 to 0.568. The results demonstrated that the Ulan Mulun River Basin ecological environment quality spatial distribution was positively correlated, and the clustering degree decreased gradually. Local spatial auto-correlation of RSEI showed that high-high(H-H) was mainly distributed in the basin’s eastern and southern regions, where the population density was low and the vegetation was in good condition. Low-low(L-L) was mainly distributed in the basin’s central regions and western regions, where the population density was high, and the industrial and mining enterprises were concentrated. This study provided a theoretical basis for the sustainable development of the Ulan Mulun River Basin, which is crucial for the local ecological environment and economic development.

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“…Verhoef and Nijkamp (2002) discussed urban agglomeration sustainability from the perspective of externalities. Xia et al (2022) assessed the environmental sustainability in Beijing-Tianjin-Hebei urban agglomeration and found it showed a trend of first rising, then falling, and then rising again in the past 30 years. Cui et al (2021) analyzed the environmental sustainability of the three major urban agglomerations in China from the aspect of input-output efficiency by using the dynamic DEA model.…”

Section: Introductionmentioning

confidence: 99%

Assessment on sustainable development of three major urban agglomerations in China based on sustainability‐differentiation‐combined weighting method

et al. 2023

Sustainable Development

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Sustainability is an important topic in the development process of urban agglomerations. Sustainability assessment is an effective way to monitor the sustainable development of urban agglomerations. Based on the three-pillars model, the paper assessed the sustainability of the three major urban agglomerations in China by selecting 18 indicators from economic, social, and environmental dimensions. A new sustainability-differentiation-combined weighting method was proposed, which considers the sustainability at indicator level as well as highlights the difference at urban agglomeration level. The assessment results show that the Pearl River Delta (PRD) urban agglomeration performed best, followed by the Yangtze River Delta (YRD) urban agglomeration and the Beijing-Tianjin-Hebei (BTH) urban agglomeration. However, the sustainability level of the three urban agglomerations was not high. It primarily caused by the downward development trend of economic sustainability of the three urban agglomerations. And the YRD and PRD urban agglomerations had lower performances on social sustainability. The spatial sustainability of the urban agglomerations was also analyzed, and the results show the spatial linkage development within the urban agglomerations was poor. Based on these findings, some valuable suggestions were provided as reference for the sustainability construction of the urban agglomerations.

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Developing a method for assessing environmental sustainability based on the Google Earth Engine platform

Cited by 7 publications

References 46 publications

Long‐term and seasonal variation of open‐surface water bodies in the Yellow River Basin during 1990–2020

Long‐term and seasonal variation of open‐surface water bodies in the Yellow River Basin during 1990–2020

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

Assessment on sustainable development of three major urban agglomerations in China based on sustainability‐differentiation‐combined weighting method

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