Evolution of environmental quality and its response to human disturbances of the urban agglomeration in the northern slope of the Tianshan Mountains
•The study area has a special geographical location and fragile environment.•Google Earth Engine can ensure the consistency and stability of index retrieval.•Focus on relations between integrated human disturbances and environmental quality.•A spatial relationship between environmental quality and h...
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| Published in | Ecological indicators Vol. 153; p. 110481 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.09.2023
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | •The study area has a special geographical location and fragile environment.•Google Earth Engine can ensure the consistency and stability of index retrieval.•Focus on relations between integrated human disturbances and environmental quality.•A spatial relationship between environmental quality and human disturbances is positive.•The increasing human disturbances improved the environmental quality of the cropland.
Increased human disturbances have disturbed the global ecosystem at various scales. A growing need exists for effective techniques that can be used to quantify human disturbances and detect the resulting spatial and temporal variations in ecological status. Remote sensing can be used as an effective means to detect changes in environmental quality. Consequently, this study employed multi-source remote sensing data and principal component analysis (PCA) to create a remote sensing ecological index (RSEI) and a human footprint index used to clarify the human disturbances intensity (HDI). Then, unary linear regression was used to evaluate the change trends of HDI and the RSEI; spatial autocorrelation analysis and correlation analysis were used to reveal the evolution characteristics of environmental quality and its response to human disturbances of the Urban Agglomeration in the Northern Slope of the Tianshan Mountains (UANST) from 2000 to 2020. The results show the following: (1) An RSEI is applicable to both medium- and large-scale regions and can be used to effectively quantify and reveal the environmental quality of the UANST. (2) From 2000 to 2020, the annual average RSEI of UANST was 0.29, with the environmental quality graded as poor for more than 40% of the region. Although high proportions of the UANST were ranked as having poor and fair environmental quality, the overall trend was toward increasing the areas with good and excellent grades. The spatial characteristics of environmental quality showed that the environment was generally better in the central area and poorer in the northern and southern areas. (3) From 2000 to 2020, the HDI in the majority of the UANST was low but showed an increase over time and gradually attenuated from the city center to the edge of each county. (4) A significant positive correlation was found between environmental quality and human disturbances in the cropland area in the central part of the UANST, while a significant negative correlation was found in the surrounding urban areas of cities, in counties or rural residential areas, and some areas of scattered grasslands. |
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| AbstractList | Increased human disturbances have disturbed the global ecosystem at various scales. A growing need exists for effective techniques that can be used to quantify human disturbances and detect the resulting spatial and temporal variations in ecological status. Remote sensing can be used as an effective means to detect changes in environmental quality. Consequently, this study employed multi-source remote sensing data and principal component analysis (PCA) to create a remote sensing ecological index (RSEI) and a human footprint index used to clarify the human disturbances intensity (HDI). Then, unary linear regression was used to evaluate the change trends of HDI and the RSEI; spatial autocorrelation analysis and correlation analysis were used to reveal the evolution characteristics of environmental quality and its response to human disturbances of the Urban Agglomeration in the Northern Slope of the Tianshan Mountains (UANST) from 2000 to 2020. The results show the following: (1) An RSEI is applicable to both medium- and large-scale regions and can be used to effectively quantify and reveal the environmental quality of the UANST. (2) From 2000 to 2020, the annual average RSEI of UANST was 0.29, with the environmental quality graded as poor for more than 40% of the region. Although high proportions of the UANST were ranked as having poor and fair environmental quality, the overall trend was toward increasing the areas with good and excellent grades. The spatial characteristics of environmental quality showed that the environment was generally better in the central area and poorer in the northern and southern areas. (3) From 2000 to 2020, the HDI in the majority of the UANST was low but showed an increase over time and gradually attenuated from the city center to the edge of each county. (4) A significant positive correlation was found between environmental quality and human disturbances in the cropland area in the central part of the UANST, while a significant negative correlation was found in the surrounding urban areas of cities, in counties or rural residential areas, and some areas of scattered grasslands. •The study area has a special geographical location and fragile environment.•Google Earth Engine can ensure the consistency and stability of index retrieval.•Focus on relations between integrated human disturbances and environmental quality.•A spatial relationship between environmental quality and human disturbances is positive.•The increasing human disturbances improved the environmental quality of the cropland. Increased human disturbances have disturbed the global ecosystem at various scales. A growing need exists for effective techniques that can be used to quantify human disturbances and detect the resulting spatial and temporal variations in ecological status. Remote sensing can be used as an effective means to detect changes in environmental quality. Consequently, this study employed multi-source remote sensing data and principal component analysis (PCA) to create a remote sensing ecological index (RSEI) and a human footprint index used to clarify the human disturbances intensity (HDI). Then, unary linear regression was used to evaluate the change trends of HDI and the RSEI; spatial autocorrelation analysis and correlation analysis were used to reveal the evolution characteristics of environmental quality and its response to human disturbances of the Urban Agglomeration in the Northern Slope of the Tianshan Mountains (UANST) from 2000 to 2020. The results show the following: (1) An RSEI is applicable to both medium- and large-scale regions and can be used to effectively quantify and reveal the environmental quality of the UANST. (2) From 2000 to 2020, the annual average RSEI of UANST was 0.29, with the environmental quality graded as poor for more than 40% of the region. Although high proportions of the UANST were ranked as having poor and fair environmental quality, the overall trend was toward increasing the areas with good and excellent grades. The spatial characteristics of environmental quality showed that the environment was generally better in the central area and poorer in the northern and southern areas. (3) From 2000 to 2020, the HDI in the majority of the UANST was low but showed an increase over time and gradually attenuated from the city center to the edge of each county. (4) A significant positive correlation was found between environmental quality and human disturbances in the cropland area in the central part of the UANST, while a significant negative correlation was found in the surrounding urban areas of cities, in counties or rural residential areas, and some areas of scattered grasslands. |
| ArticleNumber | 110481 |
| Author | Yu, Tingting Xu, Fujin Ou, Bin Xu, Zhonglin Jiang, Jiao Abulizi, Abudukeyimu Akbar, Adila |
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| Keywords | Remote sensing ecological index Human disturbances intensity Environmental quality Urban agglomeration |
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| SubjectTerms | autocorrelation cropland ecosystems Environmental quality Human disturbances intensity humans principal component analysis regression analysis Remote sensing ecological index topographic slope Urban agglomeration |
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| Title | Evolution of environmental quality and its response to human disturbances of the urban agglomeration in the northern slope of the Tianshan Mountains |
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