MRSEILA: A modified remote sensing ecological index using local adaptability for enhancing ecological environment quality assessment
The Remote Sensing Ecological Index (RSEI) has been widely applied in ecological environment quality (EEQ) assessments by integrating multiple environmental factors. To enhance RSEI's ability to capture local ecological variations, a locally adapted version (RSEILA) was designed and widely adop...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S157495412500247X |
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| author | Yingzhang Guo Mingjin Zhan Hanzeyu Xu Xiao Li Junjun Fang Xingchen Zhou Dan Lin Wenhui Chen |
| author_facet | Yingzhang Guo Mingjin Zhan Hanzeyu Xu Xiao Li Junjun Fang Xingchen Zhou Dan Lin Wenhui Chen |
| author_sort | Yingzhang Guo |
| collection | DOAJ |
| description | The Remote Sensing Ecological Index (RSEI) has been widely applied in ecological environment quality (EEQ) assessments by integrating multiple environmental factors. To enhance RSEI's ability to capture local ecological variations, a locally adapted version (RSEILA) was designed and widely adopted using moving windows. However, the randomness in eigenvector directions generated by principal component analysis (PCA) can introduce bias, affecting the accuracy of RSEILA's assessment. To enhance the effectiveness of RSEILA in EEQ, we propose a modified RSEILA model (MRSEILA) implemented on the Google Earth Engine (GEE) platform, consisting of three components: (1) optimization of moving window sizes tailored to each target region; (2) automatic recognition and correction of PCA-induced eigenvector direction inconsistencies; and (3) refinement of PCA computation within each circular window to improve the accuracy of EEQ evaluations. We validated MRSEILA using Landsat Collection 2 Level-2 surface reflectance data and compared its performance with RSEILA across four typical areas in China. The results showed that, compared to RSEILA, MRSEILA consistently produces aligned eigenvector directions and more accurate EEQ assessments that better reflect actual land surface conditions across all four testing areas, making it an effective tool for regional and large-scale ecological monitoring. |
| format | Article |
| id | doaj-art-7ead8e34e3b44885a2eff3222459d75e |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-7ead8e34e3b44885a2eff3222459d75e2025-08-20T05:05:15ZengElsevierEcological Informatics1574-95412025-12-019010323810.1016/j.ecoinf.2025.103238MRSEILA: A modified remote sensing ecological index using local adaptability for enhancing ecological environment quality assessmentYingzhang Guo0Mingjin Zhan1Hanzeyu Xu2Xiao Li3Junjun Fang4Xingchen Zhou5Dan Lin6Wenhui Chen7Fujian Mapping Institute, Fuzhou 350001, China; School of Geographical Sciences, School of Carbon Neutrality Future Technology, Fujian Normal University, Fuzhou 350117, ChinaJiangxi Vocational and Technical College of Information Application, Nanchang 330043, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology / Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, China; Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China; Research Institute of Smart Agriculture, Yangzhou University, Yangzhou 225009, China; Corresponding authors at: Yangzhou University, No. 48 Wenhui East Road, Yangzhou, Jiangsu Province, China; Fujian Normal University, No.1 Keji Road, University Town, Fuzhou, Fujian Province, China.Transport Studies Unit, University of Oxford, Oxford OX1 3QY, UK; Linacre College, University of Oxford, Oxford OX1 3JA, UKState Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaSchool of Geographical Sciences, School of Carbon Neutrality Future Technology, Fujian Normal University, Fuzhou 350117, China; Liaoning Natural Resources Affairs Service Center, Shenyang 110033, ChinaFujian Mapping Institute, Fuzhou 350001, ChinaSchool of Geographical Sciences, School of Carbon Neutrality Future Technology, Fujian Normal University, Fuzhou 350117, China; Corresponding authors at: Yangzhou University, No. 48 Wenhui East Road, Yangzhou, Jiangsu Province, China; Fujian Normal University, No.1 Keji Road, University Town, Fuzhou, Fujian Province, China.The Remote Sensing Ecological Index (RSEI) has been widely applied in ecological environment quality (EEQ) assessments by integrating multiple environmental factors. To enhance RSEI's ability to capture local ecological variations, a locally adapted version (RSEILA) was designed and widely adopted using moving windows. However, the randomness in eigenvector directions generated by principal component analysis (PCA) can introduce bias, affecting the accuracy of RSEILA's assessment. To enhance the effectiveness of RSEILA in EEQ, we propose a modified RSEILA model (MRSEILA) implemented on the Google Earth Engine (GEE) platform, consisting of three components: (1) optimization of moving window sizes tailored to each target region; (2) automatic recognition and correction of PCA-induced eigenvector direction inconsistencies; and (3) refinement of PCA computation within each circular window to improve the accuracy of EEQ evaluations. We validated MRSEILA using Landsat Collection 2 Level-2 surface reflectance data and compared its performance with RSEILA across four typical areas in China. The results showed that, compared to RSEILA, MRSEILA consistently produces aligned eigenvector directions and more accurate EEQ assessments that better reflect actual land surface conditions across all four testing areas, making it an effective tool for regional and large-scale ecological monitoring.http://www.sciencedirect.com/science/article/pii/S157495412500247XEcological environment quality (EEQ)Remote sensing ecological index (RSEI)Moving windowPrincipal component analysis (PCA)Eigenvector direction |
| spellingShingle | Yingzhang Guo Mingjin Zhan Hanzeyu Xu Xiao Li Junjun Fang Xingchen Zhou Dan Lin Wenhui Chen MRSEILA: A modified remote sensing ecological index using local adaptability for enhancing ecological environment quality assessment Ecological Informatics Ecological environment quality (EEQ) Remote sensing ecological index (RSEI) Moving window Principal component analysis (PCA) Eigenvector direction |
| title | MRSEILA: A modified remote sensing ecological index using local adaptability for enhancing ecological environment quality assessment |
| title_full | MRSEILA: A modified remote sensing ecological index using local adaptability for enhancing ecological environment quality assessment |
| title_fullStr | MRSEILA: A modified remote sensing ecological index using local adaptability for enhancing ecological environment quality assessment |
| title_full_unstemmed | MRSEILA: A modified remote sensing ecological index using local adaptability for enhancing ecological environment quality assessment |
| title_short | MRSEILA: A modified remote sensing ecological index using local adaptability for enhancing ecological environment quality assessment |
| title_sort | mrseila a modified remote sensing ecological index using local adaptability for enhancing ecological environment quality assessment |
| topic | Ecological environment quality (EEQ) Remote sensing ecological index (RSEI) Moving window Principal component analysis (PCA) Eigenvector direction |
| url | http://www.sciencedirect.com/science/article/pii/S157495412500247X |
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