Low-cost, miniaturized hyperspectral imaging technology is becoming available for small unmanned aerial vehicle (UAV) platforms. This technology can be efficient in carrying out small-area inspections of anomalous reflectance characteristics of trees at a very high level of detail. Increased frequency and intensity of insect induced forest disturbance has established a new demand for effective methods suitable in mapping and monitoring tasks. In this investigation, a novel miniaturized hyperspectral frame imaging sensor operating in the wavelength range of 500–900 nm was used to identify mature Norway spruce (Picea abies L. Karst.) trees suffering from infestation, representing a different outbreak phase, by the European spruce bark beetle (Ips typographus L.). We developed a new processing method for analyzing spectral characteristic for high spatial resolution photogrammetric and hyperspectral images in forested environments, as well as for identifying individual anomalous trees. The dense point clouds, measured using image matching, enabled detection of single trees with an accuracy of 74.7%. We classified the trees into classes of healthy, infested and dead, and the results were promising. The best results for the overall accuracy were 76% (Cohen’s kappa 0.60), when using three color classes (healthy, infested, dead). For two color classes (healthy, dead), the best overall accuracy was 90% (kappa 0.80). The survey methodology based on high-resolution hyperspectral imaging will be of a high practical value for forest health management, indicating a status of bark beetle outbreak in time.

Accurate three-dimensional (3D) data from indoor spaces are of high importance for various applications in construction, indoor navigation and real estate management. Mobile scanning techniques are offering an efficient way to produce point clouds, but with a lower accuracy than the traditional terrestrial laser scanning (TLS). In this paper, we first tackle the problem of how the quality of a point cloud should be rigorously evaluated. Previous evaluations typically operate on some point cloud subset, using a manually-given length scale, which would perhaps describe the ranging precision or the properties of the environment. Instead, the metrics that we propose perform the quality evaluation to the full point cloud and over all of the length scales, revealing the method precision along with some possible problems related to the point clouds, such as outliers, over-completeness and misregistration. The proposed methods are used to evaluate the end product point clouds of some of the latest methods. In detail, point clouds are obtained from five commercial indoor mapping systems, Matterport, NavVis, Zebedee, Stencil and Leica Pegasus: Backpack, and three research prototypes, Aalto VILMA , FGI Slammer and the Würzburg backpack. These are compared against survey-grade TLS point clouds captured from three distinct test sites that each have different properties. Based on the presented experimental findings, we discuss the properties of the proposed metrics and the strengths and weaknesses of the above mapping systems and then suggest directions for future research.

This paper investigated the potential of multispectral airborne laser scanning (ALS) data for individual tree detection and tree species classification. The aim was to develop a single-sensorsolution for forest mapping that is capable of providing species-specific information, required for forest management and planning purposes. Experiments were conducted using 1903 ground measured trees from 22 sample plots and multispectral ALS data, acquired with an Optech Titan scanner over a boreal forest, mainly consisting of Scots pine (Pinus Sylvestris), Norway spruce (Picea Abies), and birch (Betula sp.), in southern Finland. ALS-features used as predictors for tree species were extracted from segmented tree objects and used in random forest classification. Different combinations of features, including point cloud features, and intensity features of single and multiple channels, were tested. Among the field-measured trees, 61.3% were correctly detected. The best overall accuracy (OA) of tree species classification achieved for correctly-detected trees was 85.9% (Kappa = 0.75), using a point cloud and single-channel intensity features combination, which was not significantly different from the ones that were obtained either using all features (OA = 85.6%, Kappa = 0.75), or single-channel intensity features alone (OA = 85.4%, Kappa = 0.75). Point cloud features alone achieved the lowest accuracy, with an OA of 76.0%. Field-measured trees were also divided into four categories. An examination of the classification accuracy for four categories of trees showed that isolated and dominant trees can be detected with a detection rate of 91.9%, and classified with a high overall accuracy of 90.5%. The corresponding detection rate and accuracy were 81.5% and 89.8% for a group of trees, 26.4% and 79.1% for trees next to a larger tree, and 7.2% and 53.9% for trees situated under a larger tree, respectively. The results suggest that Channel 2 (1064 nm) contains more information for separating pine, spruce, and birch, followed by channel 1 (1550 nm) and channel 3 (532 nm) with an overall accuracy of 81.9%, 78.3%, and 69.1%, respectively. Our results indicate that the use of multispectral ALS data has great potential to lead to a single-sensor solution for forest mapping.

