About the Journal
In Memory of Professor Jerzy Radecki
Editorial Office
Editorial Board
Scope
Impact Factor
Indexed in...
Journal Impact Factor contributing items
Contact
Subscription
Current issue
Online first
Notes to Authors
Frequently Asked Questions
Editorial policies
PJOES Publication Policy Overview
PJOES Peer Review Policy
PJOES Research Ethics and Malpractice Policy
PJOES Plagiarism Policy
PJOES Conflict of Interest Policy
PJOES Open Access Policy
PJOES Instructions for Reviewers
Generative AI and AI-Assisted Technologies Policy
Archive
ORIGINAL RESEARCH
Research on High-Resolution Prediction Method
of Sichuan Province’s Natural Resources
Based on Multi-Source Information Fusion
1
Power Internet of Things Key Laboratory of Sichuan Province, State Grid Sichuan Electric Power Research Institute,
Chengdu 610095, China
2
College of Carbon Neutrality Future Technology, Sichuan University, Chengdu 610065, China
Submission date: 2024-09-07
Final revision date: 2024-11-01
Acceptance date: 2024-11-14
Online publication date: 2025-03-31
Publication date: 2026-01-29
Corresponding author
Zhengwei Chang
Power Internet of Things Key Laboratory of Sichuan Province, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
Power Internet of Things Key Laboratory of Sichuan Province, State Grid Sichuan Electric Power Research Institute, Chengdu 610095, China
Pol. J. Environ. Stud. 2026;35(1):453-464
KEYWORDS
TOPICS
ABSTRACT
With the intensification of global climate change and ecological degradation, the protection
and sustainable management of vegetation resources in Sichuan Province have become critical research
areas. This paper, leveraging multi-source information fusion technology, integrates remote sensing
data, Geographic Information System (GIS) data, meteorological data, and ground observation data
to propose a high-resolution spatiotemporal prediction model for vegetation resources across
the province. Using an XGBoost algorithm combined with high-precision spatial grid data,
the study accurately predicts the distribution of vegetation resources and provides an in-depth analysis
of the impact of urbanization on vegetation cover across various cities in Sichuan. For example, plateau
areas such as Ganzi Prefecture (MEAN = 68.03, STD = 12.23) and Aba Prefecture (MEAN = 49.81,
STD = 10.93) exhibit rich and uniform vegetation cover. In contrast, urbanized regions like Chengdu
(MEAN = 2 6.18, S TD = 21.77) s how s ignificantly l ower v egetation c overage, a lthough t he s uburban
areas around Chengdu still maintain considerable natural resource richness. The model achieved an
RMSE of 8.7 and an R² of 0.82, demonstrating high accuracy and robustness. The results offer crucial
insights for improving ecological management and promoting sustainable development in Sichuan
Province while also serving as a technical foundation for environmental protection in other regions with
similar ecological challenges.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
REFERENCES (27)
1.
MONDAL S., PALIT D. Challenges in natural resource management for ecological sustainability. In Natural Resources Conservation and Advances for Sustainability, Elsevier: pp. 29, 2022. https://doi.org/10.1016/B978-0....
2.
WASSIE S.B. Natural resource degradation tendencies in Ethiopia: a review. Environmental Systems Research. 9 (1), 1, 2020. https://doi.org/10.1186/s40068....
3.
WU H., YU L., SHEN X., HUA F., MA K. Maximizing the potential of protected areas for biodiversity conservation, climate refuge and carbon storage in the face of climate change: A case study of Southwest China. Biological Conservation. 284, 110213, 2023. https://doi.org/10.1016/j.bioc....
4.
SPARROW B.D., FOULKES J.N., WARDLE G.M., LEITCH E.J., CADDY-RETALIC S., VAN LEEUWEN S.J., TOKMAKOFF A., THURGATE N.Y., GUERIN G.R., LOWE A.J. A vegetation and soil survey method for surveillance monitoring of rangeland environments. Frontiers in Ecology and Evolution. 8, 157, 2020. https://doi.org/10.3389/fevo.2....
5.
LAWLEY V., LEWIS M., CLARKE K., OSTENDORF B. Site-based and remote sensing methods for monitoring indicators of vegetation condition: An Australian review. Ecological Indicators. 60, 1273, 2016. https://doi.org/10.1016/j.ecol....
6.
SALCEDO-SANZ S., GHAMISI P., PILES M., WERNER M., CUADRA L., MORENO-MARTÍNEZ A., IZQUIERDO-VERDIGUIER E., MUÑOZ-MARÍ J., MOSAVI A., CAMPS-VALLS G. Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources. Information Fusion. 63, 256, 2020. https://doi.org/10.1016/j.inff....
7.
GHAMISI P., RASTI B., YOKOYA N., WANG Q., HOFLE B., BRUZZONE L., BOVOLO F., CHI M., ANDERS K., GLOAGUEN R. Multisource and multitemporal data fusion in remote sensing: A comprehensive review of the state of the art. IEEE Geoscience and Remote Sensing Magazine. 7 (1), 6, 2019. https://doi.org/10.1109/MGRS.2....
8.
DI CURZIO D., CASTRIGNANÒ A., FOUNTAS S., ROMIĆ M., ROSSEL R.A.V. Multi-source data fusion of big spatial-temporal data in soil, geo-engineering and environmental studies. Science of the Total Environment. 788, 147842, 2021. https://doi.org/10.1016/j.scit... PMid:34034183.
