Real-time automated forest field inventory using a compact low-cost helmet-based laser scanning system
Forest field inventory plays a crucial role in forestry management and the estimation of carbon circular economy, as it provides information on forest parameters, assesses carbon storage, and identifies the impact factors of ecological change. Terrestrial Laser Scanning (TLS) and Mobile Mapping Syst...
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sg-ntu-dr.10356-1714782023-10-27T15:40:30Z Real-time automated forest field inventory using a compact low-cost helmet-based laser scanning system Li, Jianping Yang, Bisheng Yang, Yandi Zhao, Xin Liao, Youqi Zhu, Ningning Dai, Wenxia Liu, Rundong Chen, Ruibo Dong, Zhen School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Point Cloud Wearable Laser Scanning Forest field inventory plays a crucial role in forestry management and the estimation of carbon circular economy, as it provides information on forest parameters, assesses carbon storage, and identifies the impact factors of ecological change. Terrestrial Laser Scanning (TLS) and Mobile Mapping Systems (MMS) are commonly used for forest field inventory, but they are unable to verify the inventory results in real time. With real-time forest inventory, forestry workers can immediately verify if all trees have been correctly extracted without requiring expertise in point clouds. The advences of miniaturized, low-cost 3D sensors (such as solid-state laser scanners and Inertial Measurement Units [IMUs]) and edge computing units have made it possible to achieve real-time forest inventory using a compact, low-cost helmet. To this end, this paper presents a real-time automated forest field inventory method, which is validated on a compact, low-cost helmet-based laser scanning system. Firstly, a Fast Candidate Tree Detection (FCTD) approach is proposed to identify individual trees as candidates by utilizing a novel 2D corner detection technique based on point cloud projection, taking into account point density and geometry features. Secondly, a Spatiotemporal Consistency-based Tree Parameter Estimation (SCTPE) method is proposed to estimate the tree parameters by considering both the current submap and submaps that have been scanned in real-time. The proposed method was tested in three typical forest areas in Wuhan, China, where the main tree species present include Sapindaceae, dawn redwoods, and Platycladus. The results showed that the proposed method achieves high accuracy in tree detection (recall=0.97,precision=0.94,F=0.96). The average error and root-mean-square error (RMSE) of DBH are 0.033 m and 0.038 m, which outperforms the existing one-circle fitting model. The average error and RMSE of tree height are 0.231 m and 0.294 m. Overall, these results demonstrate the high potential of the helmet-based laser scanning system for real-time forest field inventory. Published version This study was jointly supported by the National Natural Science Foundation Project (No. 42130105, No. 42201477) , the National Sci- ence Fund for Distinguished Young Scholars (No. 41725005) , and China Postdoctoral Science Foundation (2022M712441, 2022TQ0234). 2023-10-26T02:46:22Z 2023-10-26T02:46:22Z 2023 Journal Article Li, J., Yang, B., Yang, Y., Zhao, X., Liao, Y., Zhu, N., Dai, W., Liu, R., Chen, R. & Dong, Z. (2023). Real-time automated forest field inventory using a compact low-cost helmet-based laser scanning system. International Journal of Applied Earth Observation and Geoinformation, 118, 103299-. https://dx.doi.org/10.1016/j.jag.2023.103299 0303-2434 https://hdl.handle.net/10356/171478 10.1016/j.jag.2023.103299 2-s2.0-85152663617 118 103299 en International Journal of Applied Earth Observation and Geoinformation © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). application/pdf |
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Engineering::Electrical and electronic engineering Point Cloud Wearable Laser Scanning Li, Jianping Yang, Bisheng Yang, Yandi Zhao, Xin Liao, Youqi Zhu, Ningning Dai, Wenxia Liu, Rundong Chen, Ruibo Dong, Zhen Real-time automated forest field inventory using a compact low-cost helmet-based laser scanning system |
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Forest field inventory plays a crucial role in forestry management and the estimation of carbon circular economy, as it provides information on forest parameters, assesses carbon storage, and identifies the impact factors of ecological change. Terrestrial Laser Scanning (TLS) and Mobile Mapping Systems (MMS) are commonly used for forest field inventory, but they are unable to verify the inventory results in real time. With real-time forest inventory, forestry workers can immediately verify if all trees have been correctly extracted without requiring expertise in point clouds. The advences of miniaturized, low-cost 3D sensors (such as solid-state laser scanners and Inertial Measurement Units [IMUs]) and edge computing units have made it possible to achieve real-time forest inventory using a compact, low-cost helmet. To this end, this paper presents a real-time automated forest field inventory method, which is validated on a compact, low-cost helmet-based laser scanning system. Firstly, a Fast Candidate Tree Detection (FCTD) approach is proposed to identify individual trees as candidates by utilizing a novel 2D corner detection technique based on point cloud projection, taking into account point density and geometry features. Secondly, a Spatiotemporal Consistency-based Tree Parameter Estimation (SCTPE) method is proposed to estimate the tree parameters by considering both the current submap and submaps that have been scanned in real-time. The proposed method was tested in three typical forest areas in Wuhan, China, where the main tree species present include Sapindaceae, dawn redwoods, and Platycladus. The results showed that the proposed method achieves high accuracy in tree detection (recall=0.97,precision=0.94,F=0.96). The average error and root-mean-square error (RMSE) of DBH are 0.033 m and 0.038 m, which outperforms the existing one-circle fitting model. The average error and RMSE of tree height are 0.231 m and 0.294 m. Overall, these results demonstrate the high potential of the helmet-based laser scanning system for real-time forest field inventory. |
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School of Electrical and Electronic Engineering |
author_facet |
School of Electrical and Electronic Engineering Li, Jianping Yang, Bisheng Yang, Yandi Zhao, Xin Liao, Youqi Zhu, Ningning Dai, Wenxia Liu, Rundong Chen, Ruibo Dong, Zhen |
format |
Article |
author |
Li, Jianping Yang, Bisheng Yang, Yandi Zhao, Xin Liao, Youqi Zhu, Ningning Dai, Wenxia Liu, Rundong Chen, Ruibo Dong, Zhen |
author_sort |
Li, Jianping |
title |
Real-time automated forest field inventory using a compact low-cost helmet-based laser scanning system |
title_short |
Real-time automated forest field inventory using a compact low-cost helmet-based laser scanning system |
title_full |
Real-time automated forest field inventory using a compact low-cost helmet-based laser scanning system |
title_fullStr |
Real-time automated forest field inventory using a compact low-cost helmet-based laser scanning system |
title_full_unstemmed |
Real-time automated forest field inventory using a compact low-cost helmet-based laser scanning system |
title_sort |
real-time automated forest field inventory using a compact low-cost helmet-based laser scanning system |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/171478 |
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1781793792460849152 |