Evaluation and prediction of ecological environment of karst world heritage sites based on google earth engine: a case study of Libo–Huanjiang karst
The assessment and prediction of ecological quality can help to quickly and systematically understand the ecological status of World Natural Heritage and assist with developing appropriate strategies to ensure the sustainable and healthy development of that heritage. Using the google earth engine pl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2023-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/acba2f |
| _version_ | 1827869863164510208 |
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| author | Ning Zhang Kangning Xiong Juan Zhang Hua Xiao |
| author_facet | Ning Zhang Kangning Xiong Juan Zhang Hua Xiao |
| author_sort | Ning Zhang |
| collection | DOAJ |
| description | The assessment and prediction of ecological quality can help to quickly and systematically understand the ecological status of World Natural Heritage and assist with developing appropriate strategies to ensure the sustainable and healthy development of that heritage. Using the google earth engine platform, the remote sensing ecological index (RSEI) is rapidly constructed based on principal component analysis to evaluate the spatial and temporal distribution characteristics and the main impact indicators of the ecological environment, and the cellular automata (CA)-Markov model are used to simulate and predict the ecological quality, taking the Libo–Huanjiang karst world natural heritage site as the study area. The results show that: (a) the contribution rate of the four ecological indicators on the first principal component (PC1) are more than 90%. The construction of RSEI based on PC1 are applicable in karst world nature heritage sites and can be used to monitor and evaluate the spatial and temporal variation characteristics of the ecological environment. (b) The mean values of RSEI of the Libo–Huanjiang heritage site during 2000, 2007, 2013 and 2020 were 0.5435, 0.5465, 0.6009 and 0.5101, The overall ecological quality is mainly moderate and good, but in the eastern part of the heritage site the ecological quality is poor. (c) The evolution of the ecological environment quality during 20 years is mainly divided into the development trend of rapidly getting better, slowly getting better, and maintaining stability. (d) In the analysis of the relationship between RSEI and altitude, it is found that the ecological environment quality is mainly inferior, less favorable and moderate in areas with altitudes below 600 m, and there is a positive relationship between ecological quality level and altitude. (e) By analyzing the apparent spatial aggregation between the ecological environment quality, and then simulating the ecological grades in 2027 and 2033 using CA-Markov model, it is predicted that the area of medium and excellent ecological grades will increase in the future, but the ecological environment quality still needs to be improved in the eastern region due to the development of the tourism industry. Overall, the remote sensing ecological index is an effective model for evaluating and monitoring the ecological environment quality of karst heritage sites; the ecological environment quality of the Libo–Huanjiang heritage site is in a steady state of improvement, and the conservation measures of relevant departments are beginning to bear fruit; further coordination between conservation and development is needed to promote the sustainable development of heritage sites and to provide effective solutions for monitoring other karst-like heritage sites. |
| first_indexed | 2024-03-12T15:50:03Z |
| format | Article |
| id | doaj.art-9d7f9587b56e432692c034d8c80b36ab |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:50:03Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-9d7f9587b56e432692c034d8c80b36ab2023-08-09T15:13:59ZengIOP PublishingEnvironmental Research Letters1748-93262023-01-0118303403310.1088/1748-9326/acba2fEvaluation and prediction of ecological environment of karst world heritage sites based on google earth engine: a case study of Libo–Huanjiang karstNing Zhang0Kangning Xiong1Juan Zhang2Hua Xiao3School of karst Science, Guizhou Normal University/State Engineering Technology Institute for Karst Desertification Control of China , 116 Baoshan North Road, Guiyang 550001, People’s Republic of ChinaSchool of karst Science, Guizhou Normal University/State Engineering Technology Institute for Karst Desertification Control of China , 116 Baoshan North Road, Guiyang 550001, People’s Republic of ChinaSchool of karst Science, Guizhou Normal University/State Engineering Technology Institute for Karst Desertification Control of China , 116 Baoshan North Road, Guiyang 550001, People’s