Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in Shenzhen
Tree height is a key indicator in forest ecology, reflecting tree growth status and ecosystem structure. Traditional methods of tree height measurement rely on ground-based measurements, which are limited by cost and time. In recent years, the development of machine learning and multi-source remotel...
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Journal of Remote Sensing |
| Online Access: | https://spj.science.org/doi/10.34133/remotesensing.0379 |
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| author | Hang Song Xuemei Zhang Ting Hu Jinglei Liu Bing Xu |
| author_facet | Hang Song Xuemei Zhang Ting Hu Jinglei Liu Bing Xu |
| author_sort | Hang Song |
| collection | DOAJ |
| description | Tree height is a key indicator in forest ecology, reflecting tree growth status and ecosystem structure. Traditional methods of tree height measurement rely on ground-based measurements, which are limited by cost and time. In recent years, the development of machine learning and multi-source remotely sensed technologies has provided new ways to measure tree height. In this study, we utilized light detection and ranging and satellite data to extract spectral, vegetation, texture, polarization, terrain, and season features. By integrating these features with machine learning, deep learning, and optimization methods, we dynamically estimated tree heights in Shenzhen during summer and winter from 2018 to 2023 and validated seasonal and regional scalability. It was found that (a) the seasonal tree height neural network demonstrated the highest prediction accuracy in tree height estimation (R2 = 0.72, mean absolute error = 1.89 m), and the optimization process of Shapley additive explanations reduced 23 features, which improved the prediction accuracy (R2 = 0.80, mean absolute error = 1.58 m) and saved computational resources; (b) the seasonal tree height neural network has a strong generalizability for estimating tree height across seasons and regions; and (c) during 2018 to 2023, tree heights in Shenzhen were mainly concentrated in 6 to 14 m, and the spatial distribution has a strong autocorrelation. Tree canopy heights in winter are generally lower than those in summer, and the tree growth rate shows spatial heterogeneity. Overall, this study uncovers the intricate interplay between tree growth and seasonal variations in its traits throughout the urbanization process in Shenzhen. It offers valuable data support and a theoretical foundation for urban greening management and ecological protection. |
| format | Article |
| id | doaj-art-2f4a3d37bd844348be1a14a5fe74cc46 |
| institution | Kabale University |
| issn | 2694-1589 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Journal of Remote Sensing |
| spelling | doaj-art-2f4a3d37bd844348be1a14a5fe74cc462025-01-24T08:00:22ZengAmerican Association for the Advancement of Science (AAAS)Journal of Remote Sensing2694-15892025-01-01510.34133/remotesensing.0379Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in ShenzhenHang Song0Xuemei Zhang1Ting Hu2Jinglei Liu3Bing Xu4Department of Earth System Science, Tsinghua University, Beijing 100084, China.Department of Earth System Science, Tsinghua University, Beijing 100084, China.Nanjing University of Information Science & Technology, Nanjing 210044, China.College of Resources and Environment, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.Department of Earth System Science, Tsinghua University, Beijing 100084, China.Tree height is a key indicator in forest ecology, reflecting tree growth status and ecosystem structure. Traditional methods of tree height measurement rely on ground-based measurements, which are limited by cost and time. In recent years, the development of machine learning and multi-source remotely sensed technologies has provided new ways to measure tree height. In this study, we utilized light detection and ranging and satellite data to extract spectral, vegetation, texture, polarization, terrain, and season features. By integrating these features with machine learning, deep learning, and optimization methods, we dynamically estimated tree heights in Shenzhen during summer and winter from 2018 to 2023 and validated seasonal and regional scalability. It was found that (a) the seasonal tree height neural network demonstrated the highest prediction accuracy in tree height estimation (R2 = 0.72, mean absolute error = 1.89 m), and the optimization process of Shapley additive explanations reduced 23 features, which improved the prediction accuracy (R2 = 0.80, mean absolute error = 1.58 m) and saved computational resources; (b) the seasonal tree height neural network has a strong generalizability for estimating tree height across seasons and regions; and (c) during 2018 to 2023, tree heights in Shenzhen were mainly concentrated in 6 to 14 m, and the spatial distribution has a strong autocorrelation. Tree canopy heights in winter are generally lower than those in summer, and the tree growth rate shows spatial heterogeneity. Overall, this study uncovers the intricate interplay between tree growth and seasonal variations in its traits throughout the urbanization process in Shenzhen. It offers valuable data support and a theoretical foundation for urban greening management and ecological protection.https://spj.science.org/doi/10.34133/remotesensing.0379 |
| spellingShingle | Hang Song Xuemei Zhang Ting Hu Jinglei Liu Bing Xu Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in Shenzhen Journal of Remote Sensing |
| title | Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in Shenzhen |
| title_full | Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in Shenzhen |
| title_fullStr | Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in Shenzhen |
| title_full_unstemmed | Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in Shenzhen |
| title_short | Seasonal Tree Height Dynamic Estimation Using Multi-source Remotely Sensed Data in Shenzhen |
| title_sort | seasonal tree height dynamic estimation using multi source remotely sensed data in shenzhen |
| url | https://spj.science.org/doi/10.34133/remotesensing.0379 |
| work_keys_str_mv | AT hangsong seasonaltreeheightdynamicestimationusingmultisourceremotelysenseddatainshenzhen AT xuemeizhang seasonaltreeheightdynamicestimationusingmultisourceremotelysenseddatainshenzhen AT tinghu seasonaltreeheightdynamicestimationusingmultisourceremotelysenseddatainshenzhen AT jingleiliu seasonaltreeheightdynamicestimationusingmultisourceremotelysenseddatainshenzhen AT bingxu seasonaltreeheightdynamicestimationusingmultisourceremotelysenseddatainshenzhen |