9.
HIMEUR Y., RIMAL B., TIWARY A., AMIRA A. Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives. Information Fusion. 86, 44, 2022. https://doi.org/10.1016/j.inff....
10.
YANG D., DU S., ZHANG W., FAN R., LIU Q., ZENG W., LI G., LI C., YIN L., LI J. Study on vegetation restoration technology in ecologically sensitive area of power transmission and transformation project in Sichuan-Chongqing area. Advances in Education, Humanities and Social Science Research. 8 (1), 83, 2023. https://doi.org/10.56028/aehss....
11.
ZHOU Z., DING Y., SHI H., CAI H., FU Q., LIU S., LI T. Analysis and prediction of vegetation dynamic changes in China: Past, present and future. Ecological Indicators. 117, 106642, 2020. https://doi.org/10.1016/j.ecol....
12.
KOEHLER J., KUENZER C. Forecasting spatio-temporal dynamics on the land surface using earth observation data - A review. Remote Sensing. 12 (21), 3513, 2020. https://doi.org/10.3390/rs1221....
13.
TAYLOR S.J., LETHAM B. Forecasting at scale. The American Statistician. 72 (1), 37, 2018. https://doi.org/10.1080/000313....
14.
SEIDL R., FERNANDES P.M., FONSECA T.F., GILLET F., JÖNSSON A.M., MERGANIČOVÁ K., NETHERER S., ARPACI A., BONTEMPS J.-D., BUGMANN H. Modelling natural disturbances in forest ecosystems: a review. Ecological Modelling. 222 (4), 903, 2011. https://doi.org/10.1016/j.ecol....
15.
XIE G., NICULESCU S. Mapping and monitoring of land cover/land use (LCLU) changes in the crozon peninsula (Brittany, France) from 2007 to 2018 by machine learning algorithms (support vector machine, random forest, and convolutional neural network) and by post-classification comparison (PCC). Remote Sensing. 13 (19), 3899, 2021. https://doi.org/10.3390/rs1319....
16.
WANG J., BRETZ M., DEWAN M.A.A., DELAVAR M.A. Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects. Science of The Total Environment. 822, 153559, 2022. https://doi.org/10.1016/j.scit... PMid:35114222.
17.
ZHU X., CAI F., TIAN J., WILLIAMS T.K.-A. Spatiotemporal fusion of multisource remote sensing data: Literature survey, taxonomy, principles, applications, and future directions. Remote Sensing. 10 (4), 527, 2018. https://doi.org/10.3390/rs1004....
18.
LIU Q., QIAO J., LI M., HUANG M. Spatiotemporal heterogeneity of ecosystem service interactions and their drivers at different spatial scales in the Yellow River Basin. Science of The Total Environment. 908, 168486, 2024. https://doi.org/10.1016/j.scit... PMid:37952663.
19.
LIU Y., WANG L., LU Y., ZOU Q., YANG L., HE Y., GAO W., LI Q. Identification and optimization methods for delineating ecological red lines in Sichuan Province of southwest China. Ecological Indicators. 146, 109786, 2023. https://doi.org/10.1016/j.ecol....
20.
MENG B., WANG X., ZHANG Z., HUANG P. Spatio-Temporal Pattern and Driving Force Evolution of Cultivated Land Occupied by Urban Expansion in the Chengdu Metropolitan Area. Land. 11 (9), 1458, 2022. https://doi.org/10.3390/land11....
21.
ZHANG X., LIU L., CHEN X., GAO Y., XIE S., MI J. GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth System Science Data Discussions. 2020, 1, 2020. https://doi.org/10.5194/essd-2....
22.
MUKUL M., SRIVASTAVA V., MUKUL M. Accuracy analysis of the 2014-2015 global shuttle radar topography mission (SRTM) 1 arc-sec C-Band height model using international global navigation satellite system service (IGS) network. Journal of Earth System Science. 125, 909, 2016. https://doi.org/10.1007/s12040....
23.
LI T., ZHENG X., LIU X., ZHANG H., GRIENEISEN M.L., HE C., JI M., ZHAN Y., YANG F. Enhancing Space-Based Tracking of Fossil Fuel CO₂ Emissions via Synergistic Integration of OCO-2, OCO-3, and TROPOMI Measurements. Environmental Science & Technology. 2024. https://doi.org/10.1021/acs.es... PMid:39453935.
24.
LIU L., CAO X., LI S., JIE N. A 31-year (1990-2020) global gridded population dataset generated by cluster analysis and statistical learning. Scientific Data. 11 (1), 124, 2024. https://doi.org/10.1038/s41597... PMid:38267476 PMCid:PMC10808219.
25.
ZOU Y., ZHOU J. Quantitative analysis of resident consumption structure in Sichuan Province. IOP Publishing, 2021. https://doi.org/10.1088/1742-6....
26.
KAVZOGLU T., TEKE A. Predictive Performances of ensemble machine learning algorithms in landslide susceptibility mapping using random forest, extreme gradient boosting (XGBoost) and natural gradient boosting (NGBoost). Arabian Journal for Science and Engineering. 47 (6), 7367, 2022. https://doi.org/10.1007/s13369....
27.
CHICCO D., WARRENS M.J., JURMAN G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science. 7, e623, 2021. https://doi.org/10.7717/peerj-... PMid:34307865 PMCid:PMC8279135.
Share
RELATED ARTICLE
| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
