Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data
Soil organic carbon (SOC) is an essential component for plant growth and a pivotal factor in the global carbon cycle. <italic>Spartina alterniflora</italic> (<italic>S. alterniflora</italic>), an invasive species characterized by high primary productivity and rapid carbon seq...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10854657/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823857170524930048 |
|---|---|
| author | Jiannan He Yongbin Zhang Mingyue Liu Lin Chen Weidong Man Hua Fang Xiang Li Xuan Yin Jianping Liang Wenke Bai Fuping Li |
| author_facet | Jiannan He Yongbin Zhang Mingyue Liu Lin Chen Weidong Man Hua Fang Xiang Li Xuan Yin Jianping Liang Wenke Bai Fuping Li |
| author_sort | Jiannan He |
| collection | DOAJ |
| description | Soil organic carbon (SOC) is an essential component for plant growth and a pivotal factor in the global carbon cycle. <italic>Spartina alterniflora</italic> (<italic>S. alterniflora</italic>), an invasive species characterized by high primary productivity and rapid carbon sequestration capabilities, exerts a substantial impact on SOC concentrations. The precise quantification of SOC content in <italic>S. alterniflora</italic> is extremely importance. Based on 73 measured samples, along with multispectral imagery and LiDAR data collected via unmanned aerial vehicles, machine learning techniques, including random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were employed to predict the SOC content of <italic>S. alterniflora</italic> and map its spatial distribution. We compared the predictive performance of these different machine learning algorithms to identify the most effective one. The results show that the following. 1) The prediction accuracy is improved by classifying the data into three types: unlodging <italic>S. alterniflora</italic> (ULSA), lodging <italic>S. alterniflora</italic> (LSA), and mudflats. 2) XGBoost outperformed RF and SVM in accurately predicting SOC content, with <italic>R</italic><sup>2</sup>; values of 0.743 for ULSA, 0.731 for LSA, and 0.705 for mudflats; 3) In the XGBoost models constructed for ULSA, LSA, and mudflats, spectral features contributed 75.7%, 73.1%, and 63.1%, respectively, with the normalized difference vegetation index emerging as the most critical spectral feature. Slope aspect (AS) was identified as the most influential topographic feature. 4) The spatial distribution of SOC exhibited marked heterogeneity, with higher SOC content in ULSA and lower in mudflats, demonstrating a gradient of decreasing SOC content from land to sea. These results hold significant implications for the study of SOC content in <italic>S. alterniflora</italic>. |
| format | Article |
| id | doaj-art-edf9a4b01a064a56b23907cfa01bab84 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-edf9a4b01a064a56b23907cfa01bab842025-02-12T00:00:11ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01184895490610.1109/JSTARS.2025.353423810854657Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR DataJiannan He0https://orcid.org/0009-0008-6197-6038Yongbin Zhang1Mingyue Liu2https://orcid.org/0000-0002-0503-3544Lin Chen3https://orcid.org/0000-0002-9270-1626Weidong Man4https://orcid.org/0000-0003-1960-1976Hua Fang5Xiang Li6Xuan Yin7Jianping Liang8Wenke Bai9https://orcid.org/0009-0007-8308-8843Fuping Li10College of Mining Engineering, North China University of Science and Technology, Tangshan, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan, ChinaZhejiang Provincial Key Laboratory of Urban Wetlands and Regional Change, Institute of Remote Sensing and Earth Sciences, Hangzhou Normal University, Hangzhou, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan, ChinaSINOTECH LAND SPATIO CORP, Beijing, ChinaFifth Geological Brigade of Hebei Bureau of Geology and Mineral Resources, Tangshan, ChinaCollege of Mining Engineering, North China University of Science and Technology, Tangshan, ChinaSoil organic carbon (SOC) is an essential component for plant growth and a pivotal factor in the global carbon cycle. <italic>Spartina alterniflora</italic> (<italic>S. alterniflora</italic>), an invasive species characterized by high primary productivity and rapid carbon sequestration capabilities, exerts a substantial impact on SOC concentrations. The precise quantification of SOC content in <italic>S. alterniflora</italic> is extremely importance. Based on 73 measured samples, along with multispectral imagery and LiDAR data collected via unmanned aerial vehicles, machine learning techniques, including random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), were employed to predict the SOC content of <italic>S. alterniflora</italic> and map its spatial distribution. We compared the predictive performance of these different machine learning algorithms to identify the most effective one. The results show that the following. 