For the accurate and automatic mapping of forest stands based on very-high-resolution satellite imagery and digital orthophotos, precise object detection at the individual tree level is necessary. Currently, supervised deep learning models are primarily applied for this task. To train a reliable model, it is crucial to have an accurate tree crown annotation dataset. The current method of generating these training datasets still relies on manual annotation and labeling. Because of the intricate contours of tree crowns, vegetation density in natural forests and the insufficient ground sampling distance of the imagery, manually generated annotations are error-prone. It is unlikely that the manually delineated tree crowns represent the true conditions on the ground. If these error-prone annotations are used as training data for deep learning models, this may lead to inaccurate mapping results for the models. This study critically validates manual tree crown annotations on two study sites: a forest-like plantation on a cemetery and a natural city forest. The validation is based on tree reference data in the form of an official tree register and tree segments extracted from UAV laser scanning (ULS) data for the quality assessment of a training dataset. The validation results reveal that the manual annotations detect only 37% of the tree crowns in the forest-like plantation area and 10% of the tree crowns in the natural forest correctly. Furthermore, it is frequent for multiple trees to be interpreted in the annotation as a single tree at both study sites.

Plants signal their health in a broader spectrum than we can see with our eyes. We compared sunlight reflectance on plants at spectral wavelengths ranging from 430 nm to 870 nm in our study. These are based on multispectral images captured at a distance of 2 m. Indoor plants were observed over a period of 18 days and stressed due to a lack of sunlight or water. Wild sedge photographed on the forest floor at close range and with a difficult capture setup produced results comparable to published multispectral signatures derived from aerial imagery. Changes of leaf reflectance were noticed in spectral signatures and in vegetation indices. When calculating vegetation indices, our results show that comparing red and red edge reflectance values is superior to comparing red and NIR reflectance values.

A 3D model can be useful for inventory management and monitoring of forests. For this task, we present our prototype mobile mapping backpack system for collecting 3D point clouds of forest vegetation. However, data collection in forests is challenging due to unreliable GNSS positioning, moving objects caused by winds, unclear object edges, and uneven ground to walk on. We include LiDAR and IMU with Simultaneous Localisation and Mapping (SLAM). We describe in detail our backpack system, its development for the forest environment, and evaluate it. Furthermore, we compare two open code SLAM algorithms for ROS, as well as data collection and laser scan quality between TLS and MLS for forest environments. Finally, we installed and tested the MicaSense multi-spectral camera on our backpack and discuss the advantages and drawbacks of it. We conclude, that the backpack is convenient to use in forest environments and produces a good point cloud. It can be carried easily off trail and on rough terrain. The system needs less storage space, computation and less collection time than TLS.

3D indoor mapping is becoming increasingly critical for a variety of applications such as path planning and navigation for robots. In recent years, there is a growing interest in how low-cost sensors, such as monocular or depth cameras, can be used for 3D mapping. In our paper, we present an octree-based approach for real-time 3D indoor mapping using a handheld RGB depth camera. One benefit of the generated octree map is that it requires less storage and computational resources than point cloud models. Moreover, it explicitly represents free space and unmapped areas, which are essential for the robot's navigation tasks. In this work, on the basis of the ORB-SLAM3 system (Campos et al., 2021), we developed an octree mapping system, which directly calls the keyframes and estimated poses provided by ORB-SLAM3 algorithms. Furthermore, we used point cloud library (PCL) for the dense point cloud mapping and then OctoMap for the point cloud to octree map conversion. Finally, we implemented an efficient probabilistic 3D mapping in the robot operating system (ROS) environment. We used the TUM RGB-D dataset to evaluate the estimated trajectories of the camera. The evaluation shows an average translational RMSE of 5.9 cm on the TUM RGB-D dataset. Besides, we also compared the ground truth point clouds and our generated point clouds. The result shows the mean cloud-to-cloud distance in the corridor scene is about 6 cm. All the evaluation results show our proposed approach is a promising solution for advanced indoor voxel mapping and robotic navigation systems.

Forests are irreplaceable and are being studied extensively. Better forest inventory and understanding necessitate effective mapping, modeling, and automatic analysis. As a result, considerable research effort is being devoted to digitizing forest environments. Recently, digital twins have come to the attention of the geospatial community as a virtual representation of the Earth’s surface linked to its corresponding physical asset. This concept is applicable to forests and has been studied in the literature. This requires initial input data obtained through reality capture. Among mapping techniques, laser scanning has emerged as a state-of-the-art technology for vegetation modeling. In this paper, we look into the potential of mobile laser scanning for forest digital twinning. While most studies concentrate on single tree detection, modeling, and estimation of dendrometric parameters, we also include lower vegetation in our investigations. To accomplish this, we first detect single trees and then investigate different vegetation densities and levels using geometric metrics. We also demonstrate how to model the underlying layers of vegetation in a digital twin. We perform the tests on data from mobile laser scanning (MLS) and compare the results to those from airborne laser scanning (ALS).We show that single tree detection based on crown separation using MLS data works similarly to or slightly better than ALS data. Furthermore, we demonstrate that MLS data allows for more detailed analysis of understory vegetation taking into account different height levels and a multi-level representation, whereas ALS data only allows for rough analysis of the lower parts of forest vegetation.

Recent advancements in remote sensing have enabled increasingly detailed analysis of forest canopies using a range of platforms, from satellites to ground-based systems. This study focuses on zenith multispectral imaging beneath deciduous tree crowns to assess canopy openness and vegetation activity. We conducted two experiments: First, we evaluated the spectral similarity between a 10-lens multispectral camera and a spectrometer in a controlled environment. Using Spectral AngleMapping (SAM) and common Vegetation Indices (VIs), we found that reflectance data acquired at 0.5 m distance under diffuse light closely matched spectrometer measurements. Second, we monitored seasonal foliage development between February and July 2025, capturing ground-based reflectance from 400–850 nm. The inclusion of near-infrared (NIR) bands significantly improved the detection of vegetation activity throughout the growth season. Our results indicate that absolute reflectance values from images captured at greater zenith distance are uncertain due to angular and illumination effects, but relative changes between bands remain informative. Among the tested indices, the Green Normalised Difference Vegetation Index (GNDVI) aligned well with reference data and remained unsaturated during peak foliage months.