Microwave Radar is an attractive solution for forest mapping and inventories because microwave signals penetrates into the forest canopy and the backscattering signal can provide information regarding the whole forest structure. Satellite-borne and airborne imaging radars have been used in forest resources mapping for many decades. However, their accuracy with respect to the main forest inventory attributes substantially varies depending on the wavelength and techniques used in the estimation. Systems providing canopy backscatter as a function of canopy height are, practically speaking, missing. Therefore, there is a need for a radar system that would enable the scientific community to better understand the radar backscatter response from the forest canopy. Consequently, we undertook a research study to develop an unmanned aerial vehicle (UAV)-borne profiling (i.e., waveform) radar that could be used to improve the understanding of the radar backscatter response for forestry mapping and inventories. A frequency modulation continuous waveform (FMCW) profiling radar, termed FGI-Tomoradar, was introduced, designed and tested. One goal is the total weight of the whole system is less than 7 kg, including the radar system and georeferencing system, with centimetre-level positioning accuracy. Achieving this weight goal would enable the FGI-Tomoradar system to be installed on the Mini-UAV platform. The prototype system had all four linear polarization measuring capabilities, with bistatic configuration in Ku-band. In system performance tests in this study, FGI-Tomoradar was mounted on a manned helicopter together with a Riegl VQ-480-U laser scanner and tested in several flight campaigns performed at the Evo site, Finland. Airborne laser scanning data was simultaneously collected to investigate the differences and similarities of the outputs for the same target area for better understanding the penetration of the microwave signal into the forest canopy. Preliminary analysis confirmed that the profiling radar measures a clear signal from the canopy structure and has substantial potential to improve our understanding of radar forest mapping using the UAV platform.

Photogrammetric aerial film image archives are scanned into digital form in many countries. These data sets offer an interesting source of information for scientists from different disciplines. The objective of this investigation was to contribute to the automation of a generation of 3D environmental model time series when using small-scale airborne image archives, especially in forested scenes. Furthermore, we investigated the usability of dense digital surface models (DSMs) generated using these data sets as well as the uncertainty propagation of the DSMs. A key element in the automation is georeferencing. It is obvious that for images captured years apart, it is essential to find ground reference locations that have changed as little as possible. We studied a 68-year-long aerial image time series in a Finnish Karelian forestland. The quality of candidate ground locations was evaluated by comparing digital DSMs created from the images to an airborne laser scanning (ALS)-originated reference DSM. The quality statistics of DSMs were consistent with the expectations; the estimated median root mean squared error for height varied between 0.3 and 2 m, indicating a photogrammetric modelling error of 0.1‰ with respect to flying height for data sets collected since the 1980s, and 0.2‰ for older data sets. The results show that of the studied land cover classes, “peatland without trees” changed the least over time and is one of the most promising candidates to serve as a location for automatic ground control measurement. Our results also highlight some potential challenges in the process as well as possible solutions. Our results indicate that using modern photogrammetric techniques, it is possible to reconstruct 3D environmental model time series using photogrammetric image archives in a highly automated way.

The continuous flow of autonomous vehicle-based data could revolutionize current map updating procedures and allow completely new types of mapping applications. Therefore, in this article, we demonstrate the feasibility of using perception data of autonomous vehicles to replace traditionally conducted mobile mapping surveys with a case study focusing on updating a register of roadside city trees. In our experiment, we drove along a 1.3-km-long road in Helsinki to collect laser scanner data using our autonomous car platform ARVO, which is based on a Ford Mondeo hybrid passenger vehicle equipped with a Velodyne VLS-128 Alpha Prime scanner and other high-grade sensors for autonomous perception. For comparison, laser scanner data from the same region were also collected with a specially-planned high-grade mobile mapping laser scanning system. Based on our results, the diameter at breast height, one of the key parameters of city tree registers, could be estimated with a lower root-mean-square error from the perception data of the autonomous car than from the specially-planned mobile laser scanning survey, provided that time-based filtering was included in the post-processing of the autonomous perception data to mitigate distortions in the obtained point cloud. Therefore, appropriately performed post-processing of the autonomous perception data can be regarded as a viable option for keeping maps updated in road environments. However, point cloud-processing algorithms may need to be adapted for the post-processing of autonomous perception data due to the differences in the sensors and their arrangements compared to designated mobile mapping systems. We also emphasize that time-based filtering may be required in the post-processing of autonomous perception data due to point cloud distortions around objects seen at multiple times. This highlights the importance of saving the time stamp for each data point in the autonomous perception data or saving the temporal order of the data points.