Estimation of Tree Canopy Closure Based on U-Net Image Segmentation and Machine Learning Algorithms

Zhou, Yuefei; Wang, Jinghan; Song, Zengjing; Zhou, Miaohang; Lv, Mengnan; Han, Xujun

doi:10.3390/rs17111828

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Estimation of Tree Canopy Closure Based on U-Net Image Segmentation and Machine Learning Algorithms

by

Yuefei Zhou

¹,

Jinghan Wang

¹,

Zengjing Song

^1,2

,

Miaohang Zhou

¹,

Mengnan Lv

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(11), 1828; https://doi.org/10.3390/rs17111828

Submission received: 25 March 2025 / Revised: 17 May 2025 / Accepted: 20 May 2025 / Published: 23 May 2025

(This article belongs to the Special Issue Advances in Multimodal Remote Sensing Data: Processing, Fusion and Applications)

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Abstract

Canopy closure is a critical indicator reflecting forest structure, biodiversity, and ecological balance. This study proposes an estimation method integrating U-Net segmentation with machine learning, significantly improving accuracy through multi-source remote sensing data and feature selection. Covering eight U.S. continental states, the study utilized 13,000 stratified samples equally split for model training and validation. Four states were used to train models based on XGBoost, random forest (RF), LightGBM, and support vector machine (SVM), while the remaining four states served for validation. The results indicate that (1) U-Net effectively extracted tree crowns from aerial imagery to construct the sample dataset; (2) among the tested algorithms, XGBoost achieved the highest accuracy of 0.88 when incorporating Sentinel-1, Sentinel-2, vegetation indices, and land cover features, outperforming models using only Sentinel-2 data by 25.7%; and (3) XGBoost-estimated tree canopy cover (Model TCC) showed finer spatial details than the National Land Cover Database Tree Canopy Cover (NLCD TCC), with R² against the true tree canopy closure from U-Net (True TCC) up to 49.1% higher. This approach offers a cost-effective solution for regional-scale canopy monitoring.

Keywords: canopy closure; machine learning; high-resolution aerial imagery; U-Net

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MDPI and ACS Style

Zhou, Y.; Wang, J.; Song, Z.; Zhou, M.; Lv, M.; Han, X. Estimation of Tree Canopy Closure Based on U-Net Image Segmentation and Machine Learning Algorithms. Remote Sens. 2025, 17, 1828. https://doi.org/10.3390/rs17111828

AMA Style

Zhou Y, Wang J, Song Z, Zhou M, Lv M, Han X. Estimation of Tree Canopy Closure Based on U-Net Image Segmentation and Machine Learning Algorithms. Remote Sensing. 2025; 17(11):1828. https://doi.org/10.3390/rs17111828

Chicago/Turabian Style

Zhou, Yuefei, Jinghan Wang, Zengjing Song, Miaohang Zhou, Mengnan Lv, and Xujun Han. 2025. "Estimation of Tree Canopy Closure Based on U-Net Image Segmentation and Machine Learning Algorithms" Remote Sensing 17, no. 11: 1828. https://doi.org/10.3390/rs17111828

APA Style

Zhou, Y., Wang, J., Song, Z., Zhou, M., Lv, M., & Han, X. (2025). Estimation of Tree Canopy Closure Based on U-Net Image Segmentation and Machine Learning Algorithms. Remote Sensing, 17(11), 1828. https://doi.org/10.3390/rs17111828

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Estimation of Tree Canopy Closure Based on U-Net Image Segmentation and Machine Learning Algorithms

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