Republic of China; School of Management Science, Guizhou University of Finance and Economics , Guiyang 550025, People’s Republic of ChinaSchool of karst Science, Guizhou Normal University/State Engineering Technology Institute for Karst Desertification Control of China , 116 Baoshan North Road, Guiyang 550001, People’s Republic of ChinaThe assessment and prediction of ecological quality can help to quickly and systematically understand the ecological status of World Natural Heritage and assist with developing appropriate strategies to ensure the sustainable and healthy development of that heritage. Using the google earth engine platform, the remote sensing ecological index (RSEI) is rapidly constructed based on principal component analysis to evaluate the spatial and temporal distribution characteristics and the main impact indicators of the ecological environment, and the cellular automata (CA)-Markov model are used to simulate and predict the ecological quality, taking the Libo–Huanjiang karst world natural heritage site as the study area. The results show that: (a) the contribution rate of the four ecological indicators on the first principal component (PC1) are more than 90%. The construction of RSEI based on PC1 are applicable in karst world nature heritage sites and can be used to monitor and evaluate the spatial and temporal variation characteristics of the ecological environment. (b) The mean values of RSEI of the Libo–Huanjiang heritage site during 2000, 2007, 2013 and 2020 were 0.5435, 0.5465, 0.6009 and 0.5101, The overall ecological quality is mainly moderate and good, but in the eastern part of the heritage site the ecological quality is poor. (c) The evolution of the ecological environment quality during 20 years is mainly divided into the development trend of rapidly getting better, slowly getting better, and maintaining stability. (d) In the analysis of the relationship between RSEI and altitude, it is found that the ecological environment quality is mainly inferior, less favorable and moderate in areas with altitudes below 600 m, and there is a positive relationship between ecological quality level and altitude. (e) By analyzing the apparent spatial aggregation between the ecological environment quality, and then simulating the ecological grades in 2027 and 2033 using CA-Markov model, it is predicted that the area of medium and excellent ecological grades will increase in the future, but the ecological environment quality still needs to be improved in the eastern region due to the development of the tourism industry. Overall, the remote sensing ecological index is an effective model for evaluating and monitoring the ecological environment quality of karst heritage sites; the ecological environment quality of the Libo–Huanjiang heritage site is in a steady state of improvement, and the conservation measures of relevant departments are beginning to bear fruit; further coordination between conservation and development is needed to promote the sustainable development of heritage sites and to provide effective solutions for monitoring other karst-like heritage sites.https://doi.org/10.1088/1748-9326/acba2fgoogle earth engineworld natural heritageSouth China karstecological environment |
| spellingShingle | Ning Zhang Kangning Xiong Juan Zhang Hua Xiao Evaluation and prediction of ecological environment of karst world heritage sites based on google earth engine: a case study of Libo–Huanjiang karst Environmental Research Letters google earth engine world natural heritage South China karst ecological environment |
| title | Evaluation and prediction of ecological environment of karst world heritage sites based on google earth engine: a case study of Libo–Huanjiang karst |
| title_full | Evaluation and prediction of ecological environment of karst world heritage sites based on google earth engine: a case study of Libo–Huanjiang karst |
| title_fullStr | Evaluation and prediction of ecological environment of karst world heritage sites based on google earth engine: a case study of Libo–Huanjiang karst |
| title_full_unstemmed | Evaluation and prediction of ecological environment of karst world heritage sites based on google earth engine: a case study of Libo–Huanjiang karst |
| title_short | Evaluation and prediction of ecological environment of karst world heritage sites based on google earth engine: a case study of Libo–Huanjiang karst |
| title_sort | evaluation and prediction of ecological environment of karst world heritage sites based on google earth engine a case study of libo huanjiang karst |
| topic | google earth engine world natural heritage South China karst ecological environment |
| url | https://doi.org/10.1088/1748-9326/acba2f |
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