Temporal and spatial variations of eco-environment in Association of Southeast Asian Nations from 2000 to 2021 based on information granulation

Liao, Weihua

doi:10.1016/j.jclepro.2022.133890

Cited by 17 publications

(5 citation statements)

References 46 publications

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“…Since no studies have been conducted to assess the EEQ specifically around the JBHSR, the results were compared with those of previous large-scale studies in terms of the general spatial distribution pattern: (1) A comparison with the regional distribution in EEQ in Southeast Asian countries conducted by Liao et al [62] indicated that the EEQ in the Jakarta region was generally at a moderate level or below, while the surrounding areas exhibited significantly better EEQ; (2) Hong et al [61] depicted the spatiotemporal distribution in the EEQ in the Pan-Third Pole region for 2000 and 2020, uncovering changes over the past 20 years. The EEQ in Jakarta was relatively moderate compared to the overall Pan-Third Pole region but lower than other regions in Indonesia.…”

Section: Comparative Analysis Of the Eeq Resultsmentioning

confidence: 99%

Spatiotemporal Patterns and Driving Factors of the Ecological Environmental Quality along the Jakarta–Bandung High-Speed Railway in Indonesia

2023

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Economic globalization and rapid urbanization have affected ecological environments in global regions to different degrees. Timely, objective and quantitative evaluations of the spatiotemporal variations in regional ecological environmental quality are the key to environmental protection and decision making. The spatial distributions of ecological environment quality levels along the Jakarta–Bandung high-speed railway from 2000 to 2020 were obtained based on Google Earth Engine and the Remote Sensing Ecological Index (RSEI). Then, the Theil–Sen median and Mann-Kendall methods were used to assess the temporal RSEI trend; the spatial autocorrelation evaluation index was used to evaluate RSEI clustering on a spatial scale. The results show that the overall ecological environmental quality from 2000 to 2020 was moderate, with temporally fluctuating changes and very significant spatial clustering. Approximately 20% of the area showed very strong changes (improvement or degradation). Areas with relatively better ecological quality were located mainly in relatively high-elevation and high-vegetation-coverage regions, while areas with poorer ecological quality were located mainly in the four major cities, including Jakarta, with concentrated populations and developed economies. The research results provide local governments with scientific suggestions regarding the synergistic development of high-speed railway construction and ecological environmental protection.

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Section: Comparative Analysis Of the Eeq Resultsmentioning

confidence: 99%

Spatiotemporal Patterns and Driving Factors of the Ecological Environmental Quality along the Jakarta–Bandung High-Speed Railway in Indonesia

2023

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“…The heat indicator shows an optimum value, while higher or lower values signify deteriorating eco-environmental conditions. The formulas for calculating greenness, wetness, dryness and heat have appeared in a number of studies [29,37,38]. Greenness is conveyed by the normalized differential vegetation index (NDVI).…”

Section: Indicators Of the Eco-environmentmentioning

confidence: 99%

“…In the actual calculation of the eco-environment, the values of eco-environmental indicators are continuous. Since the numbers of the value of p i tend to be infinite, it is challenging to calculate Er directly [38]. Instead, the RSEI images are granulated into information spatial granule images with multiple levels of partitioning through spatial data information granulation, and this partition refinement can be known as the RSI knowledge granularity:…”

Section: Knowledge Granularity Entropymentioning

confidence: 99%

Using Knowledge Granularity Entropy to Measure Eco-Environmental Impacts of Land Cover Changes in ASEAN from 2001 to 2020

Liao

2023

Sustainability

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The eco-environment is the basis for the political, economic and social development of any nation or group of nations, such as the Association of Southeast Asian Nations (ASEAN). There is an inseparable association between land cover change (LCC) and eco-environmental change. The effects of the regional eco-environment are seen in the spatial and temporal variations in the eco-environment of different land cover types (LCTs). The Remote Sensing Ecological Index (RSEI), which is based on information granulation and spatial information granules, is used in this study to characterize the ecosystem. This issue is solved by breaking down the intricate ecosystem structure into straightforward spatially granular computational units; this approach greatly reduces the complexity of spatial data computation. The main contributions of this study are as follows: (1) A model based on the concept of “granularity entropy” of the RSEI values of the different LCTs has been proposed by condensing and combining the spatial information granules. This model seeks to evaluate the stability or change of the eco-environment over time. (2) The main LCC factors for the decline in eco-environmental quality in ASEAN from 2001 to 2020 are the interval changes in eco-environmental indicator values caused by the decrease in forest area and the increase in grassland area; climate alteration is also a significant contributor to changes in regional eco-environmental quality.

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“…The RSEI integrated various remote sensing indicators through principal component transformation (PCA), which was a more objective and reasonable way to estimate the weight of factors [27,28]. The RSEI has been successfully applied to many regions for the analysis, modeling, and prediction of regional eco-environment characteristics, which proved the effectiveness of RSEI [29][30][31][32]. Though the RSEI has some limitations in the influence of season and water bodies, it has been tested to be more reasonable compared with other remote 2 > IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING < sensing index for EEQ (such as MRSEI) [33][34][35].…”

Section: Introductionmentioning

confidence: 96%

The Diurnal Variation and Development Status of Ecoenvironmental Quality in Hainan, China

Shen,

Wu,

Lin

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Temporal and spatial variations of eco-environment in Association of Southeast Asian Nations from 2000 to 2021 based on information granulation

Cited by 17 publications

References 46 publications

Spatiotemporal Patterns and Driving Factors of the Ecological Environmental Quality along the Jakarta–Bandung High-Speed Railway in Indonesia

Spatiotemporal Patterns and Driving Factors of the Ecological Environmental Quality along the Jakarta–Bandung High-Speed Railway in Indonesia

Using Knowledge Granularity Entropy to Measure Eco-Environmental Impacts of Land Cover Changes in ASEAN from 2001 to 2020

The Diurnal Variation and Development Status of Ecoenvironmental Quality in Hainan, China

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