3D Reconstruction of deciduous Trees using low-cost UAV- and Crane-based Photogrammetry for Monitoring Shoot Elongation across entire Canopies
- URL: http://arxiv.org/abs/2607.07905v1
- Date: Wed, 08 Jul 2026 20:39:19 GMT
- Title: 3D Reconstruction of deciduous Trees using low-cost UAV- and Crane-based Photogrammetry for Monitoring Shoot Elongation across entire Canopies
- Abstract summary: Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass.<n>This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Tree growth determines how much CO2 is sequestered from the atmosphere and temporarily stored in woody biomass. At the same time tree growth is affected by increasing temperatures, more frequent drought periods, late frosts and other extreme events associated with climate change. While continuous measurements of radial (secondary) tree growth using dendrometers are well established, monitoring of shoot elongation (primary growth) has largely been neglected because suitable measurement techniques are lacking. As a result, the effects of climate change on primary tree growth remain insufficiently understood. This work aims at reconstructing native deciduous trees in 3D as a basis for measuring and monitoring shoot elongation over entire tree canopies. Here we explored the use of low-cost UAV photogrammetry and of a multi-camera CraneCam system under real-world conditions. Data were collected in two study areas over an entire growing season. We present sensor evaluations, photogrammetric data acquisition and processing strategies. A special focus is placed on the analysis of the resulting photogrammetric 3D point clouds in terms of accuracy, resolution and completeness. Results demonstrate 3D point accuracies of 5-6 mm for entire trees using consumer-grade UAVs weighing less than 250 g and a 3D reconstruction completeness between 92% and 98% depending on the UAV type. The paper introduces a novel 3Dprinted ground-truth branch to evaluate the capability to reconstructing fine-detail structures such as thin tree shoots. Finally, we discuss operational challenges and initial experiments towards a skeletonization of entire trees based on photogrammetric point clouds.
Related papers
- Estimating Individual Tree Height and Species from UAV Imagery [16.423929827492493]
We introduce BIRCH-Trees, the first benchmark for individual tree height and species estimation from tree-centered UAV images.<n>We also present DINOvTree, a unified approach using a Vision Foundation Model (VFM) backbone with task-specific heads for simultaneous height and species prediction.
arXiv Detail & Related papers (2026-03-24T19:16:24Z) - ECHOSAT: Estimating Canopy Height Over Space And Time [17.73069647964353]
Existing global tree height maps provide only static snapshots and do not capture temporal forest dynamics.<n>We introduce ECHOSAT, a global and temporally consistent tree height map at 10 m resolution spanning multiple years.<n>We also provide the first global-scale height map that accurately quantifies tree growth and disturbances over time.
arXiv Detail & Related papers (2026-02-24T22:46:58Z) - Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery [68.69685477556682]
Current monitoring methods involve ground measurements, requiring extensive cost, time and labor.<n>Drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale.<n>We compare methods leveraging Segment Anything Model (SAM) for the task of automatic tree crown instance segmentation in high resolution drone imagery.<n>We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery.
arXiv Detail & Related papers (2025-06-05T12:43:11Z) - Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation [49.13393683126712]
Urban forests play a key role in enhancing environmental quality and supporting biodiversity in cities.<n> accurately detecting trees is challenging due to complex landscapes and the variability in image resolution caused by different satellite sensors or UAV flight altitudes.<n>We propose a novel pipeline that integrates domain adaptation with GANs and Diffusion models to enhance the quality of low-resolution aerial images.
arXiv Detail & Related papers (2025-05-21T03:57:10Z) - Estimating the Diameter at Breast Height of Trees in a Forest With a Single 360 Camera [52.85399274741336]
Forest inventories rely on accurate measurements of the diameter at breast height (DBH) for ecological monitoring, resource management, and carbon accounting.<n>While LiDAR-based techniques can achieve centimeter-level precision, they are cost-prohibitive and operationally complex.<n>We present a low-cost alternative that only needs a consumer-grade 360 video camera.
arXiv Detail & Related papers (2025-05-06T01:09:07Z) - Assessing SAM for Tree Crown Instance Segmentation from Drone Imagery [68.69685477556682]
Current monitoring methods involve measuring trees by hand for each species, requiring extensive cost, time, and labour.<n>Advances in drone remote sensing and computer vision offer great potential for mapping and characterizing trees from aerial imagery.<n>We compare SAM methods for the task of automatic tree crown instance segmentation in high resolution drone imagery of young tree plantations.<n>We find that methods using SAM out-of-the-box do not outperform a custom Mask R-CNN, even with well-designed prompts, but that there is potential for methods which tune SAM further.
arXiv Detail & Related papers (2025-03-26T03:45:36Z) - Comparative Analysis of Novel View Synthesis and Photogrammetry for 3D Forest Stand Reconstruction and extraction of individual tree parameters [2.153174198957389]
Photogrammetry is commonly used for reconstructing forest scenes but faces challenges like low efficiency and poor quality.
NeRF, while better for canopy regions, may produce errors in ground areas with limited views.
3DGS method generates sparser point clouds, particularly in trunk areas, affecting diameter at breast height (DBH) accuracy.
arXiv Detail & Related papers (2024-10-08T07:53:21Z) - Tree Counting by Bridging 3D Point Clouds with Imagery [31.02816235514385]
Two-dimensional remote sensing imagery primarily shows overstory canopy, and it does not facilitate easy differentiation of individual trees in areas with a dense canopy.
We leverage the fusion of three-dimensional LiDAR measurements and 2D imagery to facilitate the accurate counting of trees.
We compare a deep learning approach to counting trees in forests using 3D airborne LiDAR data and 2D imagery.
arXiv Detail & Related papers (2024-03-04T11:02:17Z) - Evaluating the point cloud of individual trees generated from images
based on Neural Radiance fields (NeRF) method [2.4199520195547986]
In this study, based on tree images collected by various cameras, the Neural Radiance Fields (NeRF) method was used for individual tree reconstruction.
The results show that the NeRF method performs well in individual tree 3D reconstruction, as it has higher successful reconstruction rate, better reconstruction in the canopy area.
The accuracy of tree structural parameters extracted from the photogrammetric point cloud is still higher than those of derived from the NeRF point cloud.
arXiv Detail & Related papers (2023-12-06T09:13:34Z) - Vision Transformers, a new approach for high-resolution and large-scale
mapping of canopy heights [50.52704854147297]
We present a new vision transformer (ViT) model optimized with a classification (discrete) and a continuous loss function.
This model achieves better accuracy than previously used convolutional based approaches (ConvNets) optimized with only a continuous loss function.
arXiv Detail & Related papers (2023-04-22T22:39:03Z) - Neuroevolution-based Classifiers for Deforestation Detection in Tropical
Forests [62.997667081978825]
Millions of hectares of tropical forests are lost every year due to deforestation or degradation.
Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals.
This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
arXiv Detail & Related papers (2022-08-23T16:04:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.