Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery
Mapping urban top of canopy height (UTCH) is essential for quantifying urban vegetation carbon storage and developing effective vegetation management strategies. However, the scarcity and uneven distribution of urban measurement samples pose significant challenges to accurately estimating UTCH on a...
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| Format: | Article |
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Elsevier
2025-02-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224007064 |
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| author | Xiaodi Xu Ya Zhang Peng Fu Chaoya Dang Bowen Cai Qingwei Zhuang Zhenfeng Shao Deren Li Qing Ding |
| author_facet | Xiaodi Xu Ya Zhang Peng Fu Chaoya Dang Bowen Cai Qingwei Zhuang Zhenfeng Shao Deren Li Qing Ding |
| author_sort | Xiaodi Xu |
| collection | DOAJ |
| description | Mapping urban top of canopy height (UTCH) is essential for quantifying urban vegetation carbon storage and developing effective vegetation management strategies. However, the scarcity and uneven distribution of urban measurement samples pose significant challenges to accurately estimating UTCH on a large scale in complex urban environments. To address this issue, this study utilized ICESat-2 photon spot height data as reference samples, in conjunction with high-resolution GF-2 remote sensing data, to estimate UTCH. To achieve UTCH mapping at a resolution of 4 m, a synergistic model integrating data from the GF-2 and ICESat-2 grid-based canopy height was constructed using the Random Forest technique. The model’s performance was evaluated using 111 urban tree canopy height samples collected across different urban areas. The experimental results demonstrated a moderate correlation between estimated and actual canopy heights, with a coefficient of determination (R) = 0.53, root mean square error (RMSE) = 2.9 m, and mean absolute error (MAE) = 2.04 m. Texture information, the red band, and MNDVI are key indicators for determining UTCH, with contribution percentages of 25.29 %, 13.7 %, and 25.75 %, respectively. As a result, the UTCH model created by fusing remote sensing spectral data with satellite-based lidar data can accurately estimate UTCH and offer a practical solution for predicting UTCH on a regional or even global scale. |
| format | Article |
| id | doaj-art-17ddb45905ae48e6af29488e75f2b34a |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-17ddb45905ae48e6af29488e75f2b34a2025-02-02T05:26:58ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-01136104348Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imageryXiaodi Xu0Ya Zhang1Peng Fu2Chaoya Dang3Bowen Cai4Qingwei Zhuang5Zhenfeng Shao6Deren Li7Qing Ding8School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Plant, Environmental, and Soil Sciences, Louisiana State University AgCenter, Baton Rouge, LA 70803, USAState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; Corresponding author at: School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, ChinaMapping urban top of canopy height (UTCH) is essential for quantifying urban vegetation carbon storage and developing effective vegetation management strategies. However, the scarcity and uneven distribution of urban measurement samples pose significant challenges to accurately estimating UTCH on a large scale in complex urban environments. To address this issue, this study utilized ICESat-2 photon spot height data as reference samples, in conjunction with high-resolution GF-2 remote sensing data, to estimate UTCH. To achieve UTCH mapping at a resolution of 4 m, a synergistic model integrating data from the GF-2 and ICESat-2 grid-based canopy height was constructed using the Random Forest technique. The model’s performance was evaluated using 111 urban tree canopy height samples collected across different urban areas. The experimental results demonstrated a moderate correlation between estimated and actual canopy heights, with a coefficient of determination (R) = 0.53, root mean square error (RMSE) = 2.9 m, and mean absolute error (MAE) = 2.04 m. Texture information, the red band, and MNDVI are key indicators for determining UTCH, with contribution percentages of 25.29 %, 13.7 %, and 25.75 %, respectively. As a result, the UTCH model created by fusing remote sensing spectral data with satellite-based lidar data can accurately estimate UTCH and offer a practical solution for predicting UTCH on a regional or even global scale.http://www.sciencedirect.com/science/article/pii/S1569843224007064Canopy heightUrbanRemote SensingMachine LearningFeature Selection |
| spellingShingle | Xiaodi Xu Ya Zhang Peng Fu Chaoya Dang Bowen Cai Qingwei Zhuang Zhenfeng Shao Deren Li Qing Ding Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery International Journal of Applied Earth Observations and Geoinformation Canopy height Urban Remote Sensing Machine Learning Feature Selection |
| title | Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery |
| title_full | Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery |
| title_fullStr | Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery |
| title_full_unstemmed | Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery |
| title_short | Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery |
| title_sort | synergistic mapping of urban tree canopy height using icesat 2 data and gf 2 imagery |
| topic | Canopy height Urban Remote Sensing Machine Learning Feature Selection |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224007064 |
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