Long time-series and high-frequency ecological evaluation of Henan section of the Yellow River
Clarifying ecological status and dynamic changes is crucial to ecological conservation and high-quality development of the Yellow River basin. However, due to the limitations of the existing model's solution mechanism, current ecological environment assessment models have poor automation capabi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Global Ecology and Conservation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2351989424005559 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832590974927241216 |
|---|---|
| author | Jianzhong Guo Daozhu Xu Jian Xu Ruoxin Zhu Ning Li |
| author_facet | Jianzhong Guo Daozhu Xu Jian Xu Ruoxin Zhu Ning Li |
| author_sort | Jianzhong Guo |
| collection | DOAJ |
| description | Clarifying ecological status and dynamic changes is crucial to ecological conservation and high-quality development of the Yellow River basin. However, due to the limitations of the existing model's solution mechanism, current ecological environment assessment models have poor automation capabilities. So the research tends to select images from limited periods, making it difficult to conduct batch calculations of high-frequency, long-term sequence, and multi-time scales of ecological environment quality automatically, dynamically, and rapidly. This study proposed a new Spatial-Temporal Comprehensive Evaluation of Eco-environment Quality Index (SCEQI) model. Then taking Henan section of the Yellow River as an example, 960 images from 2001 to 2021 were used to calculate the comprehensive ecological environment quality quickly and in batches. Subsequently, comprehensive ecological environment quality data at different time scales (monthly, quarterly, and annually) were obtained using the average value method in SCEQI. The results showed that the regional ecological environment quality exhibited significant temporal heterogeneity. Spring is the best, followed by autumn and summer, and winter is the worst. In terms of monthly ecological environment quality, the ranking from best to worst was May, September, October, November, December, January, August, April, July, March, June, and February, exhibiting a pattern of ‘double peaks’ and ‘double valleys’. To further elucidate the spatial-temporal distribution pattern, fluctuations, change trends, and future evolution trends of the ecological environment quality, we conducted coefficient of variation fluctuation analysis, Theil-Sen's slope estimation, Mann-Kendall test, and Hurst index calculation. This study realized rapid and batch calculations of ecological environment quality, thereby enabling comprehensive research on ecological environment quality with high-frequency, long time-sequence, and multiple time-scale. And the findings can provide scientific methodologies and data support for formulating policies of ecological conservation and high-quality development in the Yellow River basin. |
| format | Article |
| id | doaj-art-ad81474a15f84f52a255300c0e5d6dd5 |
| institution | Kabale University |
| issn | 2351-9894 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Global Ecology and Conservation |
| spelling | doaj-art-ad81474a15f84f52a255300c0e5d6dd52025-01-23T05:26:52ZengElsevierGlobal Ecology and Conservation2351-98942025-01-0157e03351Long time-series and high-frequency ecological evaluation of Henan section of the Yellow RiverJianzhong Guo0Daozhu Xu1Jian Xu2Ruoxin Zhu3Ning Li4College of Remote Sensing and Spatial Information Engineering, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou, China; Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China; Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, ChinaState Key Laboratory of Geo-Information Engineering, Xi’an 710054, ChinaState Key Laboratory of Geo-Information Engineering, Xi’an 710054, ChinaState Key Laboratory of Geo-Information Engineering, Xi’an 710054, ChinaCollege of Remote Sensing and Spatial Information Engineering, Faculty of Geographical Science and Engineering, Henan University, Zhengzhou, China; Henan Industrial Technology Academy of Spatio-Temporal Big Data, Henan University, Zhengzhou 450046, China; Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng 475004, China; Corresponding author at: College of Geography and Environmental Science, Henan University, Zhengzhou 450046, China.Clarifying ecological status and dynamic changes is crucial to ecological conservation and high-quality development of the Yellow River basin. However, due to the limitations of the existing model's solution mechanism, current ecological environment assessment models have poor automation capabilities. So the research tends to select images from limited periods, making it difficult to conduct batch calculations of high-frequency, long-term sequence, and multi-time scales of ecological environment quality automatically, dynamically, and rapidly. This study proposed a new Spatial-Temporal Comprehensive Evaluation of Eco-environment Quality Index (SCEQI) model. Then taking Henan section of the Yellow River as an example, 960 images from 2001 to 2021 were used to calculate the comprehensive ecological environment quality quickly and in batches. Subsequently, comprehensive ecological environment quality data at different time scales (monthly, quarterly, and annually) were obtained using the average value method in SCEQI. The results showed that the regional ecological environment quality exhibited significant temporal heterogeneity. Spring is the best, followed by autumn and summer, and winter is the worst. In terms of monthly ecological environment quality, the ranking from best to worst was May, September, October, November, December, January, August, April, July, March, June, and February, exhibiting a pattern of ‘double peaks’ and ‘double valleys’. To further elucidate the spatial-temporal distribution pattern, fluctuations, change trends, and future evolution trends of the ecological environment quality, we conducted coefficient of variation fluctuation analysis, Theil-Sen's slope estimation, Mann-Kendall test, and Hurst index calculation. This study realized rapid and batch calculations of ecological environment quality, thereby enabling comprehensive research on ecological environment quality with high-frequency, long time-sequence, and multiple time-scale. And the findings can provide scientific methodologies and data support for formulating policies of ecological conservation and high-quality development in the Yellow River basin.http://www.sciencedirect.com/science/article/pii/S2351989424005559Ecological environmentHigh frequencyLong time seriesFluctuation analysisChange trendSustainability |
| spellingShingle | Jianzhong Guo Daozhu Xu Jian Xu Ruoxin Zhu Ning Li Long time-series and high-frequency ecological evaluation of Henan section of the Yellow River Global Ecology and Conservation Ecological environment High frequency Long time series Fluctuation analysis Change trend Sustainability |
| title | Long time-series and high-frequency ecological evaluation of Henan section of the Yellow River |
| title_full | Long time-series and high-frequency ecological evaluation of Henan section of the Yellow River |
| title_fullStr | Long time-series and high-frequency ecological evaluation of Henan section of the Yellow River |
| title_full_unstemmed | Long time-series and high-frequency ecological evaluation of Henan section of the Yellow River |
| title_short | Long time-series and high-frequency ecological evaluation of Henan section of the Yellow River |
| title_sort | long time series and high frequency ecological evaluation of henan section of the yellow river |
| topic | Ecological environment High frequency Long time series Fluctuation analysis Change trend Sustainability |
| url | http://www.sciencedirect.com/science/article/pii/S2351989424005559 |
| work_keys_str_mv | AT jianzhongguo longtimeseriesandhighfrequencyecologicalevaluationofhenansectionoftheyellowriver AT daozhuxu longtimeseriesandhighfrequencyecologicalevaluationofhenansectionoftheyellowriver AT jianxu longtimeseriesandhighfrequencyecologicalevaluationofhenansectionoftheyellowriver AT ruoxinzhu longtimeseriesandhighfrequencyecologicalevaluationofhenansectionoftheyellowriver AT ningli longtimeseriesandhighfrequencyecologicalevaluationofhenansectionoftheyellowriver |