Dynamic Monitoring of Environmental Quality in the Loess Plateau from 2000 to 2020 Using the Google Earth Engine Platform and the Remote Sensing Ecological Index
Abstract:The Loess Plateau is a typical ecologically sensitive area that can easily be perturbed by the effects of human activities and global climate change. Therefore, it is necessary to develop tools to monitor the environmental quality in the LP quickly and accurately. To reveal the spatio-temporal changes in environmental quality in the LP from 2000 to 2020, we used the Moderate-Resolution Imaging Spectroradiometer (MODIS) products on the Google Earth Engine platform and constructed the remote sensing ecological i… Show more
“…The degraded areas are mainly located in the Inner Mongolia Autonomous Region, the Ningxia Hui Autonomous Region, and the northern and northwestern regions of Qinghai Province. For Shaanxi, Shanxi, and Henan, the degraded areas are mostly located in urban areas, such as Xi'an, Xianyang, Taiyuan, Luoyang, and other urban areas, which is consistent with previous research findings [74,75].…”
Section: Trends Of Rsei and Srsei In The Loess Plateausupporting
confidence: 91%
“…In addition, there have been some improvements to the RSEI [34][35][36]. Zhang Jing, Yang Liping, and others [37] incorporated aerosol optical thickness data (AOD) into the RSEI, proposed an improved remote sensing ecological index, and used this index to evaluate the ecological environment quality of Xi'an City. The results showed that the newly constructed index considers atmospheric pollution factors and contributes to a more comprehensive evaluation of the ecological environment quality of the research area.…”
Ecosystems in arid and semi-arid areas are delicate and prone to different erosive effects. Monitoring and evaluating the environmental ecological condition in such areas contribute to the governance and restoration of the ecosystem. Remote sensing ecological indices (RSEIs) are widely used as a method for environmental monitoring and have been extensively applied in various regions. This study selects the arid and semi-arid Loess Plateau as the research area, in response to existing research on ecological monitoring that predominantly uses vegetation indices as monitoring indicators for greenness factors. A fluorescence remote sensing ecological index (SRSEI) is constructed by using monthly synthesized sun-induced chlorophyll fluorescence data during the vegetation growth period as a new component for greenness and combining it with MODIS product data. The study generates the RSEI and SRSEI for the research area spanning from 2001 to 2021. The study compares and analyzes the differences between the two indices and explores the evolution patterns of the ecosystem quality in the Loess Plateau over a 21-year period. The results indicate consistent and positively correlated linear fitting trend changes in the RSEI and SRSEI for the research area between 2001 and 2021. The newly constructed ecological index exhibits a higher correlation with rainfall data, and it shows a more significant decrease in magnitude during drought occurrences, indicating a faster and stronger response of the new index to drought in the research area. The largest proportions are found in the research area’s regions with both substantial and minor improvements, pointing to an upward tendency in the Loess Plateau’s ecosystem development. The newly constructed environmental index can effectively evaluate the quality of the ecosystem in the research area.
“…The degraded areas are mainly located in the Inner Mongolia Autonomous Region, the Ningxia Hui Autonomous Region, and the northern and northwestern regions of Qinghai Province. For Shaanxi, Shanxi, and Henan, the degraded areas are mostly located in urban areas, such as Xi'an, Xianyang, Taiyuan, Luoyang, and other urban areas, which is consistent with previous research findings [74,75].…”
Section: Trends Of Rsei and Srsei In The Loess Plateausupporting
confidence: 91%
“…In addition, there have been some improvements to the RSEI [34][35][36]. Zhang Jing, Yang Liping, and others [37] incorporated aerosol optical thickness data (AOD) into the RSEI, proposed an improved remote sensing ecological index, and used this index to evaluate the ecological environment quality of Xi'an City. The results showed that the newly constructed index considers atmospheric pollution factors and contributes to a more comprehensive evaluation of the ecological environment quality of the research area.…”
Ecosystems in arid and semi-arid areas are delicate and prone to different erosive effects. Monitoring and evaluating the environmental ecological condition in such areas contribute to the governance and restoration of the ecosystem. Remote sensing ecological indices (RSEIs) are widely used as a method for environmental monitoring and have been extensively applied in various regions. This study selects the arid and semi-arid Loess Plateau as the research area, in response to existing research on ecological monitoring that predominantly uses vegetation indices as monitoring indicators for greenness factors. A fluorescence remote sensing ecological index (SRSEI) is constructed by using monthly synthesized sun-induced chlorophyll fluorescence data during the vegetation growth period as a new component for greenness and combining it with MODIS product data. The study generates the RSEI and SRSEI for the research area spanning from 2001 to 2021. The study compares and analyzes the differences between the two indices and explores the evolution patterns of the ecosystem quality in the Loess Plateau over a 21-year period. The results indicate consistent and positively correlated linear fitting trend changes in the RSEI and SRSEI for the research area between 2001 and 2021. The newly constructed ecological index exhibits a higher correlation with rainfall data, and it shows a more significant decrease in magnitude during drought occurrences, indicating a faster and stronger response of the new index to drought in the research area. The largest proportions are found in the research area’s regions with both substantial and minor improvements, pointing to an upward tendency in the Loess Plateau’s ecosystem development. The newly constructed environmental index can effectively evaluate the quality of the ecosystem in the research area.
“…The RSEI values for cropland and wetland were above 0.7, with 0.724 and 0.744 respectively. Barren is the most environmentally vulnerable land use class [ 50 ], having the lowest average RSEI value of 0.53. Fig.…”
“…After the four component indexes have been processed, the RSEI indicators are combined using the principal component analysis method. Principal component analysis, or PCA, has the advantage of automatically and impartially determining each indicator weight by examining each index's contribution to the first principle component (PC1) [31]. Equation (11) illustrates that the first output component is the initial remote sensing ecological index (RSEI PC1): RSEI0 = PC1[f(NDVI, LST,WET, NDBSI)] (11) RSEI0 must also be normalized for the computed RSEI value during the measurement procedure to enable comparability.…”
Section: Remote Sensing Ecological Index Estimationmentioning
The significance of green spaces and ecological priorities in urban areas–two major concepts and advancements in Vietnam's recent urban growth, particularly in Can Tho City–is addressed by urban planning. An ecological assessment necessitates participating in urban planning and the eco-environment protection system. This study evaluated the change in the ecological quality of Can Tho City in the period 2015 - 2020 by approaching remote sensing indicators and statistical methods. Using Landsat 8 OLI satellite data from 2015 to 2020, four indicators were calculated in this study: heat (LST), dryness (NDBSI), wetness (WET), and greenness (NDVI). These indices were then applied using the Principal Component Analysis (PCA) method to estimate the Remote Sensing Ecological Index (RSEI). According to the data, the RSEI mean was 3.66 in 2015 and 3.60 in 2020. In 2015, areas with good and very good ecological quality (EQ) accounted for 88.78% of the total natural area; by 2020, it was 84.75%. The areas with low quality increased from 1.97% in 2015 to 3.49% in 2020. This indicates a decreasing trend in ecological quality within the area. The declining trend in urban ecological quality (UEQ) at Can Tho City between 2015 and 2020 can be objectively reflected by the RSEI and the shifting proportions of the ecological level. The results of RSEI provide fundamental data on the ecological quality of the urban environment to support policymakers, managers, and authorities in implementing and developing sustainable approaches to urban ecological conservation and environmental quality preservation.
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