1) The prediction accuracy is improved by classifying the data into three types: unlodging <italic>S. alterniflora</italic> (ULSA), lodging <italic>S. alterniflora</italic> (LSA), and mudflats. 2) XGBoost outperformed RF and SVM in accurately predicting SOC content, with <italic>R</italic><sup>2</sup>; values of 0.743 for ULSA, 0.731 for LSA, and 0.705 for mudflats; 3) In the XGBoost models constructed for ULSA, LSA, and mudflats, spectral features contributed 75.7%, 73.1%, and 63.1%, respectively, with the normalized difference vegetation index emerging as the most critical spectral feature. Slope aspect (AS) was identified as the most influential topographic feature. 4) The spatial distribution of SOC exhibited marked heterogeneity, with higher SOC content in ULSA and lower in mudflats, demonstrating a gradient of decreasing SOC content from land to sea. These results hold significant implications for the study of SOC content in <italic>S. alterniflora</italic>.https://ieeexplore.ieee.org/document/10854657/Extreme gradient boostingmachine learningrandom forest (RF)soil organic carbon content<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Spartina alterniflora</italic>support vector machine (SVM) |
| spellingShingle | Jiannan He Yongbin Zhang Mingyue Liu Lin Chen Weidong Man Hua Fang Xiang Li Xuan Yin Jianping Liang Wenke Bai Fuping Li Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Extreme gradient boosting machine learning random forest (RF) soil organic carbon content <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Spartina alterniflora</italic> support vector machine (SVM) |
| title | Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data |
| title_full | Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data |
| title_fullStr | Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data |
| title_full_unstemmed | Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data |
| title_short | Prediction of Soil Organic Carbon Content in <italic>Spartina alterniflora</italic> by Using UAV Multispectral and LiDAR Data |
| title_sort | prediction of soil organic carbon content in italic spartina alterniflora italic by using uav multispectral and lidar data |
| topic | Extreme gradient boosting machine learning random forest (RF) soil organic carbon content <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Spartina alterniflora</italic> support vector machine (SVM) |
| url | https://ieeexplore.ieee.org/document/10854657/ |
| work_keys_str_mv | AT jiannanhe predictionofsoilorganiccarboncontentinitalicspartinaalternifloraitalicbyusinguavmultispectralandlidardata AT yongbinzhang predictionofsoilorganiccarboncontentinitalicspartinaalternifloraitalicbyusinguavmultispectralandlidardata AT mingyueliu predictionofsoilorganiccarboncontentinitalicspartinaalternifloraitalicbyusinguavmultispectralandlidardata AT linchen predictionofsoilorganiccarboncontentinitalicspartinaalternifloraitalicbyusinguavmultispectralandlidardata AT weidongman predictionofsoilorganiccarboncontentinitalicspartinaalternifloraitalicbyusinguavmultispectralandlidardata AT huafang predictionofsoilorganiccarboncontentinitalicspartinaalternifloraitalicbyusinguavmultispectralandlidardata AT xiangli predictionofsoilorganiccarboncontentinitalicspartinaalternifloraitalicbyusinguavmultispectralandlidardata AT xuanyin predictionofsoilorganiccarboncontentinitalicspartinaalternifloraitalicbyusinguavmultispectralandlidardata AT jianpingliang predictionofsoilorganiccarboncontentinitalicspartinaalternifloraitalicbyusinguavmultispectralandlidardata AT wenkebai predictionofsoilorganiccarboncontentinitalicspartinaalternifloraitalicbyusinguavmultispectralandlidardata AT fupingli predictionofsoilorganiccarboncontentinitalicspartinaalternifloraitalicbyusinguavmultispectralandlidardata |