Backpack System Development and Image-LiDAR Integration for Improved Geospatial Data Alignment in Forest Mapping.
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| Title: | Backpack System Development and Image-LiDAR Integration for Improved Geospatial Data Alignment in Forest Mapping. |
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| Authors: | Manish, Raja1 (AUTHOR), Fei, Songlin2 (AUTHOR), Habib, Ayman1 (AUTHOR) ahabib@purdue.edu |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1443. 34p. |
| Subjects: | Geospatial data, Multisensor data fusion, Geographical positions, Forest mapping, Forest surveys |
| Abstract: | Highlights: What are the main findings? The proposed image–LiDAR data enhancement strategy reduces feature misalignments from as much as 1.1 m (planimetric) and 2 m (vertical) to within 5 cm in both directions. While imagery alone is less reliable than LiDAR for extracting structural attributes of a tree, it remains effective for visual characterization and contextual interpretation. What are the implications of the main findings? The findings underscore the complementary strengths of LiDAR and imaging sensors and highlight the importance of their effective integration as a key step toward comprehensive and accurate tree inventory. Backpack mobile mapping systems (MMS) equipped with LiDAR and RGB cameras, as well as an optional GNSS/INS direct georeferencing unit, are increasingly utilized in forest inventory applications. In general, LiDAR point clouds provide detailed structural information, whereas imagery offers visual specifics of surface features. However, cameras typically operate at lower acquisition rates compared to LiDAR. In proximal mapping, another challenge is the inconsistent reception of GNSS signals beneath forest canopies. Additionally, georeferencing accuracy may differ between LiDAR and imagery due to biases in the system calibration parameters and variations in post-processing approaches. To address these challenges, this study introduces a Backpack MMS that uses cameras configured at elevated frame rates to enhance image overlap. Concurrently, this study presents an algorithmic approach to addressing georeferencing issues by integrating imagery and LiDAR data, thereby enhancing system calibration and improving platform trajectory. The method is based on the hypothesis that forest environments are rich with geometrically well-defined features, such as tree trunks and ground patches. By identifying conjugate primitives in point clouds from both imagery and LiDAR, the procedure optimizes feature models while simultaneously minimizing calibration biases and/or trajectory errors. The proposed approach is validated using multiple field datasets collected in diverse forest environments. Quantitative results show that the procedure reduces image–LiDAR feature misalignment across all datasets from up to 1.1 m in the planimetric direction and 2 m in the vertical direction to within 5 cm in both. The feature fitting accuracy also improves from 2.9 cm to 0.85 cm for LiDAR point clouds and from 10 cm to 0.9 cm for image-based point clouds. However, the results indicate that despite increased data availability, imagery alone remains less reliable than LiDAR for extracting structural information. Nevertheless, the proposed image–LiDAR alignment strategy represents a crucial step toward developing a comprehensive tree inventory. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? The proposed image–LiDAR data enhancement strategy reduces feature misalignments from as much as 1.1 m (planimetric) and 2 m (vertical) to within 5 cm in both directions. While imagery alone is less reliable than LiDAR for extracting structural attributes of a tree, it remains effective for visual characterization and contextual interpretation. What are the implications of the main findings? The findings underscore the complementary strengths of LiDAR and imaging sensors and highlight the importance of their effective integration as a key step toward comprehensive and accurate tree inventory. Backpack mobile mapping systems (MMS) equipped with LiDAR and RGB cameras, as well as an optional GNSS/INS direct georeferencing unit, are increasingly utilized in forest inventory applications. In general, LiDAR point clouds provide detailed structural information, whereas imagery offers visual specifics of surface features. However, cameras typically operate at lower acquisition rates compared to LiDAR. In proximal mapping, another challenge is the inconsistent reception of GNSS signals beneath forest canopies. Additionally, georeferencing accuracy may differ between LiDAR and imagery due to biases in the system calibration parameters and variations in post-processing approaches. To address these challenges, this study introduces a Backpack MMS that uses cameras configured at elevated frame rates to enhance image overlap. Concurrently, this study presents an algorithmic approach to addressing georeferencing issues by integrating imagery and LiDAR data, thereby enhancing system calibration and improving platform trajectory. The method is based on the hypothesis that forest environments are rich with geometrically well-defined features, such as tree trunks and ground patches. By identifying conjugate primitives in point clouds from both imagery and LiDAR, the procedure optimizes feature models while simultaneously minimizing calibration biases and/or trajectory errors. The proposed approach is validated using multiple field datasets collected in diverse forest environments. Quantitative results show that the procedure reduces image–LiDAR feature misalignment across all datasets from up to 1.1 m in the planimetric direction and 2 m in the vertical direction to within 5 cm in both. The feature fitting accuracy also improves from 2.9 cm to 0.85 cm for LiDAR point clouds and from 10 cm to 0.9 cm for image-based point clouds. However, the results indicate that despite increased data availability, imagery alone remains less reliable than LiDAR for extracting structural information. Nevertheless, the proposed image–LiDAR alignment strategy represents a crucial step toward developing a comprehensive tree inventory. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091443 |
