Improving Vegetation Mapping from LiDAR Point Clouds Using a Transmissivity-Based Feature.
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| Title: | Improving Vegetation Mapping from LiDAR Point Clouds Using a Transmissivity-Based Feature. |
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| Authors: | Hess, Max1 (AUTHOR) hess1@uni-potsdam.de, Rheinwalt, Aljoscha1 (AUTHOR), Bookhagen, Bodo1 (AUTHOR) |
| Source: | Remote Sensing. Aug2026, Vol. 18 Issue 15, p2602. 24p. |
| Subjects: | Vegetation classification, Point cloud, Spatial resolution, LIDAR, Vegetation mapping, Permeability |
| Geographic Terms: | Germany |
| Abstract: | Highlights: What are the main findings? A transmissivity feature improves urban vegetation classification from LiDAR point clouds. Transmissivity is highly informative across multiple spatial scales and enhances model interpretability. What are the implications of the main findings? Transmissivity can be computed efficiently from standard multi-return LiDAR data without additional inputs. Echo-enriched point clouds support scalable and interpretable urban vegetation monitoring. Urban vegetation provides essential ecosystem services, including temperature regulation, air purification, noise reduction, and carbon storage. However, urban densification and climate-induced stresses increasingly threaten these ecosystems. Accurate classification of urban vegetation is critical for sustainable urban planning, yet remains challenging due to the structural complexity and high data density of urban LiDAR (Light Detection and Ranging) point clouds. To address current research gaps, including insufficient model interpretability, high computational demands, and limited generalization capabilities, we introduce transmissivity, a novel feature that combines echo-based LiDAR properties with spatial context to more effectively characterize urban vegetation structures. This feature enhances vegetation classification by estimating whether laser beams tend to traverse or terminate within a local neighborhood, independent of the specific return order of individual beams, thereby characterizing vegetation's volumetric permeability. This makes transmissivity highly interpretable, unlike other echo-based statistical features. Transmissivity was evaluated alongside 52 conventional features using three different feature-importance measures across two distinct urban LiDAR datasets from Berlin and Hessigheim 3D (both datasets are from Germany). Transmissivity consistently ranked among the most influential features in both datasets across multiple scales and achieved the highest average gain (Berlin: 0.465; Hessigheim: 0.286) and the second highest mean absolute Shapley value (Berlin: 2.066; Hessigheim: 0.824). Permutation importance confirmed that transmissivity has the strongest impact on the mean decrease in F1-score (Berlin: 0.53; Hessigheim: 0.34) if used in uncorrelated feature subsets. Our findings support that echo-enriched point clouds allow efficient and accurate monitoring of urban vegetation. The feature is simple to compute and does not require additional data beyond standard multi-return LiDAR attributes. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A transmissivity feature improves urban vegetation classification from LiDAR point clouds. Transmissivity is highly informative across multiple spatial scales and enhances model interpretability. What are the implications of the main findings? Transmissivity can be computed efficiently from standard multi-return LiDAR data without additional inputs. Echo-enriched point clouds support scalable and interpretable urban vegetation monitoring. Urban vegetation provides essential ecosystem services, including temperature regulation, air purification, noise reduction, and carbon storage. However, urban densification and climate-induced stresses increasingly threaten these ecosystems. Accurate classification of urban vegetation is critical for sustainable urban planning, yet remains challenging due to the structural complexity and high data density of urban LiDAR (Light Detection and Ranging) point clouds. To address current research gaps, including insufficient model interpretability, high computational demands, and limited generalization capabilities, we introduce transmissivity, a novel feature that combines echo-based LiDAR properties with spatial context to more effectively characterize urban vegetation structures. This feature enhances vegetation classification by estimating whether laser beams tend to traverse or terminate within a local neighborhood, independent of the specific return order of individual beams, thereby characterizing vegetation's volumetric permeability. This makes transmissivity highly interpretable, unlike other echo-based statistical features. Transmissivity was evaluated alongside 52 conventional features using three different feature-importance measures across two distinct urban LiDAR datasets from Berlin and Hessigheim 3D (both datasets are from Germany). Transmissivity consistently ranked among the most influential features in both datasets across multiple scales and achieved the highest average gain (Berlin: 0.465; Hessigheim: 0.286) and the second highest mean absolute Shapley value (Berlin: 2.066; Hessigheim: 0.824). Permutation importance confirmed that transmissivity has the strongest impact on the mean decrease in F1-score (Berlin: 0.53; Hessigheim: 0.34) if used in uncorrelated feature subsets. Our findings support that echo-enriched point clouds allow efficient and accurate monitoring of urban vegetation. The feature is simple to compute and does not require additional data beyond standard multi-return LiDAR attributes. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18152602 |
