Advancing Plant Biomass Measurements: Integrating Smartphone-based 3D Scanning Techniques for Enhanced Ecosystem Monitoring
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Abstract
New technological developments open novel possibilities for widely applicable methods of ecosystem analyses. We investigated a novel approach using smartphone-based 3D scanning for non-destructive, high-resolution monitoring of above-ground plant biomass. This method leverages Structure from Motion (SfM) techniques with widely accessible smartphone apps and subsequent computing to generate detailed ecological data. By implementing a streamlined pipeline for point cloud processing and voxel-based analysis, we enable frequent, cost-effective, and accessible monitoring of vegetation structure and plant community biomass. Conducted in long-term experimental grasslands, our study reveals a high correlation (R² up to 0.9) between traditional biomass harvesting and 3D volume estimates derived from smartphone-generated point clouds, validating the method's accuracy and reliability. Additionally, results indicate significant effects of plant species richness and fertilization on biomass production and volume estimates, underscoring the potential for high-resolution temporal and spatial analyses of vegetation dynamics. This method's innovation extends beyond traditional practices with implications for future integration of AI to automate species segmentation, ecological trait extraction, and predictive modeling. The simplicity and accessibility of the smartphone-based approach facilitate broader engagement in ecosystem monitoring, encouraging citizen science participation and enhancing data collection efforts. Future research will make it possible to refine the accuracy of point cloud processing, expand applications across diverse vegetation types, and explore new possibilities in ecological monitoring, modeling, and its application in ecosystem analyses and biodiversity research.
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Abstract
New technological developments open novel possibilities for widely applicable methods of ecosystem analyses. We investigated a novel approach using smartphone-based 3D scanning for non-destructive, high-resolution monitoring of above-ground plant biomass. This method leverages Structure from Motion (SfM) techniques with widely accessible smartphone apps and subsequent computing to generate detailed ecological data. By implementing a streamlined pipeline for point cloud processing and voxel-based analysis, we enable frequent, cost-effective, and accessible monitoring of vegetation structure and plant community biomass. Conducted in long-term experimental grasslands, our study reveals a high correlation (R² up to 0.9) between traditional biomass harvesting and 3D volume estimates derived from smartphone-generated point clouds, validating the method's accuracy and reliability. Additionally, results indicate significant effects of plant species richness and fertilization on biomass production and volume estimates, underscoring the potential for high-resolution temporal and spatial analyses of vegetation dynamics. This method's innovation extends beyond traditional practices with implications for future integration of AI to automate species segmentation, ecological trait extraction, and predictive modeling. The simplicity and accessibility of the smartphone-based approach facilitate broader engagement in ecosystem monitoring, encouraging citizen science participation and enhancing data collection efforts. Future research will make it possible to refine the accuracy of point cloud processing, expand applications across diverse vegetation types, and explore new possibilities in ecological monitoring, modeling, and its application in ecosystem analyses and biodiversity research.
DOI
https://doi.org/10.32942/X2T92X
Subjects
Life Sciences
Keywords
biodiversity, photogrammetry, Scaniverse, vegetation height, Vegetation structure
Dates
Published: 2024-12-07 00:30
Last Updated: 2024-12-09 05:34
Older Versions
License
CC BY Attribution 4.0 International
Additional Metadata
Conflict of interest statement:
There is no conflict of interest.
Data and Code Availability Statement:
The original image data, the clipped image data, as well as the data processing scripts within a Jupyter Notebook are published under an CC BY 4.0 license at https://doi.org/10.5281/zenodo.14024991 and can be reused accordingly.
Language:
English
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License: CC-BY-4.0