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Remote Sensing
Volume 17
Issue 23
10.3390/rs17233859
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Application of High-Precision Classification Method Based on Spatiotemporal Stable Samples and Land Use Policy in Oasis–Desert Mosaic Landscape Areas

by
Jinghan Wang
1,
Yuefei Zhou
1,
Miaohang Zhou
1,
Zengjing Song
1,2,
Xiangyu Ji
1 and
Xujun Han
1,*
1
Chongqing Engineering Research Center for Remote Sensing Big Data Application, Chongqing Jinfo Mountain National Field Scientific Observation and Research Station for Karst Ecosystem, School of Geographical Sciences, Southwest University, Chongqing 400715, China
2
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(23), 3859; https://doi.org/10.3390/rs17233859 (registering DOI)
Submission received: 24 October 2025 / Revised: 24 November 2025 / Accepted: 25 November 2025 / Published: 28 November 2025
(This article belongs to the Special Issue Intelligent Image Analysis: Advancing Remote Sensing with Artificial Intelligence)
Versions Notes

Abstract

Land cover products are essential tools in environmental and ecological research. However, limited attention has been paid to their data quality issues. Many existing products suffer from pronounced spatiotemporal inconsistencies, characterized by frequent and repetitive classification fluctuations in specific regions and years, which substantially compromise the accuracy of analyses and models that rely on them. To address these challenges, this study introduces a method for deriving spatiotemporally stable samples to support high-precision land cover classification. The approach integrates national and regional land-use policies to assess temporal stability and incorporates advanced time-series processing techniques together with innovative vegetation indices to facilitate effective sample reuse. Experimental results show that this method markedly improves classification accuracy across vegetation types and reduces the extent of areas prone to frequent land-cover changes by 22.64%. Compared with existing products of similar spatial resolution, our approach achieves an overall classification accuracy of 91.1%, providing stable, high-quality input data that underpin precise and reliable regional-scale environmental and ecological modeling.
Keywords: land use policies; Landsat; data aggregation; LULC; XGBoost land use policies; Landsat; data aggregation; LULC; XGBoost

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MDPI and ACS Style

Wang, J.; Zhou, Y.; Zhou, M.; Song, Z.; Ji, X.; Han, X. Application of High-Precision Classification Method Based on Spatiotemporal Stable Samples and Land Use Policy in Oasis–Desert Mosaic Landscape Areas. Remote Sens. 2025, 17, 3859. https://doi.org/10.3390/rs17233859

AMA Style

Wang J, Zhou Y, Zhou M, Song Z, Ji X, Han X. Application of High-Precision Classification Method Based on Spatiotemporal Stable Samples and Land Use Policy in Oasis–Desert Mosaic Landscape Areas. Remote Sensing. 2025; 17(23):3859. https://doi.org/10.3390/rs17233859

Chicago/Turabian Style

Wang, Jinghan, Yuefei Zhou, Miaohang Zhou, Zengjing Song, Xiangyu Ji, and Xujun Han. 2025. "Application of High-Precision Classification Method Based on Spatiotemporal Stable Samples and Land Use Policy in Oasis–Desert Mosaic Landscape Areas" Remote Sensing 17, no. 23: 3859. https://doi.org/10.3390/rs17233859

APA Style

Wang, J., Zhou, Y., Zhou, M., Song, Z., Ji, X., & Han, X. (2025). Application of High-Precision Classification Method Based on Spatiotemporal Stable Samples and Land Use Policy in Oasis–Desert Mosaic Landscape Areas. Remote Sensing, 17(23), 3859. https://doi.org/10.3390/rs17233859

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