Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China
Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources such...
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MDPI AG
2018-03-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/10/4/532 |
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| author | Luodan Cao Jianjun Pan Ruijuan Li Jialin Li Zhaofu Li |
| author_facet | Luodan Cao Jianjun Pan Ruijuan Li Jialin Li Zhaofu Li |
| author_sort | Luodan Cao |
| collection | DOAJ |
| description | Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources such as Light Detection and Ranging (LiDAR) and optical data. In this study, we constructed and compared the accuracies of five models for estimating AGB of forests in the upper Heihe River Basin in Northwest China. The five models were constructed using field and remotely-sensed data (optical and LiDAR) and algorithms including Random Forest (RF), Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), K-Nearest Neighbor (KNN) and the Generalized Linear Mixed Model (GLMM). Models based on the RF algorithm emerged as being the best among the five algorithms irrespective of the datasets used. The Random Forest AGB model, using only LiDAR data (R2 = 0.899, RMSE = 14.0 t/ha) as the input data, was more effective than the one using optical data (R2 = 0.835, RMSE = 22.724 t/ha). Compared to LiDAR or optical data alone, the AGB model (R2 = 0.913, RMSE = 13.352 t/ha) that used the RF algorithm and integrated LiDAR and optical data was found to be optimal. Incorporation of terrain variables with optical data resulted in only slight improvements in accuracy. The models developed in this study could be useful for using integrated airborne LiDAR and passive optical data to accurately estimate forest biomass. |
| first_indexed | 2024-12-13T10:42:00Z |
| format | Article |
| id | doaj.art-9545014c2de5456c850cf8e2c690b5d8 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-13T10:42:00Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9545014c2de5456c850cf8e2c690b5d82022-12-21T23:50:27ZengMDPI AGRemote Sensing2072-42922018-03-0110453210.3390/rs10040532rs10040532Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of ChinaLuodan Cao0Jianjun Pan1Ruijuan Li2Jialin Li3Zhaofu Li4College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, Jiangsu, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, Jiangsu, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, Jiangsu, ChinaDepartment of Geography & Spatial Information Techniques, Ningbo University, Ningbo 315211, Jiangsu, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, Jiangsu, ChinaForest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources such as Light Detection and Ranging (LiDAR) and optical data. In this study, we constructed and compared the accuracies of five models for estimating AGB of forests in the upper Heihe River Basin in Northwest China. The five models were constructed using field and remotely-sensed data (optical and LiDAR) and algorithms including Random Forest (RF), Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), K-Nearest Neighbor (KNN) and the Generalized Linear Mixed Model (GLMM). Models based on the RF algorithm emerged as being the best among the five algorithms irrespective of the datasets used. The Random Forest AGB model, using only LiDAR data (R2 = 0.899, RMSE = 14.0 t/ha) as the input data, was more effective than the one using optical data (R2 = 0.835, RMSE = 22.724 t/ha). Compared to LiDAR or optical data alone, the AGB model (R2 = 0.913, RMSE = 13.352 t/ha) that used the RF algorithm and integrated LiDAR and optical data was found to be optimal. Incorporation of terrain variables with optical data resulted in only slight improvements in accuracy. The models developed in this study could be useful for using integrated airborne LiDAR and passive optical data to accurately estimate forest biomass.http://www.mdpi.com/2072-4292/10/4/532forest AGBairborne LiDARprediction modelterrain variables |
| spellingShingle | Luodan Cao Jianjun Pan Ruijuan Li Jialin Li Zhaofu Li Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China Remote Sensing forest AGB airborne LiDAR prediction model terrain variables |
| title | Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China |
| title_full | Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China |
| title_fullStr | Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China |
| title_full_unstemmed | Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China |
| title_short | Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China |
| title_sort | integrating airborne lidar and optical data to estimate forest aboveground biomass in arid and semi arid regions of china |
| topic | forest AGB airborne LiDAR prediction model terrain variables |
| url | http://www.mdpi.com/2072-4292/10/4/532 |
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